diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..6bdba7be20bf3cf107c88e5269c6bf7f25b730ec
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,29 @@
+FROM misakiminato/cuda-python:cu12.0.0-py3.8.16-devel-ubuntu18.04
+WORKDIR /app
+
+COPY ./requirements.txt /app/requirements.txt
+COPY ./packages.txt /app/packages.txt
+RUN pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
+RUN pip3 install --no-cache-dir -r /app/requirements.txt
+
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+
+EXPOSE 8501
+CMD streamlit run app.py --server.maxUploadSize 1024 --server.enableWebsocketCompression=false --server.enableXsrfProtection=false
diff --git a/Dockerfile.back b/Dockerfile.back
new file mode 100644
index 0000000000000000000000000000000000000000..66f1ddf79bc65bbab100a3aacd1b8b97eb6ee2d2
--- /dev/null
+++ b/Dockerfile.back
@@ -0,0 +1,35 @@
+FROM nvidia/cuda:12.0.0-base-ubuntu20.04
+ARG DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+# Install python, git & ffmpeg
+RUN apt-get update && apt-get install --no-install-recommends -y \
+  build-essential \
+  python3.8=3.8.10* \
+  python3-pip \
+  git \
+  ffmpeg \
+  && apt-get clean && rm -rf /var/lib/apt/lists/*
+COPY ./requirements.txt /app/requirements.txt
+COPY ./packages.txt /app/packages.txt
+RUN pip install --upgrade pip
+RUN pip install pyproject-toml
+RUN pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
+RUN pip3 install --no-cache-dir -r /app/requirements.txt
+RUN pip3 install --no-cache-dir numba==0.56.3
+RUN pip install --no-binary :all: pyworld
+
+WORKDIR /app
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+EXPOSE 8501
+CMD nvidia-smi -l
+CMD streamlit run app.py --server.maxUploadSize 1024 --server.enableWebsocketCompression=false --server.enableXsrfProtection=false
diff --git a/Dockerfile.latestbackup b/Dockerfile.latestbackup
new file mode 100644
index 0000000000000000000000000000000000000000..af25edbc8c0f0ab8d964b5856767c75495862b1a
--- /dev/null
+++ b/Dockerfile.latestbackup
@@ -0,0 +1,42 @@
+FROM nvidia/cuda:12.0.0-base-ubuntu20.04
+ARG DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+ENV PYTHON_INCLUDE /usr/include/python3.8
+ENV PYTHON_LIB /usr/lib/x86_64-linux-gnu/libpython3.8.so
+
+WORKDIR /app
+# Install python, git & ffmpeg
+RUN apt-get update && apt-get install --no-install-recommends -y \
+  build-essential \
+  python3.9 \
+  python3-pip \
+  git \
+  ffmpeg \
+  && apt-get clean && rm -rf /var/lib/apt/lists/*
+COPY ./requirements.txt /app/requirements.txt
+COPY ./packages.txt /app/packages.txt
+RUN pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install -y python3-dev
+RUN apt-get update && apt-get install -y build-essential
+RUN which python3
+RUN which python3-config
+RUN pip3 install --no-cache-dir -r /app/requirements.txt
+RUN apt-get update && apt-get install -y build-essential
+RUN pip3 install --no-cache-dir numba==0.56.3
+RUN pip install --upgrade pip setuptools wheel
+RUN pip3 install --no-binary :all: pyworld
+RUN pip install soundfile
+
+WORKDIR /app
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+EXPOSE 8501
+CMD nvidia-smi -l
+CMD streamlit run app.py --server.maxUploadSize 1024 --server.enableWebsocketCompression=false --server.enableXsrfProtection=false
\ No newline at end of file
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..0ad25db4bd1d86c452db3f9602ccdbe172438f52
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
+you this License which gives you legal permission to copy, distribute
+and/or modify the software.
+
+  A secondary benefit of defending all users' freedom is that
+improvements made in alternate versions of the program, if they
+receive widespread use, become available for other developers to
+incorporate.  Many developers of free software are heartened and
+encouraged by the resulting cooperation.  However, in the case of
+software used on network servers, this result may fail to come about.
+The GNU General Public License permits making a modified version and
+letting the public access it on a server without ever releasing its
+source code to the public.
+
+  The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
+to the community.  It requires the operator of a network server to
+provide the source code of the modified version running there to the
+users of that server.  Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+  An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals.  This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
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+  A patent license is "discriminatory" if it does not include within
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+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
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+
+  13. Remote Network Interaction; Use with the GNU General Public License.
+
+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
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+
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+3 of the GNU General Public License.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU Affero General Public License from time to time.  Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
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+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
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+
+  If the Program specifies that a proxy can decide which future
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+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+  16. Limitation of Liability.
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
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+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
diff --git a/README.md b/README.md
index 4d797e1dd44058d82277d7d011aa4ef59a5a01b7..65ca11661c2db5e302bf32c36e8a6fe9da7d034c 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,10 @@
 ---
-title: Diffsvc Test
-emoji: 🔥
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
+title: DiffSVC Inference
+emoji: 🎙
+colorFrom: red
+colorTo: orange
+sdk: docker
+app_port: 8501
 pinned: false
+duplicated_from: DIFF-SVCModel/Inference
 ---
-
-Check out the configuration reference at https://huggingface.co./docs/hub/spaces-config-reference
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..d898aab686c0c2271b8d9c8217aa30197eaa6a83
--- /dev/null
+++ b/app.py
@@ -0,0 +1,106 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import json
+import os
+import tempfile
+import shutil
+import requests
+from pathlib import Path
+global ckpt_temp_file
+global audio_temp_file
+global config_temp_file
+###################################################
+from utils.hparams import hparams
+from preprocessing.data_gen_utils import get_pitch_parselmouth,get_pitch_crepe
+import numpy as np
+import matplotlib.pyplot as plt
+import IPython.display as ipd
+import utils
+import librosa
+import torchcrepe
+from infer import *
+import logging
+from infer_tools.infer_tool import *
+import io
+
+spk_dict = {
+    "雷电将军": {"model_name": './models/genshin/raiden.ckpt', "config_name": './models/genshin/config.yaml'}
+}
+
+project_name = "Unnamed"
+model_path = spk_dict['雷电将军']['model_name']
+config_path= spk_dict['雷电将军']['config_name']
+hubert_gpu = False
+svc_model = Svc(project_name, config_path, hubert_gpu, model_path)
+
+def vc_fn(sid, audio_record, audio_upload, tran, pndm_speedup=20):
+    print(sid)
+    if audio_upload is not None:
+        audio_path = audio_upload
+    elif audio_record is not None:
+        audio_path = audio_record
+    else:
+        return "你需要上传wav文件或使用网页内置的录音！", None
+
+    tran = int(tran)
+    pndm_speedup = int(pndm_speedup)
+    print('model loaded')
+    # demoaudio, sr = librosa.load(audio_path)
+    key = tran # 音高调整，支持正负（半音）
+    # 加速倍数
+    pndm_speedup = 20
+    wav_gen='queeeeee.wav'
+    
+    # Show the spinner and run the run_clip function inside the 'with' block
+    f0_tst, f0_pred, audio = run_clip(svc_model, file_path=audio_path, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.05,
+                                            use_gt_mel=False, add_noise_step=500, project_name=project_name, out_path=wav_gen)
+
+    return "Success", (hparams['audio_sample_rate'], audio)
+
+
+app = gr.Blocks()
+with app:
+    with gr.Tabs():
+        with gr.TabItem("Basic"):
+            gr.Markdown(value="""
+            本模型为sovits_f0（含AI猫雷2.0音色），支持**60s以内**的**无伴奏**wav、mp3（单声道）格式，或使用**网页内置**的录音（二选一）
+            
+            转换效果取决于源音频语气、节奏是否与目标音色相近，以及音域是否超出目标音色音域范围
+            
+            猫雷音色低音音域效果不佳，如转换男声歌声，建议变调升 **6-10key** 
+            
+            该模型的 [github仓库链接](https://github.com/innnky/so-vits-svc)，如果想自己制作并训练模型可以访问这个 [github仓库](https://github.com/IceKyrin/sovits_guide)
+            """)
+            speaker_id = gr.Dropdown(label="音色", choices=['雷电将军'], value="雷电将军")
+            record_input = gr.Audio(source="microphone", label="录制你的声音", type="filepath", elem_id="audio_inputs")
+            upload_input = gr.Audio(source="upload", label="上传音频（长度小于60秒）", type="filepath",
+                                    elem_id="audio_inputs")
+            vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
+            vc_speedup = gr.Number(label="加速倍数", value=20)
+            vc_submit = gr.Button("转换", variant="primary")
+            out_audio = gr.Audio(label="Output Audio")
+            gr.Markdown(value="""
+                        输出信息为音高平均偏差半音数量，体现转换音频的跑调情况（一般平均小于0.5个半音）
+                        """)
+            out_message = gr.Textbox(label="Output")
+            gr.Markdown(value="""f0曲线可以直观的显示跑调情况，蓝色为输入音高，橙色为合成音频的音高
+                        若**只看见橙色**，说明蓝色曲线被覆盖，转换效果较好
+                        """)
+            # f0_image = gr.Image(label="f0曲线")
+        vc_submit.click(vc_fn, [speaker_id, record_input, upload_input, vc_transform, vc_speedup],
+                        [out_message, out_audio])
+        with gr.TabItem("使用说明"):
+            gr.Markdown(value="""
+                        0、合集：https://github.com/IceKyrin/sovits_guide/blob/main/README.md
+                        1、仅支持sovit_f0（sovits2.0）模型
+                        2、自行下载hubert-soft-0d54a1f4.pt改名为hubert.pt放置于pth文件夹下（已经下好了）
+                            https://github.com/bshall/hubert/releases/tag/v0.1
+                        3、pth文件夹下放置sovits2.0的模型
+                        4、与模型配套的xxx.json，需有speaker项——人物列表
+                        5、放无伴奏的音频、或网页内置录音，不要放奇奇怪怪的格式
+                        6、仅供交流使用，不对用户行为负责
+                        """)
+
+    app.launch()
diff --git a/app.py.back2 b/app.py.back2
new file mode 100644
index 0000000000000000000000000000000000000000..2c960e3a027a2abdaddcc798c8e021f101f8d14f
--- /dev/null
+++ b/app.py.back2
@@ -0,0 +1,123 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import json
+import os
+import tempfile
+import shutil
+import requests
+from pathlib import Path
+temp_dir = os.path.expanduser("~/app")
+global ckpt_temp_file
+global audio_temp_file
+global config_temp_file
+###################################################
+from utils.hparams import hparams
+from preprocessing.data_gen_utils import get_pitch_parselmouth,get_pitch_crepe
+import numpy as np
+import matplotlib.pyplot as plt
+import IPython.display as ipd
+import utils
+import librosa
+import torchcrepe
+from infer import *
+import logging
+from infer_tools.infer_tool import *
+import io
+
+clip_completed = False
+def render_audio(ckpt_temp_file, config_temp_file, audio_temp_file, title):
+    logging.getLogger('numba').setLevel(logging.WARNING)
+    title = int(title)
+    project_name = "Unnamed"
+    model_path = ckpt_temp_file
+    config_path= config_temp_file
+    hubert_gpu=True
+    svc_model = Svc(project_name,config_path,hubert_gpu, model_path)
+    print('model loaded')
+    wav_fn = audio_temp_file
+    demoaudio, sr = librosa.load(wav_fn)
+    key = title # 音高调整，支持正负（半音）
+    # 加速倍数
+    pndm_speedup = 20
+    wav_gen='queeeeee.wav'#直接改后缀可以保存不同格式音频，如flac可无损压缩
+    
+    # Show the spinner and run the run_clip function inside the 'with' block
+    with st.spinner("Rendering Audio..."):
+      f0_tst, f0_pred, audio = run_clip(svc_model,file_path=wav_fn, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.05,
+                                        use_gt_mel=False, add_noise_step=500,project_name=project_name,out_path=wav_gen)
+    clip_completed = True
+    if clip_completed:
+    # If the 'run_clip' function has completed, use the st.audio function to show an audio player for the file stored in the 'wav_gen' variable
+        st.audio(wav_gen)
+
+#######################################################
+st.set_page_config(
+    page_title="DiffSVC Render",
+    page_icon="🧊",
+    initial_sidebar_state="expanded",
+)
+############
+st.title('DIFF-SVC Render')
+
+###CKPT LOADER
+with tempfile.TemporaryDirectory(dir=os.path.expanduser("~/app")) as temp_dir:
+    ckpt = st.file_uploader("Choose your CKPT", type= 'ckpt')
+    # Check if user uploaded a CKPT file
+    if ckpt is not None:
+      #TEMP FUNCTION
+      with tempfile.NamedTemporaryFile(mode="wb", suffix='.ckpt', delete=False) as temp:
+        # Get the file contents as bytes
+        bytes_data = ckpt.getvalue()
+        # Write the bytes to the temporary file
+        temp.write(bytes_data)
+        ckpt_temp_file = temp.name
+        # Print the temporary file name
+        print(temp.name)
+
+    # Display the file path
+    if "ckpt_temp_file" in locals():
+        st.success("File saved to: {}".format(ckpt_temp_file))
+
+    # File uploader
+    config = st.file_uploader("Choose your config", type= 'yaml')
+
+    # Check if user uploaded a config file
+    if config is not None:
+      #TEMP FUNCTION
+      with tempfile.NamedTemporaryFile(mode="wb", suffix='.yaml', delete=False) as temp:
+        # Get the file contents as bytes
+        bytes_data = config.getvalue()
+        # Write the bytes to the temporary file
+        temp.write(bytes_data)
+        config_temp_file = temp.name
+        # Print the temporary file name
+        print(temp.name)
+
+    # Display the file path
+    if "config_temp_file" in locals():
+        st.success("File saved to: {}".format(config_temp_file))
+
+    audio = st.file_uploader("Choose your audio", type=["wav", "mp3"])
+
+    # Check if user uploaded an audio file
+    if audio is not None:
+  #TEMP FUNCTION
+      with tempfile.NamedTemporaryFile(mode="wb", suffix='.wav', delete=False) as temp:
+    # Get the file contents as bytes
+        bytes_data = audio.getvalue()
+    # Write the bytes to the temporary file
+        temp.write(bytes_data)
+        audio_temp_file = temp.name
+    # Print the temporary file name
+        print(temp.name)
+
+# Display the file path
+    if "audio_temp_file" in locals():
+        st.success("File saved to: {}".format(audio_temp_file))
+# Add a text input for the title with a default value of 0
+title = st.text_input("Key", value="0")
+# Add a button to start the rendering process
+if st.button("Render audio"):
+  render_audio(ckpt_temp_file, config_temp_file, audio_temp_file, title)
\ No newline at end of file
diff --git a/app.pyback b/app.pyback
new file mode 100644
index 0000000000000000000000000000000000000000..028481c47efd6e4ae06c1ae1054783176aedc6e0
--- /dev/null
+++ b/app.pyback
@@ -0,0 +1,123 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import json
+import os
+import tempfile
+import shutil
+import requests
+from pathlib import Path
+temp_dir = os.path.expanduser("/~app")
+global ckpt_temp_file
+global audio_temp_file
+global config_temp_file
+###################################################
+from utils.hparams import hparams
+from preprocessing.data_gen_utils import get_pitch_parselmouth,get_pitch_crepe
+import numpy as np
+import matplotlib.pyplot as plt
+import IPython.display as ipd
+import utils
+import librosa
+import torchcrepe
+from infer import *
+import logging
+from infer_tools.infer_tool import *
+import io
+
+clip_completed = False
+def render_audio(ckpt_temp_file, config_temp_file, audio_temp_file, title):
+    logging.getLogger('numba').setLevel(logging.WARNING)
+    title = int(title)
+    project_name = "Unnamed"
+    model_path = ckpt_temp_file
+    config_path= config_temp_file
+    hubert_gpu=True
+    svc_model = Svc(project_name,config_path,hubert_gpu, model_path)
+    print('model loaded')
+    wav_fn = audio_temp_file
+    demoaudio, sr = librosa.load(wav_fn)
+    key = title # 音高调整，支持正负（半音）
+    # 加速倍数
+    pndm_speedup = 20
+    wav_gen='queeeeee.wav'#直接改后缀可以保存不同格式音频，如flac可无损压缩
+    
+    # Show the spinner and run the run_clip function inside the 'with' block
+    with st.spinner("Rendering Audio..."):
+      f0_tst, f0_pred, audio = run_clip(svc_model,file_path=wav_fn, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.05,
+                                        use_gt_mel=False, add_noise_step=500,project_name=project_name,out_path=wav_gen)
+    clip_completed = True
+    if clip_completed:
+    # If the 'run_clip' function has completed, use the st.audio function to show an audio player for the file stored in the 'wav_gen' variable
+        st.audio(wav_gen)
+
+#######################################################
+st.set_page_config(
+    page_title="DiffSVC Render",
+    page_icon="🧊",
+    initial_sidebar_state="expanded",
+)
+############
+st.title('DIFF-SVC Render')
+
+###CKPT LOADER
+with tempfile.TemporaryDirectory(dir=os.path.expanduser("/~app")) as temp_dir:
+    ckpt = st.file_uploader("Choose your CKPT", type= 'ckpt')
+    # Check if user uploaded a CKPT file
+    if ckpt is not None:
+      #TEMP FUNCTION
+      with tempfile.NamedTemporaryFile(mode="wb", suffix='.ckpt', delete=False, dir=temp_dir) as temp:
+        # Get the file contents as bytes
+        bytes_data = ckpt.getvalue()
+        # Write the bytes to the temporary file
+        temp.write(bytes_data)
+        ckpt_temp_file = temp.name
+        # Print the temporary file name
+        print(temp.name)
+
+    # Display the file path
+    if "ckpt_temp_file" in locals():
+        st.success("File saved to: {}".format(ckpt_temp_file))
+
+    # File uploader
+    config = st.file_uploader("Choose your config", type= 'yaml')
+
+    # Check if user uploaded a config file
+    if config is not None:
+      #TEMP FUNCTION
+      with tempfile.NamedTemporaryFile(mode="w", suffix='.yaml', delete=False, dir=temp_dir) as temp:
+        # Get the file contents as bytes
+        bytes_data = config.getvalue()
+        # Write the bytes to the temporary file
+        temp.write(bytes_data)
+        config_temp_file = temp.name
+        # Print the temporary file name
+        print(temp.name)
+
+    # Display the file path
+    if "config_temp_file" in locals():
+        st.success("File saved to: {}".format(config_temp_file))
+
+    audio = st.file_uploader("Choose your audio", type=["wav", "mp3"])
+
+    # Check if user uploaded an audio file
+    if audio is not None:
+  #TEMP FUNCTION
+      with tempfile.NamedTemporaryFile(mode="wb", suffix='.wav', delete=False, dir=temp_dir) as temp:
+    # Get the file contents as bytes
+        bytes_data = audio.getvalue()
+    # Write the bytes to the temporary file
+        temp.write(bytes_data)
+        audio_temp_file = temp.name
+    # Print the temporary file name
+        print(temp.name)
+
+# Display the file path
+    if "audio_temp_file" in locals():
+        st.success("File saved to: {}".format(audio_temp_file))
+# Add a text input for the title with a default value of 0
+title = st.text_input("Key", value="0")
+# Add a button to start the rendering process
+if st.button("Render audio"):
+  render_audio(ckpt_temp_file, config_temp_file, audio_temp_file, title)
\ No newline at end of file
diff --git a/batch.py b/batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..07b283e6de56d70c3dac2883830ab84f132ec4c5
--- /dev/null
+++ b/batch.py
@@ -0,0 +1,43 @@
+import soundfile
+
+from infer_tools import infer_tool
+from infer_tools.infer_tool import Svc
+
+
+def run_clip(svc_model, key, acc, use_pe, use_crepe, thre, use_gt_mel, add_noise_step, project_name='', f_name=None,
+             file_path=None, out_path=None):
+    raw_audio_path = f_name
+    infer_tool.format_wav(raw_audio_path)
+    _f0_tst, _f0_pred, _audio = svc_model.infer(raw_audio_path, key=key, acc=acc, singer=True, use_pe=use_pe,
+                                                use_crepe=use_crepe,
+                                                thre=thre, use_gt_mel=use_gt_mel, add_noise_step=add_noise_step)
+    out_path = f'./singer_data/{f_name.split("/")[-1]}'
+    soundfile.write(out_path, _audio, 44100, 'PCM_16')
+
+
+if __name__ == '__main__':
+    # 工程文件夹名，训练时用的那个
+    project_name = "firefox"
+    model_path = f'./checkpoints/{project_name}/clean_model_ckpt_steps_100000.ckpt'
+    config_path = f'./checkpoints/{project_name}/config.yaml'
+
+    # 支持多个wav/ogg文件，放在raw文件夹下，带扩展名
+    file_names = infer_tool.get_end_file("./batch", "wav")
+    trans = [-6]  # 音高调整，支持正负（半音），数量与上一行对应，不足的自动按第一个移调参数补齐
+    # 加速倍数
+    accelerate = 50
+    hubert_gpu = True
+    cut_time = 30
+
+    # 下面不动
+    infer_tool.mkdir(["./batch", "./singer_data"])
+    infer_tool.fill_a_to_b(trans, file_names)
+
+    model = Svc(project_name, config_path, hubert_gpu, model_path)
+    count = 0
+    for f_name, tran in zip(file_names, trans):
+        print(f_name)
+        run_clip(model, key=tran, acc=accelerate, use_crepe=False, thre=0.05, use_pe=False, use_gt_mel=False,
+                 add_noise_step=500, f_name=f_name, project_name=project_name)
+        count += 1
+        print(f"process:{round(count * 100 / len(file_names), 2)}%")
diff --git a/checkpoints/0102_xiaoma_pe/config.yaml b/checkpoints/0102_xiaoma_pe/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..69a88444205377d48573d53bb4fb500860976588
--- /dev/null
+++ b/checkpoints/0102_xiaoma_pe/config.yaml
@@ -0,0 +1,172 @@
+accumulate_grad_batches: 1
+audio_num_mel_bins: 80
+audio_sample_rate: 24000
+base_config:
+- configs/tts/lj/fs2.yaml
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_f0cwt: true
+  with_spk_embed: true
+  with_txt: true
+  with_wav: false
+binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer
+binary_data_dir: data/binary/xiaoma1022_24k_128hop
+check_val_every_n_epoch: 10
+clip_grad_norm: 1
+cwt_add_f0_loss: false
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_std_scale: 0.8
+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+decoder_type: fft
+dict_dir: ''
+dropout: 0.1
+ds_workers: 4
+dur_enc_hidden_stride_kernel:
+- 0,2,3
+- 0,2,3
+- 0,1,3
+dur_loss: mse
+dur_predictor_kernel: 3
+dur_predictor_layers: 2
+enc_ffn_kernel_size: 9
+enc_layers: 4
+encoder_K: 8
+encoder_type: fft
+endless_ds: true
+ffn_act: gelu
+ffn_padding: SAME
+fft_size: 512
+fmax: 12000
+fmin: 30
+gen_dir_name: ''
+hidden_size: 256
+hop_size: 128
+infer: false
+lambda_commit: 0.25
+lambda_energy: 0.1
+lambda_f0: 1.0
+lambda_ph_dur: 1.0
+lambda_sent_dur: 1.0
+lambda_uv: 1.0
+lambda_word_dur: 1.0
+load_ckpt: ''
+log_interval: 100
+loud_norm: false
+lr: 2.0
+max_epochs: 1000
+max_eval_sentences: 1
+max_eval_tokens: 60000
+max_frames: 5000
+max_input_tokens: 1550
+max_sentences: 100000
+max_tokens: 20000
+max_updates: 60000
+mel_loss: l1
+mel_vmax: 1.5
+mel_vmin: -6
+min_level_db: -120
+norm_type: gn
+num_ckpt_keep: 3
+num_heads: 2
+num_sanity_val_steps: 5
+num_spk: 1
+num_test_samples: 20
+num_valid_plots: 10
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+out_wav_norm: false
+pitch_ar: false
+pitch_enc_hidden_stride_kernel:
+- 0,2,5
+- 0,2,5
+- 0,2,5
+pitch_extractor_conv_layers: 2
+pitch_loss: l1
+pitch_norm: log
+pitch_type: frame
+pre_align_args:
+  allow_no_txt: false
+  denoise: false
+  forced_align: mfa
+  txt_processor: en
+  use_sox: false
+  use_tone: true
+pre_align_cls: data_gen.tts.lj.pre_align.LJPreAlign
+predictor_dropout: 0.5
+predictor_grad: 0.1
+predictor_hidden: -1
+predictor_kernel: 5
+predictor_layers: 2
+prenet_dropout: 0.5
+prenet_hidden_size: 256
+pretrain_fs_ckpt: ''
+processed_data_dir: data/processed/ljspeech
+profile_infer: false
+raw_data_dir: data/raw/LJSpeech-1.1
+ref_norm_layer: bn
+reset_phone_dict: true
+save_best: false
+save_ckpt: true
+save_codes:
+- configs
+- modules
+- tasks
+- utils
+- usr
+save_f0: false
+save_gt: false
+seed: 1234
+sort_by_len: true
+stop_token_weight: 5.0
+task_cls: tasks.tts.pe.PitchExtractionTask
+test_ids:
+- 68
+- 70
+- 74
+- 87
+- 110
+- 172
+- 190
+- 215
+- 231
+- 294
+- 316
+- 324
+- 402
+- 422
+- 485
+- 500
+- 505
+- 508
+- 509
+- 519
+test_input_dir: ''
+test_num: 523
+test_set_name: test
+train_set_name: train
+use_denoise: false
+use_energy_embed: false
+use_gt_dur: false
+use_gt_f0: false
+use_pitch_embed: true
+use_pos_embed: true
+use_spk_embed: false
+use_spk_id: false
+use_split_spk_id: false
+use_uv: true
+use_var_enc: false
+val_check_interval: 2000
+valid_num: 348
+valid_set_name: valid
+vocoder: pwg
+vocoder_ckpt: ''
+warmup_updates: 2000
+weight_decay: 0
+win_size: 512
+work_dir: checkpoints/0102_xiaoma_pe
diff --git a/checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt b/checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..fb8ffa57bddeef3b1def9ebf3e12311f0a1ea799
--- /dev/null
+++ b/checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1863f12324e43783089ab933edeeb969106b851e30d71019ebbaa9b82099d82a
+size 39141959
diff --git a/checkpoints/0109_hifigan_bigpopcs_hop128/config.yaml b/checkpoints/0109_hifigan_bigpopcs_hop128/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..95fc5414ba1aff1bad8284ebfba52f5636b4d76d
--- /dev/null
+++ b/checkpoints/0109_hifigan_bigpopcs_hop128/config.yaml
@@ -0,0 +1,241 @@
+accumulate_grad_batches: 1
+adam_b1: 0.8
+adam_b2: 0.99
+amp: false
+audio_num_mel_bins: 80
+audio_sample_rate: 24000
+aux_context_window: 0
+#base_config:
+#- egs/egs_bases/singing/pwg.yaml
+#- egs/egs_bases/tts/vocoder/hifigan.yaml
+binarization_args:
+  reset_phone_dict: true
+  reset_word_dict: true
+  shuffle: false
+  trim_eos_bos: false
+  trim_sil: false
+  with_align: false
+  with_f0: true
+  with_f0cwt: false
+  with_linear: false
+  with_spk_embed: false
+  with_spk_id: true
+  with_txt: false
+  with_wav: true
+  with_word: false
+binarizer_cls: data_gen.tts.singing.binarize.SingingBinarizer
+binary_data_dir: data/binary/big_popcs_24k_hop128
+check_val_every_n_epoch: 10
+clip_grad_norm: 1
+clip_grad_value: 0
+datasets: []
+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+dict_dir: ''
+disc_start_steps: 40000
+discriminator_grad_norm: 1
+discriminator_optimizer_params:
+  eps: 1.0e-06
+  lr: 0.0002
+  weight_decay: 0.0
+discriminator_params:
+  bias: true
+  conv_channels: 64
+  in_channels: 1
+  kernel_size: 3
+  layers: 10
+  nonlinear_activation: LeakyReLU
+  nonlinear_activation_params:
+    negative_slope: 0.2
+  out_channels: 1
+  use_weight_norm: true
+discriminator_scheduler_params:
+  gamma: 0.999
+  step_size: 600
+dropout: 0.1
+ds_workers: 1
+enc_ffn_kernel_size: 9
+enc_layers: 4
+endless_ds: true
+ffn_act: gelu
+ffn_padding: SAME
+fft_size: 512
+fmax: 12000
+fmin: 30
+frames_multiple: 1
+gen_dir_name: ''
+generator_grad_norm: 10
+generator_optimizer_params:
+  eps: 1.0e-06
+  lr: 0.0002
+  weight_decay: 0.0
+generator_params:
+  aux_channels: 80
+  dropout: 0.0
+  gate_channels: 128
+  in_channels: 1
+  kernel_size: 3
+  layers: 30
+  out_channels: 1
+  residual_channels: 64
+  skip_channels: 64
+  stacks: 3
+  upsample_net: ConvInUpsampleNetwork
+  upsample_params:
+    upsample_scales:
+    - 2
+    - 4
+    - 4
+    - 4
+  use_nsf: false
+  use_pitch_embed: true
+  use_weight_norm: true
+generator_scheduler_params:
+  gamma: 0.999
+  step_size: 600
+griffin_lim_iters: 60
+hidden_size: 256
+hop_size: 128
+infer: false
+lambda_adv: 1.0
+lambda_cdisc: 4.0
+lambda_energy: 0.0
+lambda_f0: 0.0
+lambda_mel: 5.0
+lambda_mel_adv: 1.0
+lambda_ph_dur: 0.0
+lambda_sent_dur: 0.0
+lambda_uv: 0.0
+lambda_word_dur: 0.0
+load_ckpt: ''
+loud_norm: false
+lr: 2.0
+max_epochs: 1000
+max_frames: 2400
+max_input_tokens: 1550
+max_samples: 8192
+max_sentences: 20
+max_tokens: 24000
+max_updates: 3000000
+max_valid_sentences: 1
+max_valid_tokens: 60000
+mel_loss: ssim:0.5|l1:0.5
+mel_vmax: 1.5
+mel_vmin: -6
+min_frames: 0
+min_level_db: -120
+num_ckpt_keep: 3
+num_heads: 2
+num_mels: 80
+num_sanity_val_steps: 5
+num_spk: 100
+num_test_samples: 0
+num_valid_plots: 10
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+out_wav_norm: false
+pitch_extractor: parselmouth
+pitch_type: frame
+pre_align_args:
+  allow_no_txt: false
+  denoise: false
+  sox_resample: true
+  sox_to_wav: false
+  trim_sil: false
+  txt_processor: zh
+  use_tone: false
+pre_align_cls: data_gen.tts.singing.pre_align.SingingPreAlign
+predictor_grad: 0.0
+print_nan_grads: false
+processed_data_dir: ''
+profile_infer: false
+raw_data_dir: ''
+ref_level_db: 20
+rename_tmux: true
+rerun_gen: true
+resblock: '1'
+resblock_dilation_sizes:
+- - 1
+  - 3
+  - 5
+- - 1
+  - 3
+  - 5
+- - 1
+  - 3
+  - 5
+resblock_kernel_sizes:
+- 3
+- 7
+- 11
+resume_from_checkpoint: 0
+save_best: true
+save_codes: []
+save_f0: true
+save_gt: true
+scheduler: rsqrt
+seed: 1234
+sort_by_len: true
+stft_loss_params:
+  fft_sizes:
+  - 1024
+  - 2048
+  - 512
+  hop_sizes:
+  - 120
+  - 240
+  - 50
+  win_lengths:
+  - 600
+  - 1200
+  - 240
+  window: hann_window
+task_cls: tasks.vocoder.hifigan.HifiGanTask
+tb_log_interval: 100
+test_ids: []
+test_input_dir: ''
+test_num: 50
+test_prefixes: []
+test_set_name: test
+train_set_name: train
+train_sets: ''
+upsample_initial_channel: 512
+upsample_kernel_sizes:
+- 16
+- 16
+- 4
+- 4
+upsample_rates:
+- 8
+- 4
+- 2
+- 2
+use_cdisc: false
+use_cond_disc: false
+use_fm_loss: false
+use_gt_dur: true
+use_gt_f0: true
+use_mel_loss: true
+use_ms_stft: false
+use_pitch_embed: true
+use_ref_enc: true
+use_spec_disc: false
+use_spk_embed: false
+use_spk_id: false
+use_split_spk_id: false
+val_check_interval: 2000
+valid_infer_interval: 10000
+valid_monitor_key: val_loss
+valid_monitor_mode: min
+valid_set_name: valid
+vocoder: pwg
+vocoder_ckpt: ''
+vocoder_denoise_c: 0.0
+warmup_updates: 8000
+weight_decay: 0
+win_length: null
+win_size: 512
+window: hann
+word_size: 3000
+work_dir: checkpoints/0109_hifigan_bigpopcs_hop128
diff --git a/checkpoints/0109_hifigan_bigpopcs_hop128/model_ckpt_steps_1512000.ckpt b/checkpoints/0109_hifigan_bigpopcs_hop128/model_ckpt_steps_1512000.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..ed55eaa98f86e3e22f4eb4e8115f254745cea155
--- /dev/null
+++ b/checkpoints/0109_hifigan_bigpopcs_hop128/model_ckpt_steps_1512000.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cb68f3ce0c46ba0a8b6d49718f1fffdf5bd7bcab769a986fd2fd129835cc1d1
+size 55827436
diff --git a/checkpoints/Unnamed/config.yaml b/checkpoints/Unnamed/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e49935b8b7ff2a83ba1008f3617497e8c3c6da53
--- /dev/null
+++ b/checkpoints/Unnamed/config.yaml
@@ -0,0 +1,445 @@
+K_step: 1000
+accumulate_grad_batches: 1
+audio_num_mel_bins: 128
+audio_sample_rate: 44100
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_hubert: true
+  with_spk_embed: false
+  with_wav: false
+binarizer_cls: preprocessing.SVCpre.SVCBinarizer
+binary_data_dir: data/binary/Unnamed
+check_val_every_n_epoch: 10
+choose_test_manually: false
+clip_grad_norm: 1
+config_path: training/config_nsf.yaml
+content_cond_steps: []
+cwt_add_f0_loss: false
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_std_scale: 0.8
+datasets:
+- opencpop
+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+decay_steps: 60000
+decoder_type: fft
+dict_dir: ''
+diff_decoder_type: wavenet
+diff_loss_type: l2
+dilation_cycle_length: 4
+dropout: 0.1
+ds_workers: 4
+dur_enc_hidden_stride_kernel:
+- 0,2,3
+- 0,2,3
+- 0,1,3
+dur_loss: mse
+dur_predictor_kernel: 3
+dur_predictor_layers: 5
+enc_ffn_kernel_size: 9
+enc_layers: 4
+encoder_K: 8
+encoder_type: fft
+endless_ds: false
+f0_bin: 256
+f0_max: 1100.0
+f0_min: 40.0
+ffn_act: gelu
+ffn_padding: SAME
+fft_size: 2048
+fmax: 16000
+fmin: 40
+fs2_ckpt: ''
+gaussian_start: true
+gen_dir_name: ''
+gen_tgt_spk_id: -1
+hidden_size: 256
+hop_size: 512
+hubert_gpu: true
+hubert_path: checkpoints/hubert/hubert_soft.pt
+infer: false
+keep_bins: 128
+lambda_commit: 0.25
+lambda_energy: 0.0
+lambda_f0: 1.0
+lambda_ph_dur: 0.3
+lambda_sent_dur: 1.0
+lambda_uv: 1.0
+lambda_word_dur: 1.0
+load_ckpt: ''
+log_interval: 100
+loud_norm: false
+lr: 0.0008
+max_beta: 0.02
+max_epochs: 3000
+max_eval_sentences: 1
+max_eval_tokens: 60000
+max_frames: 42000
+max_input_tokens: 60000
+max_sentences: 14
+max_tokens: 128000
+max_updates: 1000000
+mel_loss: ssim:0.5|l1:0.5
+mel_vmax: 1.5
+mel_vmin: -6.0
+min_level_db: -120
+no_fs2: true
+norm_type: gn
+num_ckpt_keep: 10
+num_heads: 2
+num_sanity_val_steps: 1
+num_spk: 1
+num_test_samples: 0
+num_valid_plots: 10
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+out_wav_norm: false
+pe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
+pe_enable: false
+perform_enhance: true
+pitch_ar: false
+pitch_enc_hidden_stride_kernel:
+- 0,2,5
+- 0,2,5
+- 0,2,5
+pitch_extractor: parselmouth
+pitch_loss: l2
+pitch_norm: log
+pitch_type: frame
+pndm_speedup: 10
+pre_align_args:
+  allow_no_txt: false
+  denoise: false
+  forced_align: mfa
+  txt_processor: zh_g2pM
+  use_sox: true
+  use_tone: false
+pre_align_cls: data_gen.singing.pre_align.SingingPreAlign
+predictor_dropout: 0.5
+predictor_grad: 0.1
+predictor_hidden: -1
+predictor_kernel: 5
+predictor_layers: 5
+prenet_dropout: 0.5
+prenet_hidden_size: 256
+pretrain_fs_ckpt: ''
+processed_data_dir: xxx
+profile_infer: false
+raw_data_dir: data/raw/Unnamed
+ref_norm_layer: bn
+rel_pos: true
+reset_phone_dict: true
+residual_channels: 384
+residual_layers: 20
+save_best: true
+save_ckpt: true
+save_codes:
+- configs
+- modules
+- src
+- utils
+save_f0: true
+save_gt: false
+schedule_type: linear
+seed: 1234
+sort_by_len: true
+speaker_id: Unnamed
+spec_max:
+- 0.47615352272987366
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diff --git a/checkpoints/Unnamed/config_nsf.yaml b/checkpoints/Unnamed/config_nsf.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0f07e15dbd2382be4a77521411e52b9214e5444f
--- /dev/null
+++ b/checkpoints/Unnamed/config_nsf.yaml
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+K_step: 1000
+accumulate_grad_batches: 1
+audio_num_mel_bins: 128
+audio_sample_rate: 44100
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+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_hubert: true
+  with_spk_embed: false
+  with_wav: false
+binarizer_cls: preprocessing.SVCpre.SVCBinarizer
+binary_data_dir: data/binary/Unnamed
+check_val_every_n_epoch: 10
+choose_test_manually: false
+clip_grad_norm: 1
+config_path: training/config_nsf.yaml
+content_cond_steps: []
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+cwt_hidden_size: 128
+cwt_layers: 2
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+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+decay_steps: 20000
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+dict_dir: ''
+diff_decoder_type: wavenet
+diff_loss_type: l2
+dilation_cycle_length: 4
+dropout: 0.1
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+  txt_processor: zh_g2pM
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diff --git a/checkpoints/Unnamed/lightning_logs/lastest/hparams.yaml b/checkpoints/Unnamed/lightning_logs/lastest/hparams.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0967ef424bce6791893e9a57bb952f80fd536e93
--- /dev/null
+++ b/checkpoints/Unnamed/lightning_logs/lastest/hparams.yaml
@@ -0,0 +1 @@
+{}
diff --git a/checkpoints/Unnamed/model_ckpt_steps_192000.ckpt b/checkpoints/Unnamed/model_ckpt_steps_192000.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..c758264b63b667b86aa648c71426f07993c9d55e
--- /dev/null
+++ b/checkpoints/Unnamed/model_ckpt_steps_192000.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c441462923580893a6170dd00126084be0a20b387b1c4fb1860755acd36c881b
+size 391390823
diff --git a/checkpoints/hubert/hubert_soft.pt b/checkpoints/hubert/hubert_soft.pt
new file mode 100644
index 0000000000000000000000000000000000000000..5ccd36b11dc124c97a0b73fa5f39eed8d1a6f27a
--- /dev/null
+++ b/checkpoints/hubert/hubert_soft.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e82e7d079df05fe3aa535f6f7d42d309bdae1d2a53324e2b2386c56721f4f649
+size 378435957
diff --git a/checkpoints/nsf_hifigan/NOTICE.txt b/checkpoints/nsf_hifigan/NOTICE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..228fc663c20c3166dc16dca0b3b94dca38a489b8
--- /dev/null
+++ b/checkpoints/nsf_hifigan/NOTICE.txt
@@ -0,0 +1,74 @@
+--- DiffSinger Community Vocoder ---
+
+ARCHITECTURE: NSF-HiFiGAN
+RELEASE DATE: 2022-12-11
+
+HYPER PARAMETERS:
+ - 44100 sample rate
+ - 128 mel bins
+ - 512 hop size
+ - 2048 window size
+ - fmin at 40Hz
+ - fmax at 16000Hz
+
+
+NOTICE:
+
+All model weights in the [DiffSinger Community Vocoder Project](https://openvpi.github.io/vocoders/), including
+model weights in this directory, are provided by the [OpenVPI Team](https://github.com/openvpi/), under the
+[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
+
+
+ACKNOWLEDGEMENTS:
+
+Training data of this vocoder is provided and permitted by the following organizations, societies and individuals:
+
+孙飒              https://www.qfssr.cn
+赤松_Akamatsu     https://www.zhibin.club
+乐威              https://www.zhibin.club
+伯添              https://space.bilibili.com/24087011
+雲宇光             https://space.bilibili.com/660675050
+橙子言             https://space.bilibili.com/318486464
+人衣大人           https://space.bilibili.com/2270344
+玖蝶              https://space.bilibili.com/676771003
+Yuuko
+白夜零BYL          https://space.bilibili.com/1605040503
+嗷天              https://space.bilibili.com/5675252
+洛泠羽            https://space.bilibili.com/347373318
+灰条纹的灰猫君      https://space.bilibili.com/2083633
+幽寂              https://space.bilibili.com/478860
+恶魔王女           https://space.bilibili.com/2475098
+AlexYHX 芮晴
+绮萱              https://y.qq.com/n/ryqq/singer/003HjD6H4aZn1K
+诗芸              https://y.qq.com/n/ryqq/singer/0005NInj142zm0
+汐蕾              https://y.qq.com/n/ryqq/singer/0023cWMH1Bq1PJ
+1262917464
+炜阳
+叶卡yolka
+幸の夏            https://space.bilibili.com/1017297686
+暮色未量           https://space.bilibili.com/272904686
+晓寞sama          https://space.bilibili.com/3463394
+没头绪的节操君
+串串BunC          https://space.bilibili.com/95817834
+落雨              https://space.bilibili.com/1292427
+长尾巴的翎艾        https://space.bilibili.com/1638666
+声闻计划           https://space.bilibili.com/392812269
+唐家大小姐         http://5sing.kugou.com/palmusic/default.html
+不伊子
+
+Training machines are provided by:
+
+花儿不哭           https://space.bilibili.com/5760446
+
+
+TERMS OF REDISTRIBUTIONS:
+
+1. Do not sell this vocoder, or charge any fees from redistributing it, as prohibited by
+   the license.
+2. Include a copy of the CC BY-NC-SA 4.0 license, or a link referring to it.
+3. Include a copy of this notice, or any other notices informing that this vocoder is
+   provided by the OpenVPI Team, that this vocoder is licensed under CC BY-NC-SA 4.0, and
+   with a complete acknowledgement list as shown above.
+4. If you fine-tuned or modified the weights, leave a notice about what has been changed.
+5. (Optional) Leave a link to the official release page of the vocoder, and tell users
+   that other versions and future updates of this vocoder can be obtained from the website.
diff --git a/checkpoints/nsf_hifigan/NOTICE.zh-CN.txt b/checkpoints/nsf_hifigan/NOTICE.zh-CN.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b206a0bd1d3b80feb66c52a7452856021d06805a
--- /dev/null
+++ b/checkpoints/nsf_hifigan/NOTICE.zh-CN.txt
@@ -0,0 +1,72 @@
+--- DiffSinger 社区声码器 ---
+
+架构：NSF-HiFiGAN
+发布日期：2022-12-11
+
+超参数：
+ - 44100 sample rate
+ - 128 mel bins
+ - 512 hop size
+ - 2048 window size
+ - fmin at 40Hz
+ - fmax at 16000Hz
+
+
+注意事项：
+
+[DiffSinger 社区声码器企划](https://openvpi.github.io/vocoders/) 中的所有模型权重，
+包括此目录下的模型权重，均由 [OpenVPI Team](https://github.com/openvpi/) 提供，并基于
+[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+进行许可。
+
+
+致谢：
+
+此声码器的训练数据由以下组织、社团和个人提供并许可：
+
+孙飒              https://www.qfssr.cn
+赤松_Akamatsu     https://www.zhibin.club
+乐威              https://www.zhibin.club
+伯添              https://space.bilibili.com/24087011
+雲宇光             https://space.bilibili.com/660675050
+橙子言             https://space.bilibili.com/318486464
+人衣大人           https://space.bilibili.com/2270344
+玖蝶              https://space.bilibili.com/676771003
+Yuuko
+白夜零BYL          https://space.bilibili.com/1605040503
+嗷天              https://space.bilibili.com/5675252
+洛泠羽            https://space.bilibili.com/347373318
+灰条纹的灰猫君      https://space.bilibili.com/2083633
+幽寂              https://space.bilibili.com/478860
+恶魔王女           https://space.bilibili.com/2475098
+AlexYHX 芮晴
+绮萱              https://y.qq.com/n/ryqq/singer/003HjD6H4aZn1K
+诗芸              https://y.qq.com/n/ryqq/singer/0005NInj142zm0
+汐蕾              https://y.qq.com/n/ryqq/singer/0023cWMH1Bq1PJ
+1262917464
+炜阳
+叶卡yolka
+幸の夏            https://space.bilibili.com/1017297686
+暮色未量           https://space.bilibili.com/272904686
+晓寞sama          https://space.bilibili.com/3463394
+没头绪的节操君
+串串BunC          https://space.bilibili.com/95817834
+落雨              https://space.bilibili.com/1292427
+长尾巴的翎艾        https://space.bilibili.com/1638666
+声闻计划           https://space.bilibili.com/392812269
+唐家大小姐         http://5sing.kugou.com/palmusic/default.html
+不伊子
+
+训练算力的提供者如下：
+
+花儿不哭           https://space.bilibili.com/5760446
+
+
+二次分发条款：
+
+1. 请勿售卖此声码器或从其二次分发过程中收取任何费用，因为此类行为受到许可证的禁止。
+2. 请在二次分发文件中包含一份 CC BY-NC-SA 4.0 许可证的副本或指向该许可证的链接。
+3. 请在二次分发文件中包含这份声明，或以其他形式声明此声码器由 OpenVPI Team 提供并基于 CC BY-NC-SA 4.0 许可，
+   并附带上述完整的致谢名单。
+4. 如果您微调或修改了权重，请留下一份关于其受到了何种修改的说明。
+5.（可选）留下一份指向此声码器的官方发布页面的链接，并告知使用者可从该网站获取此声码器的其他版本和未来的更新。
diff --git a/checkpoints/nsf_hifigan/config.json b/checkpoints/nsf_hifigan/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..79821fb218253a51b8bcbaa2eaed539a79c78d32
--- /dev/null
+++ b/checkpoints/nsf_hifigan/config.json
@@ -0,0 +1,38 @@
+{
+    "resblock": "1",
+    "num_gpus": 4,
+    "batch_size": 10,
+    "learning_rate": 0.0002,
+    "adam_b1": 0.8,
+    "adam_b2": 0.99,
+    "lr_decay": 0.999,
+    "seed": 1234,
+
+    "upsample_rates":        [ 8, 8, 2, 2, 2],
+    "upsample_kernel_sizes": [16,16, 4, 4, 4],
+    "upsample_initial_channel": 512,
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "discriminator_periods": [3, 5, 7, 11, 17, 23, 37],
+
+    "segment_size": 16384,
+    "num_mels": 128,
+    "num_freq": 1025,
+    "n_fft"   : 2048,
+    "hop_size": 512,
+    "win_size": 2048,
+
+    "sampling_rate": 44100,
+
+    "fmin": 40,
+    "fmax": 16000,
+    "fmax_for_loss": null,
+
+    "num_workers": 16,
+
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321",
+        "world_size": 1
+    }
+}
diff --git a/ckpt.jpg b/ckpt.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..589c02413686da308443a1b03b5f19e1c13d6d47
Binary files /dev/null and b/ckpt.jpg differ
diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d49f0ecb2e118cc255d97e6077eb8e62046f4a05
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,349 @@
+K_step: 1000
+accumulate_grad_batches: 1
+audio_num_mel_bins: 80
+audio_sample_rate: 24000
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_hubert: true
+  with_spk_embed: false
+  with_wav: false
+binarizer_cls: preprocessing.SVCpre.SVCBinarizer
+binary_data_dir: data/binary/atri
+check_val_every_n_epoch: 10
+choose_test_manually: false
+clip_grad_norm: 1
+config_path: training/config.yaml
+content_cond_steps: []
+cwt_add_f0_loss: false
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_std_scale: 0.8
+datasets:
+- opencpop
+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+decay_steps: 30000
+decoder_type: fft
+dict_dir: ''
+diff_decoder_type: wavenet
+diff_loss_type: l2
+dilation_cycle_length: 4
+dropout: 0.1
+ds_workers: 4
+dur_enc_hidden_stride_kernel:
+- 0,2,3
+- 0,2,3
+- 0,1,3
+dur_loss: mse
+dur_predictor_kernel: 3
+dur_predictor_layers: 5
+enc_ffn_kernel_size: 9
+enc_layers: 4
+encoder_K: 8
+encoder_type: fft
+endless_ds: False
+f0_bin: 256
+f0_max: 1100.0
+f0_min: 50.0
+ffn_act: gelu
+ffn_padding: SAME
+fft_size: 512
+fmax: 12000
+fmin: 30
+fs2_ckpt: ''
+gaussian_start: true
+gen_dir_name: ''
+gen_tgt_spk_id: -1
+hidden_size: 256
+hop_size: 128
+hubert_gpu: true
+hubert_path: checkpoints/hubert/hubert_soft.pt
+infer: false
+keep_bins: 80
+lambda_commit: 0.25
+lambda_energy: 0.0
+lambda_f0: 1.0
+lambda_ph_dur: 0.3
+lambda_sent_dur: 1.0
+lambda_uv: 1.0
+lambda_word_dur: 1.0
+load_ckpt: ''
+log_interval: 100
+loud_norm: false
+lr: 5.0e-05
+max_beta: 0.02
+max_epochs: 3000
+max_eval_sentences: 1
+max_eval_tokens: 60000
+max_frames: 42000
+max_input_tokens: 60000
+max_sentences: 24
+max_tokens: 128000
+max_updates: 1000000
+mel_loss: ssim:0.5|l1:0.5
+mel_vmax: 1.5
+mel_vmin: -6.0
+min_level_db: -120
+norm_type: gn
+num_ckpt_keep: 10
+num_heads: 2
+num_sanity_val_steps: 1
+num_spk: 1
+num_test_samples: 0
+num_valid_plots: 10
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+out_wav_norm: false
+pe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
+pe_enable: false
+perform_enhance: true
+pitch_ar: false
+pitch_enc_hidden_stride_kernel:
+- 0,2,5
+- 0,2,5
+- 0,2,5
+pitch_extractor: parselmouth
+pitch_loss: l2
+pitch_norm: log
+pitch_type: frame
+pndm_speedup: 10
+pre_align_args:
+  allow_no_txt: false
+  denoise: false
+  forced_align: mfa
+  txt_processor: zh_g2pM
+  use_sox: true
+  use_tone: false
+pre_align_cls: data_gen.singing.pre_align.SingingPreAlign
+predictor_dropout: 0.5
+predictor_grad: 0.1
+predictor_hidden: -1
+predictor_kernel: 5
+predictor_layers: 5
+prenet_dropout: 0.5
+prenet_hidden_size: 256
+pretrain_fs_ckpt: pretrain/nyaru/model_ckpt_steps_60000.ckpt
+processed_data_dir: xxx
+profile_infer: false
+raw_data_dir: data/raw/atri
+ref_norm_layer: bn
+rel_pos: true
+reset_phone_dict: true
+residual_channels: 256
+residual_layers: 20
+save_best: false
+save_ckpt: true
+save_codes:
+- configs
+- modules
+- src
+- utils
+save_f0: true
+save_gt: false
+schedule_type: linear
+seed: 1234
+sort_by_len: true
+speaker_id: atri
+spec_max:
+- 0.2987259328365326
+- 0.29721200466156006
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+- 0.208412766456604
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+spec_min:
+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
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+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
+- -6.0
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+- -6.0
+- -6.0
+- -6.0
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+- -6.0
+- -6.0
+- -6.0
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+- -6.0
+- -6.0
+- -6.0
+- -6.0
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+- -6.0
+- -6.0
+spk_cond_steps: []
+stop_token_weight: 5.0
+task_cls: training.task.SVC_task.SVCTask
+test_ids: []
+test_input_dir: ''
+test_num: 0
+test_prefixes:
+- test
+test_set_name: test
+timesteps: 1000
+train_set_name: train
+use_crepe: true
+use_denoise: false
+use_energy_embed: false
+use_gt_dur: false
+use_gt_f0: false
+use_midi: false
+use_nsf: true
+use_pitch_embed: true
+use_pos_embed: true
+use_spk_embed: false
+use_spk_id: false
+use_split_spk_id: false
+use_uv: false
+use_var_enc: false
+use_vec: false
+val_check_interval: 2000
+valid_num: 0
+valid_set_name: valid
+vocoder: network.vocoders.hifigan.HifiGAN
+vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128
+warmup_updates: 2000
+wav2spec_eps: 1e-6
+weight_decay: 0
+win_size: 512
+work_dir: checkpoints/atri
+no_fs2: false
\ No newline at end of file
diff --git a/doc/train_and_inference.markdown b/doc/train_and_inference.markdown
new file mode 100644
index 0000000000000000000000000000000000000000..eed0d6e9ad470db1d3fa8d8410d5c51fb026a19f
--- /dev/null
+++ b/doc/train_and_inference.markdown
@@ -0,0 +1,210 @@
+# Diff-SVC(train/inference by yourself)
+## 0.环境配置
+>注意:requirements文件已更新，目前分为3个版本，可自行选择使用。\
+1. requirements.txt 是此仓库测试的原始完整环境，Torch1.12.1+cu113,可选择直接pip 或删除其中与pytorch有关的项目(torch/torchvision)后再pip，并使用自己的torch环境
+```
+pip install -r requirements.txt
+```
+>2. (推荐)requirements_short.txt 是上述环境的手动整理版，不含torch本体，也可以直接
+```
+pip install -r requirements_short.txt
+```
+>3. 根目录下有一份@三千整理的依赖列表requirements.png，是在某品牌云服务器上跑通的，不过此torch版本已不兼容目前版本代码,但是其他部分版本可以参考，十分感谢
+
+## 1.推理
+>使用根目录下的inference.ipynb进行推理或使用经过作者适配的@小狼的infer.py\
+在第一个block中修改如下参数：
+```
+config_path='checkpoints压缩包中config.yaml的位置'
+如'./checkpoints/nyaru/config.yaml'
+config和checkpoints是一一对应的，请不要使用其他config
+
+project_name='这个项目的名称'
+如'nyaru'
+
+model_path='ckpt文件的全路径'
+如'./checkpoints/nyaru/model_ckpt_steps_112000.ckpt'
+
+hubert_gpu=True
+推理时是否使用gpu推理hubert(模型中的一个模块)，不影响模型的其他部分
+目前版本已大幅减小hubert的gpu占用，在1060 6G显存下可完整推理，不需要关闭了。
+另外现已支持长音频自动切片功能(ipynb和infer.py均可)，超过30s的音频将自动在静音处切片处理，感谢@小狼的代码
+
+```
+### 可调节参数：
+```
+wav_fn='xxx.wav'#传入音频的路径，默认在项目根目录中
+
+use_crepe=True 
+#crepe是一个F0算法，效果好但速度慢，改成False会使用效果稍逊于crepe但较快的parselmouth算法
+
+thre=0.05
+#crepe的噪声过滤阈值，源音频干净可适当调大，噪音多就保持这个数值或者调小，前面改成False后这个参数不起作用
+
+pndm_speedup=20
+#推理加速算法倍数，默认是1000步，这里填成10就是只使用100步合成，是一个中规中矩的数值，这个数值可以高到50倍(20步合成)没有明显质量损失，再大可能会有可观的质量损失,注意如果下方开启了use_gt_mel, 应保证这个数值小于add_noise_step，并尽量让其能够整除
+
+key=0
+#变调参数，默认为0(不是1!!)，将源音频的音高升高key个半音后合成，如男声转女生，可填入8或者12等(12就是升高一整个8度)
+
+use_pe=True
+#梅尔谱合成音频时使用的F0提取算法，如果改成False将使用源音频的F0\
+这里填True和False合成会略有差异，通常是True会好些，但也不尽然，对合成速度几乎无影响\
+(无论key填什么 这里都是可以自由选择的，不影响)\
+44.1kHz下不支持此功能，会自动关闭，开着也不报错就是了
+
+use_gt_mel=False
+#这个选项类似于AI画图的图生图功能，如果打开，产生的音频将是输入声音与目标说话人声音的混合，混合比例由下一个参数确定
+注意!!!：这个参数如果改成True，请确保key填成0，不支持变调
+
+add_noise_step=500
+#与上个参数有关，控制两种声音的比例，填入1是完全的源声线，填入1000是完全的目标声线，能听出来是两者均等混合的数值大约在300附近(并不是线性的，另外这个参数如果调的很小，可以把pndm加速倍率调低，增加合成质量)
+
+wav_gen='yyy.wav'#输出音频的路径，默认在项目根目录中，可通过改变扩展名更改保存文件类型
+```
+如果使用infer.py，修改方式类似，需要修改__name__=='__main__'中的部分，然后在根目录中执行\
+python infer.py\
+这种方式需要将原音频放入raw中并在results中查找结果
+## 2.数据预处理与训练
+### 2.1 准备数据
+>目前支持wav格式和ogg格式的音频数据，采样率最好高于24kHz，程序会自动处理采样率和声道问题。采样率不可低于16kHz（一般不会的）\
+音频需要切片为5-15s为宜的短音频，长度没有具体要求，但不宜过长过短。音频需要为纯目标人干声，不可以有背景音乐和其他人声音，最好也不要有过重的混响等。若经过去伴奏等处理，请尽量保证处理后的音频质量。\
+目前仅支持单人训练，总时长尽量保证在3h或以上，不需要额外任何标注，将音频文件放在下述raw_data_dir下即可，这个目录下的结构可以自由定义，程序会自主找到所需文件。
+
+### 2.2 修改超参数配置
+>首先请备份一份config.yaml(此文件对应24kHz声码器, 44.1kHz声码器请使用config_nsf.yaml)，然后修改它\
+可能会用到的参数如下(以工程名为nyaru为例):
+```
+K_step: 1000
+#diffusion过程总的step,建议不要修改
+
+binary_data_dir: data/binary/nyaru
+预处理后数据的存放地址:需要将后缀改成工程名字
+
+config_path: training/config.yaml
+你要使用的这份yaml自身的地址，由于预处理过程中会写入数据，所以这个地址务必修改成将要存放这份yaml文件的完整路径
+
+choose_test_manually: false
+手动选择测试集，默认关闭，自动随机抽取5条音频作为测试集。
+如果改为ture，请在test_prefixes:中填入测试数据的文件名前缀，程序会将以对应前缀开头的文件作为测试集
+这是个列表，可以填多个前缀，如：
+test_prefixes:
+- test
+- aaaa
+- 5012
+- speaker1024
+重要：测试集*不可以*为空，为了不产生意外影响，建议尽量不要手动选择测试集
+
+endless_ds:False
+如果你的数据集过小，每个epoch时间很短，请将此项打开，将把正常的1000epoch作为一个epoch计算
+
+hubert_path: checkpoints/hubert/hubert.pt
+hubert模型的存放地址，确保这个路径是对的，一般解压checkpoints包之后就是这个路径不需要改,现已使用torch版本推理
+hubert_gpu:True
+是否在预处理时使用gpu运行hubert(模型的一个模块)，关闭后使用cpu，但耗时会显著增加。另外模型训练完推理时hubert是否用gpu是在inference中单独控制的，不受此处影响。目前hubert改为torch版后已经可以做到在1060 6G显存gpu上进行预处理，与直接推理1分钟内的音频不超出显存限制，一般不需要关了。
+
+lr: 0.0008
+#初始的学习率:这个数字对应于88的batchsize，如果batchsize更小，可以调低这个数值一些
+
+decay_steps: 20000
+每20000步学习率衰减为原来的一半，如果batchsize比较小，请调大这个数值
+
+#对于30-40左右的batchsize，推荐lr=0.0004，decay_steps=40000
+
+max_frames: 42000
+max_input_tokens: 6000
+max_sentences: 88
+max_tokens: 128000
+#batchsize是由这几个参数动态算出来的，如果不太清楚具体含义，可以只改动max_sentences这个参数，填入batchsize的最大限制值，以免炸显存
+
+pe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
+#pe模型路径，确保这个文件存在，具体作用参考inference部分
+
+raw_data_dir: data/raw/nyaru
+#存放预处理前原始数据的位置，请将原始wav数据放在这个目录下，内部文件结构无所谓，会自动解构
+
+residual_channels: 384
+residual_layers: 20
+#控制核心网络规模的一组参数，越大参数越多炼的越慢，但效果不一定会变好，大一点的数据集可以把第一个改成512。这个可以自行实验效果，不过不了解的话尽量不动。
+
+speaker_id: nyaru
+#训练的说话人名字，目前只支持单说话人，请在这里填写（只是观赏作用，没有实际意义的参数）
+
+use_crepe: true
+#在数据预处理中使用crepe提取F0,追求效果请打开，追求速度可以关闭
+
+val_check_interval: 2000
+#每2000steps推理测试集并保存ckpt
+
+vocoder_ckpt:checkpoints/0109_hifigan_bigpopcs_hop128
+#24kHz下为对应声码器的目录, 44.1kHz下为对应声码器的文件名, 注意不要填错
+
+work_dir: checkpoints/nyaru
+#修改后缀为工程名(也可以删掉或完全留空自动生成，但别乱填)
+no_fs2: true
+#对网络encoder的精简，能缩减模型体积，加快训练，且并未发现有对网络表现损害的直接证据。默认打开
+
+```
+>其他的参数如果你不知道它是做什么的，请不要修改，即使你看着名称可能以为你知道它是做什么的。
+
+### 2.3 数据预处理
+在diff-svc的目录下执行以下命令：\
+#windows
+```
+set PYTHONPATH=.
+set CUDA_VISIBLE_DEVICES=0 
+python preprocessing/binarize.py --config training/config.yaml
+```
+#linux
+```
+export PYTHONPATH=.
+CUDA_VISIBLE_DEVICES=0 python preprocessing/binarize.py --config training/config.yaml
+```
+对于预处理，@小狼准备了一份可以分段处理hubert和其他特征的代码，如果正常处理显存不足，可以先python ./network/hubert/hubert_model.py
+然后再运行正常的指令，能够识别提前处理好的hubert特征
+### 2.4 训练
+#windows
+```
+set CUDA_VISIBLE_DEVICES=0 
+python run.py --config training/config.yaml --exp_name nyaru --reset  
+```
+#linux
+```
+CUDA_VISIBLE_DEVICES=0 python run.py --config training/config.yaml --exp_name nyaru --reset 
+```
+>需要将exp_name改为你的工程名，并修改config路径，请确保和预处理使用的是同一个config文件\
+*重要* ：训练完成后，若之前不是在本地数据预处理，除了需要下载对应的ckpt文件，也需要将config文件下载下来，作为推理时使用的config，不可以使用本地之前上传上去那份。因为预处理时会向config文件中写入内容。推理时要保持使用的config和预处理使用的config是同一份。
+
+
+### 2.5 可能出现的问题：
+>2.5.1 'Upsample' object has no attribute 'recompute_scale_factor'\
+此问题发现于cuda11.3对应的torch中，若出现此问题,请通过合适的方法(如ide自动跳转等)找到你的python依赖包中的torch.nn.modules.upsampling.py文件(如conda环境中为conda目录\envs\环境目录\Lib\site-packages\torch\nn\modules\upsampling.py)，修改其153-154行
+```
+return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,recompute_scale_factor=self.recompute_scale_factor)
+```
+>改为
+```
+return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners)
+# recompute_scale_factor=self.recompute_scale_factor)
+```
+>2.5.2 no module named 'utils'\
+请在你的运行环境(如colab笔记本)中以如下方式设置:
+```
+import os
+os.environ['PYTHONPATH']='.'
+!CUDA_VISIBLE_DEVICES=0 python preprocessing/binarize.py --config training/config.yaml
+```
+注意一定要在项目文件夹的根目录中执行
+>2.5.3 cannot load library 'libsndfile.so'\
+可能会在linux环境中遇到的错误,请执行以下指令
+```
+apt-get install libsndfile1 -y
+```
+>2.5.4 cannot load import 'consume_prefix_in_state_dict_if_present'\
+torch版本过低，请更换高版本torch
+
+>2.5.5 预处理数据过慢\
+检查是否在配置中开启了use_crepe，将其关闭可显著提升速度。\
+检查配置中hubert_gpu是否开启。
+
+如有其他问题，请加入QQ频道或discord频道询问。
diff --git a/flask_api.py b/flask_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaecd0b7305218ad61d0a5a23d44f3312b538f0b
--- /dev/null
+++ b/flask_api.py
@@ -0,0 +1,54 @@
+import io
+import logging
+
+import librosa
+import soundfile
+from flask import Flask, request, send_file
+from flask_cors import CORS
+
+from infer_tools.infer_tool import Svc
+from utils.hparams import hparams
+
+app = Flask(__name__)
+
+CORS(app)
+
+logging.getLogger('numba').setLevel(logging.WARNING)
+
+
+@app.route("/voiceChangeModel", methods=["POST"])
+def voice_change_model():
+    request_form = request.form
+    wave_file = request.files.get("sample", None)
+    # 变调信息
+    f_pitch_change = float(request_form.get("fPitchChange", 0))
+    # DAW所需的采样率
+    daw_sample = int(float(request_form.get("sampleRate", 0)))
+    speaker_id = int(float(request_form.get("sSpeakId", 0)))
+    # http获得wav文件并转换
+    input_wav_path = io.BytesIO(wave_file.read())
+    # 模型推理
+    _f0_tst, _f0_pred, _audio = model.infer(input_wav_path, key=f_pitch_change, acc=accelerate, use_pe=False,
+                                            use_crepe=False)
+    tar_audio = librosa.resample(_audio, hparams["audio_sample_rate"], daw_sample)
+    # 返回音频
+    out_wav_path = io.BytesIO()
+    soundfile.write(out_wav_path, tar_audio, daw_sample, format="wav")
+    out_wav_path.seek(0)
+    return send_file(out_wav_path, download_name="temp.wav", as_attachment=True)
+
+
+if __name__ == '__main__':
+    # 工程文件夹名，训练时用的那个
+    project_name = "firefox"
+    model_path = f'./checkpoints/{project_name}/model_ckpt_steps_188000.ckpt'
+    config_path = f'./checkpoints/{project_name}/config.yaml'
+
+    # 加速倍数
+    accelerate = 50
+    hubert_gpu = True
+
+    model = Svc(project_name, config_path, hubert_gpu, model_path)
+
+    # 此处与vst插件对应，不建议更改
+    app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)
diff --git a/infer.py b/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a671ed05af4248a13fcf9225ce21133fc766ae01
--- /dev/null
+++ b/infer.py
@@ -0,0 +1,98 @@
+import io
+import time
+from pathlib import Path
+
+import librosa
+import numpy as np
+import soundfile
+
+from infer_tools import infer_tool
+from infer_tools import slicer
+from infer_tools.infer_tool import Svc
+from utils.hparams import hparams
+
+chunks_dict = infer_tool.read_temp("./infer_tools/new_chunks_temp.json")
+
+
+def run_clip(svc_model, key, acc, use_pe, use_crepe, thre, use_gt_mel, add_noise_step, project_name='', f_name=None,
+             file_path=None, out_path=None, slice_db=-40,**kwargs):
+    print(f'code version:2022-12-04')
+    use_pe = use_pe if hparams['audio_sample_rate'] == 24000 else False
+    if file_path is None:
+        raw_audio_path = f"./raw/{f_name}"
+        clean_name = f_name[:-4]
+    else:
+        raw_audio_path = file_path
+        clean_name = str(Path(file_path).name)[:-4]
+    infer_tool.format_wav(raw_audio_path)
+    wav_path = Path(raw_audio_path).with_suffix('.wav')
+    global chunks_dict
+    audio, sr = librosa.load(wav_path, mono=True,sr=None)
+    wav_hash = infer_tool.get_md5(audio)
+    if wav_hash in chunks_dict.keys():
+        print("load chunks from temp")
+        chunks = chunks_dict[wav_hash]["chunks"]
+    else:
+        chunks = slicer.cut(wav_path, db_thresh=slice_db)
+    chunks_dict[wav_hash] = {"chunks": chunks, "time": int(time.time())}
+    infer_tool.write_temp("./infer_tools/new_chunks_temp.json", chunks_dict)
+    audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
+
+    count = 0
+    f0_tst = []
+    f0_pred = []
+    audio = []
+    for (slice_tag, data) in audio_data:
+        print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
+        length = int(np.ceil(len(data) / audio_sr * hparams['audio_sample_rate']))
+        raw_path = io.BytesIO()
+        soundfile.write(raw_path, data, audio_sr, format="wav")
+        if hparams['debug']:
+            print(np.mean(data), np.var(data))
+        raw_path.seek(0)
+        if slice_tag:
+            print('jump empty segment')
+            _f0_tst, _f0_pred, _audio = (
+                np.zeros(int(np.ceil(length / hparams['hop_size']))), np.zeros(int(np.ceil(length / hparams['hop_size']))),
+                np.zeros(length))
+        else:
+            _f0_tst, _f0_pred, _audio = svc_model.infer(raw_path, key=key, acc=acc, use_pe=use_pe, use_crepe=use_crepe,
+                                                        thre=thre, use_gt_mel=use_gt_mel, add_noise_step=add_noise_step)
+        fix_audio = np.zeros(length)
+        fix_audio[:] = np.mean(_audio)
+        fix_audio[:len(_audio)] = _audio[0 if len(_audio)<len(fix_audio) else len(_audio)-len(fix_audio):]
+        f0_tst.extend(_f0_tst)
+        f0_pred.extend(_f0_pred)
+        audio.extend(list(fix_audio))
+        count += 1
+    if out_path is None:
+        out_path = f'./results/{clean_name}_{key}key_{project_name}_{hparams["residual_channels"]}_{hparams["residual_layers"]}_{int(step / 1000)}k_{accelerate}x.{kwargs["format"]}'
+    soundfile.write(out_path, audio, hparams["audio_sample_rate"], 'PCM_16',format=out_path.split('.')[-1])
+    return np.array(f0_tst), np.array(f0_pred), audio
+
+
+if __name__ == '__main__':
+    # 工程文件夹名，训练时用的那个
+    project_name = "yilanqiu"
+    model_path = f'./checkpoints/{project_name}/model_ckpt_steps_246000.ckpt'
+    config_path = f'./checkpoints/{project_name}/config.yaml'
+
+    # 支持多个wav/ogg文件，放在raw文件夹下，带扩展名
+    file_names = ["青花瓷.wav"]
+    trans = [0]  # 音高调整，支持正负（半音），数量与上一行对应，不足的自动按第一个移调参数补齐
+    # 加速倍数
+    accelerate = 20
+    hubert_gpu = True
+    format='flac'
+    step = int(model_path.split("_")[-1].split(".")[0])
+
+    # 下面不动
+    infer_tool.mkdir(["./raw", "./results"])
+    infer_tool.fill_a_to_b(trans, file_names)
+
+    model = Svc(project_name, config_path, hubert_gpu, model_path)
+    for f_name, tran in zip(file_names, trans):
+        if "." not in f_name:
+            f_name += ".wav"
+        run_clip(model, key=tran, acc=accelerate, use_crepe=True, thre=0.05, use_pe=True, use_gt_mel=False,
+                 add_noise_step=500, f_name=f_name, project_name=project_name, format=format)
diff --git a/infer_tools/__init__.py b/infer_tools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/infer_tools/__pycache__/slicer.cpython-38.pyc b/infer_tools/__pycache__/slicer.cpython-38.pyc
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diff --git a/infer_tools/f0_temp.json b/infer_tools/f0_temp.json
new file mode 100644
index 0000000000000000000000000000000000000000..181205deeb1ff53fb21daedf40797df8db40e5fd
--- /dev/null
+++ b/infer_tools/f0_temp.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/infer_tools/infer_tool.py b/infer_tools/infer_tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..a17789952dd6e0272deb02e7a09815c9e4dd00a4
--- /dev/null
+++ b/infer_tools/infer_tool.py
@@ -0,0 +1,342 @@
+import hashlib
+import json
+import os
+import time
+from io import BytesIO
+from pathlib import Path
+
+import librosa
+import numpy as np
+import soundfile
+import torch
+
+import utils
+from modules.fastspeech.pe import PitchExtractor
+from network.diff.candidate_decoder import FFT
+from network.diff.diffusion import GaussianDiffusion
+from network.diff.net import DiffNet
+from network.vocoders.base_vocoder import VOCODERS, get_vocoder_cls
+from preprocessing.data_gen_utils import get_pitch_parselmouth, get_pitch_crepe, get_pitch_world
+from preprocessing.hubertinfer import Hubertencoder
+from utils.hparams import hparams, set_hparams
+from utils.pitch_utils import denorm_f0, norm_interp_f0
+
+if os.path.exists("chunks_temp.json"):
+    os.remove("chunks_temp.json")
+
+def read_temp(file_name):
+    if not os.path.exists(file_name):
+        with open(file_name, "w") as f:
+            f.write(json.dumps({"info": "temp_dict"}))
+        return {}
+    else:
+        try:
+            with open(file_name, "r") as f:
+                data = f.read()
+            data_dict = json.loads(data)
+            if os.path.getsize(file_name) > 50 * 1024 * 1024:
+                f_name = file_name.split("/")[-1]
+                print(f"clean {f_name}")
+                for wav_hash in list(data_dict.keys()):
+                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
+                        del data_dict[wav_hash]
+        except Exception as e:
+            print(e)
+            print(f"{file_name} error,auto rebuild file")
+            data_dict = {"info": "temp_dict"}
+        return data_dict
+
+
+f0_dict = read_temp("./infer_tools/f0_temp.json")
+
+
+def write_temp(file_name, data):
+    with open(file_name, "w") as f:
+        f.write(json.dumps(data))
+
+
+def timeit(func):
+    def run(*args, **kwargs):
+        t = time.time()
+        res = func(*args, **kwargs)
+        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
+        return res
+
+    return run
+
+
+def format_wav(audio_path):
+    if Path(audio_path).suffix=='.wav':
+        return
+    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True,sr=None)
+    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
+
+
+def fill_a_to_b(a, b):
+    if len(a) < len(b):
+        for _ in range(0, len(b) - len(a)):
+            a.append(a[0])
+
+
+def get_end_file(dir_path, end):
+    file_lists = []
+    for root, dirs, files in os.walk(dir_path):
+        files = [f for f in files if f[0] != '.']
+        dirs[:] = [d for d in dirs if d[0] != '.']
+        for f_file in files:
+            if f_file.endswith(end):
+                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
+    return file_lists
+
+
+def mkdir(paths: list):
+    for path in paths:
+        if not os.path.exists(path):
+            os.mkdir(path)
+
+
+def get_md5(content):
+    return hashlib.new("md5", content).hexdigest()
+
+
+class Svc:
+    def __init__(self, project_name, config_name, hubert_gpu, model_path):
+        self.project_name = project_name
+        self.DIFF_DECODERS = {
+            'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
+            'fft': lambda hp: FFT(
+                hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
+        }
+
+        self.model_path = model_path
+        self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        self._ = set_hparams(config=config_name, exp_name=self.project_name, infer=True,
+                             reset=True,
+                             hparams_str='',
+                             print_hparams=False)
+
+        self.mel_bins = hparams['audio_num_mel_bins']
+        self.model = GaussianDiffusion(
+            phone_encoder=Hubertencoder(hparams['hubert_path']),
+            out_dims=self.mel_bins, denoise_fn=self.DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
+            timesteps=hparams['timesteps'],
+            K_step=hparams['K_step'],
+            loss_type=hparams['diff_loss_type'],
+            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
+        )
+        self.load_ckpt()
+        self.model.to(self.dev)
+        hparams['hubert_gpu'] = hubert_gpu
+        self.hubert = Hubertencoder(hparams['hubert_path'])
+        self.pe = PitchExtractor().to(self.dev)
+        utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
+        self.pe.eval()
+        self.vocoder = get_vocoder_cls(hparams)()
+
+    def load_ckpt(self, model_name='model', force=True, strict=True):
+        utils.load_ckpt(self.model, self.model_path, model_name, force, strict)
+
+    def infer(self, in_path, key, acc, use_pe=True, use_crepe=True, thre=0.05, singer=False, **kwargs):
+        batch = self.pre(in_path, acc, use_crepe, thre)
+        spk_embed = batch.get('spk_embed') if not hparams['use_spk_id'] else batch.get('spk_ids')
+        hubert = batch['hubert']
+        ref_mels = batch["mels"]
+        energy=batch['energy']
+        mel2ph = batch['mel2ph']
+        batch['f0'] = batch['f0'] + (key / 12)
+        batch['f0'][batch['f0']>np.log2(hparams['f0_max'])]=0
+        f0 = batch['f0']
+        uv = batch['uv']
+        @timeit
+        def diff_infer():
+            outputs = self.model(
+                hubert.to(self.dev), spk_embed=spk_embed, mel2ph=mel2ph.to(self.dev), f0=f0.to(self.dev), uv=uv.to(self.dev),energy=energy.to(self.dev),
+                ref_mels=ref_mels.to(self.dev),
+                infer=True, **kwargs)
+            return outputs
+        outputs=diff_infer()
+        batch['outputs'] = self.model.out2mel(outputs['mel_out'])
+        batch['mel2ph_pred'] = outputs['mel2ph']
+        batch['f0_gt'] = denorm_f0(batch['f0'], batch['uv'], hparams)
+        if use_pe:
+            batch['f0_pred'] = self.pe(outputs['mel_out'])['f0_denorm_pred'].detach()
+        else:
+            batch['f0_pred'] = outputs.get('f0_denorm')
+        return self.after_infer(batch, singer, in_path)
+
+    @timeit
+    def after_infer(self, prediction, singer, in_path):
+        for k, v in prediction.items():
+            if type(v) is torch.Tensor:
+                prediction[k] = v.cpu().numpy()
+
+        # remove paddings
+        mel_gt = prediction["mels"]
+        mel_gt_mask = np.abs(mel_gt).sum(-1) > 0
+
+        mel_pred = prediction["outputs"]
+        mel_pred_mask = np.abs(mel_pred).sum(-1) > 0
+        mel_pred = mel_pred[mel_pred_mask]
+        mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax'])
+
+        f0_gt = prediction.get("f0_gt")
+        f0_pred = prediction.get("f0_pred")
+        if f0_pred is not None:
+            f0_gt = f0_gt[mel_gt_mask]
+        if len(f0_pred) > len(mel_pred_mask):
+            f0_pred = f0_pred[:len(mel_pred_mask)]
+        f0_pred = f0_pred[mel_pred_mask]
+        torch.cuda.is_available() and torch.cuda.empty_cache()
+
+        if singer:
+            data_path = in_path.replace("batch", "singer_data")
+            mel_path = data_path[:-4] + "_mel.npy"
+            f0_path = data_path[:-4] + "_f0.npy"
+            np.save(mel_path, mel_pred)
+            np.save(f0_path, f0_pred)
+        wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred)
+        return f0_gt, f0_pred, wav_pred
+
+    def temporary_dict2processed_input(self, item_name, temp_dict, use_crepe=True, thre=0.05):
+        '''
+            process data in temporary_dicts
+        '''
+
+        binarization_args = hparams['binarization_args']
+
+        @timeit
+        def get_pitch(wav, mel):
+            # get ground truth f0 by self.get_pitch_algorithm
+            global f0_dict
+            if use_crepe:
+                md5 = get_md5(wav)
+                if f"{md5}_gt" in f0_dict.keys():
+                    print("load temp crepe f0")
+                    gt_f0 = np.array(f0_dict[f"{md5}_gt"]["f0"])
+                    coarse_f0 = np.array(f0_dict[f"{md5}_coarse"]["f0"])
+                else:
+                    torch.cuda.is_available() and torch.cuda.empty_cache()
+                    gt_f0, coarse_f0 = get_pitch_crepe(wav, mel, hparams, thre)
+                f0_dict[f"{md5}_gt"] = {"f0": gt_f0.tolist(), "time": int(time.time())}
+                f0_dict[f"{md5}_coarse"] = {"f0": coarse_f0.tolist(), "time": int(time.time())}
+                write_temp("./infer_tools/f0_temp.json", f0_dict)
+            else:
+                md5 = get_md5(wav)
+                if f"{md5}_gt_harvest" in f0_dict.keys():
+                    print("load temp harvest f0")
+                    gt_f0 = np.array(f0_dict[f"{md5}_gt_harvest"]["f0"])
+                    coarse_f0 = np.array(f0_dict[f"{md5}_coarse_harvest"]["f0"])
+                else:
+                    gt_f0, coarse_f0 = get_pitch_world(wav, mel, hparams)
+                f0_dict[f"{md5}_gt_harvest"] = {"f0": gt_f0.tolist(), "time": int(time.time())}
+                f0_dict[f"{md5}_coarse_harvest"] = {"f0": coarse_f0.tolist(), "time": int(time.time())}
+                write_temp("./infer_tools/f0_temp.json", f0_dict)
+            processed_input['f0'] = gt_f0
+            processed_input['pitch'] = coarse_f0
+
+        def get_align(mel, phone_encoded):
+            mel2ph = np.zeros([mel.shape[0]], int)
+            start_frame = 0
+            ph_durs = mel.shape[0] / phone_encoded.shape[0]
+            if hparams['debug']:
+                print(mel.shape, phone_encoded.shape, mel.shape[0] / phone_encoded.shape[0])
+            for i_ph in range(phone_encoded.shape[0]):
+                end_frame = int(i_ph * ph_durs + ph_durs + 0.5)
+                mel2ph[start_frame:end_frame + 1] = i_ph + 1
+                start_frame = end_frame + 1
+
+            processed_input['mel2ph'] = mel2ph
+
+        if hparams['vocoder'] in VOCODERS:
+            wav, mel = VOCODERS[hparams['vocoder']].wav2spec(temp_dict['wav_fn'])
+        else:
+            wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(temp_dict['wav_fn'])
+        processed_input = {
+            'item_name': item_name, 'mel': mel,
+            'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]
+        }
+        processed_input = {**temp_dict, **processed_input}  # merge two dicts
+
+        if binarization_args['with_f0']:
+            get_pitch(wav, mel)
+        if binarization_args['with_hubert']:
+            st = time.time()
+            hubert_encoded = processed_input['hubert'] = self.hubert.encode(temp_dict['wav_fn'])
+            et = time.time()
+            dev = 'cuda' if hparams['hubert_gpu'] and torch.cuda.is_available() else 'cpu'
+            print(f'hubert (on {dev}) time used {et - st}')
+
+            if binarization_args['with_align']:
+                get_align(mel, hubert_encoded)
+        return processed_input
+
+    def pre(self, wav_fn, accelerate, use_crepe=True, thre=0.05):
+        if isinstance(wav_fn, BytesIO):
+            item_name = self.project_name
+        else:
+            song_info = wav_fn.split('/')
+            item_name = song_info[-1].split('.')[-2]
+        temp_dict = {'wav_fn': wav_fn, 'spk_id': self.project_name}
+
+        temp_dict = self.temporary_dict2processed_input(item_name, temp_dict, use_crepe, thre)
+        hparams['pndm_speedup'] = accelerate
+        batch = processed_input2batch([getitem(temp_dict)])
+        return batch
+
+
+def getitem(item):
+    max_frames = hparams['max_frames']
+    spec = torch.Tensor(item['mel'])[:max_frames]
+    energy = (spec.exp() ** 2).sum(-1).sqrt()
+    mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
+    f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
+    hubert = torch.Tensor(item['hubert'][:hparams['max_input_tokens']])
+    pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
+    sample = {
+        "item_name": item['item_name'],
+        "hubert": hubert,
+        "mel": spec,
+        "pitch": pitch,
+        "energy": energy,
+        "f0": f0,
+        "uv": uv,
+        "mel2ph": mel2ph,
+        "mel_nonpadding": spec.abs().sum(-1) > 0,
+    }
+    return sample
+
+
+def processed_input2batch(samples):
+    '''
+        Args:
+            samples: one batch of processed_input
+        NOTE:
+            the batch size is controlled by hparams['max_sentences']
+    '''
+    if len(samples) == 0:
+        return {}
+    item_names = [s['item_name'] for s in samples]
+    hubert = utils.collate_2d([s['hubert'] for s in samples], 0.0)
+    f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
+    pitch = utils.collate_1d([s['pitch'] for s in samples])
+    uv = utils.collate_1d([s['uv'] for s in samples])
+    energy = utils.collate_1d([s['energy'] for s in samples], 0.0)
+    mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
+        if samples[0]['mel2ph'] is not None else None
+    mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
+    mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
+
+    batch = {
+        'item_name': item_names,
+        'nsamples': len(samples),
+        'hubert': hubert,
+        'mels': mels,
+        'mel_lengths': mel_lengths,
+        'mel2ph': mel2ph,
+        'energy': energy,
+        'pitch': pitch,
+        'f0': f0,
+        'uv': uv,
+    }
+    return batch
diff --git a/infer_tools/new_chunks_temp.json b/infer_tools/new_chunks_temp.json
new file mode 100644
index 0000000000000000000000000000000000000000..f71eeddf2c8c881b7fa6895e75a1a3e25ab1043d
--- /dev/null
+++ b/infer_tools/new_chunks_temp.json
@@ -0,0 +1 @@
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"2468360,2479240"}, "7": {"slice": false, "split_time": "2479240,3593434"}, "8": {"slice": true, "split_time": "3593434,3629186"}, "9": {"slice": false, "split_time": "3629186,4074575"}, "10": {"slice": true, "split_time": "4074575,4078966"}, "11": {"slice": false, "split_time": "4078966,4307991"}, "12": {"slice": true, "split_time": "4307991,4316241"}, "13": {"slice": false, "split_time": "4316241,5986540"}, "14": {"slice": true, "split_time": "5986540,5990713"}, "15": {"slice": false, "split_time": "5990713,6243455"}, "16": {"slice": true, "split_time": "6243455,6491661"}, "17": {"slice": false, "split_time": "6491661,6719744"}, "18": {"slice": true, "split_time": "6719744,6739748"}, "19": {"slice": false, "split_time": "6739748,7021570"}, "20": {"slice": true, "split_time": "7021570,7069439"}, "21": {"slice": false, "split_time": "7069439,7363743"}, "22": {"slice": true, "split_time": "7363743,7376200"}, "23": {"slice": false, "split_time": "7376200,9402054"}, "24": {"slice": true, "split_time": "9402054,9403936"}, "25": {"slice": false, "split_time": "9403936,9697617"}, "26": {"slice": true, "split_time": "9697617,9878400"}}, "time": 1673468608}, "1d7d07c16652e4b7300bdaf5d38128b9": {"chunks": {"0": {"slice": true, "split_time": "0,261052"}, "1": {"slice": false, "split_time": "261052,993078"}, "2": {"slice": true, "split_time": "993078,1091294"}, "3": {"slice": false, "split_time": "1091294,2303042"}, "4": {"slice": true, "split_time": "2303042,2304533"}, "5": {"slice": false, "split_time": "2304533,3543442"}, "6": {"slice": true, "split_time": "3543442,3656782"}, "7": {"slice": false, "split_time": "3656782,4337660"}, "8": {"slice": true, "split_time": "4337660,4406921"}, "9": {"slice": false, "split_time": "4406921,6132779"}, "10": {"slice": true, "split_time": "6132779,6147466"}, "11": {"slice": false, "split_time": "6147466,6855506"}, "12": {"slice": true, "split_time": "6855506,7413094"}}, "time": 1673466029}, "dc6af7b6086c4a6f7f6ee206024988f3": {"chunks": {"0": {"slice": true, "split_time": "0,1492137"}, "1": {"slice": false, "split_time": "1492137,2132538"}, "2": {"slice": true, "split_time": "2132538,2140949"}, "3": {"slice": false, "split_time": "2140949,2362096"}, "4": {"slice": true, "split_time": "2362096,2377313"}, "5": {"slice": false, "split_time": "2377313,3190602"}, "6": {"slice": true, "split_time": "3190602,3236563"}, "7": {"slice": false, "split_time": "3236563,3795750"}, "8": {"slice": true, "split_time": "3795750,3798646"}, "9": {"slice": false, "split_time": "3798646,4037700"}, "10": {"slice": true, "split_time": "4037700,4057671"}, "11": {"slice": false, "split_time": "4057671,4828341"}, "12": {"slice": true, "split_time": "4828341,4839270"}, "13": {"slice": false, "split_time": "4839270,5069319"}, "14": {"slice": true, "split_time": "5069319,5070998"}, "15": {"slice": false, "split_time": "5070998,5722290"}, "16": {"slice": true, "split_time": "5722290,5778275"}, "17": {"slice": false, "split_time": "5778275,6346775"}, "18": {"slice": true, "split_time": "6346775,6354400"}, "19": {"slice": false, "split_time": "6354400,6965771"}, "20": {"slice": true, "split_time": "6965771,7361821"}, "21": {"slice": false, "split_time": "7361821,7649206"}, "22": {"slice": true, "split_time": "7649206,7681433"}, "23": {"slice": false, "split_time": "7681433,7921525"}, "24": {"slice": true, "split_time": "7921525,7934007"}, "25": {"slice": false, "split_time": "7934007,8183308"}, "26": {"slice": true, "split_time": "8183308,8238306"}, "27": {"slice": false, "split_time": "8238306,10906572"}, "28": {"slice": true, "split_time": "10906572,10942779"}}, "time": 1673469959}, "010900becfb5574ab3a333f941577c2c": {"chunks": {"0": {"slice": true, "split_time": "0,1083101"}, "1": {"slice": false, "split_time": "1083101,1421801"}, "2": {"slice": true, "split_time": "1421801,1453133"}, "3": {"slice": false, "split_time": "1453133,1794206"}, "4": {"slice": true, "split_time": "1794206,1800891"}, "5": {"slice": 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\ No newline at end of file
diff --git a/infer_tools/slicer.py b/infer_tools/slicer.py
new file mode 100644
index 0000000000000000000000000000000000000000..35a888b906e7df8634cfdcec914f650c6cefd26a
--- /dev/null
+++ b/infer_tools/slicer.py
@@ -0,0 +1,158 @@
+import time
+
+import numpy as np
+import torch
+import torchaudio
+from scipy.ndimage import maximum_filter1d, uniform_filter1d
+
+
+def timeit(func):
+    def run(*args, **kwargs):
+        t = time.time()
+        res = func(*args, **kwargs)
+        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
+        return res
+
+    return run
+
+
+# @timeit
+def _window_maximum(arr, win_sz):
+    return maximum_filter1d(arr, size=win_sz)[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1]
+
+
+# @timeit
+def _window_rms(arr, win_sz):
+    filtered = np.sqrt(uniform_filter1d(np.power(arr, 2), win_sz) - np.power(uniform_filter1d(arr, win_sz), 2))
+    return filtered[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1]
+
+
+def level2db(levels, eps=1e-12):
+    return 20 * np.log10(np.clip(levels, a_min=eps, a_max=1))
+
+
+def _apply_slice(audio, begin, end):
+    if len(audio.shape) > 1:
+        return audio[:, begin: end]
+    else:
+        return audio[begin: end]
+
+
+class Slicer:
+    def __init__(self,
+                 sr: int,
+                 db_threshold: float = -40,
+                 min_length: int = 5000,
+                 win_l: int = 300,
+                 win_s: int = 20,
+                 max_silence_kept: int = 500):
+        self.db_threshold = db_threshold
+        self.min_samples = round(sr * min_length / 1000)
+        self.win_ln = round(sr * win_l / 1000)
+        self.win_sn = round(sr * win_s / 1000)
+        self.max_silence = round(sr * max_silence_kept / 1000)
+        if not self.min_samples >= self.win_ln >= self.win_sn:
+            raise ValueError('The following condition must be satisfied: min_length >= win_l >= win_s')
+        if not self.max_silence >= self.win_sn:
+            raise ValueError('The following condition must be satisfied: max_silence_kept >= win_s')
+
+    @timeit
+    def slice(self, audio):
+        samples = audio
+        if samples.shape[0] <= self.min_samples:
+            return {"0": {"slice": False, "split_time": f"0,{len(audio)}"}}
+        # get absolute amplitudes
+        abs_amp = np.abs(samples - np.mean(samples))
+        # calculate local maximum with large window
+        win_max_db = level2db(_window_maximum(abs_amp, win_sz=self.win_ln))
+        sil_tags = []
+        left = right = 0
+        while right < win_max_db.shape[0]:
+            if win_max_db[right] < self.db_threshold:
+                right += 1
+            elif left == right:
+                left += 1
+                right += 1
+            else:
+                if left == 0:
+                    split_loc_l = left
+                else:
+                    sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2)
+                    rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn))
+                    split_win_l = left + np.argmin(rms_db_left)
+                    split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn])
+                if len(sil_tags) != 0 and split_loc_l - sil_tags[-1][1] < self.min_samples and right < win_max_db.shape[
+                    0] - 1:
+                    right += 1
+                    left = right
+                    continue
+                if right == win_max_db.shape[0] - 1:
+                    split_loc_r = right + self.win_ln
+                else:
+                    sil_right_n = min(self.max_silence, (right + self.win_ln - left) // 2)
+                    rms_db_right = level2db(_window_rms(samples[right + self.win_ln - sil_right_n: right + self.win_ln],
+                                                        win_sz=self.win_sn))
+                    split_win_r = right + self.win_ln - sil_right_n + np.argmin(rms_db_right)
+                    split_loc_r = split_win_r + np.argmin(abs_amp[split_win_r: split_win_r + self.win_sn])
+                sil_tags.append((split_loc_l, split_loc_r))
+                right += 1
+                left = right
+        if left != right:
+            sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2)
+            rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn))
+            split_win_l = left + np.argmin(rms_db_left)
+            split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn])
+            sil_tags.append((split_loc_l, samples.shape[0]))
+        if len(sil_tags) == 0:
+            return {"0": {"slice": False, "split_time": f"0,{len(audio)}"}}
+        else:
+            chunks = []
+            # 第一段静音并非从头开始，补上有声片段
+            if sil_tags[0][0]:
+                chunks.append({"slice": False, "split_time": f"0,{sil_tags[0][0]}"})
+            for i in range(0, len(sil_tags)):
+                # 标识有声片段（跳过第一段）
+                if i:
+                    chunks.append({"slice": False, "split_time": f"{sil_tags[i - 1][1]},{sil_tags[i][0]}"})
+                # 标识所有静音片段
+                chunks.append({"slice": True, "split_time": f"{sil_tags[i][0]},{sil_tags[i][1]}"})
+            # 最后一段静音并非结尾，补上结尾片段
+            if sil_tags[-1][1] != len(audio):
+                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1]},{len(audio)}"})
+            chunk_dict = {}
+            for i in range(len(chunks)):
+                chunk_dict[str(i)] = chunks[i]
+            return chunk_dict
+
+
+def cut(audio_path, db_thresh=-30, min_len=5000, win_l=300, win_s=20, max_sil_kept=500):
+    audio, sr = torchaudio.load(audio_path)
+    if len(audio.shape) == 2 and audio.shape[1] >= 2:
+        audio = torch.mean(audio, dim=0).unsqueeze(0)
+    audio = audio.cpu().numpy()[0]
+
+    slicer = Slicer(
+        sr=sr,
+        db_threshold=db_thresh,
+        min_length=min_len,
+        win_l=win_l,
+        win_s=win_s,
+        max_silence_kept=max_sil_kept
+    )
+    chunks = slicer.slice(audio)
+    return chunks
+
+
+def chunks2audio(audio_path, chunks):
+    chunks = dict(chunks)
+    audio, sr = torchaudio.load(audio_path)
+    if len(audio.shape) == 2 and audio.shape[1] >= 2:
+        audio = torch.mean(audio, dim=0).unsqueeze(0)
+    audio = audio.cpu().numpy()[0]
+    result = []
+    for k, v in chunks.items():
+        tag = v["split_time"].split(",")
+        result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
+    return result, sr
+
+
diff --git a/inference.ipynb b/inference.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..129c5ee984fddc9b505b3a9bd3b065ef2ba988c5
--- /dev/null
+++ b/inference.ipynb
@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "e:\\Software\\Anaconda3\\envs\\diffsvc\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "| load 'model' from './checkpoints/nyaru/model_ckpt_steps_112000.ckpt'.\n",
+      "| load 'model' from 'checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt'.\n",
+      "| load HifiGAN:  checkpoints/0109_hifigan_bigpopcs_hop128\\model_ckpt_steps_1512000.pth\n",
+      "| Loaded model parameters from checkpoints/0109_hifigan_bigpopcs_hop128\\model_ckpt_steps_1512000.pth.\n",
+      "| HifiGAN device: cuda.\n",
+      "model loaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils.hparams import hparams\n",
+    "from preprocessing.data_gen_utils import get_pitch_parselmouth,get_pitch_crepe\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import IPython.display as ipd\n",
+    "import utils\n",
+    "import librosa\n",
+    "import torchcrepe\n",
+    "from infer import *\n",
+    "import logging\n",
+    "from infer_tools.infer_tool import *\n",
+    "\n",
+    "logging.getLogger('numba').setLevel(logging.WARNING)\n",
+    "\n",
+    "# 工程文件夹名，训练时用的那个\n",
+    "project_name = \"nyaru\"\n",
+    "model_path = f'./checkpoints/{project_name}/model_ckpt_steps_112000.ckpt'\n",
+    "config_path=f'./checkpoints/{project_name}/config.yaml'\n",
+    "hubert_gpu=True\n",
+    "svc_model = Svc(project_name,config_path,hubert_gpu, model_path)\n",
+    "print('model loaded')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load chunks from temp\n",
+      "#=====segment start, 0.46s======\n",
+      "jump empty segment\n",
+      "#=====segment start, 6.702s======\n",
+      "load temp crepe f0\n",
+      "executing 'get_pitch' costed 0.066s\n",
+      "hubert (on cpu) time used 0.6847963333129883\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample time step: 100%|██████████| 50/50 [00:02<00:00, 21.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "executing 'diff_infer' costed 2.310s\n",
+      "executing 'after_infer' costed 1.167s\n",
+      "#=====segment start, 8.831s======\n",
+      "load temp crepe f0\n",
+      "executing 'get_pitch' costed 0.063s\n",
+      "hubert (on cpu) time used 0.8832910060882568\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample time step: 100%|██████████| 50/50 [00:02<00:00, 18.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "executing 'diff_infer' costed 2.749s\n",
+      "executing 'after_infer' costed 1.894s\n",
+      "#=====segment start, 5.265s======\n",
+      "load temp crepe f0\n",
+      "executing 'get_pitch' costed 0.065s\n",
+      "hubert (on cpu) time used 0.5448079109191895\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample time step: 100%|██████████| 50/50 [00:01<00:00, 28.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "executing 'diff_infer' costed 1.780s\n",
+      "executing 'after_infer' costed 1.038s\n",
+      "#=====segment start, 1.377s======\n",
+      "jump empty segment\n"
+     ]
+    }
+   ],
+   "source": [
+    "wav_fn='raw/test_input.wav'#支持多数音频格式，无需手动转为wav\n",
+    "demoaudio, sr = librosa.load(wav_fn)\n",
+    "key = 0  # 音高调整，支持正负（半音）\n",
+    "# 加速倍数\n",
+    "pndm_speedup = 20\n",
+    "wav_gen='test_output.wav'#直接改后缀可以保存不同格式音频，如flac可无损压缩\n",
+    "f0_tst, f0_pred, audio = run_clip(svc_model,file_path=wav_fn, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.05,\n",
+    "                                        use_gt_mel=False, add_noise_step=500,project_name=project_name,out_path=wav_gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
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AfoAtQCiAAwA8//I/03/Xv8uAPD/Pv85/wX/IgDZAN8AiQBC/6b/IwDh/1QA6QCIAAAAWQAZAGv/yv4u/wwAd/9Y/+b/BQB1APcA9QDSAGYA+v/X//3/qgCjAAQAs/96/6n/4f+M/5z/EABFAAIAz/8cAE4AbgBfAD4Atv/b/5EARwDT/5n/tP+1/zwAWQDd/yIAPwCeAG0Auv++//X/yP+q/xsAPABYADEAAgBsACQAGgBXAMD/7f/j/0D/q//U//T/GgDd/xcAFgDc/9P/tv+0/ycAZQAvABkAHQBkAHYAdwAzAE//K/+b/4//ZP/3/1AADwAuAAkAhf98/9b/FQARAMT/AQCqAHUA5f/A/5//v/9gAIwAJQD7//v/DgDs/+f/KAAGAFAAVgDt/04ALQDf/zoA6f9k/9f/QQAJAOP/VABTAPT/9//U/7v/sf+7/7T//v8dAM3/QwCEAC4ACQA+AEEA4v9m/2H/+P8MAAsA6v/d/2UAYgDm/7D/EgAZANP/0P+C/3b/CgCaAHMAOwAHAJj/ov/X//X/IQD+/6L/sv/z/y4AdACuAHAAyP+v/+j/FgAYADUAagAnANT/aP9m/93/9f8SACYA8f/m/zAA9f/E/y4A+v+E/5//AQAHAAAAdwC4AC8A4v/S/5b/DwCcAKoAcACv/4f/CwD4/yYAWQAhAOH/if/f//H/z/8eABkA+v/1/w4ADQAcADcAPQBDAAAA6//q/5T/xP+HAHEAyv/C/+L/EACGAKQAEQCl/6z/zf8KAAYAGwAtABAA0v97/5v/7P84ACIA4v/f/9//8/9kAJYA///e/0AACAB0/47/HAA9ADwAQgDc/4D/kf/g/zkAUABgAG4AUQAaAPv/7/8VAAIAaP9T/4n/BQB1AEUAJABAAIkADwBv/7H/5P+o//L/WwAOABYAbQB7AIAAhwAzAMv/1/+//0//ZP/9/yUAHwBOADIA1v8CAGQAOQDY/6j/p/+E/8T/CwAgAJEAawDd/+H/OQAiAOL/0P/F/5T/iv9HAE8A0f8xAI4ANADP/9T/tP/Z/04AMQC+/6f/5P8MACQAXAAjAND/8f8AAAEA7v/Y//H/FAAOACYAYQBAANX/s//b/+P/3f/p/w4AVgCtAGIA4P++/6H/uP+V/2L/xP8AABYAUQA9AEwAegB1ABEAqf+Q/4j/rP/D//r/MwBlAFkADgDr/9f/BgATAAUA0P+Q/9T/AAAZAGUAiwCVAGwA8f+e/5r/wP/N/6f/l//G/wQAJAB/AJUAXwBYAA4A3P++/6D/1v8JABsA/P9AAG0A0/+h/ygANwDZ//n/6P+b/8X/KQA7AC0AQAAcAO//9f8TACcAMwAlAP//qv+k//n/AwDi/9v/GQBQACUA6f+5/5n/4v8sAEEASgAgAPv/AQAZACYAUgBXAPb/f/9V/5X/EABhAGcALgDt/wAAAADZ/97/OQB5AC0A5//h//b/BQAoAA0A3//Z/9D/+P8AAOn/5P8GACMA1/+Y/5z/8v+IAJIAJAD9/ywAGgDm/wMAEwD6/9b/1P+0/4P/2f8YAOj/8P9CAHEANADH/7f/7/8aAPr/wP/B//v/YwCOAH8ASAD0/8T/p/+a/5L/yv/8/97/v//Z/y0AfgCXAGAAHQDS/73/of+h//z/+v/0//z/CQAlABcAQQBrAPT/n//S//f/0f/V////BgBLADoAGwAEAPD/CADy//f/1//b/+P/p/+7/xcAMwAXADYAOAAGAPz/FAAcAAkA+/8GAE8AMQD4//v/nv+r/+b/2v/3/xQA7v/D/woAVABoACwAHgAcAMv/v//B//f/QQA3ABgAuf+K/+r/JQAyAD4AJQDv/+L/+f/x//P/FQAkAOT/2v8HAA8AMgBrAFAA1/+T/3v/lv/b/xAASQBYAE8A6//K/0EAVQAcAAoAEQD2/8f/uf+4/+b/QQBLAPH/y/8xADAA3P/n/xQAJwDR//H/SABAAFEARgARAOr/5f/h////7/+//+X/GAArACYAGAABAA4AEwDo/7//rv/A/xEATwBDADUAKAAtAAcAv//B/+f/8v/m/7j/6/9JAGYAWQBFAEkAGQD7/9T/2//A/1H/k//5/y4AUwARAOn/DAAeAGUAUQAdAOr/hv+c/9H/IwA4AEMAWwBDAPf/1f8CAPb/FQDQ/8L/CQDk/9v/tf/d/y4ASgApAL3/3P8RANz/nv/s/1UAGwAbAAkA1//t/wwAMQAEAND/8P8oAO3/u/8OACQAHAArABoAJgDv/4v/nP/O/9D/zf9EAIsA///I/yYAbwAeAKD/qP+9/8r/BwAZAAIAOQB6AGEAHQDn//L///8CANr/kv9u/47/4P8CADcAVABCAPf/s/8EAEUAIwA3AEMAHwCz/6H/QQBUADgAOAAiAMn/n//8/+X/x/8OABAAvP/j/3cAlgBOAFEARADr//f/wv97/7//0//0/woAGgBDACEAGgBiAIAADQDw/83/cv+F/8//OgAzAFsAjAAxAOv/5v/4/+D/2P/z/wMA7/+w/5n/1v8xAIwAhwAfABkALwD8/9P/xP+d/3z/sP/v/wcAOwA1ACMAZABaABUA4f/V/4r/ff/s/xUAEgD0/0wAaAA4ACsAu/+i/8n/BgA0AAcAFwAyAAwA5v/W//T/7v/P/9T/9//y/9D/3f/S/+//EQBAAH0AOwD0/8b/nv/A/zIAOAD3/0MAHgCz/97/AwAMAAAA/f/f/6z/9v8QABMAYgCXAJIAOADn/5j/t/80ABIAvf///wMAo/+H/7D/NwAyAAYAFQD1/0AAfAA4AOb/y/+v/8f/BQA5ABkAAwASAOj/+v/r/7v/5/8cAMT/df+3/0YAoACTAKMASgDp//f/+//Z/9b/CACr/1T/kv/Z/xoAHwBaAB4Ao//O/9//9v8LADQAPgBBAEYAEgASAP3/MwAAAKr/9v/A/3r/v/81AFYAHgAgAAEAuP/4/zAAFADY/9T/RgBOABYAGQAAAPv/7//C/6H/v//o/+X/7v8BADUAMgASADEAWAAcANv/5v8KACsAAgAWACIA5v8BAAcAyf+a/8f/RgA/ABQAMAD9/+7/JgBMAAQA5/8vABEA2//w//n/mv9+//n/MgAAAO7/7f+1/9T/KgBeAIYAWwAGAKH/pP8pAHQAQgAYABoA1v/N/8D/x/8TABQAFwDA/6f/HwBWAFUATgA8AP7/zf+4/8//+f/1/9X/nP+Y/wQAaQBqAGMATQAYAOn/zP/M/87/zP+0/8L/OgCgAIAAQQAtABAA4//5//X/uv+K/6H/GgBCAGgARQAPAEcAAgCp/47/qP/l/9z/2v/n/9H//P89AEwASQBbAEwAEQDf/6r/pv/S/z4AbQAaAPX/2P+t/9H/GgANAA8AHwCs/7j/QwB8AGIACwD7//D/6f8QAPv/FQBCADwACADE/77/wP/1//f/oP/C/0sAWwAvAEsAdgB+APr/sP/P/wQAawBRAMP/gv+p/7//7/8sABUAt//H/w4A7P8SAFoAPAAjABQAxv+W/83/NQAWAN3/FwApAB0AKgAmAOb/6v8IAKv/mP/A/9v/4//l/1IAWQBGAFIAEADt/8L/t//M/xMAGgDm/9X/z/8uABAA+P9BAPH/qv/L/xMAGgDg/9v/vP+6/xkAQQA2AAwAFQBPABYA0//O/+b/HAAwACoAMQArAAQA7v/k/+//FAD8/8//1/8KAAkABgATADYASgDj/9//7f/N/wUABQD1/9j/4/9SADkAEQATAMr/y/8HACkA/P/5/zsAKwD9/xMAKQDd/7z/7/8LAPv/BQAIAMn/y//Q/+b/OQBMAE0ANQAQABAABQDS/7T/3P///xkA9v/g//b/AwBKACUA9f8FAPv/8v/2/woAFQASAPf/8v///+f/wf+t/9X/IwBFAEMAXQA+AO//xP/G/93//v8nAPv/1P8rAGgAOwAPABkAKQASACkAEQDM/8v/v//P/+n/LwAhAPD/QwAmANr/5v/5//X/uf++/+3/y//r/xoADAAhAEIAMAD0/+f/+//4/+z/FgAfAO7//P/f/7X/2P8WACIAMQCBAE8ACQA2ACUA2f+m/8b/0v+x//n/LgAfAAQA6//r/+f/AwAAAA8ALQDa/53/9v9TAEMALQA5AEcADwAOACAA4P8bAFgA3v9F/0j/kP+p/+r/UgBhAE4ATAAPANX/3//h/9T//f8AAO7/8v8zAEgAAAAmACwA//8qADIA1f+T//X/HQDY//j/SQBkACwAIQD+/6f/zv/k/73/rv/i//f/0P8EAE4ARQAOADAAMQDL/9f/EAD0/+H/FgA5APf/5v83ADAA9v/v/yMACACu/8T/5v/x/xMACwADABcAIQD8/93/9f8PAA0A//8aACYAFwAUAAwAHAAmADEAJwACANv/wf/P/7z/vv/4//r/+//f/7v/0P/f/zcAOADi//j/FQAoABgADgAvAC4AHQAbAAwA5f/p//D/vf/G/xQANwAdABEABwDr//3/GgAVAPP/7f8AAP//9P8CABAA9P/6/yIANQDy/7//7f8HAAMAFABGAF4AOgD3/9j/6P8GABoAAwDv/wEAEQD3//v/HQAUAAkADwD1/83/3f8BAPL/+f8QAPv/4P/x/yUA+v/k/xUAAAAOADsAVgATAOT/CQDW/8T/CAAbAOn/yf+9/7z/6f8BAPf/HQByAFEA2f/M/wMAFwAQAAcAAADb/+H/6//f/w8AHQD8/9z/2f/Q/8H/7//2/wMAMQAzAPj/zP/+/y8AEgAaAFgAFwC+/57/rP/R/9f/DABHAG0ATgADAOn/1v/k/wkAGAAZABYAJAAJAPz/DQD+//z/EwAXAPz/DQAWAND/uv8CADoAIAD5/wUAGQAaABYAAgATACkAIQAAAOP/AQAQAPr/EAAUAOv/2v/m/x8AHgABAAUAAAD9/97/1v/R/+b//v/y//z/LgBoAEoAJQAXAPr/4f/b/wYA+P/Y/83/ov+7/xMAOwAfABgAPQAjAOH/x//J/+X/9v8QAAAAEgBUAP3/xv/Z/9v/1P/N/wMA/P///zAAFQDv/xEAJAD8/wMAGAAKAOX/1v/h//T/AAAEABgARwBiACsA7//q/w0A8P++/+7/KgA2AAsA+P8KAP///f8OACMAQQAoAAoA9//o/+3/+P8PAA4AIwAsABAA+v/a/8T/0P/8/zwATgAoAAgAyP+8/9b/9P8oADgAIQD7/9T/tf+8//T/PgAuAAoACQDT/6D/nP/N/wMAOABJAA4ACwAiAOv/rP/Q/wcAyv+w//H/FgAKAPP/GQA3ACQABwDp/+f//f8DAAIAIwBPADoA+//W/9P/0/+r/6v/0P/5/x4AAQD9/zUAbwBcAAoA8//s/9T/0v/q/wcAJwAyABkA+P8TADsACwD//xEA8f+j/3L/tP8DACEAIwApAC0AIgAQABAAHQArABsA/P/K/7X/+v8eADAAOgA+ACQA9v/u/8L/q//I/9H/uP/W/ycARgBFADoAHAAQABAA/P/t//P/AQAVAP3/7f/s/8T/xv/w/yYALwAbAAQA3/+8/6z/3//3/xEAKwAuAD0ASABgAFsAUABUAB0Auv9//3P/cv+D/+f/RQBhAG0AQQAeABwAGgDp/7n/6v/9/+L/7v8IABAAHAA0AD0ALgBDADkA+v/y/+P/wP+a/8H/GQBKAFMALAAVAPX/3f/X/93/DQAxADYAMgAfABEA+//j/+r/7v/+////9//q/9f/yP/d/woADgAnADkALwAfAPf/2P/l/xkAFwAIABcAFgDx/8H/1P8HACUAJgAAAN//7//z/+3/6f8AAAMA/P/z/+r/+f8QACkAIAAbACoALgDp/7r/t//I/wQAFwAQAP//9P/P/7z/1v/1//b/AAAiACIAMQA1ACwAHwAeAP3/s/+p/9n/8f8DADkAWQBYAD4AFwDL/57/wv/n/wAACAABAOX/yf/g/+L/CQBLAGQAVAACAOT/3//m/wIADQAPAP7/EgDy/9T/CgAZAPz/6f/9////0v/H/+j/CwAfABUADAAFAA4ADAD1/wQAGAD7/9L/wv/o/w0AGQAlADwAUwA5APr/yP/G/9z/8f/m/9z/7v8OABcA/P8AAPv/7f/i/9L/2f/t/ycAZABiAFQAKQDw/+H/8/8NAP7/BAAXAP//6P/i/+f/6f8HACsAKwAoABkA9//R/8P/zf/y/ygAPQAwABcAFQANAOf/0f/h/wIAEQAYACcANAAwAB4AAwDf/9b/z//J/+H/EQA5ADIAIgAPAPr/4P/Y/+X/8f8OACsAGgDt//X/EQABAO3//f8TAPn/z/+p/7H/9f8fABoAEgA5AFMAIwAIAPr/6f/j/9D/0f/F/9b/5v/n/w0AEgD9/+T/7/8SAA8AHgA7AD8AKgAAANT/ov9//4f/of/W/xoATQBzAI4AegA8ABgA9v/Q/8b/5P8MAAcAAgADABgAQwBQAEYAMAAeAO7/p/+l/8v/8f8TADMATAA9ACcABQDu//j/+//x//H/CQAUAP7/DABKAFQAKgDy/8n/x//V/+7//P8eAEIAOgAgAA4A+//N/8H/0//f/+3//P8NAB8AOABDACAAHgA9ABkA3P+7/8f/5P/2/wkABwAOACgAGgAAAPP////w/97/4P/T/9D/4P8ZADMAFwD7/97/2v/o//r//P/+/xAADwAFAP3/AQADAPv//P/s/8b/w//k/wQABgAKABUA/P/b/9v/+f8SAAkA8f/t//7/CAAIABMALAAnAPr/3//m//P/7//b/+L/6//4//3/BwAiACoAFAD1/+7/6v/f/+b/4v/H/7z/zf///yIAMwA8AC4AMAAnABYAFAAaAAkAyf+U/4//qf/G/+//OQBjAFwASwBEAC0ACgD5/+//5v/b/9P/1P/2/yoAPQA8AE0AWwAxAAAA7f/n/+P/4v/v//T//P8MAP//4f/c//L/FAAvADoAKgAUABgAEwD8//X/AAD8/+3/6v/q/+n/+v8MABoAIwAUAPL/4v/w/+//2P/f/+//8f/4/x8ASwBKADwAJgAIAPH/3//a/+T/9//x/9r/6/8OABIA9v/s/wYAEgAbABcAEgAlAC8AGAD3/+3/6P/W/8X/zv/o/+//AAAfACkAEQD4//X//P8JACMANAAsABIA9//h/9H/3//+/xkAIwASAP3//P8QACQAKwAsACoACwDd/8L/1P/o/+j/6//0/wcABgD///3/DAAiAB0AAgD2//D/3//R/8z/3f/v//r/9v/w//j/BAAYACwAIgD5/+T/3v/V/9T/6v8RACsAPAAyAAoA//8HAP3/3v/f//j/7//W/8r/5P/+/xgANQAuACIAHQATAAgAEgA0AEUANAALANn/vP/H/+j/9v8DABoADADv/+X/5f/m//7/IAAZAPD/4//5/xIAJAAuACwAHQAJAPH/2f/d//b//f/4//r/CwAdACQAMQArABEA4//A/7j/vv/e/w4AMQA5ADIAJQD7/9P/2f/u//v//v8GAAgAAgAMAA8AEgAgACcAGAAFAAUAAADq/9X/0//i//T/BwAbAB0AEQAHAPf/7v/8/w0AEQAIAAYA///s/+j/7v/s/+n/9v/+/wcADQAEAPv/AAAYAB8ADQD9//T/7P/q//b/AQAOACsAMQARAP3/7P/L/67/tv/Q/+X/AQAeADIARwBIACsADwAKAAQA5v/U/9L/y//L/9b/7/8CACQASwBUAD4AFADv/9z/3f/u//n/BwAVAAkA8v/o//z/EQAdACAAFQAQAA4ADAASABsAGAAEAOf/zv/J/8z/1//3/xMAHwAdACAAIQAYAA8AEQAfACIAEQD+/+f/3v/d/+L/8P/+/woACwAIAAkAEwAgACYAHwASAAwA///a/7//0f/w//T/3//T/+P/+f/+//n//v8IAAUA+//9/wYADgAVAA0A/f/m/9P/wv+1/7v/wP/K//b/KQA8ADUALQAuACkAIAASAP3/6v/Y/8P/u//S//3/FwAfACIAIgAaABIAIQA0ADYAJAADAOL/yf+//8L/3P8EABwAHwAYABoAFwAVAC8AUgBdAE8ALQADAOD/0P/F/7j/u//W//j/EwAlADQAPQA5ACkAFwAVABkAFQAKAPv/6v/W/8j/0v/w/wgADQAHAAQABAALABgAKwA3AC8AGgD6/8v/qP+X/6D/uP/K/9n/6f8KADgATABXAGUAZABJACAAAQDv/9v/w/+y/7T/yf/q/wQAEgAZACIAJQAlACMAHQARAAUA/f/y/+r/6//t/+z/7//4//z/AgAKABcAJAAuADcAMwAXAOj/u/+j/6j/w//l/wMAGwApAC0AMQAyACoAGgAUABAAAADp/9z/2P/S/9D/1f/b/+n///8KABMAHgAcABIACQANAAsABAAGAAUABwAIAAQA/P/v/+j/7v/+/wsACQAEAAIA/f/s/+H/7v8KABcACQD0/+L/0f+8/7f/yf/c/+H/2P/a//L/EgAzAEgAUwBLADoAJAAJAPT/4//R/8f/wv+//8D/y//q/wsAHwApAC0ALwAjABAABQAAAAIABwANAA8ACQABAPX/9P/7/wIACgAXACUAKAAeABIACAD+//H/6//n/93/1f/b/+v///8OABsAKwA8AEAANQAqACYAIwASAPn/5v/Z/87/0f/d//P/BgASABsAIAAfABYAFQAbACEAGgAOAAYA/v/z/+7/8//3//7/DAAWACEAIwAaABEACgAIAP//8f/v//D/8v/w//H/8//6/wIAAwAJABcAGgASAAMA/v/9/wIADgAPAA0ADAAIAAQAAAAAAAAAAAD+/wEA///4//n/BQAUABoAHAAfABwADwD8//H/8f/z//L/8//9/wYACQAGAAAA+f/2//b/AQASABgAEwAIAAEAAAACAAkAEQARAAcA+v/4//7/BgAMAA4ADgAKAAEA9f/n/+T/5P/j/93/3P/e/+D/6f/2//7/AQADAPz/6P/b/97/8f8GABYAGQAWABcACgD5//D/8v/3//P/9P/2//L/5v/n//X/AQAIAAkADgAUABIACwAFAAoAEgAUABMADgAHAP3/9//0/+7/7P/l/+H/3//e/9z/3//w/wUACwAEAPz/9v/z//P/9v/5//r//v///////v8CAA0AGgAlACUAFwAFAPP/3v/M/8P/zf/h/+7/8P/q/+P/3P/j//P/CAAeACoAKQAWAP3/5P/Z/+j/AAATAB0AHQAWAAUA9//0////EAAcACAAGAAKAPf/4v/Q/87/2//w/wgAGAAfABgADgAJAAUAAwAEAAoADQAMAAcACAAQABoAHAATAAwACgAEAPr/7f/n/+f/7P/z//X/+P/5//n//f8HABMAEwATABMAEgARABEAEAARABEACgD6/+j/5P/m/+r/8v/8/wgADgANAAUA+f/w/+z/7v/y//b/AAAIABAAEwASABYAGgAbABUAEAAMAAUA///2//T//P8EAAwAEAAPAAsAAQD0/+z/8P/z//f/+f8AAAQABwAQACAALgA1ADIAKAAaAA8AAwD5//L/8f/4//7/AQAGAA0AFAAbAB0AHAAbABgAEAAKAA0ADgAHAPr/7//n/93/2P/Z/+X/+v8JABEAFQAXABIABQD5//X/9//4//L/6v/n/+T/4v/k/+f/6v/y//v/AgAGAAcABQAAAP7//f/4//L/7P/l/97/2P/d/+r/+P8BABAAHgApAC0AKQAfAA4A+P/h/8//xf/F/87/3f/p//D/9P8AAA4AHQAsADEALAAiABUABAD3//H/9P/1/+//5P/g/+b/8P/5////BgANAA4ADAAHAAUAAwAAAPz/+v/6//3/AQAHABQAIQArAC4AKAAfABEAAADu/+X/4f/k/+j/6f/s//H//P8GAAwAFgAhACUAIQAYABMAEQAUABUAFQATAA0AAwD0/+f/4//p/+//8v/1//7/BwAQABcAGAAXABQADAAFAAAA///9/wAAAwAJABAAFAAUAAwA///0//H/9/8AAAIAAQD//wAAAgAHABEAGwAiACIAIgAfABkADQAAAPT/8P/v/+z/6f/q/+3/7f/v//f/+v/9/wAACAAPABYAHQAfACIAJgAoACgAIwAeABoAFgANAP3/7//p/+P/2P/Q/9D/1//e/+P/6f/t//D/7//y//j/AAAIAA4AEAAPAAgA/v/x/+z/8P/z/+3/5f/l/+r/8f/7/wMABwAJAAwADgAIAP7/9v/v/+j/3v/b/+T/8/8FABQAGwAcABkAEQAEAP3/9//y//L/9v/6//7/AAAAAAAAAAAEAAwAEQATABYAFAALAAMAAQD///n/7//g/9n/3f/o/+7/9/8EABQAHAAeAB4AFgASAA4ABgAAAPr/+f/5//v//P8AAAMACgAMAAMAAAABAAEAAAABAAUABwANAA4ABAD/////BAAEAAYAEAAYAB8AIAAaABEABwD8/+v/3//W/83/0P/h//r/DQAdACUAIgAeABQABgD9//n//P/5//T/9v/7//n/9v/4/wEACgATABcAFAAJAAAA+f/2//j/AAAGAA0ADwALAP7/9P/x//X/+v/8/wEABgAFAAAA+P/y/+7/6//u//P//P8LABsAKQAwADMALwAoAB4ADgD+/+7/5f/i/9//4P/m//D/+P/7//n/9f/x//L/9f/4//3/BgAGAAMAAQABAAMABwAJAAMAAQAFAAgACQAJAA8ADwAJAP3/7//h/9f/1P/Y/+H/7v/1//v//v8DAAkADAAOABIAGQAdAB4AFAAIAP//+P/6//3///////7//f/5/+//6v/q/+v/8P/y//P/8f/0//r//v8CAAcADQAPAA4ADAAIAAEAAQAAAAAAAwAFAAUAAQD5//X/8f/s/+b/5v/r//H/9v/4//r/9//z/+3/7f/3/wEADQAXABoAFgAMAAIA/v/8//r/+f/4//P/7f/n/+T/6P/z//n//P8AAAQABgAGAAcACAAAAPr/+v8AAAIAAwAEAAcABgAAAPz//f8EAA4AEAANAAkABgAEAAAA/v/6//r//f8AAAcAEAAZAB4AIAAbABEACAAFAAIAAAD+//v/9v/0//P/8//6/wUADQAOAA4ADwAOAAgACAAIAAoADAAQABQAFAAQAAkAAgACAAQAAwABAAEABAACAP3/9//4/wAABgAMAA0ACgAKAAcAAgABAAEAAQAHAAsACAACAAAAAQAAAP7//P/2//L/8f/1//v/AwAQAB0AIgAZAAwAAwAAAAAAAAABAAUACAAFAAAA+//4//X/9P/3//7/AQAAAAAAAgADAAMAAgABAAIAAwACAP///f8AAAEABAAGAAoACgADAPz/8f/m/+H/5f/s//D/9P/4//3/AQAFAAAA/P/9//n/8v/v//T//f8AAAEAAwABAPX/7f/p/+v/8//8/wMACgALAAcAAwAAAAAAAAACAAUABQD///r/9//y//T/9P/2//v/AAADAP7/+//+/wMABAACAAAAAgAFAAoADAAMABAAEgARAA8ACwAFAAIAAAD///7/+//5//v//f/8//7/AgACAAMABQAHAAMAAwAIABAAGAAdAB4AGgAQAAEA8f/r//D/+P///wQABwAIAAcABwAKAAsACQAHAAIAAAD//wIAAwAGAAYAAwD+//v//v/+////AAACAAQABwAKAAsACwAHAAQAAgAAAAMABQAIABAAEgAQAAwACgAHAAQAAgD///r/+v/6//z//v8AAAEABAAHAAgACQAIAAQABAAIAAoABwAGAAcABQADAAQAAwAAAAAAAwAGAAYABQAEAAUABAABAAAAAAAAAP7/+P/0//P/9f/2//n//P///wAA+v/z//P/9v/9/wIAAgD///b/7P/s//P/9f/4/wAABAAEAAIABQAJAAsADQALAAcAAwABAP//+v/z/+//8v/2//n//P8AAAEAAAD8//r//v8CAAUABgAGAAAA+P/1//n//f/8//v/+f/6////AAAAAP///v8AAAAA/f/7//j/9v/3//v///8BAAQAAgD6//L/8f/1//n//P/6//f/9//6//z/AAAFAAoACQABAPr/+v/9/wAABwANAAsABwACAP7/9//u/+n/6v/q/+n/7P/y//T/9f/0//f/+v///wkADgAQABQAEAAIAAQABQAGAAUAAgD+//n/9P/y//D/8f/0//f/9P/w/+7/7v/x//T/+f/9/wAABAALAA4ADAAIAAUAAQD///7/AAACAAkADAAIAAEA/v/6//r//v8AAAEABwATABkAFQARAAwABwACAP////8AAAUACgAMAAsACwAMAAYAAgAAAAAAAgAIAA0ADgAOAAwACgAEAAIABgAJAAsACwAFAAAAAAACAAYABgAHAAoABAD8//b/9P/3//v/AQAHAA0ADwAKAAYAAwAAAAIAAgD///v/+//6//r//P8BAAIAAgAGAAoABAD7//X/8f/0//v/AAACAAAAAAD9//n/8v/x//n/AwAKAAsACAAIAAkADAAMAAoACQAJAAsACAADAAMABQAHAAcABgAIAAsADgAQAA0AAgD8//7/AAACAAUACQAIAAUA///3/+//6f/o/+r/8v8AAAsAEgASAA0AAwD6//T/9f/7/wIACQAHAAAA/v////3//v8EAAgABAD7//T/9P/z//P/9v/8/wAAAgACAAAA/f/9/wAAAgAGAAkABwACAP///v/6//j///8BAAAA///7//z/AAAEAAUAAwAEAAYABAAAAP////////z/+P/3//n//v8FAAcACQAIAAYACQANAAkAAAD//wAAAAADAAUACgAKAA4AEQAOAAoACQAFAP3/+v/9/wAACAAPABEAEAAKAAAA+v/7/wAAAQAAAAEABQAFAAIAAAACAAMABAADAAEA//8AAAIAAwADAAcABgAEAAAAAAAEAAcACQAMAAoACgAKAAcAAgD+/wIABgADAAAA/P/0/+//7f/x//r/AwAJAAoABwAAAPv/9//3//7/CQARABYAFQANAAEA+f/2//b/+v/7//z///8AAPz/9//1//X/+f/8//7/AAAAAPz/9//1//j///8GAAgACQAIAAUAAQD+//r/9f/0//T/9v/4//z/AAACAAYABAD7//P/+P8AAAIAAgAGAAQAAAD7//f/8v/y//P/8P/v/+7/7//1//f/+P/5//7////5//X/9f/0//P/9P/9/wcACwAIAAYABQAAAPn/9f/0//H/8f/y//P/+P/9/wEAAAD///z////7//b/+/8BAAQABwANAA0ABwAAAP7//f/6//n/+v/+/////v/8//r//P8AAAEAAwAEAAUAAQABAAIAAAD9//3/AAABAAUACQAIAAIA/////wEAAAACAAgACwAMAAoABwACAAUABwADAAMACAAPABAADAAQABMAEwATABEAEAAJAAIAAAD///3/+/8AAAQACAAIAAYABgADAAQACAALAAsADQARABMAFgAVABEAEAARABMADAAAAPv/+v/6//v///8FAAwADgAFAAIABAADAAIAAwAGAAYACQAQAA4ACQAIAAQA/v/2//X/9//1//P/+/////z//P8BAAgACwALAAoABwAIAAUAAgABAAMABAAAAPv//P/+/////P/6//z/+v/5//z//v/+//7//f/7//j/+P/8//3////+//3/+f/3//T/7v/s//D/+f/6//b/9v/3//T/8//1//f/+f/5//n/9v/1//v//v/9////AAABAAAAAAD7//X/+v/7//n/+v/+/wMAAQADAAgACgAGAAQABwAIAAYAAwACAAcACQAIAAIA/f/4//P/7P/m/+X/5//t//P/9/8AAAIAAgAFAAkACgAFAAMABgAIAAoADQAOABAAFwAaABMACAACAPn/8P/s//D/9//9/wMADAAMAAoACAAHAAUAAgAAAAAAAAAEAAcADAAKAAsADQALAAoABwABAPz/+//3//D/8v/7/wIACwAOAA8ADQAJAAMAAAD8//j/+P/4//z/AQAEAAEABAAGAAEA/f/8/////////wUACQAJAAkACQAHAAgACAAIAAoADAAMAAMA//8BAAIAAQABAAEAAAAAAAQAAQAAAAYABwAJAAwADQAKAAwADQAKAAgACAACAAAAAgADAAAAAAADAAIAAgAIAAcAAwABAAEAAAD8//r/+//+/wMABQAGAAMABAAFAAEAAAAAAP//+//5//v//v8AAP3/+P/5//b/9P/2//b/+//7//f/+v8AAP7//v8BAAYABgAFAAQAAgABAAEA/f/7/wAACQANAA4ADwAMAAYAAAD5//b/9/8AAAUAAwAEAAYAAgD+//z//f/6//n/+v/9/wAAAAAAAAAAAgAEAAEA/v/4//b/+P/4//j//f////z/+//8//j/9P/1//b/8f/w//P/9//8/wAAAAD9////AQD9//n//P8BAAMAAgABAAEA///9/wAAAQAAAAAA+//4//j/9f/3////AgAAAP//AgADAAMAAwAEAAAA+f/6//r/+/8DAAcAAgAAAAMABAD///r/9v/z//L/8//z//X/+v8AAAAAAAABAAIAAQD///3//f/9////AwAHAAcABAAEAAMABAAFAAUAAgAEAAgACgAIAAkABgAEAAUAAwD///7/BwAJAAIAAQABAAEABAAGAAYABgAIAAYAAQD9//3////8//v/AAAEAAgADgAOAAcAAQD7//j/9v/5//3//v8AAAMAAwACAAYABAACAAEAAgAHAAMA/////wAABgAKAAYAAAABAAQAAAD+/wAAAQAEAAQA/f/1//f//P/7//7/AwABAP7///8DAAMAAQACAAIABQAGAAEA/v/5//r/+//8//n/+P///wQABQADAAAAAwAFAAAA+//2//j//f8DAAQAAAAEAAkACgAIAAUABAADAAAA/v8BAAQACAAQABEAEQAOAAwACgADAP///f8BAAcACwAOAA8ADwALAAQAAAD7//r/+f8AAAcACAAGAAkACgADAAAAAAD8//T/8//y//X/+P/6//7/AAAFAAUA/v/1//D/8f/x//H/8//5//3/+//6//3//v/+/wEAAAD4//b/+P/2//n/+//8//z///8EAAEA/f/+/wAA///2//L/8//2//v//f8AAP//AQABAP7////9//z//f8AAAIAAgAFAAYABQABAP///f/4//v/AgADAAUACQALAAYABQAFAAYACQAGAAIABgAKAAoACgAKAA8AEQAQABAADgANAA0ADgAPAA8AEAAUABUAFAARAA0ABwACAAEAAgADAAkACgALAAUABQAIAAYABgAJABAAEQAIAAkACgAGAAgACQAJAAcABgAFAAEAAAADAAQAAQAAAAMABAAAAPv/9//0//X/+/8CAAUABwALAAsABwAEAAEA/v8BAAAA/v/+////AwACAP///f8AAAAAAAD+//v/+v/9//3/+f/2//P/9P/5/wAABgALAAwABwD8//L/7P/p/+z/9v/+//z//f////v//P///wEA//8AAAAA+v/7//n/9//7//v/+v/6//r/+f/1//T/+P/7//j/+P/9/wAA/v/8//v/+P/z/+//8P/2//n///8AAPv/+f/y/+j/3//g/+r/8//1//H/8f/0//T/+//+//7/AAADAAsACQAHAAgABgAEAP7/+f/9/wAABQAHAAQAAAD7//f/8v/1//n/+v8AAAcACgALAAwACwAGAAEA//8BAAUACAARABcAFAANAAQA/v/7//v/+f/8/wcADAALAAEA+/8AAAQABQAEAAIAAgABAAAA///9//3///8EAAUABwAIAAwAEAAMAAgABAAAAAEABAAFAAUADQATABIAEwAQAA4ACAACAAQABAAGAAYABgAFAAUABQACAAUACAAEAAQABgAKAAsACwAJAAIABgAJAAUABQAFAAkACwALAAsACAAAAPn/+f/1/+//7v/x//j/+//9//v/+f/8//3//f8BAAgADwAQAA0ACAADAAEA/f/8//z//f8EAAsADgAMAAQAAAD7//j//v8CAAYABQAFAAEAAAABAAEA/v/8////AgAAAP3///8BAAEA/v/8//3/+//8/////v8AAAkABgAAAPT/7//w//T/9v/u//H/9//6//j/9P/6//r//P/8///////6//3/AAACAAQAAgAEAAIABQAKAAMA+v/x//D/7//u/+//8//5//z///8AAPv/9//4//v/9//w//P/9v/5/wAAAQAAAAAABAAFAAAA/f/9//z/+//+//3/+//5/wIAAwABAAMABQAGAAEAAQAFAAUAAwACAAcABwAGAAsACwANABAADwAMAAsADgAHAAMABQAGAAoACgAHAAMA/v/8//z/+//4//n/+P/4/wAACgAHAAAA/P/1//L/9/8AAAEAAAAFAAcAAwD///7///8CAAQAAwD///3/AQACAP3/AQAHAAwAEAATABMAEAASABAACAADAAAAAAAFABEAGgAgACEAHgAaABgADQAAAAAAAgACAAMACAAHAAIABgAJAAMA/f/9/wIAAAABAAgABgAGAAUABgAAAPz/AwADAAAA/v8AAP7/9//3//z/AgADAAEAAgD///r/+P/6//7//////wAACgALAAoACgAJAAQA/v///wAAAQAMABUAFwAVABkAGgARAAYA///5//b/9v/4//j///8KAAoAAwADAAUA///4//r/+f/0//P/+P/9////AgADAP7///8CAAAA+f/1//f/+P/1//b/9//3//X/+P/4//X/9v/8//r/9f/7/wAABQAIAAcAAAD4////AAD7//z/AwAMAAkABwAIAAIA/f/5//z/9v/z//T/7//v/+3/7//4//7//v/5//r/+//4//j/9//0/+v/7P/z//L/+v8DAAQA/P/7/wEABAACAAAA/v///wMABQABAPz//f////r/+v8AAAYACAAKAAwACgAGAAoADAAIAAIAAwAHAAcADAASAA0ABgAEAAkABgAEAAAA+P/4//r/+f/x//H/+P/2//T/9v/5//r///8GAAYAAgABAAAA+v/5//z/9v/v//P//v8AAAMACgAKAAQABAALAAkABAAHAAkACAAKAA0ACAAJAA8AFQAXABkAHgATAAEA+P/y/+7/7f/v/+7/9P8BAAkACgAOABEAEQAMAAcAAAD7//r/9//3//v/+f/z//X/+v/3//P/+/8BAAYACwAOAAgA+//3//3/AgADAAMABgALABQAGwAVABAADQAOAA8ADAANAAsACQAFAAQACgAOABAADgAMAAcAAAD//wQACAAAAPr/+v/4//n//v8GAAcAAwACAAAA/P/0/+3/5//q//b/+P/0/+//7f/y//X/9v/0//T//f8DAPz/7f/s//f/+P/0//T/9f/3//n//P8AAAAA/f/8//v/+f/9/wEABQAIAA0ADwAGAPz/9//1//X/+f/+//j/8v/1//L/7v/u//T/AAAIAAYA+f/u//H/+v8AAAEAAAD+//n/8//s/+//8P/y//P/7v/w//L/+v8JAA8ABAD5//7//f/3//v//v8FAAwAGgAjABsAEwAKAAAA+v/9/wQAAQAFABEAGgAcABYAFQATABEADwAOAAwADwAUABMACwAFAAUABgAGAAcAAwD7//b//P8EAAYACAAHAAcABwAKAAoAAAD4//j/9f/6//j/7//u////FAAQAAUAAwADAAMAAQD7/+7/6//z////BQACAAAAAAAAAAEAAwABAP3/BwASABcAGAATAA8ACgAKAAwACQABAPj/9P/5/////P8AAAoADwAIAPz/9v/7/wUADQAKAAIAAAALABIAEQARABEABQD7//b/7//q/+n/6P/r//j/BwAIAAkAEgAaABMACgAKAAkACQACAPj/9v/4/wAAAwD///b/9v8GABIAFAAMAAcADgARABYAGgAYABMABwACAAAA/f////3/AAAEAAUAAgABAP////8AAPX/8f/7/woAHgArAC8AJgAaABIABgD3/+T/3f/k//T/AwD9/+//6v/y/wEACQAJAAIA+//9/wQABwD6//f/AgACAPz/AAAHAAgAAAD7//v/+f/5//T/8P/0////BQACAP7/+P/4//X/8/8CABgAHwAXABIADwAEAAoADQD+//P/8f/1//r/AgAHAPz/9P/2//H/4//Z/9//5v/o/+r/7v/4//v/AwAGAAcAEQASAA0AAADy/+X/5P/y//f/9P/p/+b/6//s//j//P/x/+n/5v/o/+//BAAZABQABwABABAAJQAuACUAFAAMABQAFwAPAAwABAD6//L/8f/2/+//5v/l/+f/+P8JABYAFgAKAAAA9//6//3/AgANAPv/6//p//L/BgATABMA+P/j/+7/7//l/9//5//x//T//P8DAAoACwANAA0ACQALAAsA/v/t//X/AwACAAcAFAAWAAsACQANAAYA///8//T/6f/t//T//v8IAA8AFwAgACwANgAnAAkA9f/9/xcAKwAoAAsA7v/p//H/+//8//H/5f/i/+//CQAdACUAFQAHAAwACwAIAAgADAD+/+j/6f/q//X//P/7//X/6v/4////9//w/+f/7P/z/woAHgAdACQAIAALAPT/+f8WABYABgDs/93/9f8XACMADgD9//n/BAARABMAGgAKAPb/8f/w//D/8P8IAB0AGwATAAgACwAQABkAGwAFAPX//P8WACYADwDt/+P/+v8SABIABAD+/wsAFAAIAAkAHQAoABMA8v/r//X/AAAGAPv/6f/i/+L/4v/t//j/7v/d/+T/AQAgABsAAAAAABgAJAAkACMAEgAMAAsA/v/z//P//f/v/9j/2P/U/93/7v/1//T/7v8AAAwABwD0//H/BQADAPH/1//S/+z////8/+r/6f/7/wQA8v/K/7z/yv/f/+L/yf/J/+b//v/+/wAAEAAdAB0ADgD4/+j/6P/6/wMA/v/8/wYAGAAjACgAJgAuADUAJwAcABMAJAAyAC8AKwAgACYAJgAdACIAIwAhAAwA+P/z/wAAHgAdAAQA7//w//z/8f/i/9P/4v/8//n/5f/d//H/AAD3/+H/2P/d/9H/xP/A/87/3v/j/9f/0v/v////9P/k/+r/+v/u/+j/9/8UACUAJAAZABUAJwA2AD4AOQArAB8ABwD9/wUAFAAfAB4AIwAuAEEATAA+ADMAKwAlACMAJAAeABAAAwD///P/7f/y//b//v8CAAAA/P8AAAUA+//9/wMA/v/n/9L/3v/m/+T/4v/j//D/8P/7/wIA+f/s/9X/0P/W/9z/zv+u/63/v//d/+b/2v/m//n/9//i/87/zv/k//r/BAAaACQALAA4ADUAMAAmAC8AKgARAAIA+f/+/wEAFAA1ADsAOwBMAG0AiAB3AFUARwBRAFoAUQBCADoAPQA/AD4ASwBKADAAEwAPACQAJgAJAPL/9f8HAAkA6v/Q/8n/vf+l/5H/lP+b/5r/jv+I/5P/nf+V/53/qv+d/5z/nP+S/4z/k/+w/67/oP+g/6f/yf/b/9L/yv/o/x0AKQAYABgAUQB9AG8AXwBoAIYAkQB7AHAAiAC+ANUAwgClAJ8AwQDZAOEAzgCyAL8AxACgAGMAPAA6AEYAQAAYAOv/0P/D/63/lf+Q/5j/m/+G/3H/Yv9V/0v/Of8w/yT/A//W/qz+s/7D/rv+sP64/uD+BP8e/zL/R/9i/27/gv+S/6r/1f/w/xAAMQBVAH8AqgDtACABOQFNAXEBmgGrAbUBxgHRAdMBzwHDAbEBoQGTAYsBdgFlAVIBMAERAfQAxwCLAFQALwAIAMf/fP9E/yD/C//e/p3+Zf49/hz++f3Y/br9lf1a/Sj9KP1K/W79gf2F/Y39q/3L/en9CP4h/kP+cf61/g//Y//A/yAAhQD4AHEB2QEeAmECiQKmAtIC9AIhAzUDRwNIAzwDWAOGA74D3APDA5QDYgNIAx8DygJKAsoBbwElAcUAPwDI/3H/Jv/A/jH+zf2X/Vf98PyK/Gn8S/z++577a/uI+6j7pPt6+2z7l/vN+wj8KPxU/J783fwt/Y398v1Z/rD+H/+i/zEAyABQAdQBVgLfAmEDxgMWBGoEuwTbBNUE2QT+BEsFewWFBXgFhQW7BbcFUgWkBPMDbQP1AnkCzgH/ADwArP9G/+P+gf4d/p/9/vxY/N37o/t3+x77qvpO+k36cvp6+oP6pPrt+j37efu4+/j7I/wz/E/8h/zn/Eb9gf3D/SH+v/6N/28AVwE2AhwD3gOCBAMFZgXMBfYF5AWlBXIFkAXBBcYFmgWRBekFXAaOBjYGogUhBZgECARXA7AC6gHYAMH/9P6c/kH+ff1//ND7n/ui+3r7EvvY+s76tPqI+n36vvro+qb6PPoK+lH60voi+zb7Mfto+9X7Wvzi/E79sv0R/on+Mf8NABoBIgLtAmgD3wOiBJMFPAZTBvgFoAW+BSgGjAanBp0G4gZsB+cH5AdMB4sG3gVHBYcEcQMcAtcA4/9c/xP/sv4D/iL9g/wv/Af8yftR+8v6RPr1+dD5tfmi+W/5Lfny+PL4QPmm+e357fnv+Tv67/rJ+1P8lfy9/Az9hf0k/gL/DAAWAeUBkAJXA2YEkQViBrEGpAadBuoGTAdmBycH+QY/B9MHPgg4CMIHIAdnBqIF7gQ0BDcDxgE4AFL/G/8N/3X+QP0n/KP7q/uk+yz7b/rM+Yn5kPm5+dH5yfmr+X35dvmm+RD6hfqY+l/6Qfqd+lL71/v9++/7MPzb/Kf9eP5V/00AUwFkApED3wQABqwG8gYOB0oHewd6B0kH6Aa/BvsGjQc7CJwIhAj0BywHeQYFBpgFwgQ4Ay0Bjv/b/tH+gv5h/Qf8JfsJ+0z7c/tn+xv7q/o++iP6gfrY+qn65/kR+cv4NfnL+Qr63Pml+dr5hfpm+yf8nvzi/BD9af0v/nj/2QDYAXACBwMzBKQFvQYXB90GugbUBgQHJAdFB40HwwfiBzUI/QjgCeIJpwjVBogFCgVtBPQCuwDH/sf9fv1T/eP8SfzH+3P7O/tC+3T7gPsm+176uPmL+bH5p/kV+XH4QPii+Cr5ffm4+fz5Yfqy+ur6Rfuz+yv8ePzF/HD9f/7T/xYBTAKlAwYFNQbiBhkHOgdyB54Hbwf9BpwGoQYDB5MHTAgRCakJiQmWCEgHPAafBboEBgOvAKD+uf20/cb9Rf0v/FT7I/uD+/378ftl+4L6vPl3+ZX5wfly+bD4+/fZ93L4PPnM+df5lvmI+cr5ePoi+2n7Yvtx+yT8av33/mUAuAEPA2cEtAW9BnwH2QfDB30HQAcVBwgHAQcXB1oHyQd6CEMJ6gkACioJwwdmBn0FsAQ5AwMBwP5o/T/9eP0//WL8iftK+5X7Bfwq/O77UPuE+t/5pPnR+dL5Qvlb+ND3FvjG+D35UflK+YL54flH+rn6TfsV/L/8S/36/RH/lQAuAo4DpASJBUYG1gYaByEHFQcWBwkHxQZqBnAGIgcyCCcJrwnpCfIJgQluCAkHxAWoBBcDuQBB/rj8iPzk/Lb81vsV+y/7Dvzu/Cf9tvzu+y37lPo3+uP5W/mO+Jn3MPeN91L49vgi+TL5nvlv+kL7yfsH/EP8vvxi/S3+Ev/z/wQBKAJ5A+gEDAbbBiQHQwdyB68HzAeMByIH6AYkB7AHVQjmCFAJYgm7CHoHJwZMBbgEjwNlAeH+Mf3e/Db9If1J/GD7GPuR+2H82PzY/FH8hPva+mr6SPrq+R/5IPhy95X3NvjK+AX5Evlq+R768vqT+/H7WfzP/Hf9Of4V/xUAFgE5AncDywT6BawGFAdZB58H3AewB04H5wbPBhsHiAcfCKsIMAlZCdIIxgeQBrcFAAXEA7IBQ/+p/U/9jv1n/WP8Tvvn+mH7Nfyi/Jf8HPyA+wL7tvqr+nb62/nj+O/3qvcF+Lb4K/kk+Sb5XPkU+gn7yvtg/Lv8Nf3s/e3+HwAyASIC8gL7AzIFMQa5BrcGtwYBB1sHagcLB8sG8AaHBzYIvQg1CVEJ4gjMB4UGkwW9BKIDsQGD/+r9Sf1y/Wj9+Pwi/Jj7pvsF/KL8svxs/N37LPvp+qz6jfoo+kT5fPjz9x/4rvgN+TT5G/lV+eP5o/pZ+8P7K/yK/DP9Kv46/08AHgEEAicDeQS3BV4GpQbGBjUHtQfYB6EHKgc4B6IHLwilCMkIEgn+CEQI+AZ3BZwE5AOxArUAf/59/X79+f3V/cL84/tq+9j7avx//Fn8l/sB+7T6o/rn+pb68/kA+Ur4ZPic+Pn45vin+MD4APnM+Yj6MfvX+038EP30/QP/KAAPAf0B8wIVBEQFEQaVBtkGXAcXCHEIXwjgB7QHLQi1CCQJBwnoCO0IZAh/Bw0G2wQUBNACLAEa/8f9n/2w/ZH9lvy8+4v7w/s5/Bv84Pue+1D7Kfvj+uf63vqB+uD5DPnV+PH4C/nh+G74Y/ir+Eb50vk3+t36mPt8/Hb9av5z/3QAfAGhAtoDAQXOBUYGsQZZBxkIlwiJCBII4wcvCN4IdAmDCWQJNgnlCDcI8QaZBW0EPgOcAYL/0/0V/Ur9WP3C/AD8kfsb/LX8+vzn/HH8Q/ze+3r7OPvk+q765vn4+F74MfiM+Kf4g/hV+G34/fin+V76Afuq+0D85vyl/Xn+k/+GAJgBuALbAxYF7gW0BmwHMwj5CD0JCwmcCIUIzghGCXQJKgnjCGkIAAgmB+MFzwSEA1UCqADK/qz9H/1H/Qf9UPy7+4P7JfyY/Mj8pPxE/Cn8tPtp+x/71/qj+sP5C/mM+IL47PjJ+JH4T/hr+Av5fPkT+oH6E/vz+7T8xf3G/un/KQFcAtED+gQaBuwGgAdECLMIKAkdCdcIvQiaCPkIJglECV0J/AiWCJgHcwZ5BV8EVAOVAbD/Of5a/Ub98vxb/Lb7W/ux+xL8bPyM/IX8l/xj/DT88vu9+4v73/oV+kP53fjT+Kb4VPjP96f37fd7+DT5zvmF+lT7S/xn/YD+pf+oAM8BDQNWBIMFKwa9BkIHDQjTCA0J9giECJcI/wiGCfoJ3QnJCWAJzAjNB1QGGQW2A2UCkgCP/kT9tfz2/LX8GPyR+4z7gvz7/DT9Dv3X/BT9m/wn/Jn7Kvsa+0z6ffnL+Ir40Pim+F74+/f091z4vPgm+XT5B/rR+rn7tfyg/cn+CwCFAQsDhwTjBdIGlQcOCLUISglcCTQJkQh/COoISQnWCaUJoAmCCZwImwfaBbUEyAM1AmkA8/3M/Kz8wvyo/In7Jftc+x384/zQ/Bb9Ff0m/ej8NPwU/LX7Xvtx+jD5p/hV+H34Rfi995r3n/cm+J/49/iR+SX6Cvv3+978B/4e/3MA8wGaA1gFkQZvB+EHgQhjCdoJ9Qk+Cc4I7AhJCQgKAAoXCvkJSglzCK0GeQVqBBwDegHU/ir9cfyc/MP8xPsP+7b6bPtZ/J/88fzx/Ff9Vv3g/Kn8SPxI/Ir7PPo7+Y/4tPh2+Nz3RvcD94b35/cp+Hz4EfkQ+hH7Bvwb/W7+1v9LAdcCmgRMBlIHzAcICKwIkwn6CagJ9Qi7CCYJ6AlNCmYKVgr+CVcJ6QdxBhUF1QNKAur/wf1W/CX8Vvz++1P7zPpD+xH8z/wf/TL9k/2L/WT9yPxS/EX8vvv7+rL54PjC+L34kvjS92X3bffb9yv4K/h3+Bb5QPpC+zD8V/2o/n0AJwLRA3UFswa6ByYIpghmCfgJLQp+Ce0I7wh1CS4KOwpJCikK6QkeCW0H/AWrBJ8D2AE//w393vsZ/CP8n/uy+lP6LPsQ/Or8J/2A/fb9v/1X/aP8mfy6/Pz7mfrl+Ev4eviO+Bz4C/er9sn2PvfA9wb45/ir+Zv6k/ug/Hf+EQC4Af8CQgTtBREHEghlCMIIUgmLCZ0JJQkcCXcJ4QlQCjcKPgo9CqEJmAjXBmYFLgSEAqcAIP6g/Ar8w/ue+6L6cfrL+pv7nfzL/HH9yP3+/cz9+fza/JL8TPwm+4r5t/hU+J34QPh59+n2uvY69573BfiY+Ff5T/o3+zX8n/1L/+oASgJ8A/cEbAaMBx8ITgjdCFkJswmBCRkJdAnlCacKxgqTCr0KSAqzCfMHEAbHBDkDzQFB/xv9AvyS+8P74fpd+lb68Pok/HD8H/2X/Q3+Pf5k/SL9vPyR/Nv7Nfou+Vf4cfhC+Jb3Jfef9vb2Qvef9yz4wvjT+b/61Pvx/Fn+AAB6AeECBgRgBYwGXwfiBzcI3QhlCaIJgAmTCQoKuAooCwQLEAvGClkK+wj0BmAF1gO1AqkAX/69/Aj8Pfzd+yv7d/qe+mj7+Ptn/JX8Of2R/V/99fyJ/NP8l/ys+1H6Ovkr+RH5ofij9+P2z/b29i73OffT98r47vn3+uH7cP0v/wIBYQJmA7cE+QUgB6EH4wd9CB8Jswl9CUEJsAmDCoELaQsZC/MK0wpNCk0IKwZcBD0D3AF+/139F/xJ/D/8gPuO+i36J/vM+yv8Hvxr/F/9lf1r/b78mPy4/Cv8GvuO+ez4ufh3+NT3ufZ99pr2APc+90/3RPhm+bv6wvuq/Ev+7v+hAcgC4ANaBYQGdAeWB/EHxAiJCQsKuQm9CVoKIQu6C3YLFQvzCi8KEwn3Bg8F9wNCAtUAYv7V/Jb8KPxm/Br7q/rT+gH79/uT+zn8pvwQ/W/9nfzj/J/8ofzr+076o/nM+L34HPg399X2mfbw9gn3Rff39/b4Jfon+zb8o/1I/9UANAJ5AwUFUAYlB48HywfFCHEJ7wneCZgJcwr9CtAL1QtBC0oLPwpyCYcHdwU3BFsCJwHT/i39evwv/J38lvv6+oD62PrU+8H7EPwN/Kz8Of3g/OD8oPzc/Fj81/qi+cv4EfnL+NT30PZT9uv2TfeI94b3HfhJ+Wf6jPuY/Db+/P+eAfUCLQSkBfAGxAcKCGsIIgn5CWgKHgo5CpEKagvSCz0L9AoNCq0JVghxBgUFNANdAkcAov5B/Zn88Pz3+5n7cPqa+jH7Ofvn+5b7oPz8/CH9Pv23/EL9ivzX+3P6T/k5+Wn4Gfjd9oz2nvav9jv38fbt96/4B/pv+zv87v39/tUAQQKZAx4F+AUfB2AHJwjPCJ4JSgr7CScKIQoEC6ILhQs+C08K5wnnCHIH0gX9A8gCGQFx/9n90vy//F786PsO+6X6Dvto++b77/sn/LP84/wf/ev81fy4/O77+/rB+R35wvgi+Gr3dvZN9pT2/vZo94v3Wfht+dX6LPwt/Z/+CADGAUUDeATTBbgGtAcjCJkIUAnMCUkKGQowCo8KHguECy4LtgoSCj8J+AcwBnIE+wKVAfP/M/7k/Hj8fPxK/Lv7DvsV+3T7+vtC/FH8rPzB/OT8sPyl/LH8Lvxg+wf6VPn1+LD4Hvgp98j2tvYm92f3ofc0+AD5RPpy+778MP6e/zMBjwLwA2AFagZeB6cHAAisCDoJFQrZCdkJ+gllCmMLDQvgCvsJXAmTCLoGRwVsA5QCEQFJ/7/9m/we/cn8hPyK+wT7rfvS+4P8Wfyi/BH9+vwd/bH8u/xo/JP7hPpL+eP4gvgb+H33sPap9sn2Sfeh9+b3r/i4+T/7kPzq/UD/oQA5AnMD1wTSBacGMQdfBxMIuQiCCbIJiwmxCU4KCgtOC/gKUAryCeAIrgeeBQsEAgNtASkA4f0r/fH8Bv0G/bT7tPtd+yT8Zfw+/Nn8nfxW/bv8lvyM/DP8Mfy3+uX59/jn+M/4HviG99j2OfeA9+v3EviF+K359Ppp/H790f47AMsBBgMYBDIFEwb2BvwGagfrB7QIfAk2CWkJmgl3ChsLwQpeCpkJHQkECE4GkgQAAwUCmAAj/7z9Ef0r/fL8qPzq+6f75vsZ/GD8Tvxf/KX8pfyO/EX8A/z1+1/7p/q9+Tb5Gfm8+Cv4Xfcl90L3rPfq9zb4EPkW+oD7vfwk/pv/6gAqAk0DoQTTBZ0G3QYKB6QHXQgZCSEJBwlRCcMJvQrACqkKKAp0CeMIIAe9BekDzAKUAbn/bv4L/Vn9KP3f/ED8cPsa/BX8r/yF/Hr8Cv2j/OL8MPxW/Fr8sPsm+7/5rvlG+Sr5ffiC93L3Ffex96j3KvgJ+bT5IPvi+4L95f4pAG4BMwLLA9MEAgZUBocGEgeCB2oIcAi4CNIIXgkmCmcKlwpCCgoKWQkaCGoGwQSjA3YC7wD9/p/9Of1f/TT9aPy1+6D7Fvxp/If8hfy9/OL8t/yR/Hv8o/xb/Jf7tvoa+gL6tPka+UH4m/eB9333rffa91f4JPkB+jb7gvwF/nP/jQDJAQoDlATJBWkGxgbqBqYHPgjICNIIqwgfCa0JkwqSCk8KAQqHCekIFwdmBdADzAKZAZ3/Mf47/YX9Wv29/C784PuA/H/8lvxm/J/8/PzI/Lr8Vfyf/IH8KPyC+7X6iPoY+t/5GPle+Oj3lffB98T3L/ir+Hr5jvq5+z/9kv7r/xoBagLiAxAF2wUwBpYGKwfaBzcIdgh0CN8IWQnNCU0KGwo6CngJ4wifBwQGvAQYAy8COQDg/pz9TP2c/ev8h/yX+wz8ePyX/JX8U/zy/AH97vyK/IT84vyK/NT74fqA+pT6Gvp1+Yv4IPj496T3s/fc94v4Mfnq+RD7cfwZ/kb/agCtAQsDgwRHBQYGZQboBoAH4gdzCI8I3wgaCcQJQgpkCjcKuAloCTEIwwa4BGwDaAIAAYX/mP1L/Q39R/3B/AX8L/wJ/K/8YPzA/Pj88Pz8/FP8zvzJ/PT8XvyB+yz7qfqm+gH6ivnZ+DX43few9zz4nPhC+cn5t/ok/Ij9Cv8dAHUBoALmA+kEpwVkBqYGUAejB0AIlQikCA8JUAn0CQIK1QmpCUUJogj2BiYFrgOvApsBsv8I/gb9KP04/aj8C/y9+zP8Zfxd/FP8rfz+/PD8mPyQ/P/8Gv3S/Of7Yvsl++b6iPq/+SD5dvgg+P33MfiD+M/4ZflL+rr7Gv1e/pj/ywAvAm8DnQSiBUsGqwboBo8HPAjUCLkIuwg9CcIJcQrxCeYJkwnuCMcHdwVQBOMC6wH8/9f9Ev2G/A/9RfzY+6v7wfuL/B/8uPyo/An9If2X/Bn96vxz/eD8Pfy8+zb7Vfug+mT6U/nB+GX4Ffil+F/45vgE+dH5Pfss/OP9wv5OAIUBuQI1BB8FQAZDBq0GCgcECLgIlAioCKgItQkXChsKyAmGCVwJBwhZBn4EdQNfAoYAfP7p/Lr8y/x//Nz7cvvA+xr8RvxU/KD88Pz9/NL8x/ww/WT9Q/2j/AT80Pt9+037tfoa+mv5u/h/+HX4wvjF+Nj4XPlg+uv7HP1L/nn/1AB0AqgDBgXSBWYGqwbmBuQHiQgTCcwI7wi0CU4KuAr8CdsJYgmQCNEGdARjAysCIAG9/sn8Pvxe/N381vtb+zb7zPs+/Ov7RvyG/D39/vzO/Cb9vf1W/nX9u/wO/DP8JPxK+236W/kV+YP4UPgn+EH4pvja+M351Pp7/N/9Jv97AKMBLQNQBJsFJwZ9BrQGaweECPgIJgmmCIQJKQrLCmYKngm7CbQImgcFBZQDkgJYAYb///yI/F/8+vwg/GX7kvu8+1784ftK/NX8Nv0i/bb8T/0R/m3+1P3h/Lj8k/xd/JH7jvoG+iD5mvgc+Aj4Uvg9+Jb4LPmO+v77Lv1h/mb/EQFsAgwELQXDBUYGagaWB4EIVwk9CRwJnQlcCh4LqQpxCqwJMwmRB3IF2ANQAoEBKP9e/eP77vti/Nr7kfvF+nX7fvvT+wr8R/wv/dj8Tf1b/VP+7f5z/vz9H/1N/dz8TPwz+zn6r/nd+Kr4E/hS+G34ovhH+f35nPvU/Db+Ff9PANsBdwMJBXcFMgZ3BqoHtQg/CZYJNQnQCSwKzwrCCl8KAgoTCQQI8gU9BJkCNwGp/3T9JPxg+6v7s/s0++f6yvpi+6z78fsu/MH8Lf1u/az9Kf4L/wj/tP7P/W79R/2f/Kz7WPqt+RT5r/gJ+Mj3//dl+Az5j/nB+hL8Yv2T/rH/XQETA6EEjQUrBs8GjQeVCCkJsQmTCZQJDQpoCgILggofCoYJiwhCB94ENAOJAS8ANP4S/Dv74/p6+/n60PoO+237K/zE+1786/ya/df9Z/0S/p7+Vf/p/jj+5f2Q/Ur9F/wz+0v6qfkJ+TT4HPj+91b4gfgS+TX6c/vk/ND9Of+pAH0CFQQ+BTQGqQZyBxcILwmeCbwJiwmtCXIKnwq5CusJvAkACagHfwU2AxwCdgAM/3X8Nfvu+vv6Ovt3+vT6KfvB+8X7+vvu/F/90v1d/e79rf4d/w3/N/4s/tL9S/1K/DL7rvrL+ef49ve49/73CPg2+J/42/lY+2r8f/2m/nkAOwKvA/oE6QXsBmcHFwjaCJQJ/gmtCegJJgrECrMKTwoWCi4JPQjOBQ8EZQL3ADz/m/xi+276zvpm+kT6b/rI+pD7LfsM/Fz8iP3R/YT9Gv49/lr//v7l/mj+B/6f/V/83/uj+hz64fj89+T3lvcC+L73lPic+Qn7TPwW/cD+BAArAlED2AQTBsoGwwfzBzwJqQkTCrcJgAkQCk0KsAr0CdEJNglhCMMGogQ7A2QBu/9K/Yv7lfoz+vX5pPnS+VD68voy+7z7ffxg/fP9Ev58/gr/cv+D/xr/4P5o/qT9fvxz+7766fkI+eD3h/d598X3DPiB+J/5yPoN/B/9eP78/5EBDwNkBM0FmgY/B8gHlAibCcsJzAmcCf4JkgqFCnMKHQolCloJugeaBYADGwIMABX+nvsX+n/5MPmt+Zz5Pvqj+hP73ftZ/Ez9zf1E/nX+h/7R/g7/df8n/6r+vv3c/Gn8ZfuE+if5Ovje92f3cPcq99j3xPgf+ob7rfxb/qr/dQHDAk0ElQV9BicHUAcSCJIIhgmsCa8JswnLCYoKkgrKCk0K1Am4CN0G1QSpAkABL/8K/Zf6Sfk/+XD5tvlw+fT5v/q0+0f8vPyU/UT+1f7M/u3+Ov9q/1L/sf4J/kf9Wfxo+1z6Zfli+H33DPcE9133x/d5+Lf5UPsD/V7+sv8jAcMCVQR+BVsGzwY2B5oHMAjaCCAJQglVCb8JRQqZCq0KggpbCnkJ5AezBZkD6AHm/6b9Ofux+Rr5Gvkc+TH5tvlZ+iP7jPtE/Dz9E/6D/nL+qf4R/5f/gv8i/4X+x/0K/df77/rd+c/4zffs9sz2/PaF9yr4QPm6+mX87/1H/94AVwLeAwwFBwbpBlEHqgfmB2wI+AguCRoJIAmlCTcKhwpeCjoK3wniCAsHwgTgAhQBDP9o/Cr69/i5+MT4k/jL+GX5Sfr0+qD7mvzN/Y/+1f4A/2L/GQAKAL//Gf+K/uH9tfyK+3L6kflu+HP3vfbP9ib3e/dD+E/5GPvL/Fz+AgCuAWgD1QQJBvQGtgfqB/gHRgiHCPkIuAiuCCQJtgl0CkcKPwozCt8Jpwg6BswDqwELAOX9ZPtL+Vr4h/ip+ML45fiF+aD6a/tB/Bz9EP7z/iP/Zf+//zcARACu/9z++v1K/Qz8Afuo+aj4x/fH9qP2lfZm9w34E/lp+v377v1e/wcBTALqAz4FYAYlB1YHnweuB10Ijgi8CIAIkghqCdsJUQoEChoKAwpmCawHawWhA/ABMABN/fH6dfkm+RP5gvhJ+LT4yfm5+nH7Jfxg/Wz+IP9Y/7D/KQBcAA0ALf9p/lP9YPwu+wX6B/ni9yT3n/bL9i/3vPeb+Mn5ePsC/aL+HQCuAU4DlgTyBeUGoQfSB9YHIAhlCK0IUAgXCDAI2AhsCXMJjwl2CZsJpQjzBuUE9gKKAU//7/xw+iX5vPiQ+Fv4Aviv+Hf5s/p8+1T8kP2A/lz/ev/B/wAAMgDG/8v+uP2D/LX7hfp9+Xr4jvcK97D28PaD91/4Svlu+sr7Yv0r/7UAYALdA1cFpwaFBxkISQhmCKQIxQiSCCcI1QchCKgIAwkFCQMJOAkpCTUIZgagBCEDqQFS/578ivqT+U35q/gE+Mr3pPi0+aH6QftS/NP9wv5n/zf/rv8UABEAWP/o/er8Cvxx+0v6Ofk++MT3n/dM95r3APgQ+S36MPuV/Af+0/9TAbACEQSCBd8GbwfQB7kHCAhYCEcIZwjmBx0IVQjXCJAJqwn6CawJXwlDCHYGfAR1AuYAgP4O/Kv5jvio+Gj4MPiw93f44vki+yf81/wl/ir/t/+R/6T/+v/Q//L+KP0j/HL78/oK+qz4B/i099v35/c6+PT45PnN+qT7AP2a/mgApwHQAikEtAUhB54H3AfKBzsIgwhVCBwIlAfvB08I3wgmCTsJpgmfCWoJ9AdPBoAE4gI+AZX+Rfw2+nX5JPlm+Ob3sfeU+LD5evpA+y38nv2H/t/+4/4g/7L/RP9a/vz8J/zC+9v65fmy+EX4/PfP98z3GfgT+cz53PrM+1n9G/96APwBKgP1BFYGZQf1BwgIWQg/CFgI+Qe9B4MHXgfnBy8I3gj6CD4JXAm/CMoH4AV/BOUCHAGp/gr8kfqV+Uj5IPh193n3N/hu+fD5Cfsn/Jf9ZP6q/gv/af/P/xT/L/4I/VP8xfu8+ur55Phj+Aj45fca+HP4Dfm5+d76L/zQ/VP/0QBuAvsDpAX9BhgIwAjmCK0IfwhwCEcI1AcgBwEHaQcaCIYImQjiCBUJygiYB/wFnwRbA38Bx/41/Jj65fk6+Qz4SfdC9zf4JPm6+d76CPyC/RH+V/7f/nb/2f8J/xP+Bv2V/Nb7zvrR+av4S/ib94j30vc++Bv5pvnf+lb8Bf6t/xYB3gJjBBoGJwcXCMAIuQjoCGoIjQhfCNcHjAcrB9MHMgiKCJkIxQgVCXIIRAdbBToE7QLrAGL+pfuP+tX5Ivnr9wL3j/d8+GH5zPm++lb86v2R/nv+1f5i/+r/G/+w/Y382vt4+2z6P/lP+P335vfT99T3QPgy+UH6g/us/Dz+6P+xAVsDuwQ7BlwHgQjUCK0IfghICHQICgh2B+MGEQfDBzsIkAijCEoJWwmqCD0HoQW9BC0D5wD//ar7kvq2+af4Vvff9mL3P/gC+af5EPum/Ob9Zv5Y/uX+XP9b/17+5Pz7+1L7zfqu+Zz46vfD99X3r/cO+JX4wvnO+tT7P/3N/toAZQLkAzwFogbwB4UIpgg8CDgIIQj1B5AH7QYCBzAH3AdICKkIQQlyCWMJTwgbB8IFbwSiAvz/jP1R+zz6Mfkn+GP3Dve792z4Yfll+tv7Pf0L/mD+c/4V/07/7P7O/YL8yPsf+2j6cfmi+Cr44vet98H3Y/hK+VP6OftU/OH9sf+GASEDkQQHBloHOAicCHsISAgaCN4HkAcHB6sGtQYwB9oHZAjcCEQJjgk+CT4I2AZlBRAEDwJ7/8j83Pr1+RD5GfgO9+b2ufes+KD5evrc+z79C/42/lf+7v4y/7v+Wv02/Kb7Hvtn+jz5k/hW+Dr4Kvgz+Oj40fnL+qX71vyU/kEA8AEjA6cENwaDB34IgQhyCBMIAwgLCLYHRAeRBqkGNgf8B30Iswg1CUwJ6AiPBxoGKQXaA+IByf46/NT6CPpJ+cr3CvcW9+n3tPhu+dX6MvyF/a39+P2U/iH/Qv/o/cD8wPtC+6f6l/nN+CT44PeR9773S/g3+Qf6uPrm+2z9T//mAFACxwNYBb8GzAdqCKIIgggcCM4HmAdYB+cGfQaKBjgH+webCAAJZwmVCQ8J0wdcBikFogNwAXr++ft3+oz5l/hG97j2+/bx99f4uPkV+5L8wP0S/kv+xv45//7+xf2A/Iv7+fo5+kP5lPgV+OX3qPfL94D4cPlx+j77fvwZ/vb/qgEXA6EEGQaAB2MIyQjXCH0IIAi3B3wHJAeqBn0GvQaaB1EI5whfCdEJAQoWCaEHCgbiBDcDgQBx/Qb7DPo5+Rj43Paa9n33cPgs+fX5ivso/QD+9/37/Z/+EP97/gH9tfsA+4z6tfnE+Cr49/fX97v3KvgF+Sn65fq2+//8uP6yABUCiAPXBDAGdwc3CKYIgAgYCI8HWgcpB9QGegZOBvUGswdmCA4JpQlGCvMJvghDB/AFvwSmAtb/9vz8+tf5tfiz99T26fZr9wb47Pj7+Y77wfxr/a79Af6N/qf+I/7n/NX7+PpG+pP5svg7+NH3pfel9xL4AfkS+hv7Avx7/S//JQG6AhIEgwW9BhIIpgjRCH8I9AeyB2QHRAe1BlUGZQYaBxUIiAgmCZoJOArUCVkI1QZ0BYgETQI4/0/8ovr7+er4j/d19rj2dvc3+OD45fmm+8L8W/2Q/TP+Bf8R/zn+9PwR/FT7nPqK+bf4L/iu94f3hPc6+DH5HPoc+zz85/3V/6ABMAOWBOAFFwciCMUI7Ah3CM0HgQdnB1cH1AZXBpIGYQdYCNAIEgmPCd8JWQnzB0sGLgW/A3UBf/7Y+4D6fvlW+O32RfaM9k73AvjK+Ez6t/vz/Hb9D/7v/kX/Dv/7/QP9Nvxb+2D6Ufmh+AH4m/dJ95H3Yvgu+TT6GfuQ/Fv+FQDVATADwQQ0BoQHhQjiCNcISwjyB7UHqwduB8cGhAapBrMHlQgECXQJpQn3CUIJzQc+BtgEgwMCAef9P/v4+VT5OvjP9vH1h/ag96H4bvmH+ir8V/3+/VH+8f55/yn/C/6r/ML7BvsZ+vn4//de9x73FveW93P4X/lb+lH78/zE/ogA9wErA5kEBwZkBxEIeQhkCB0I+AfAB9QHcQf/BsgGKwcUCKMIDwlPCdoJ6gn+CHwH4gXjBDgDmgCX/UD7QfpV+R/4zvZi9gv35veK+Dv5nvos/Ej9vv0F/rD+F//U/sP9efyf+8r6Afr2+Br4ivdD92H3yPem+Iv5nfrB+zv93P5hAPkBUAPDBAMGDwfkB1AIeAgxCOkHwge9B4gHDQf2BkUHOQjqCEIJnwnFCeIJ/giWBw8GpATpAlkArv1g+zP6GPnt99n2UfbY9nL3QPg/+ZX6/vvx/Jn9Qv4M/1//9f7W/bj86fsR+x76//gj+Kf3dfeG9+P3nfie+ar6u/sP/aX+UADFAREDUQSzBQYHFwilCKEIdAg1CD8IQwjyB2IH5QY3Bw4IxAgVCSMJdAmjCQkJsQcYBs8ESgP6AA3+pfs4+ir59vd29uH1Q/Yc9/n3wvgy+sj7Hf3g/Wn+Fv+M/1z/Vv43/Uj8hfug+lb5Uvif92n3gfeq90H4AfkZ+kb7mPwn/rj/WgG3AhoEZgW9BusHggjMCJQIiwiDCHAIUAi5B38HiQcNCKgI/whhCZcJuAkNCcEHOQbTBHIDDQFL/o373fkH+dH3xvba9Sb2Fvfy9/z4APqn+/L81v1U/t/+g/9m/5f+MP07/JD73/rK+Yr4tfdU92r3mvce+ND4xPnQ+v/7gP0+//MAYAKnA/IEbAa0B5II3gi4CI4IgQiJCGII4AdqB2EH4gegCBYJeQnWCR0Kugl+CPUGmQVDBBYCHv8f/DD6I/kR+MP2nvWQ9Ur2R/dK+HH5EPt//Hr9Gv7D/p3/yP8V/7z9mvz4+1r7avoL+Qz4lPeT97j34vdt+B/5H/ow+3D8B/6n/0wBtgIqBMEFQQeVCFgJmAlsCUYJRQk4CdwICwiXB7EHWwjrCBQJcAndCfYJ+QhgB/8F7ARIA1UAEP2o+oT5lfgJ95/1LPXz9fr2yPfo+Ir6VvyC/f/9pP6B/wkAqP9M/h79WPzM+/j6mPlw+KH3afdh93334/eN+Gz5Vvql+yf9+P6nABUCigP2BKYGCwjyCFsJQwk7CTcJPAkFCWMI4Qe6BzIIwggeCY0J9AksCnwJDAiaBnIFFQSJAUP+Z/vR+dn4jvcm9lj11vXO9q33rvgS+vH7Qv3r/W3+Lv/n/7r/l/46/Xj8+ftP+xD6tPjh93X3evdw9673O/jj+LX5p/og/N/9pP8UAVMC3QOdBWEHbwjtCB8JOQlyCVAJCAmICBQI7gcUCJoIMQnUCS0KYQoNCiwJ9wdqBtYEdwKH/5P8Mvrb+Kf3XfZM9Sj1D/ZK9134jPkq+8/8F/6m/iT/3f8qAKr/Uf4d/YD86Pvb+mP5M/iy9473afdD94n3LfgW+f35Fvus/FX+GQCUAQkDzQSMBiMIFglRCWcJignDCc4JLgl2CPwHFAiPCOwISgmyCQ4K8QlUCSYICAeqBZgDHgEd/sP76/lo+Cz30vVD9Wv1MvZc94/4Gfq8+0f9Wf4v/+r/fACXAKr/iP5r/Z78zPtV+vv4yvc69/v2xvbw9lj3Ifj++Br6hPsr/e3+dgAKApkDbQUxB3YISAlrCZIJ0An6CdcJDglSCAkIUAi5CAQJUAnBCSUKzAnWCIAHUgYWBeUC/v/f/JX6NPn296z2i/VH9QP2Cvcx+H/5IvsJ/UT+/P53/w4AnwAlAAD/lv21/B78D/ud+S34WvcC9+z2z/YC95T3Vfhk+ZX6RfwX/r7/WgHPApYEbAbzBwcJYQl4CXoJpQmzCT0JgQjKB8YHNwi/CB8JXAnICdwJVQk4CPsG1wUrBKYBif7u+0n6D/m99zf2fvWy9af2x/fc+Ff65ftl/VL++/6j/x0AKQBl/0j+Pf2A/Kn7jPpI+SL4dfcY9wH3D/dU9/731vgC+mT7A/29/lYA+gGfA3oFKgd+CDQJYwmCCZ0JxAl4CdMICQiwB/4HiQgRCVEJrgn5Cc8J+giUB0oG+gQAAxwABf3U+qP5kPgU97n1ZvU/9nT3dPiJ+RT74fw9/uL+Yv/6/2IAIAAJ/9b93vwr/EX79/me+I73D/fh9uT2+/Zn9034cvnI+ir8yf2b/08B1wJOBOwFogfJCDwJKAkkCWUJYwn4CCAIigd7B/YHkQgECXQJwgkWCr8JwwhgB+wFhQQ8Akb/O/wc+gb56veK9k/1T/Vn9sP33vjw+bj7g/3T/kr/ef8UAF8AEQDY/ov9wvwx/HH7HvrF+Mz3ZfdA9xX3EPdj90D4Sfl3+tr7fP12/0AB4QJ9BEAGHghbCcsJtAmiCdEJ1wlSCWIIwwfTB3sI9QgbCV0JwQn+CSsJgQfhBcMEhwMLAaD90PqQ+Tn5P/h+9nv1FfbV9yT50PnQ+ob8c/5Z/33/vP9eALUA5P9B/t78SvzH+6X60vg795T2hvak9o72t/aS9+L4ZvrP+1j9L/8OAb8CHgSLBRAHWAgDCfEIxQjFCO0IyQgiCIsHcAf8B64IHAl1CegJTQryCbUIJAfiBboEpwKU/2v8h/q/+db4O/eu9YX1o/b999v4tflX+2798/6N/+b/kwBbASUBwP8T/vn8d/yB+7v51/eV9kH2MvYO9hT2sfbn90z5svon/Av+///AATYDkwQwBqsHtgj8COEIywjpCP4IlwjpB1EHaQccCMIIJwlpCbwJ6wlfCR4IrwZgBfQDqwGm/vj7WvqD+YH4+PbU9eP1+PZQ+ET5Vvr7+9P9PP/Z/0oA7wBuAf4Am/8d/h79c/w2+275u/e39mX2IPbq9Qz2wfb891L5vPpj/D3+NwD3AZADIwWyBh4I7QgQCeAI/Qg+CUMJlQisB30H9Ae0CPoIHQmQCeoJpQllCOEGvgWzBN8CCwAv/Tz7XPp3+SD42PZU9gb3/Pfd+Mf5IPsO/Z/+jf8IALQAhwGvAbwAKP/s/RT9D/xQ+lf45/Ye9sn1Z/VX9fr1Kfeg+A/6tPuo/aj/bgHaAjgEugU8By0IYgg3CC0ImwjRCH4IvwcwB2IH6gdsCLQIDwmbCccJLQnmB50GuAWLBHoCnf/s/GT7m/q6+TD4wvaI9l/3gfg7+Qr6jft9/dv+av/S/5oAaQEpAen/fv6c/Qf90Pv++Ub4Nfe29jf2y/XP9Xb2k/fK+Dn62vuo/Wv/8AB4AhQEzwU9Bw4IVwhnCLgIMwlPCb4I1gdjB7cHZAi4CL0I7whTCUwJQAjVBssFFgWtAwUBIP4//Ir70/pg+dP3NPeu95T4Pfnz+Tz70Pwv/gX/pf9jAPUADgF1AGL/Uv5W/Uz86/pW+eD33vZG9un14fUk9vr2MPic+S370PyH/hsAtQFPAwkFiQZ9B/wHJwiCCOYI/givCAAIaAdTB7sHPwh8CJgI7whBCfQI0wdnBmIFegSxAvj/Tf3K+yb7EvqA+En3Sfcg+Mr4TPkr+uf7vP0F/7H/QQANAX8BaAGPAGX/Pv4O/dz7V/rA+Gr3efb19bD1yfVi9mn3xfgy+sL7e/0+/9AAMwKlAwwFewZbB8MH7Af+B2AImgiDCNIHBwcDB6QHSAhoCHEI2whoCRUJpQchBhMFTQRjAmL/yPw1+5D6gfkW+Cv3Hff199f47/lY+wb9nP7D/60ATwHSAeoBjwGfAD//9P2h/HL7Evpt+BH3JPbd9eb1F/ag9nT3p/gb+rr7R/3B/igAoQFoAxoFmwaAB+0HRQibCB4JNgm7CN8HOwd5BwcIQAgnCC8I1wgmCTkIjgZBBeIE7wNhARv+3PtW+wf7u/kM+F33A/hG+TH6+PpA/M39Xf94ADYBxgHqAbgBBAHk/3v+6fyF+xf6l/gJ99H1VvVE9Yv1+vW/9vb3avkU+8P8dP70/0MBrwJlBA0GHweKB68HFgipCN8IcwiMB9cGswYEB3EHnQfmB4YIOQk1CSAIyQboBSYFagNjAGb9wPsM+yX6nfhi91/3Mvg3+Tr6nPtt/Qn/KgAGAdkBZgJIApABegA+/779J/yt+jf5w/dP9kr17fQa9ZL1QfZU97H4VfoT/Ob9qf8NAVECoQMrBaAGdgexB7AH6wdYCH0IBQgZB10GYAbvBlsHaQeOB0oIIAn3CK4HOQZrBakEuAKl/9L8T/uj+q35WPh697L3m/jM+S/7zvx7/sX/zwC/AXICkwL5AQsB8f+Y/v38VPvo+Zb4QPcZ9mr1VvWo9Tr2D/cu+GX5sfo//Pr9pf/gAOsBMwPGBEQGFgdaB30HvwcTCDAI4gdLB+IG9waMBxsIXgjDCF4JygkpCY8HDQYGBcQDKAHA/SL7CPqm+cT4o/cw9+f3Qvm7+l38E/6z/9YA1AHYAnQDXwNxAiYBuf8c/mj8o/re+An3a/Vh9BH0J/RY9AH1LfbW9475RftW/WP/KgFnApoDKAWcBo4HvQeoB7IHyQfPB4oH9QZKBgYGcQY7B+cHbAgZCdUJ5wn4CKQHiwZWBSgDAAAm/X/7g/pe+QP4QPeA91r4gvkA+978vv4qAFUBWwIVA0IDxQLcAX8Atv7F/P36bvm+9/f1iPTV89fzQvT69AT2Xffi+KL6nfyQ/ioAXQGWAg4EgwWRBvwGLgdaB48HwAerB2UH8Qa0BvYGogdKCKQIHgn/Ca0KNQqYCMwGlAVNBLoBIP4B+4X5E/la+E/30faQ90D5HPvz/ML+fgDUAc4CZgOTAy8DDgKOALf+w/zR+g75hPcO9tr0/fPO8zL0B/Uo9lP3qvgh+uL74f2p/wUB/QEYA40E9AXpBiYHEgchB1QHmQeIBx8HpAaKBisHFgjRCDAJqQlwCsEK1gnuByYGxgQYAy4AvPxY+oX5XPmS+JT3cveC+Ez68Pt6/QP/eAC2AZECKAM7A5ICUQG1/xX+X/x/+pv45PaD9YP06/PH8yv0C/VP9sD3OPne+rf8tf51AK8BtALSAy8FbgYcB0AHJwc1B3wHtgeEBwQHswYAB9kHoQgjCZgJVwrUCj0KmAirBiwFlwMpAer9BPt3+er4jPjy96r3N/iR+Xb7Zv09/6oAsgF1AgQDSQPPAp4B6v8g/mD8nvrt+DD3yfWr9PTzv/P38+D0IPaS9/z4dPpK/EL+IQBzAVkCNANJBHEFQgaeBokGgAasBgwHYQdPBx0HFweYB3wITQnwCXYK5Aq4CqEJCghyBu4EtAKg/4H8Xvp0+cT42vcj9073h/gQ+q77gP10/y8BQwLmAmIDhwP2An0Bh/+L/an7zPkD+Fj26vTk80vzV/ME9A71Uvao9zf5BPsA/ff+iAC4AbwC6gM/BVEG6gYdB0EHbwevB9MHvQeBB0MHbgcKCOQImQkaCqMK4QpRCtAI+AZGBWEDugCE/eH6f/nj+DP4b/dh90j40/lt+x/9Ff/8AHICOQOUA6gDOwMNAjUAJv4U/DP6Zfit9i318fM58wLzVvM69Hv1A/ee+Gj6e/yW/msAqQGYAq4D2gTeBWUGhwaeBr0G+QYtB00HRAcjB1MHCggPCfAJhArxClALEguwCacHuAUSBOoB3P7X++f5N/nH+Pz3dvfh90j58vqN/FD+IwDJAbgCFQM0A/wCNQK0AMv+0Pz6+kT5jPfv9Zv0tfNC803z5/ME9Xv2+Pd7+Sv7HP0j/7gA3wHrAiIEbQVkBuIGBgcoB2EHoAfZB9oHoweWB/oHwwiSCQsKZwrKCt0K/AkkCDcGoATSAh0A6vyB+mf57fgq+GT3aPd5+Av6k/tL/UT/IAFVAtkCLANNA+kCogHJ/9j99/sn+jH4WPbf9MrzIfPn8lPzZ/Ti9Wv36PiT+pb8nv46AGsBeALGAy4FOwbHBvQGIAdyB8QHCAgQCOMHvAcCCM8ImgkHCjoKqgoKC4oK7wjpBmQF8QORAUz+VvvW+Ur5i/h499/2e/fs+Hj6EvzX/df/hAGWAjcDkAN8A6oCLAE8/0T9Qfs1+Uf3hvUt9Cbzo/LE8oHzyPQv9qH3U/ll+6z9o/8aAVACvgNCBYkGPQdcB2YHZAefB+YH9wfQB3AHlQdtCJAJQgpgCqYKNgtXCwoKtAegBUQEnAKO//77gfm/+KP48fcg9xz3aPhQ+jH8Df7Q/1gBVAILA4QDkwPbAj4BZf+C/bX7xvmx9+L1dfSG8/3y7fKA84/05/VN98L4mvqy/MP+cQCkAdUCLQSfBaoGEgcfByUHfgcACE4IKwjJB6sHPAhBCecJFwpGCvAKhQvDCtAIswZbBRQEeAHo/eX6nPlQ+Y34V/ef9lH34viM+kD8Bv7e/04BSwIEA1oDHgMHAlsAef6M/J36oPjC9gz1q/O98mzyz/K18wz1evYO+OH5//tE/igAiwGYAtwDYgWiBkEHRgciB1cH1AczCD4I3geiBxQIJwk7Co0KmAr9Co4LVgusCX4HvAVMBA4C0v7V+xD6Uvlx+FH3q/YR90/4u/lu+2X9cf8OATACEAOcA6EDtQIeAUz/Y/1w+1r5Qfdg9ePz5/Jq8mvyEvM69Kz1Pvf5+Aj7SP1J/80ADAJwAxMFcAYxB2cHegekB+MHMQhKCBoIugeqB2IIgQlLCmkKgAoYC34LuQqtCJsGNgWQA8kASf2s+of5z/iq92r2PPZc9/r4jvpB/Ev+SADCAbQCaQPHA1sDGgJaAJ7+0vzA+nr4MvZs9DzzjPJK8njyT/O39IL2YPhc+mr8d/5UAMUBJQNvBK0FmQYCBzUHUQekBxMIeQiOCEcINQiwCLAJaAqCCncK3gpiC74K1giXBhEF2wOtAY7+ovsZ+nn5pfiA98X2G/cS+Gb5BPsJ/Rz/jwCZAWcCLgNsA6YCOQF//9v9J/w0+iP4GvZt9E3ztvK68inzCvRb9fP2z/jJ+tD8sP5OANUBbQMWBXMGNAd8B60HCQh7CMcIowg0COMHNAggCeQJIgooCqsKeQtqCwMK+gdwBkUFUQNCAA798/ra+dL4iPdu9jj23/YW+MP5sfu8/YH/DgFoAnQD7gOWA64CUwG0/8T9n/ts+Tz3Z/Xn8+LyVvJW8gbzPvTR9Xn3X/mE+6v9lv8zAcICZgT0BfAGXgd9B6EH+QdHCHMILgi4B5sHJwg+CQQKPQpgCvoKlgsrC3kJXAfRBVsEDALK/r/76fnj+Pf35PZN9pj2oPcm+QL7I/0a/6MA6QH9As4D+wNbAzMCsQD1/uv8pvpt+Fb2nvRK827yLPKI8ozz9vSV9lr4XPqW/LX+egD0AWQD1wQjBvEGMwdGB2kH0Ac8CGYIOwjgBwAIvAitCUIKZAq6Ck8Legt0CnwIqgZBBZMD1gCI/f76ovnN+LT3lPYm9qX24Pdz+Vn7Y/1S//IAVQKFAywEHAROAyICygAV/+r8a/oO+Bz2kvRL81/yBfJn8oHzAPW29oz4l/rI/OL+1QCPAj8EygX7Bp8HtAeqB6cH0AftB7wHUQcLB20HTwgtCZEJ0Ql4CkgLaAsqCk8IxwaIBZ0DhAA6/dH6ivmE+ED3OPbl9Xj2qfdi+Y/7sv2J/w0BpQInBPcEwAS9A3UCAwEn/8H8LPrR9+j1WPQJ8zDy8fF68qTzHvXG9pj4rvrp/Az/9gClAkkEzQX8BqsH5gcHCDMIbwh+CCYIngdQB6UHewgoCW4JsQlZCggLxwprCbkHZQYUBcQChP9u/G/6Tvke+Lj2v/XD9aP2Gfjv+QX8G/7w/6cBPAN7BPwEnwS8A4QC/gD2/nb84fmV97b1H/TM8vPx3/Gd8t/zUPXk9tn4GPta/Vj/JQH/AsQEPwYnB6QH9Ac2CHoImgh2CPAHWQcvB5sHMwhyCKIIMQkPCmkKtAmECIoHtgYpBaMC8P/W/VD8vPoF+Zn31Pav9uj2rfcd+f/65/yY/isAswH9As4D+QOYA7sCWwGT/2z9L/sF+QD3MPWw89bysvIh8+jz7vRU9ir4JvoB/N/97v9CAm0ECgYcB9IHfwgTCXEJjwlLCc0IVAg9CJII2gjnCOoIWQn9Ce8J8QiWB6gG1wUoBG4Bjv6b/Hb7Sfq7+GH32PYU98P3tfgN+rv7fv03/8AAIAIRA38DfAP7AgECfACO/mb8SvpX+I329PSt8/jy9vKO82H0cPXW9qz4svqA/Dn+DQA1AloE+wUIB7wHcQgiCagJywmICQAJdghUCIIIuwiuCJsI6AhUCUoJZwg8B1MGeQX6A5YBJf9X/Rb80PpR+Rz4k/eo9wz4yfgG+rD7WP3E/gIALgFCAugCAAOCAogBOACG/pz8rvrQ+Bj3evUy9IXzhPMQ9Mf0ufUC97T4n/pq/DX+GwBBAlYE6wUGB8QHdAgmCZwJvwlrCdQITQggCGQIpQisCKYI6ghnCWcJlQhbB2wGpAU7BOUBT/9o/Sz8+/px+fX3Ivf+9mL3JfhI+cf6Yfz//YD/6QAcAvUCSwMGA0MCDgGM/7L9p/uU+Z737PWB9KDzVvOV8yz0//RB9ub31vnJ+7b92P8qAmsEJwZVBzEIAwnNCVMKdgogCpUJIwkXCWAJgglNCREJUQm+CZMJdgj6Bu4FBgVxA+YAP/5y/Gn7Xfrn+IT37/ZH9xz4Ffkn+pj7S/0H/3oAiAFWAtQC9wKLApYBNACE/q/8y/rx+CX3dfUI9DvzI/OA8x306/Qw9vr3APr9++P9+f9HAoQEVQaFB2wILQntCX0KigoZClUJywjKCAUJAwmfCG4IzQhJCQ0J4gecBtcFKgWqAzwB0f4l/Rj83/pM+eD3KfdV9/j34fjy+UT72/x//vv/FAHdAVYCeQItAlQB/f9Q/nD8lvrX+Bb3YfXs8zHzQPO+82P0LvVy9kf4Yfpt/Gv+kgDkAiUF3gYECNsImwleCs4Kvgo0CpMJRQlZCZIJdAkXCeoILwl+CQgJ3QemBs8F7AQ0A8QAk/4m/RD8m/rU+H73+/Ys96T3Vvhy+eT6h/ww/rr/FAH9AXUCmAJhArEBXQCc/rX84voS+Sn3XPXv80HzLvNv8/zz4fRW9jX4NPox/D/+kQAJAzwF4Qb7B+oIwgltCsYKtApZCukJqQmyCdMJtQliCUQJewmXCegIigdFBnEFhASrAhoA0P1K/Cf7xPkz+BX3wvYQ97P3qPju+Y37Q/3i/kgAWAEgAp0CyQJqAm8B6P8Z/lH8h/q1+L/20/Rd86jyp/ID85nzfPT+9Q74TfqS/Nj+YQECBFMGGAhCCSwK6wqMC70LWwuUCrUJXglcCVkJFAm7COIIMQkiCVEIOQd/BtIFlAR2AiEAUf70/I/72/ks+Aj3j/az9k73T/iS+f/6j/wr/q7/3QClARoCSgIDAhIBlv/R/RH8VPps+HP2q/SF8x3zK/OA8wv0GfW89rj44foS/Xz/EwKXBLYGPQhVCScK1ApWC2oL/AosCo4JhAnJCcMJTgkNCWAJ2wmlCYkIVweYBvEFegQaArD/2v2H/AL7HPlT90T2DfZt9ir3K/iL+U/7O/0C/20AegE9AsACzgIqAtIADf86/W/7kPl792X10vMA89vyB/Nr8zj0qPWY96352vsb/qYATQOzBZMH4gjiCa0KXwu8C40L3goCCpgJnwmyCWwJ9gj0CGgJpgkQCeIH8wZSBmAFagPUAKj+Jv3m+zv6UPjU9i72TvbB9nT3fPju+br7g/0R/0QARgEUAoMCWwJsAf7/VP6o/OX60vii9qL0VPPS8s/yFfOW86z0Z/aQ+NH6BP14/yoC1wQUB6EIvwmjCm0L8QvwC2ILfArECY0Jtgm1CUoJ9wgzCcwJ1wnuCKsHuAYFBrUEawLY/8P9RfzW+gz5N/fu9X/1y/WF9n73uPhI+jf8Lv7c/wwB3QFqApcCLAL1ADf/RP1k+435l/eY9drz1PKI8sryVPMn9Iv1ave2+Rz8nf4/AeADXQZbCM4JrgozC5oLyQukC+8KBgpyCWYJnAmRCVwJZAnMCQ4KiQlgCDwHaAZYBX0D9gCO/r78O/ug+cj3OPZY9TH1sfWc9uP3dflG+0P9Hv+jAK4BWAKtAn8CqQEOABP+CvwW+i34H/Yu9LfyBPIX8qnylPPT9In2zvhn+x/+1gB+AxIGSAjbCckKVAu5CwMM6wtJC2UKrAl7CasJzAmzCYQJrAn4CdoJEQnsB/MG8wVXBPEBX/9B/Zj76/n19yL27PSI9Nn0qPX19pb4b/pr/Hj+YgDkAeACQgMjA3MCJwFf/039GPvB+Gn2PPSN8onxNPGH8V3yuPOP9dr3g/pr/XIAeQNOBqIIVwp6CzcMswy/DEMMTgsyCl4JBQkDCfwIzgjMCEAJ8wkoCnIJUwhlB6gGZAUUA04A6v0s/IT6cPg+9pb0AfRL9AP1/vVT9yT5avvT/er/ewGMAj8DjAMvAwQCFADR/ZD7WfkV97n0r/Jl8QXxa/Ex8mDzFvVe9y36L/1AADMD+wV2CGUKsgtRDHMMWAwMDGULVwpKCasIpAjFCMYI0QgpCcAJ8wl+Ca0I5wcdB74FnAMUAcT+y/zY+rz4mvbs9A30BfST9JX18fa++O36Pv1v/ysBdgJJA40DLwMMAkoALv72+6f5Rffn9NvyiPEG8Tzx9fEt8/D0QvcS+iP9RQBPAyoGpwiHCsALWQyUDIoMIgxRCzkKOwmtCJYIsAiyCKwI8Qh5CckJegmoCNwHKAf+BewDUgH+/in9U/sO+bX2//RC9DP0hvQ/9Zf2efil+tj87v7MAD4CIANfA/wCBAJ/AJ/+gfwm+qv3SvVY8wvyYPFV8dTx8/K89A331PnX/Oj/9ALmBYcImArlC44M0gzIDFkMZAssCiAJgQhTCEYIMwhECK8IXgnGCXQJoAjtB2kHeQaNBOMBfP+p/eT7l/n99hD1QPQ69Gb06fQr9iv4gfqh/Ir+VwDvAf0CTAPhAs0BRABT/h38vvlJ9wj1MvPw8U7xPvHR8RrzBvVo9y/6Lf1UAI8DkwYxCQ4LMAy4DNgMtQwdDBgL1gm6CCoIBwgDCPAH/QeHCFUJtQlPCYwIBgiuB6kGcASyAW//zP0L/In5w/bQ9B/0HfQ79JT0uvXV91j6ofyA/iwAyAEAA18DxAJ0Aen/P/5I/OH5OffY9BjzBPJq8UPxwPEL8xr1qPeG+p/93QAeBCUHqQlvC38MBg0nDfMMRQwuC+4J8gh7CFYIPQggCEcI0ghhCXQJ8AhLCNoHTwf/BcoDXgFh/7P9qvsX+Zn2/fRY9DP0SPTi9Gr2pvjp+uX8sf6IACwCBAPaAvMBtwBT/5r9Ufu4+Dr2JvSl8p3xE/Er8Q/ywfMK9rn4rPvq/kgChgVfCJ0KLwwDDTgNEg2nDNoLkQoiCRsIvAe/B6gHjwfZB5IIPAlFCcQIMwjVBzMHtQVzAw0BEP9O/SX7fPgT9pz0KvQx9Fn0BvWx9hr5f/tW/fH+uABhAjQD0wK+AX8AHf86/bH6BPit9eLzffJs8fXwT/GL8m/0tfZT+UT8kv8EAzYG5Aj1Cl0MGQ1IDQANaAxtCyUK8ggcCL4HnQeLB7MHMAjPCCUJ8AhtCAQIqQfbBiUFzAJ9AJT+wfx0+t73xPWw9Fb0O/R99Jz1s/cd+ij82P2Z/30B6AJBA4QCNgHC/xf+DPyW+Q331fQd8+vxMPEb8dPxYfN09db3nPrK/VABvwS/ByoK7AsIDX4NcA0JDUMMGguwCWEIlAdaB18HcAebBxcIxQgXCc4IMwjJB3EHWwY7BKABbf+8/dX7RvmM9qL01fO98+nzkPQd9mn44PoF/er+zAB3AnUDdQOEAg4BZ/+U/V37xPgo9uLzOvIh8YHwlfCQ8VzztfV0+KD7QP8GA4QGbgmZCwoNww3pDZwNzQyXCx4KtQimBxIH6QYBBz4HpQc+CMkI/wjMCGQI9AcqB6IFWgPtAN7+5PyN+tn3gfUf9KHzqvMg9E71Wffa+VT8kf6hAH4C2QNFBLEDcALXAAD/sfzu+Qr3ePR98gDxCvDM74jwLPJ19Cf3QPrW/aYBUwVqCMgKfwyTDQQO2A0yDTEM1gpNCfAHHQfhBuIG8AY5B/QH2whTCTMJyAh3CPkHqQZuBN4BmP+V/UX7b/i89ezzLfMe82fzSfQk9tD4rvsX/iAAGQLpA/sExwSGA8oB7P+z/eP6vvfJ9HTyvvCP7w3vdu/j8BbzwvXV+E78JAAPBJUHaApzDMQNYQ5dDssN0AxvC7oJEwjnBngGfgacBtMGbQdfCCcJYAkYCcIIaQh4B4UF4gJBAAT+2vs9+Wf2GvT78uHyP/MC9IL18vfm+qT98P/6AdMDHQVXBVkEmQKMADr+g/tg+DL1fPKM8F7v0O4G7yjwUvIj9VT41/ua/5kDVgdsCqgMBQ6nDqsOJg4oDa8L4AkqCOgGSwYqBjgGigZFB04IMgl/CUwJ/witCOgHHgZvA5wAMf7x+1z5ffYQ9MXyiPLt8r3zQPW598L6pP0AAA0C9QNQBYwFjwTXAtIAbP56+yT48fRj8m7w+u417mLuqO/g8Z/0w/dd+1z/kQNnB24KmgwZDgAPGA9fDhcNhwvWCSkIzgYSBvQFIwaCBjkHRghSCcsJugl5CRoJQAhgBr8DAgGJ/iH8YPl19hT02fKh8vPyrvMs9av3xfq6/SAAOQInBIAFxwXOBA8DBQGj/rv7XfgJ9UjyPvDQ7gTuG+5G74DxUfSK9zL7Sv+UA2sHhArLDGYOSg9OD5cOZA3gCxUKNwjABgkG8AUOBksG+gYmCEsJzQmdCVoJLgmBCK0G2gP/AK7+b/yf+Xv2+PPP8rnyBPOl8xz1vff4+tD9BwD1AeUDWgWMBWcEhAJ8ACn+NPu19030ovG271vul+3Q7UXvtPGt9Pb3q/vg/zkEEwgRCz0NwA6UD4oPqw5IDZcLvQnkB2sGtwWrBewFUgYVB0IIbAkMCgEKuglaCWYIZwaaA7IAIP6u+9747fWj85XyjvIM8/7zu/Vh+Hf7P/6NAI4CQAQ+BSYF8AMDArz/Dv31+aH2gfPw8BTv9e2m7WPuJ/DF8tb1Svkz/WEBhAUNCdEL3w0xD7IPYw94Dh8NXQtPCXYHQwbNBcoF+AWABogHvwiWCccJnwl2CQAJrwdNBXkC6f+Y/QP7A/hD9X/z1PLT8lfzoPTY9qL5XPy6/twA0gJUBNsEMwS5At4As/75+8b4mvXl8s/wKu8X7hLuTu9x8QP06vZe+n/+1wLQBhMKlAxvDocP3Q94D2MO0AzjCvwIcwd8BgAG1AUTBtEG4AeqCPAI8AgBCfsIEwgJBmAD7gDm/qH8zPnh9sX0xPOD87TzifRF9rP4PvuF/ab/tgFjAzEE3gO0AicBSf/q/PD5yPb587Hx6u+j7kDuDu/u8FLzAPYt+QP9SwFbBcoIdguGDQQPvw+tD+QOlA3sCx4KdghDB54GcwakBjIHBgivCOEIwgi2CJoI0QfrBUwD4QDt/uX8Uvp49zP1GvTu8yn0w/Qo9m74Avsu/e3+kgA5AloDUgM/AqQA4f7W/Ez6efe99HDyl/BR7+/uqO9O8W3z2/XF+Fr8bQBrBOMHzQoqDdEOkA+GD/sOFQ62DOUKDAm1BxIH3AblBiQHnQcjCGUIVwgjCN8HMweyBW0D7QCp/pv8X/rY96X1UfTy8y702/Qo9hf4Wvp//Fb+DwCnAcUC+QIsArYA+v79/Jv68/dU9RjzX/Eq8K7vJ/C08fnzk/Zx+db8yADFBD8I8goNDaIOdg9/D+sO7w2kDAMLXwkaCFgHAQf3BkQHzwczCCEIxAdyBygHTwZoBOIBfP+f/dj7l/kt91n1hPRc9Iz0M/Wv9uH4Hfvz/Hj+EQClAZECVQIpAbn/M/4//Lv5Fvfg9DLzyfHB8IDwb/FG82T1tveF+gT+2wFzBXcI+Qr0DFkOIg8/D9AO0g17DBALuQmzCPQHhQdyB6MH1we+B2QHBge3Bg8GswSiAmAAfP7G/OP6r/iu9pD1OfVJ9Zr1hvZA+Gb6Wfzm/VX/wwDXAQYCTgELAJz+AP3p+nn4FvYf9LPysfEv8XPxjfJR9Hj2CPkz/Nb/bwOrBmIJpQt2DZsOGQ/oDjEOFw3EC4QKgQm/CDII3wfvBz4IVgjsBy4HqAZKBl0FawPjAMX+PP23+6L5Wvfg9Xv1rPXp9Vn2jPec+cH7Sf1m/pv/+wC6AUwBCQCU/j39nftq+fv25vRu82TyrPGR8Vzy+/ME9lj4IvuH/j0CpgV5CLEKkgwfDgMPGA99DoUNcwxZCz8KPAl8CBwIFwggCPYHjAcAB40G9gXXBAoD5wD//mP9w/vb+e/3i/bm9cb14vVH9kz3DPkD+7L8A/47/2gAGQH3AAcAtf5F/Z/7n/li90j1oPOO8uzx3fGC8uDz6vVi+EL7fP4HAokFigjmCq0MJQ4fD1QPvA6XDW8MaQt0CocJwQhbCFwIbgg6CK8HAQdoBqsFcASfAooAqv4D/Uf7W/mD9zD2lPV79cb1h/bh97z5pPs//ZL+y//LADEBwwC1/2X+7/xA+zv5E/cf9ZjzhfLT8cnxn/JP9ID28fjd+2P/QAPQBosJngtlDdUOiQ9PD2cOUQ1HDDMLLApECaEIUQg3CCwI6wdtB84GGgY7BesDEgL2/wb+Xvyz+s/4+/bU9XD1m/X+9aP27ffO+ej7mf3A/rj/oQApAc4Alf/5/WH8s/qt+GD2P/S+8t3xk/Hh8dnyo/QY9xv6if0jAcIEGQjpChENgQ5YD6YPTQ9QDu0MfQtpCqIJ6QhICP8HRwidCGEIigetBjMGkgX+A3cB6/4a/bz7+/mu96X10PQQ9af1JPbx9qz4+Prv/Cr+Ef8dAP8A9QDe/0z+tvwl+0v5FPfz9FLzVfLX8dXxmPIp9Fz24vjI+y7/7AKJBnkJuwt+DesOzw/ND/gOsA1zDGcLZQpvCaQIUAh2CKUIXQihB9cGLAZiBe4DxwF1/479IPyS+qf40vbJ9bX1APZK9tL2F/j8+cX7Df0E/hj/HgBhAIv/Mv74/Mn7Qvoy+CD2k/Sk8xbzwfIB8yP0CfZZ+Of60f0rAbUE4Qd/CpkMOQ5VD7UPTg9QDiAN4gu+CswJGAnBCKIInQhtCN8HHQdUBoMFSARpAkAAYv7k/FT7X/li9wT2dfVv9Zf1D/Yb97f4f/oO/FP9bf5h/9r/qv/a/rX9bvzm+gv5EvdO9ffzKPPX8hXz6vNd9YP3RPp0/eMAagTGB68KBA2wDq0P+w+hD8QObA3uC7QK1gk2CbEIWAhRCGcICQgcBxQGUQWPBA8DxQB3/sf8dvuy+Yz3zvUE9QL1PPWj9ab2cfh6+iP8Xf18/pf/GQC2/57+N/3R+zz6cviE9sH0hPPs8uvyUPM69Nj1QPg1+1n+iAHMBBcI8gogDYgOTA+4D4kPmA4rDc0L8QpYCqoJAwnUCAsJCQlBCAwHJgZuBVAESwLm//H9evzy+tX4sfZM9e30I/Vt9f71NPcM+e36Vvxb/U/+I/9x/+f+wP1w/Bn7tPkI+Dn2uvTD83rzq/M39ET1Dvd++VD8Tf9QAnEFgQg5CzMNVQ4BD04PIw8+DtgMqAsDC6kKFQpuCScJQgkYCRcIuQatBfkExAOMART/Pf3y+2T6QvhR9mf1cfWf9a/1JPaC92T57frk+6b8nP17/pj+2P2o/H77XPr/+GX3yvWi9Bv0FPSB9FX1nvaM+ED7SP5VAU0ENwcXCoAMNA4XD3UPcA/eDscNewxlC5YK7glACcIIkwhNCKUHsQbQBfcEzAMMAgQARv65/BX7JPla9z72vvWW9Z71GvY897X4AvoG+wj8Jf3+/S7+tv3k/AD8+/q5+Tv4sPZg9Yf0RfSH9Ef1gPZK+Kv6a/1qAHsDgQZNCaoLbw2EDhUPKg/NDu0NtwyZC8YKOwq+CU8JCAm4CCgISQdZBoQFgQT3As4Am/7C/Bv7WPlj98L15fTP9B71ovWS9v/3tvkg+yj8AP3T/XH+O/5X/SP87fq0+UH4wfZx9aX0WPSQ9EH1iPZ1+MD6d/1hAG0DYgb/CFwLPg2ADv0O4w6BDt0NCw0aDEULlgoPCrMJjQlZCZUIagc8Bk0FOgRqAhgA5/07/K36vfjC9oP1NvVY9Yb11PXd9oD4C/oh+877l/xa/af9U/2J/J/7pvqM+VT4K/cj9mn1JPV69UH2TPfE+N36j/1vAD0D5wWSCDcLTw2gDj4PZg8/D7MOzw3KDOgLIAtqCsoJRAmbCIsHWgZDBUIE7gL6AOb+M/3f+1z6dfjS9gL25vXo9eX1TPZt9+H4BPrQ+oz7Vvz5/CX9z/wh/Ef7X/pn+Vn4PPc69q/1u/U99vf2CPir+QT8sP5KAecDqgaJCdQLYA1+DlgPww9sD4EOng38DB4M8grpCVcJ2AiuBx0G1QQSBFMDzwHL/xT+5fyv+wP6QfgR94/2QPby9fj1l/am97P4k/lq+kT7B/yL/Kj8bPzs+yv7WfqM+aT4rffd9nr2mvYZ9+H3EfnS+ib9sf9RAgwFvgcoCiAMtw3cDmkPUQ/pDm0O3A3yDL8LxQoUCksJCQhzBh8FJwQbA4kBqf8E/rv8hPsH+nn4TPeq9lX2DvYY9qn2nfep+JP5hvqH+3b8A/0h/QX9k/zd+/L68Pni+MX30fZC9kH2n/Ze94/4YvrB/FL/DgLJBG8H6QnwC30Ngg4NDyIPww4mDmwNkwySC58KwQn7CBEI2QaIBTUEFwPsAW0AtP7+/JP7TvoF+cj30vZY9j72XfbI9pr3mvic+Xv6QfsC/J/8+vzV/Eb8jPvB+vb5FPkP+Cf3p/ae9g/35fca+cf62/xQ//wBowQmB3MJgws7DW4OCQ8jD+IOYQ6zDdgMwQuSCpUJvAi6B04GywSTA54CigHp/yH+v/yz+5D6HvnG9xj3DPcT9/v2L/cM+En5Rfrh+lH73ftw/J78Sfyc+/L6UPqP+a/4yvcc98329/aY95z49fmq++D9fAAuA6oF1wfaCbwLRQ0oDmQONw7TDV8NuAzaC/IKKAp3CacIpgdsBi8FHQT7An8BnP/J/Vj8JvvQ+T347fZm9nn2hvar9ij3O/iJ+YT6MvvD+4P8Df3+/HT8uvsE+yP6B/nT9/X2fPZN9pD2affw+N76Bv1s/yUCEQWgB6IJKAuZDL8NNQ7/DVsN2gx0DNgL9gosCqUJOQmPCIkHWgYjBfkDjgLGANX+9fxK+7f5KPjD9s31ZfWX9TL2Evci+FH5ufoS/Az9jv2r/Z39T/2R/FT78fnF+M/34/YB9pD14vX89oH4Qfpn/P/+4gHCBE8HeAksC4wMgA3qDdsNRA1wDK4LHwuWCv8JYgkRCfcIeQhmB+gFpwSEA+IBqv8V/fH6afkW+LD2a/Xa9BD11/XV9hH4Vfmf+u37+/zd/S/+G/6z/ej86Pua+kL59ffC9r/1HPUd9eL1Y/dU+aL7L/4fAXAEggcOCsML7Qz7DckO+Q4bDqsMeAvjCokKyAm0CP8HFAg2CKQHMwaxBKwDjgK1ABD+mfvk+aP4OPeb9ZT0nfSM9Y/2X/dT+Nv5q/sR/b/94f3y/dr9bv1r/PH6gvlH+Er3VfZz9Qv1n/Ud9xP5Qvuh/ZAA/gNaB/AJkAvSDPENtA6SDpYNZQyNCxMLdgquCfcIqgizCH4Ivwd/BjcFBQSNAoYA9v14+4z5IPjK9mX1YPQ69Bf1XPaN97H4Gvrd+1/9M/5n/lv+P/6d/T/8ifoF+dv3sfaF9b700PTY9XP3UPmV+2z+hQHCBLkHJwrhC/UMyw1JDkwOew0kDAILYAr5CVIJrAhiCI8IpAj9B8wGkQVyBN4CpAD7/X/7jPnH9yn2wfQb9Ez0CPUb9mT3A/nU+of8uP1y/tH+2P5m/mX9/Pta+ur4vPe29rn18/Th9MP1c/d/+cX7Xf5fAZwEmwcMCsEL6QysDQkO2A0MDfkL+ApGCrEJNgnvCOwIBwnRCDUIOwcYBrAErwIkAGf98frX+Pv2V/UU9JLz4PPL9A72mPdZ+S/7/vxo/mD/w/+U//f+5/2I/M76A/ly9wz28fRM9Fz0R/Xz9iT5rfua/tkBRwVLCKsKWwxoDSwOcg5EDm8NHwztCiMKzAlyCfcIlwiDCHQI3we6BlQF6gMiAs3/Gf1++oD40PZE9RL0m/MY9B31Uvaq91f5SvsR/UL+6f5Q/1X/5f7X/Wb84vpW+eX3gPZ19d/04vS09Uj3f/kF/NP+5AEpBTcItgpuDJkNeg7XDowOfA1HDFsLpgr2CTAJuQizCLMIMwg4BxQG8wSUA4EB5f5C/A36Mfhi9tP00/Op8yv0I/WC9jn4KPr4+4f9uP54/7P/X/+d/nb9+/tM+qD4MfcC9hD1l/Tf9BD29Pc2+rf8ov/oAjQGEQk4C88M5w2RDr8OUA5nDTMMCwszCpYJGgmnCGMINwjIB+0GwgV0BPsCFwGH/s37cvmc9wD2bPRu81rzKvRn9bf2WfhO+lf87P33/pj/2/+e/6X+Mv2d+yP6kvj89qf12/TI9Dn1Z/ZF+Mf6nf19AJIDvQahCckLOQ0mDsQO3A48DhcN3wsAC0cKkAn0CKoImQhGCHcHVQY9BQwEVwLu/zr9yPqr+Mf2CvXF80Lzh/Na9JP1O/c8+VX7J/2M/pn/PABTALX/ev70/FX7uvkR+HP2M/V+9In0Q/W99tX4SvsS/h4BcwS7B3cKYAyrDZkOKg8sD10OLA0EDB0LXQp/Ce4InAg7CIMHcgZ3BYUESQNOAdP+XPxM+nT4ifbm9L3zavO+85D0CfbV9835nftK/dX++/92ACkAXv8y/rj8+vo8+aL3LfYD9UT0dPSc9Wb3lvkc/Cj/lQL8BfMIQQv1DCsO6w4VD7gOzg2cDIkLowoRCoYJEAmoCC8IjQeIBl0FEQSDAmgA3P1T+zr5dPe89Tv0P/NE8wv0OvWd9jz4S/pV/BX+Sv/9/00ABgAN/5n9Afxf+r/4EPeT9aH0X/Tr9Af2zfcv+gP9LgB9A9oG5QlEDOgN7A6ID5QP5Q6pDVcMUQt+CrUJ7AhnCCoIxwfxBsEFmgR5A/MBvv8u/eP6CPlQ9431GPR3857zTPRG9az2mvim+mz8yP3t/sz/CACE/2P+Bv2j+xH6Qfi59qH1+/TG9C/1nfbL+Gz7M/4oAZoE/gfSCsAMBg7yDnYPVQ9nDjkNHAwwC0QKbgneCIcIQQiPB40GXgVBBPoCKQHt/nr8P/pX+KD2FfUE9JHzyvOO9Ln1Wfch+Qr7xfw6/lf/6//3/1f/Ov7G/DH7lfkA+I72WfWs9Kb0ePUF9zj52fvF/gYCYwWgCEwLOQ2MDlYPpw9VD14OLQ0FDA4LIgpACbAIVwgBCEMHJwYIBfMDqQKuADr+0vu/+fD3KPan9MLzpPMI9Nb0I/bu9/z50Ptr/bD+qf88AP3/Gf/I/VL8wfoS+WT38fXy9G/0rPSf9Xr39Pmx/MX/+wKGBs0JUQz4DfEOlQ+tDxMPxg1tDFsLfgqTCbkIXwg5COYHCAfhBfYEDwSnAnwADP7d++b5Bvgq9rj04fOz8/LzrPQT9sj3rvlg+wr9mf6m/yAAyf8K//H9efy++gf5b/f59cj0JfSH9ML1u/cM+r78CgCiAzEHNgqRDD8OWA/dD7QP9g7SDX0MPQtACmEJtAgtCNsHawedBpcFhASCAxUC7/9m/SD7XfnM9wn2g/TH8+fzgvQ59Wz2LPhD+iP8gf24/rb/MwDM/7H+Q/3M+z36YviP9gv1PPQY9Jr09PX+97D6pf3eAGAE2wfwCicNkA5pD98PvA/TDnYNGAwJCxwKOgmLCDQIIgi0B8QGrwXFBN4DNQLX/0b9HvtW+W33ifUb9JbztPMl9Pj0dfaG+H36LfyJ/ff+DgBQAK3/eP42/a775Pnu90T2DPU09ODzX/T69VL4Fvv6/U0B7QSKCJILtQ0bD+gPRxDJD9EOYA3wC74KtQn2CEIICgjqB58H2Qa5Ba4EoQMrAr3/Fv25+un4OPdh9Rf0jvPm84X0WPXP9uT4Cvu4/Pn9Nv8tAFgAk/8o/sD8K/tJ+Tb3bfVP9KbzfvMf9N31a/hU+0v+kwFIBfQI9wvoDS4P9Q8pELAPcg70DIwLYgp7CaMILAgLCAEIsgf5BhEGIwUHBEUC2/8y/fH65vgK91j1CfSG85jzUPR/9TX3O/kZ+9z8Yv6v/2IATQB7/yj+lvy8+tb4/PZn9SL0X/Nx82P0R/ao+Hf7h/7sAZEF9wjiC9sNAQ+gD8wPWw9FDskMWQs/Cm0JzQhcCFIIZwgfCFYHTwZYBSoERAKi/9f8ePqS+LH27fS982bz2vOu9Or1n/e++cr7dP3C/sX/bgBKAGD/5v03/H/6ovi89gf1yvMu80PzHfTr9Wn4Wvtq/rUBUAXTCNQL0g31DpQPxg9KDzMOvwxoC3YKlwnsCIgImwjBCFUIfAd0BmwFHAQWAoD/1vyB+nj4kvbt9Pzzr/P389r0Q/Yy+C36APya/ej+7/9WAA4ALP++/er74/nw9zz2w/Si8w/zNvNE9Cj2oPiN+7/+HQKlBQkJ7AvwDQ8PnA+hDxMP7w1vDBALFApOCa8IcAieCPYIyAjwB9gG1QWvBK4C3//0/H76fPiK9sX0sPN68w/0//RO9ir4TvpZ/OD9BP/z/2wALAAN/139kPu0+cf3+/Vs9EvzvPLq8gn0APaf+I37m/7uAX0F+gjUC7oN0Q5VD3MPAQ/iDWEM/QoACkgJtQhxCKkICwn3CCAIAQcXBhkFOQNKADH9uvrN+O32BfW784nzKvQe9WX2PviK+pD89/3q/qf/HgDM/4v+s/zE+vH4Gvdu9Rn0LPPN8jPzaPR09hL5APwK/ysCfQWuCGQLRg1BDq0OsQ40DiYNzAuTCssJUAnqCNoIUAnzCe0JBAnJB8kGrwWJA1YA+vxf+mT4VfZf9EXzW/M/9Fb1sfaw+CX7T/2d/m7/NwCoAC8Ap/6u/M76+fgO9yf1ovO/8mvytPLl8xT24PjO+8H+7gFmBcYIYgsHDewNYg58DuwN2wyYC4wK5gl3CUIJdAkRCqMKfgqVCXQIWgfjBWUDDQCx/PX5yfet9dHz2vL08t7zNvX39ln59PsJ/m7/ZQA6AXUBngDu/r78hvpc+DT2UPTs8iTy7fFs8tnzOPYE+QL8Gv9HApoFoQgaC70MlA3wDecNfw2kDG4LUQqrCWkJWgmJCScK2wrcCvUJkwg5B9cFlwNCAL786/nN98f13/PM8vDyD/SA9Sz3W/nk+xX+j/9zABsBYwG6ABr/4fyz+qj4mPaz9CfzL/Lg8U7yp/Pe9Z34nvum/tcBMQVYCO8KngyUDQ4OJA60DacMWAtRCrwJcQlkCZ8JOArKCrQKywl8CEsH0AVcAwwApfzi+a33nfXX8/TyTPN79P/13fcq+pr8lP7l/74ASAFGAVwAmP52/Fj6TvhS9pb0UvOJ8kLyyPIq9En20Ph1+0z+XAGMBH4H0wmLC6QMNg1nDRgNWAxNC2gK+wnWCeUJOgq+Cj8LLgsuCr4IUQfDBVwD9v95/KD5dvd+9bzz9PJ089r0dPZB+Hr66vzt/hkAwwAzAScBHAA5/gn8APob+Cv2dfQq84HyZfLj8kX0cfYE+az7cP5jAWwESAejCUQLQQzJDO4MuQwXDDMLWQruCQEKPAqNCgMLfAt1C50KIAl6B60FNAPX/y78M/n79ij1j/O48i3zo/SZ9rz4IPug/an/8wCcAeQBqQGFAI3+QvwC+ur33PUW9NHyHfII8qHyDPQ79sj4dftN/jgBPwQYB2AJCQv/C5MM0QymDCEMLgtCCtsJ7wk6CpwKKQvBC88L3QpHCYsH2QWBA/7/L/wK+fH2M/V484nyCvPf9BT3KflZ+8f96P8hAYwBsAGRAY0Ai/4O/ML54vcZ9lL07PJB8lDy9/Iz9CX2kvgx+9/9qAChA5AG+giyCsILYgzdDOYMfQydC78KYgpgCpQKygohC4YLeAuKCvcIPQeOBTcD1f8n/Bn5HveD9fvzGvOL81D1d/eN+bz7C/4HAEcBsgGsAU4BPgBW/tn7ifmY99X1PvTs8jnyN/Lt8kv0MPaO+C778P3JAKcDbgbQCJsKsAs+DI4MlwwvDFALcQoNCicKcArKCkwL1gvpC/sKTAmYB+wFlAMaAEL8O/ki91/1pfOj8jrzF/VB90f5f/sP/jAAUgGxAbQBgAGJAHf+3fty+YH3s/Xq843y5/EQ8tzyKPQh9p34XPsc/tgApQNzBu4IpwqTC/kLPwxCDOILGAs7CugJDApzCtUKYgsEDBIMLQuNCc8HDgajAyEAO/wG+eP2I/V+86XyG/PO9Or2I/mX+yH+SQCBAfcBHALfAcsAxP4u/LL5ifed9ezzi/Lf8d3xj/Lp89T1S/j5+tH9rQCJA1oG0AimCrQLKAxnDG4MDgxPC3AKBQoLCkoKsgpGCwAMMwxTC6oJ5wcxBugDcACR/HL5Ofdv9bDzzPJN8+r09/b9+Fr7BP46AGoBzQH4AdkB1QDA/j780fm498D11vN28r7xwvFX8oTzifUc+PD6xP2aAHkDWAbTCKAKpAsfDG8MXwzwCxILKArMCQcKeArwCqwLlQz9DB8MSwpLCGsG7gMzAAP8r/iK9tb0I/Mp8vXyE/V797b5LPwL/08BawJ8AkYC3wGeAE3+gPvz+N/2AvVF8yHyrPH48b/yEfQN9nr4L/vS/X4ARQP/BVYILgpPC9sLJAwnDOELEgtCCuQJDAp9Cu8KjQtbDM8MHwxRCkYIeAYmBKoAgvwe+e/2TPWd84TyGPMo9Y73nvnd+4z+9AAhAjMC6gGCAX8AZf6q+x/5GfdN9a7zb/L28R7yyvIE9MX1JfjB+m39FADUAqkFJwgRCkwL2QsdDEIM9QszC0AKzwkBCl4KygpUCyoMxAw9DJ4KigiNBiMEqgCW/BH5wvYQ9YXzlPIV8/D0U/e2+SL80P4VAWMCmQJPAsIBhwB9/uf7W/kn91f11PO08iryPvLa8vzzx/UC+H/6Ef2z/3ECGgWLB24JrQpoC9oL/QvDCysLegoiCiwKfQrkCpsLfgzvDEUMiQqECGsG3QNXAGH8A/mo9u30efPF8nTzaPXC9zX6yfyE/7wB1ALqAm4CsQFXAEL+t/s/+R73VvXg893ygvKd8jnzXfQp9mL4qvoI/XH/8gGFBOgG1ggoCvQKbwuyC6ILPwusCkwKeArMCi4LogtGDL0MMgyZCmkIPQbAA3MAfvzg+GL2vPSB89jycPM+9bT3Ufrv/Jb/1QEoA2sDDAM2AtcAvf4l/IX5JPc39bXzqPIk8iPyqfLd85715vdg+uv8kf8zAuIEKAf5CDwK9wpVC14LGguSCgIKtAncCTIK0QqeC5AMIg2fDBEL5AiaBtADLwAg/In4+/VU9CLznPJc81/1E/i9+nT9LgB1AroDzwMnAwsCewBa/tD7UPk694D1MPQ587ryvPI+82H0AfYU+Ej6kfzz/nkBDwRmBmAI0wm7CjELXQtIC+MKSAr2CRgKnQonC7ALVwzDDGkMvgpWCNwFNwPl/+D7PfjQ9Xr0qvNO8/vzIfb9+MH7Nf56AIsCpgOcA8oCiAEUAB7+wftc+Uz3wfWc9NPzWvNb88rztPQT9sT3xvno+1X+1wB5A+8FAwidCZQKPAt1C14L5Ao6CssJ1Ak3CqIKKwveC5EMWAzlCqAINQaUAxgADfwk+Ir1FvQ18+HynfPw9fX4Bfyq/iMBQAOOBJYEjgMbAi4A+/1d++v44PZL9U30pfNk837zBfT59Gb2Jfgc+hX8Qv6nACgDmwWTByUJMwr1Cl0LWAvqClAK8gkRCmoKxApGC+wLhgw9DL0KcAjcBRgDxP/D+wT4ZfUS9IXzbPNP9Gv2bPl9/Cv/XAExA1cEbgSIA/gBLwAX/ur7oPmT9+710vQx9Lrzk/O584D03vWi94b5jfvl/X0AJQOBBXAH4gj4CbwKEwvuCmYK2QmnCdUJOwqpCkwLOwziDJkM4wpwCOsFIAOq/4r73fdm9Sv0mPOH82f0m/aq+bb8YP+SAW0DhwSuBNkDSQJnAFT+G/za+cP3Cfbh9Cv0wPN586vzdPTQ9Xf3L/kj+0b92v95AvcEEweuCNwJpAoaCx4LsAoDCrgJ3QlPCq4KHwvcC3sMUwzDClsIwwUUA8r/4/sa+ID1YPTz8wL0x/Ti9vz5Hv2+/7cBSANbBI4EuAMdAkYAbP54/FX6R/iT9nD11vRb9Pbzy/NM9Hv16/Z7+D36iPxK/x8CmgSoBlkIswmbCvcK5wpnCtkJhQmWCfAJXwr7CtYLqgyXDCMLsggPBkwD5f/O++v3VfUs9M3z1/PF9P72PfqT/UkAbAINBCoFPAU6BHECWgBL/jf8I/oU+Ff2NvWb9C304fPY83j0qPUe96z4VPp2/Pv+pwEdBDoG3wcuCRwKjQqdCjcKtglfCZcJIAqcCh8L4Au6DLwMSwvOCBcGagM+AEP8VfiX9Wj0FvQp9P70GvdR+qr9ZwBkAu8D9QQVBRsEVgJGADb+NPww+jz4kvaE9fT0qfRn9Fv0xfTM9Sb3fPj8+dj7Tv74AGwDjQVPB9QIAAqCCo4KXAryCZ0JjQnkCWcK3wp+Cy4MXAxPCwkJUgaaA44Aw/zQ+Nr1cvQg9DD05vTZ9vz5WP0oAFMCAwQ0BV0FewS6Ap8AhP5S/Dr6VvjG9rH1FfXA9I70jvT09Mv1+/Ze+Nv5n/vY/WQA6AIfBf4GhAirCWQKtgqSCiAKqwmPCdwJSAqsCiwL7QtEDGwLPwmABtkD7gBT/U35IvaQ9C/0TPTe9JL2g/nm/Mb/9AG0AwsFggXTBCMDBgH1/t/8w/q5+Av36PU/9cL0a/RS9Jr0avWU9gT4i/lg+7f9QACeArYEnwZHCJcJbgrPCsoKdAoeCuUJ/QlWCqMKEwunC/ILGQv6CF8G1wMvAc39/Pno9kf1tfSB9ND0XPYX+TP8+/5HAT8DyARfBb8EOgNdAXL/bv1M+zn5a/cM9if1ivQc9AL0SPQg9VT2rvdI+Sb7gP0JAIkC1QTIBnQItwmKCvEK6gqFCvsJvgn2CUUKeArSCooLEwxUCzAJiAYgBKkBUv5e+jL3kvUQ9ev0EvVX9vP4JPzq/u0ArwI7BBIFvwRCA0QBPf9Y/V77MPlG9+/1PfXM9Ff0HPRc9Ev1fPaz9xX53fo4/cX/LAJWBEwGIgiaCXoK0QrfCsAKaAoMCvQJMwqTCv4KiAvTC00LfwnVBh0EaQFR/qv6cfea9Qj1KfWf9cj2JvlN/C3/QAG+AgMEtwRcBOsC2ADU/hD9T/tm+Yr3L/Zw9Qz1lPQu9E70GPVK9oP34fic+vL8lP8QAlYEWwY4CKAJgAr1CvEKnAooCuAJ+AlICpIKAAu5C1QMzQvSCSEHdQTJAYH+q/pW92z15fT+9HD1w/Y8+WL8PP87AaQCygOABEUE3gLlAPn+V/2s+7P50fdr9rP1NfWP9Ar0EvTg9AH2HPdy+E36yfx+//4BTwR2BmsI3wmxChcLFQu6CjUK3QnTCRAKWgrsCtoLZgzpCw0KiwfjBOwBdv51+u/2zfQd9FL0EfWo9kb5jfyQ/9oBRQMwBMIEWQTYAo8Ac/7d/E/7lvnW96L2FPaz9R71hvRQ9Lr0kPWc9vz3yvlF/Dr/FwKtBPEG6giKCp8LEgzrC0wLlwoPCscJwAnoCWEKTwv+C5ILtQkIB38E4QGe/pL69Pb99H30qPQL9UT25vhr/Hn/WgF3Ap0DegQuBIkCOwBl/vj8fvuu+dT3q/Yh9rf1CfVG9PnziPSN9Y/20Pex+YL8sv+oAkIFhAegCTkLBgwPDJ0LAQtCCrAJgAm2CSoK1QrWC4QM/wvpCQYHTAR/Ae/9q/kV9lv0JvSC9CX1uPad+Sj9GwDpAQID7ANfBLQDzAF3/3X9//ua+tH4FPfm9X71U/Xl9Hf0cfQk9U32X/eb+Jn6T/1WAAgDXQWGB1wJ2QqtC/YL1gtHC6UKMQooClQKmgoVC9kLagypC1cJQgaDA9YAgv2B+Rj2lfSN9CH1tfUI9735H/31/4oBYgImA7IDNANwAR//Rf3++6b68Pga9+v1gvVs9RL1ffRV9PD0IvZc9734rvp5/b8ArQP8BdcHqAlACzcMTQy6Cw4LggpFCjUKVgq/CmoLOAx6DHwLJgkaBiEDJACY/ML4tPV69L/0hvVw9vD3pvr3/akAIwLSAkoDaQOOAqgAfv6k/En76vlR+Mn2l/UL9cb0ZfQE9AL0x/Qk9sz3mPnX+6D+ywHIBBYH9AhHCkYLygvAC0QLXAquCaMJOArWCkMLyAukDBcN2gvlCFoFagKj/xT8Efj69A/01PTt9e32h/hc+7/+RQFTAoMClAKCAqwBm/82/V77WPqZ+Tv40fbe9cj18fWJ9e70q/RX9ZT28vdh+X/7gP7pAfYELgf8CHkKyguCDFgMewtkCtEJ1AkTCjQKeApaC4sMwQw/C2IIWQWkAp7/0vvA9xr1j/RL9S72/vaz+Kn7A/9sAWAChgK5ApwCgQE9/6386frV+dv4aff69S71E/Uu9ev0j/SE9F313/aM+Fj6e/x0/8kC5QVSCP0JRAsnDKQMYgxlCxsKMgkzCb4JQwqOCisLWQzyDJoLWgjVBB8Ckf8G/Lf3sPQf9Ef1e/ZU9wf5DvyV/+4BogKAAlUC/gGoAET+nfva+f74SPgh98P1H/VJ9bH1g/US9Sb1FPaX9//4pvr1/A8AZgM2BkcI2AkmCxAMYAzrCxALIQquCeEJcgrmCi4L7gvKDMMMrgooB84DEQE//mj6kPaT9O70QPZL90z4TfqQ/YEAEQJFAhICGAJ4Ac//TP3o+oX5x/js95j2XvX79Cf1M/XS9Hr07fQt9tv3hfl0+xf+VwGoBFcHPQmUCpMLGwwLDGwLegqjCTkJkQlNCu4KpAt/DEINzAxpCtsGYAOAAGn9l/n+9TX0mPQP9nj38fg6+z3++wBNAnECHAKtAb8Azv5W/CT68/hf+Kj3i/aJ9VL1jfWg9TD10/RA9Wr2//eW+aD7cv7YARYFgwdQCawK1gteDDIMegt5Cr0JYgmlCVoKMQsHDOYMWg2GDAIKbgYFA+b/k/y6+EP1vvNR9Ob1ZPfv+Gb7mP5KAZoCpwJZAvUB3QC+/h38D/ry+ET4PPft9RL19PQ29RP1tfSt9HT15/Z9+Dj6dvx1/8kCtAXiB5EJ5wrkCzwMAwxXC2sKuwmCCfgJrwqFC1EMFg1BDfgLTQm7BXECQv+9+wH44fTU84r0AfZ29yP5wfvP/kwBZwJ7AjIC0QGvAH3+JvxS+mH5evgx9/P1JPUk9Tn1AvWl9LT0lvX/9pL4S/qn/LT/BwPQBeEHjgngCtoLPgwNDHELqQoXCgYKeQopC9ELewwZDQ4NbwtUCMgErgHT/jH7X/eo9Cn0TvWb9tb3cPkq/CP/JwHRAckBygGAAS8A6f2l+zf6jvme+FD3+PVt9ZX1ivUd9Y/00/Tq9Wn3yfiA+gv9NQB0A+8F7weWCRALCQw2DOsLUwuuCgwKygkvCu0KwQt5DPMMvQwRCywIvAScAYH+2vpG98v0aPRY9ar2/vfD+V38JP8YAdsB6QHCAUYB3v+r/WT78vke+UT49fbE9V/1e/WQ9Qr1r/QM9Tf2sPcO+dD6ZP2WAKIDHgb2B6kJIAscDGYM/AtmC7gKPwoZCmIKFgvXC58MBA2NDKQKhQdDBD8BOv6J+vn2yPSb9Kv1wvYN+Oz5xvxr/wUBoQHAAdgBUQHI/379aPsM+in5GfjB9rD1VPWC9Wz18/S19FH1n/b490n5Bfuu/csApgPSBZsHZgnvCucLEwzjC3oLFQuwCogK3gpvCzkM2AwdDUMMHgolBxkELwHR/RX6yvYb9QH1v/Wr9gr4MPrm/Dv/ogBkAc0B9QFKAbj/lf3K+5H6mfle+Mj2vfVT9WL1BfVk9En0+/RP9pP3/vj++uX9LAH6Ay0G/QfcCVkLFgwxDMYLVQu+CloKVwqzCnsLRwwlDYcN0wyWCloHEwT1AKX9rvkv9k30dvSW9Z324ff9+Qn9vv9BAacB1AH+AYkByf8u/f76zvkW+ej3X/ZP9Uz1nvWQ9fj0xPSY9QT3WPhi+Rn7yP0IAb4DvgWPB38JRQsuDEcM9wujCzALzAqXCvQKmgs3DNkMHA13DDUK7QaiA94Ax/3c+Ur2ePTb9PT11PbV9+r5Ff3L/xYBQQF9AfABjwGx/+384voB+lv5Bfgc9gT1EvWC9Ur1f/Rh9HX1Nfd9+ID5Mfsl/oMBIQToBYYHhwlRCzMMGgy4C2kLOQvxCt8KLwvPC6EMRQ2LDYkMIwrlBsgD9gCt/an5HPaF9MT0vPVr9on31/kC/aT/xgAPAYIBIgLDAbz/9/zs+hH6PvmP95f1gPTA9CL11fQb9En0t/Vr96z4mfmX+6f++AFtBAQGuge8CXQLIQwADLMLkAtUC/oK2QpZCz8MAg14DX0NqAxrCigHvwOsAG39fPnf9ePzDfQ49T72Xfdv+ar8lP8xAYYB0AFVAg8CSQBh/SD79/km+ZL3d/Uh9Az0jvR09Ovz9fND9SD3nfjI+ab7nf7wAaQEcwYKCMgJegteDH8MHQzIC5MLawubC/ILkgwzDe4NPA4gDVQKsQZ/A6sAU/0C+R/1bvPo8x311PXG9kv54PzU/+cABgGmAYwCXwIhACH9//os+kv5Yfce9d/zG/Rl9PvzLPOT82H1TveN+IX5q/sV/6wCGAWnBlQIagoXDG8MDgzGC7oLmwsxCykL8AsBDSEOrQ61DqINGwu4BwAEmwCv/GL4gPSd8tby0fPu9F72PvnN/ML/LwG4AWYC8QJYAvH/9PzO+tD5yfja9rv0zvMe9IP0IPSN8xH0mvVS91D4Zvmb++r+UQLDBLEGuAjtCoAMDA3bDIcMKAyoCzkLMQvEC6IMoQ12Dt0O6w1sC/0HjQRkAXP9vvh59F/yiPJe8yX0bvVk+Gb8gv/vAGkBbwJeA7sCCwCx/Kb6pvmJ+F32NPR88w/0rPRA9MjzdvRI9vf3ofhf+XT77v5iApcEQAZTCPIK1gwuDbcMWwxQDNgLGAu9Cm8LtAy9DXQOnQ4IDvcL4QhbBbkBs/3u+NP0ZPIJ8qfyYPP/9P73+fsm/9AAxgHUAqgD5AJaAEv9Gvvk+Wr4Efbo8xPzrPMd9LHzW/Mp9ET2/vfq+MT59vtj/4wCxwQ4BlsIrgpODKgMQAxEDC8M3AtDCyML0wvRDNwNog4BD10OQAwZCYoF8AHs/Uv5CvVi8tvxfvJ68/v0xPei+xb/FwHgAZkCRQPUApEAVP3M+oX5VPhr9jr0QfO481v0RPS78yn0svV394r4dPlf+47+/gGCBE4GGAhrCjoM1QxpDOQL0AuZCz8L+wqeC+cMNg4jD2AP5w4ZDRIKOgYmAvz9WPkI9QbyLPHd8RTz3PSN93T7G/+AAY0CEgOIA+4C1gCp/er6TfkT+F72X/RN84vzR/Ra9Af0RPSB9Sv3Vvhb+RP76/0uAd0D4gXUB+0JtQuUDKEMYAwIDMALgguyC0IMGg0eDh8P6A94D3sNMQqNBgUDCP9A+mj1JPI68c/xtfLz82n2dfqS/jkBKQKkAosDyQMWAo3+Q/t2+ZL48vaE9NjyyPLF8w/0oPOE87L0sPYg+BD5cvo4/b8AyAMEBt4H8AnMC+YMEw2kDEgM6QusC54LCAz9DBUOMA/lD8oPNg40C3cHowOX/8z61/UN8pfw7vDz8VXzr/WV+dr9+AB2AjoDAgQdBIwCXv8a/Nj5YvjO9qn0DPOl8kXz6fPR8+/zt/RB9rz32fhf+p78rP+hAjkFaAd0CUULigxJDWQNFg1sDAQMOwzsDIkNCg7jDgAQTxCbDjoLdwcWBFUAgPso9irym/C08F3xSPKU9Ib45PwkAMkB2QLiA1gEMANcAA39mvoM+YP3avVp84zy2vJv83XzZPPv82D1D/eB+A76NPxB/3ICQQWFB3MJPwuhDHoNnw0tDZAMLQxtDAINkQ0XDtEO1A8lENMOyQsUCK0EIgGr/Ib3Q/Mx8Qfxk/FK8v3zSvd/+/j+6gALAhgD+wNzAzUBJf6U++35U/hF9vfzmvJw8t/yCPPH8hnzUPRA9gP4r/nS+77+LwIxBZ0HfAktC7QMsA3tDWoNqQw4DHMMDw2nDRsO5g4aEMwQxw/ADCUJsgUrAnX97PdY89nwa/CB8Bnx3vJe9rT6Lf5sANcBSgMPBG8DUwFw/gT8/Pk8+D32PvT38pvy4fIG8wDzVPNZ9OL1f/cg+UL7Df5JAWMEEQdaCUwL5Qz3DXQOPg5/DawMYAzGDF4N0g1XDkkPOxDdD6sNMQqvBl0DSP9G+h/1v/GE8JLw+/Dw8Yf0hfiz/In/KgFWAooD0wN4Asz/5PzE+uv4/Pap9NjyIvJR8sHyx/Lo8pfzLvUk9/L4zPoV/TUAkwN6BrEIdwpaDPkN8w7ODgwOYw1DDasN4w0WDnIOYQ9REPkP2Q2MCk0HKARGACz79PVI8rrwY/BR8P7wUfNB91v7P/4QAK4BTwMTBBQDoAD7/cv77Pmm9x/1FfMl8hPyBvLx8QXy6/J89GP2ZviH+jT9TgCdA5sGAgnqCocM2Q25DsUOKQ5QDeMMMA2MDfcNaQ5QD0kQTxC2DqILQAjcBFQByPyc91Pz+PCN8KfwHvGe8tL1D/p9/ZD/zQBLAqMDjwOKAan+bPy7+t/4Pvae8wvyh/GZ8WfxPPGx8TDzaPWg99L5TPx9/yIDdgb5CM4KdQwDDiAPPA9xDn0NAQ01DbENGQ5rDhYPKxC0EJsPpAwECagFVAIE/oz4u/P48GrwnvDC8Mrxr/Qm+SH9fv+/ACQCnQPvAzICMP9u/H760/hi9qXzqvFE8cHxA/ID8i/yivPI9Sf4Lfof/Lj+EgKiBUcIEAp4CwsNlg5CD+UOtQ3oDAkNmg3iDcANNQ6CD6AQDxBSDbYJhga/AwwAwvpj9d7x9vAW8fbwQPFV8533Cfyx/sj/7wDhAiMEBAPZ/6f80/p5+UD3//Nh8a3wOPGv8WHxaPGm8hT1sfe7+cP7df78AZkFZwgLCmsLCw2QDkMPpg50DY8MpQw3DZINzQ1ZDqcPthBNEPMNnApqBz4EbQBc+yr2c/LY8ILwgPAd8Sbz4vbN+sH9i/8EAbACnAPSAnEAx/2o++/5svfm9H7yYfFi8WvxRvFV8WHyavS/9gX5TfsT/mcB5wTBB9oJfwv5DGsOLQ/3Du8N+Ay7DBsNjg24DU8Ofg+hEGUQQA4kCwYIGwViAW78SPdW81nxjfAt8IzwbPLu9bX5q/y4/nAAZAK1A2QDYwHI/qf82fqV+I318PKH8UfxOfHL8KrwcvGH8wD2QPh/+jz9zABoBJIH6gm/C1gNyA7TD7wP7g7TDToNRg1lDZoN8Q3cDuwPBRBhDlYLCggGBbEBPf0R+KTzPPGD8F3whfCr8Zj0pPg2/I7+OwAMAqoD5wNPArz/Z/1z+0b5W/Z6857x//AG8cbwofAs8d3yVfXc90765fwhAM0DQwfaCa4LMg29DuYPIRBpDz8Omw2eDQwOMA5XDv0O4Q8iEIcOXgvQB84EugGc/YP41vNi8cbwwPCq8HHxUPRw+CD8NP6B/zgBFAOzAyoCgf89/ar7xfnK9rPz0fFw8W3xz/A38JnwfvIX9Wv3mvkq/JX/bAPqBpQJfQsqDboO/g9WEJYPfw7NDfANRw5KDkUOvg64D9gPNQ4UC74H/wQAAtr93fiH9CHyfvE98QbxpfEh9Pj3bPuw/Q7/uACWAlYDUwL3/7T93fvj+Sj3IfQT8jfxB/Gc8CfwafD28YD0Kfe4+T/8d/8rA84GsgmgCygNqA4CEIAQ/Q/aDhkOGQ5xDoEOVQ62DnUPtA8/DjkL4wf5BAUCCv4t+bj0IPIq8eDw1/CS8fTzcvfj+mD9Qv8SAbgCcwOaArkAkP6T/Fz6o/fg9MPypvEC8WfwAPBC8ITxnvMb9sv4nfvW/mIC+gU+CaoLYQ27DvgPoRBUEDAPNw4xDn8OnA5lDp8OUw+BDzQOPgvtB+wE5gH8/VH5N/Wl8prxJPEw8ffxJfR797H6Nv3g/oQAKgL/An8CwQDJ/ur84fpM+Hr1PPP38Ujxo/Ac8CHwSvFz8/P1j/hh+6z+hwJSBnAJswt6DRYPWRDFECUQDQ8qDvwNDQ4VDiEOgQ5ND2oPLg52C1IIcwVqApb+4PmH9b7ym/Ej8erwafGg8xL3Z/rN/IH+egBcAlsDwwL9ACH/Tv0g+0z4YfU88zLyjfHD8DfwavDI8dLzIPaD+Cv7a/7qAWIFTQi5CsoMhw7uD4gQURCHD9AOog6iDo0OWg5/DgcP+A6ZDesK9gddBZEC3f5f+lP2o/NR8pjxKvGw8ZXzi/aK+dL7u/2l/6YB2gKTAiIBKv9V/TP7j/i79XPzT/K58Q3xZPCK8MXx6fM59oP4CvsC/nYBvgS7BzwKZwxUDqEPSBAWEIcP9A6yDr8OtA7FDu8OTQ8PD8YNZguDCMEFogIG/636qPbK8yTyYvEb8aLxXPMe9vj4dft//Y7/bwGKApsCZAGz/7b9efv2+D32APR48n3xpPAL8A3wGvER83H1+vey+t/9VgH4BDMI4woaDdAONxDvEOsQThCHDywPKQ8lD/4O4g4YDwgPwg03CwIILQVsAgD/q/o99kTzzvEw8d7wK/Hf8tP18viJ+7H9r//MATIDYQM5AjwAMP7S+y75VfbL8wvyBfFY8M7vzu+t8NTyafX896f6ev0DAZYEzQdsCm8MSw7tD+wQ8BApEEoPHQ9RD2IPHA/qDkMPRw8NDnULUAihBSQDtv9E++T26fOR8tTxI/EF8V3yJ/U/+Jr6gPxw/scApgIDAxACLgBQ/kP8v/nl9ir0VPIz8VTwiu9l70zwafLw9Jf3bPqP/WABMAWrCEILOQ0HD4AQdBFQEVIQQw/WDv8OLQ/cDmsOjg65DuYNXAsLCEcF7wIIAK779fan82PyHPKU8UPxevKL9R/5nfst/c7+GQEXA1YD7gG0/8X9Avy0+db2/PMd8kLxt/AH8JnvVvB38lf1C/h0+l799gDuBFQIqwqPDEkOAhD7EMYQ4A/jDq8OCQ9FD0UPEg8kD10PXw7JC1kISgXeAqv/VPuV9kbzFPKm8VHxHfFq8mb1zvhu+wn9y/7nALkCEwPYAcr/t/29+1H5ofbw8y7yJvFz8OLvre+D8H/yN/Xn97z6r/0iAcwEEQi3CoQMRQ7aDwARPxF3EJgPLg9qD6gPgA8YD/8O3w68DTwLyAfBBCcCSv9L+7T2T/OX8VnxXPGR8efyivUE+eP71P2L/3gBRQPOA5MCawAC/sL7bvm09jP0OPIX8UDwkO9570DwO/LQ9Kr3hfqY/e4ArAQmCMYK5gyKDkgQWRFeEYwQWA/zDh4PXg8uD/IO+Q7uDtgNPwsoCBMFmwKF/yv7jvbb8nDxNPE48XLxrvKK9Rj5EPz9/ZX/awEoA6UDjAJPAM39hPs1+bL2SvRh8izxfvAR8A7wwfB08tX0ivc++jX9fgAOBHwHQQq0DKkOiBDcEQ0SQRH+D00PVA98DyUPiA4JDvcNCA2bCjYHFwQtAsH//vs995DzT/JQ8nzyXPJH8/71cvky/Lz9+P7RAMYCSgMNArv/df2a+4r5L/ed9MLyuvH58Dnwru9A8Bnyp/RU9/D57PxwADsEvweGCtwM4Q63ENsRvhGrEDwPlA64DuIOnA4TDgIODQ4+DcAKbgfUBMMCSgAT/D735POX8p7yUPIk8jzzAvaB+fz7gP3i/swAqQIlA7sBmv+C/Zb7hPnc9o/0yvLL8Q3xNPAJ8KjwifLp9F33+vnl/F4A2AMsB+MJOwxCDgwQRxFfEY8QKg+KDqUO/g7nDlkOPQ5JDmsN0QpQB04EawINAAf8O/em837yV/I68h7yVvNo9s/5X/y9/Rj/JQEBA1ID6AGx/6L92fue+RH3r/Q482nyh/GV8PrvrPCM8t70NPeL+YH8EQC7AwgHrAkHDEwONhB4EV8RRhAaD3sOtQ7YDqkOUg5gDpIOfg22Ci4HgASdAhsApPvX9ovzc/KO8hbyFPJg86v2WPqb/Or9Jf9LARYDaAP3Adf/7P0j/AX6MffS9C7zPPI68fHvc+8U8APyU/Sf9i/5SvwIAM4DIgfqCVQMiA6EELMRnBFvEPcOTg6kDgQPyw5jDpEO8g4RDhELJQdRBIICGQCD+1H25PIA8mfyE/IP8qzzT/dG+579yv4CACMC6wPjA/ABkP+B/bz7Yvlx9ij0t/Ii8kHxFPB57x3wRPK19P32Rvk1/PH/1wMcB60JCwxIDoYQuxF/ESgQqQ5GDpgOAg/CDk0Oeg7IDvoNEws5B2IEkwItALf7gPYI8yjyifJP8h3ykPMw90L7rv2r/rL/yAGvA8IDzgE0/079mftl+Xv22fOV8uvxPPEa8GrvQfA68sf09fZF+Vj8/P/RA/kGngkiDHgOZhCUEWMRPRDUDhwOcQ6mDqQOTw6PDhQPFA5KC1QHcQSTAgEAjvs89tPyzvEK8s7xx/F/8zz3RfuZ/c/+BgBRAg0E5AMCApf/xv3P+zn5GPbO86jy/fH38JvvEe/f7xXyXvSS9gP5KPwcAMoD6wZ+CQUMhA6kEKYRURErEPAOfg6MDskOug7bDmkPag8WDgILowcNBc8Cm//O+g/2DfMC8sbxY/G78QP0pfc1+2r90/6QAKcCGQS2AwsCAgAi/vT7KflE9gv0zfLN8dXwnu887xfw5PEk9DT28/go/OH/fQOBBl0JDwzSDtcQ3xGaEaAQlw/mDgIP4Q6/DqgOBg9UD+0N8gpZB9UE/gITAFn7J/bn8v3x+PGa8aLxc/Mc9wj7b/26/kIAewJJBEEEigJOAFX+Kfxa+UP21POM8nvxSfDa7l/ufe+N8Rn0VPb++Gr8WwAtBDwH5gl1DAQPAxELEpcRdhBHD80OIA8pDzAPHA+UD7UPMA4xC6UHPQU2A/D/5/rK9bzy9fHg8TnxXfFt8173JvtI/aL+PAC6AlIE3gPXAXj/uP2e+7r4kfUm8xLyR/Er8N/ude6o78rxLfRo9hr5lfxhAPgD+AbVCa0MTQ85EfgRxxHHENoPdw9yD7QPeA98D9cPug9aDhMLsAcvBRkD6/+/+q/1pvLZ8cjxIvH/8DTzCvfH+uH8Bf4GAH0CVATXA7EBlv+u/bb7n/hF9dzyovHB8FXv+u2v7Sjve/Hs8zf2FPne/McAiwSEB4AKbg0JEP0RpRJNEkgRXRDwDwkQ6A+SD3YP2w/mDwcO0wpYBykFTAOu/176//Qn8o7xRPFd8CDwUPJs9kP6Wfyp/bb/wAK0BEEEJQK4//H9yPu2+Fn1pvI/8SXwyO6l7YXt++5G8aLzOfYx+eP82ACIBPwHLAs7DskQoRKRE3cThRJ9EQYRBBHaEBoQ3A8UEOEPEg6BCjwH9AQAA03/m/l99KLxGfG78Orv6O8i8jP2/vmF/Cb+fwA8A+MEZQQjAs//qv1l+0T46vRT8rHwlu9D7krtYu3Z7ijxqPM49jT58/wEAeQEVgh4C34OVRGXE5gUShQVEwkS1hGqETQRBxBiD7QPZA+fDegJrwbSBN4CJv9Q+Tv0pPEP8YTwYO8P727xhPVh+d37cv0TAPcC8wSkBGkC9f+g/Wf7cfj19NrxvO+N7sXtD+0Q7Wruz/Df88721fln/WoBvAVpCXEMEA9hEX8TjxRVFEcTKRL/ESMSzxHsECwQWxAIEBAOaArFBocEQgJv/tH42PNH8ZPwLPAr71fvu/HI9Yr56fsK/nwAXAPgBC0ELQK6/6L9EvvB9zP0P/GH71/ube3A7Ants+5h8TL0Avc/+vH9IgL/BV4JVAznDp0RpROnFF8UNBNzEkcSZxLhEfYQYhB7EB8QxQ0XCp4GfgQqAhf+X/iA80DxivDz79buD++t8bH1Pvmd+6j9RAAxA6gEMAQiAsn/ff3W+tT3YfSM8WzvGO5X7cDsCO1Z7tHw1PP29iT6y/3sARQG0AnNDJkPDBIjFCgVpxSPE5sSghKnEg0SChFfEFUQ5w+MDe0JdwYaBOcBrv0++GbzCfGG8OPvIe8477Dxh/Uj+aL7w/1pAB8DrAQGBE0CCwDS/Tr73vej9KHxo+8k7tzsPex+7BPuj/By85b2B/ri/TUCdwYuCmANABCUEpkUcBUDFY0TohKPEq0SLRL5EGUQfxAUENgN+wmQBmMEAgKe/bP33/LE8Fvws++17unupvG79V/5z/vg/YgAKAOuBDoEUgLN/z39ofqj93T0bvER74/t0uyY7ADtP+6i8KrzB/eP+jj+ZAJtBk4KlQ1TEMQSghR0FR4VGxQ3E+8S7hJ0EnIRzBDFEP4Psg3lCVoGCwRuAUL9mvfi8rbw/e9K71Duju4w8QH1pPg2+239SADuApAETgSNAlAAyf0K++D3gPSF8Tzvoe2Q7PfrWOzc7V3wefOy9lf6bv61AgYHmgq/DZcQGRMTFaUVEhXqExcT+xLWEjQSUBHUEOwQRxDRDUIKuAZUBKcBJv2R96PyV/B876bu5+1G7gzx7fR6+CD7df07APICkARgBMACPACE/aT6qveL9GbxAe9B7XPsNuyi7CbulvDd81X36/rj/hcDOAcBCy8O+xBQE/QUqhUSFf8TFBO5Er8SWhKtES4R5BAcEM8NPArhBiUEOAHT/E73ofLo7+XuE+6I7TLukvAw9JX3n/qG/X0AMgOwBLMEYgMVAWb+XfsQ+ND0l/ES7zftEOyj6w/st+1a8JzzIvfn+gj/jQPEB4ILjw4cEbkTdRUXFi8VlhOxEnkSvxI6EiMRdBBIENMPkA3vCXcGEASWAXH9zPez8hTwL++L7pvtk+3Q75TzifeX+hn92P+uAtEENQW9AyoBRP50+4/4MfWw8cfu5ez+67HrE+yA7UHw1vOk94X7iv/wA0kI+QsBD1QRjBNEFdMVIBVbEzwSJBJyEi4S8hAvED4Q/A/2De4JGgblA84BDf4F+Iby6e9a7wnvsO1R7YHvqPPY94n6sPxy/8MCIgVXBXAD5wCj/jL8Jvk09ZnxPe/37S3tKuz863ftifBD9IL3y/rE/ogDUgjoC3IOrRA7E3sVPhYRFfcSyxH6EYYS6xFyEJ8P7A8AELwNnwnpBe0DVgJB/hD4ofIT8PXvOu+n7STtVu/U87H3IvoW/OD+iALoBAUFSQPrAAv/yPyO+c/1LvLf713uDO0c7Nfrae1g8NHzWfe/+uX+hgP8B6oLYA7EEAITwBROFW4UzBKwEWkRihE7ETcQtw/GD5oP2g1XCvEGoARtAqn+8viJ84jwfe/F7m3tAu3k7n3yXPYr+dT7Hv+YAg8FYAUqBHQChwD6/Yb6ePb08mXwfe7y7J3rjesH7eTvdPPX9nf6gf4bA9gHoQtvDqcQwRLRFJUVwBQPE6cRoBHJEUMRUBCnD+4Ptw+wDUcK8QbYBLsCsf4Z+bHzovCN74nuge3z7I/uDPLL9Tb56vsG/2cC1ASqBZgEtgKvAFX+RPtl95Hzw/D17oftZuzs6/7soO8H86n2Lvor/q4CRgdNCzsOgRCAEj8USBXDFCQTrxEtEWURFRH6DxYPLw+PD00OHAuUBz0FmQNVAM767fT98Jnvtu5b7WXsT+2t8Kn0OvhD+07+1gGpBAQGiQWpA3EByf6Y+933z/On8HLu+OwN7J/ruexd7wjz6vaC+mD+vQJVB1MLLQ4wECQS8BMGFa4U6xJXEcQQGBEqETsQYw89D2EPQA4bC5wHKgWAA5IAMftT9Urxzu8r79rtsexh7WrwZvTO94n6gP3/AA0EbAXJBBYDFwHs/gX8Jvg99Abx+u6g7WbsA+zS7GDvA/O49oz6Vv6LAu8GxQr0DUIQChKtE3UUTxQAE1MR4hAKESwRTBANDx0Pcw/IDtAL2wdjBcoDQQH3+4P19vAo75zuW+3r6y/sB+8x8w33F/og/dMAKQQYBtMFJgQbAsX/9Pwm+fT0dvH87l/tNuyc623s2u6M8ov2P/oW/jACxgYACy4OXBDkEWgTfxRxFPwSORFYEKIQ9xBLEGQPHQ+zD0wPpwwOCQUGPASRAWD8FfYJ8e7uOO4A7bPru+t07r/ywvYn+jL9xQA7BFAGfAbwBLQCQQBd/bz5k/WZ8b/u/ez364zr7+se7q7x7/Uj+v79QwKyBgYLcQ6FEPQRCRMiFG0UYBOEEQwQEBC7EMUQvA8AD0kPaw+QDZQJ0AWRA7kBjf3r9sDwpO1I7fPspuv+6gLtiPF99kn6YP3rAJ4EXQfFB1gGIQShAd7+2fpQ9gXy2O4q7bzrEOsk6/jsvfD29HX5W/2fAWAGwwpfDnYQDBJ4E48UjhQbEwgRrg+gD+4P3g8cD+MOZA9gD8oNdAo4B+4ESwLa/WH3cvET7rzsDuy26nTqsuzt8Nf1q/kn/fYAsQRtB9IHhwZQBK4Buf7Y+of2VfL97v7s3+uF68brXO2S8Nn0bPlv/WoBngX9Cc8NQRDhEewSCBSUFJ8TwxHPD20PAhAuEJQPiw7KDjUPIA7zCu4GdQR3Ar3+N/g+8TntUexL7C/rNOrH63Dw/fVg+r79NAFFBUEI5QhTB68ETQJ5/7n7/vZP8vzuAO3j6yzrSuv07F3wi/Sv+LH82gCSBdwJTg2vD2QRIBMpFGsUfxO6EWsQsg/XD90PHQ+eDpUOEw8tDhkLlQfJBDEDtP9d+ZDyD+5N7f/se+vh6fjqwu9B9ZH5ePwpAMsESQgTCR0H3wS2AkAAVfzp9kny6O5E7UHsRuuH6wntSPBg9GP4s/zyAGMFcgmIDAIP1RBTEnsTshP2EnsR2w9XD4APvQ+AD7kO1w4JDyYOgQusBwkF4AJJ/yr5BPLf7eDs3+y26//pT+sJ8P71rvp4/bYA1QQxCFgJmwcZBa0C2P9M/FT31vJ/72ztZuxi63/ryeyr78Tz3vdD/GwAzgTtCFEMBQ/FEDES6xIgE28S7BCYD+QOTw+kD3EPGA9FDxkQnQ8CDR0JsQU2A43/gvmJ8njtu+uW68fq8ukW61PvKvX6+aj9EwH/BH0IgQlbCLwFCwNKAFH85/c/87Pvhu3t62zrfusb7e/vePO897z7EwA8BEYI/wt2DigQGxFMEk0TEROOEXwP5w6bD1sQ3w/NDgsP0w+1D6YMLgjRBIMCuv+I+S/yp+yn6kPr7eqe6k/r1O7R9HD6Wf+VAn0FLAiQCZQJFgeFA5b/1vtB+Af0/e/t7KDrluss7DztL++V8t/2jvvP/8QDpQdCC2IOWhCSEWcS9xL6Ep8R2g8UD4oPYRANEBAPEQ/6DzEQmg1HCboFUANhAFz6zvJE7Rrraevp6u/pp+op7lP0CfqG/gMC6gT7B7oJxgmuB+oDGAA7/I/4g/Q08DLtquu861XsSe1m78zyXPe++8j/kQOCB2cLNg71D+AQChL3Eu4SmRGAD8QOQw8aEOcP0w7IDn8PrA9GDekIKgWoAvn/jfpR85DtVOut68DrIeuA69ruj/Rp+v7+IwIIBZIHSQlNCRMHmAN//8L7Lfgw9E/wBO2R67brXeyk7W7v1PJp9+j7EwCPA0sHIgsHDu4PxBCTEZYSdBJoEZMPpg52DwoQGRAfD9oOsg9FD0INNQmgBfoCJf/K+fzyE+7D6/fqkuor6sLrUe9k9I/5Iv5YAqkFQAhXCR0JdQdqBMQAlvyJ+IT0wPAa7n3sAOxI7FPtrO8b8y73Lfvi/ssC3waZCmQN9Q4aEGkRWBKEEh4RbA/uDkIPIhC7D/wOFA9hD3sP8QxFCeMFsAJg/5b5U/Ng7pvrAet+6tzqcuzA75z0yvnr/jMDSgYzCFUJVwngB8UEcwBn/F/4uPQR8dbtNexm6wDsPO1k7/HykvbR+sv+DQN4B/MK4A1kD40QrBFZEoASGBFhD2gOzA7ID9wPLQ+7DmAPXg9yDYUJdwVzArb+gPnl8srthOvN6q/qf+oT7Pfv/vRs+hD/JgOpBt8IDAqtCfoHKgUxATv9+vje9E7xRe7F7AjsE+xS7T7vw/KI9m76zf7SAm0H7ApgDTgPRRDHEXMSERKsELwOMw63DlYPNA9LDoEOeQ+JDxwNxAguBZECU/9Z+U/yU+2o683rJevV6kDsxvCT9mf7qP84A+YGXwn/CUMJEQc5BGIAFfzh9+nzkvDh7VTs5+tz7OXtSfCy83z3hPt1/6UDzAcmC5kN7Q4pEFIR6xGiEfEPYg7EDTQOAQ+PDhwOTw4GDxcPWAyvCFsFqQIy//H4lvIx7m/sJ+wl6zLrd+2o8e/2Lvts/9YDGAcrCQgJDAh8BngD8P9u+0r3zfOU8KjuT+0S7YDtvO478XD0Jfip+2z/RwMrB1cKhAxDDmkPqxAvEdsQ0A9NDucNQQ7bDvsOYw6rDlUPBw+mDG4IAAU9ApX+5PgK8oLtCuwf7BrsFezk7TjycPdY/IkA7wPvBl8IoAjpB9QFBAPR/q76I/ey8/bwge5W7W/t9+1c74vx+/QR+b38IQCBAyYHkArBDNQNlg5vD0QQQxD2DqMNFA3nDRAPDQ/qDsQOig90D+MMSwksBSYCS/6T+LPyCO6L7EPs2us67AXudfKv92X8cACvA5kGgwgGCSYI/gWoAsj+wfoF97DzmPCQ7o/tqe107tLvQPJp9fX4Y/yl/ywDyAa9CboL2AzZDRkPEBA+ECwPDA7bDZMOkA9iD/4OUw/uD7cPgQwTCHwEzAHb/t/4M/Km7TvsCe3c7NTsbe5L8uz3vfysAPIDNAYKCIUI1gf1BXUCo/6w+kD3DfTt8MTu2e0e7tHu/e/18Tz18/h6/MX/rgIVBg8JXAvyDL8N3Q6vD/sPZQ8TDp8NFw4LD1YP7Q4qD8EPbg+/DJ4IDwViAtz+Bvm08nTuT+1d7cnsueyJ7u7yGfhq/B8APwMlBuIHIAhLBwAFGAJ7/rr6U/fX8y/xau+a7qzu2u4L8HDyifUY+Qj8J/+iAtQFBQkPC4UMuA2CDrEP/g98D4YOqw1ZDjEPkw9oD0APuA8UD14MSgiuBPMBbP7T+JTyeu4M7S7t8ewq7V3vUfM3+Gf8MgDcA3cG7QfdB/YGaAVtAqT+evoC9zP0cPFQ7//t/+3h7kfwXvI79Zz4BPxT/3UC2AW8COgKcAxwDdgO2A9LELUPgA5MDpoOXQ9sD/QOQw9SD4gO0QsRCBwFOQJp/sj43fIZ73rtAu247CjtRO8P83H38/sxAMoDmAa9By8IjgerBegC4f5S+6v3JPRY8TXvpu587rnu1u/f8S/1g/i5+/L+SwLLBbcIAAu9DD4Obw9dEH8Qng+BDssNSg74Du0OlA6cDloPCw9gDFIImgQIArj+Tvkx867uEe347OfsMO3q7snyufeq/NgAGwSDBugHcwjeB6sFKQLv/SD6BPfi8x3x1u4E7m3uEe9u8FHyZvUB+Sj8U/9TArIF9QguC9wM+w03D3gQyBAuENgOGg5LDr8Oqg44DmIO4w5qDqIL2webBFMCS/+z+Y7zGe+r7e7tsO277fvuePJk9/T78f/rAlIFEwevByAH8AS/AUz+D/vk92/0LPEj77/uQe/U74fwLPIr9d/4dfx3/2ICjAWUCC0LxAzvDSEPExCeEMYPdw7PDf8Nwg6UDhoOQg6gDlkO0AseCBwFZgI+/wX6K/Rl8H3uKu677bntm++58iP3jPuF/yADRgWhBiIHoQboBIgBpv0d+gP3CPQo8Sbvnu7x7q/v7PDV8vL1QvmB/KX/nQLUBY8IxgqRDMMN+g7mDz0Q2w+pDgEOEg5lDmoOzA3RDW4ORA5EDJ0IMAW3AqL/5/od9XXwcu7q7dzt9e0f70zyafYm+13/qQIfBVAGEwfKBgUF+wEv/vD6HPgc9T3yAfBF77XvJ/D+8HryCPVG+D37NP5GAXEEnwf1CcgLaw3FDk8QxxBcEGsPYg6ADqgOng47DvMNhw45DiIMoggkBb4CAQB1+6X14PCp7pPuse6n7pbvDfJS9gP7Nf91AlQEpAV0BogGKwUDAjL+2Poq+H/1bPLU79juP+8w8A/xNfJz9Ln3RPuN/o4BdwSDBzMKXAz2DQcP7Q9YEPkPKA85DgkOTg4yDgsO1Q1QDlgOhAxYCawF+gIoAKb7HPYk8bnuau5Y7mjuIO/R8UD27voc/wUCHwSzBbcGuAYCBcgBJ/4q+4f4yPWK8vTvHO+S74XwI/Et8mf0nvcl+yT+3gC5A84GvQnhC2YNig7AD68QrxDgD6oOQA5zDpcOdA4nDl8OHQ5fDEIJ8QWHA8IAXfyi9rfxbe/n7tPuhu4j79Pxy/VX+jD+VwHiA3EFYgb6BT8EcwEU/jT7XfhE9Tny0u9C7+HvkPA18RDyX/Ss9wj7If68ALAD2ga6CQ8MiQ3EDtMPnRCeELMPog4XDlUOeA5LDgQOMw48Dq0MxglUBqwD8gCx/E33OfJ076juXu5g7gzvdPFK9Y75vf0OAaIDWgUbBgUGpgQgAvP+vPvG+N717vKT8HHvZ+8M8Mnw+PEU9PH2XfqH/asA4QPXBqAJtAuADSMPWBAWEdEQ9A8zD8YO2Q6kDkAOLg4xDugNGgwQCQsGeAO+AIT8AfdM8pzv0u6z7mzuLO978Uv1zvmn/eUAVwM2BWUGPgbRBBoC5f7g++/4+vX18qnwnO+v70Dw+vA68kf0OvdN+kD9MgA/A30GXAmgC1wN1w45EEARSRFjEDYPkw6kDowOMA7KDdYNuA0xDDoJCAalAxsBPP3b9+fyJvAI77HuUe647h7xi/TM+LL83//lAtgEUgZ6BiEF8wLe/wX9E/rf9s7zRfHp76rvzu9I8GXxZPNh9nP5kfzK/yYDpgaGCboLhA0SD4wQTRESESsQBw+nDpwOdA4eDrsN8A2pDRUMLgn9BZcDAAEJ/dL38fIf8Bnvp+5w7u7uKfG99N34uPzn/5oCpgQIBjYGFwXWAhAAY/2H+or3afTi8ZfwKPA78HbwRvE78+f1A/kE/AX/YALDBfMIbws6Dc0OOhBWEY0RpRCED+EOAQ8oD54ODA7sDcQNXww6CboFFQPKAF39NPgh8wnwDe/S7oLuze6+8Dr0Nvj9+zP//wFRBNIFVwaABZED4wAW/mH7fPiH9cvyBfFU8EHwmPBG8d3yXvVF+GP7XP6oASkFVQgiCyAN/g62EM0RJRJJEUEQeg8mD+oOSw7XDYsNGw1vC5kIhQUPA8AAAv09+HfzafAy73fuRu7U7vTwd/Qj+M37G/8CApkE+AViBoIFiAMoAVH+lvuw+KT1GPNO8XnwTfBp8Dfx3PI/9RH42vrq/UoByQQqCNgK/gzlDnIQ2BE0EpoRlRCmD30PNA+gDvkNcQ30DF8LbAhNBaUCfwBL/YT42vNb8A3vj+5F7tHucvCp80v38fp7/pUBcARLBgUHZQaDBBECVv+k/J75U/ZJ8xbxCfCr79fvf/Ax8rj0rve9+sj9SgHrBFoIGgsiDfgOrRD6EXsS7RHcEPwPgA9MD9gOMA6qDcoMHQtdCDkFtwJAAOn8Y/ib82Twv+4V7vzthO6L8HXz+PaU+jb+0gGYBGcG2AZNBtwEqQIUADH9LvoR9x301vFy8Lvvve8z8Jrxt/Nk9nn5v/x3AC4EtQewCjsNXw9VEaUSBhOKEmERuRA/ENAPEg8iDrkN/Qw/CzwI5wRuAiMAz/wm+ILzZPDr7iPuvu1t7mXwafOr9vv5tv1SAXEEZgYiB+YGlgWHAwMBOf4u+9P3iPT18T/wae8t73/vxPDc8pv1yPhJ/CcALATYBwwLvg0NEPkROxOSEwIT1RGlEPEPTg+zDt8NNQ2FDKwK2AeoBFkCfQBH/Yf4lPNZ8CXvW+6z7djtn+/X8hj2gvkv/SEBkAR0Bi8H9wYXBmQE0wHL/pP7IvjW9DDyXvCE7yPvXu9+8H7yV/WR+CD89f+8A1QHkQp6DQ8QCxJuE+MTaxNJEjARmxAeEEYP/A37DCMMggqZBzsEwgHx/xH9h/jJ88Lwke+/7ubt/e3/7wTzGPYO+aT81QAzBCQGqgagBhQGfgT5AeT+r/uF+FL1k/K68JbvTe9+73TwX/Ls9Dr4y/uV/3wDEAeJCqANSRBpEqsTJRSxE6MSghGtEBMQRA8zDioNDAwUCiYH/AO2Ac//tPwH+DvzQvAo74Pu1+3/7bTvq/LO9SL59fzoADUE+AW8BvIGeQYdBa4Ctv97/Af5s/Xr8ufwoO/X7o3uZu838QL0V/cC+x//HwMcB7gK+Q3fEBoTrBQ0FZUUUhMOEmER1BCrDzAOsAyBC6QJlwZdA9oAAP8A/Gj3svK4773uUu7I7dztie9a8sP1MvkU/TcBdwSiBoUH2geHBwgGowNeAMz8JPmn9dTyp/AZ7x/u8e3m7hbx9PM29+P6vf75AggH2gpeDj4RgBPXFCoVrxSkE5MSzRHiEKQPFQ67DIsLbQk8BuQCiwC3/qv7//Z08uLvIu+l7uvt+u3I78Dy7PU7+fH8EgFmBG8GcwfDB5oHNgbOA7IAKv2j+RP2AfOl8Mfur+1L7SfuSfAB81H29/lI/v8ChQefCxEPFBJ6FAsWfhbBFT8UzxLDEaoQLw9HDcgLkgqoCLUFcwJJAKj+0PtS96fy8e8R733uv+2t7W7vZPKk9fr40PwiAdEELQcpCGoIHAjjBqAEUAFv/Xv53fXV8j3wMO7p7L7s5+3178/yJPYc+pT+KAOxB8ELaQ9hEn4UwxX/FU8V8xNyEnQRfRAvD50NGgwSC1IJVQbwAkQAfv6d+/r2LPIq737uMu6I7WjtAu9U8u31Z/ki/VEBOQWlB48Ilgg1CCAHzgRHATX9QfnD9a3y9u/c7bvspey47cDvhPIG9gb6gf4qA7kH8AtmDzQSWhTZFXIW2BVjFNgSuRG/EH4P2g1yDCkLIwktBtYCSgAb/vf6cfa78bzucu3k7Hrs2ezo7ifyz/WZ+dn9hAJ5BugIxQnDCV4J+wdQBXMBL/0v+YL1MPIt7/Ds0uvm6wHtCO/h8Yz17fmJ/nUDKwiEDDwQChM+FX4WwhbuFUgUsxJ/EXIQIA+nDWkMQQs8CSoGAQN1AEr+wvqv9dTwzO3x7JzsL+yE7HLu+PEd9k36uf4XA9QGKgkICjoKwAlVCIUFewEf/RL5mfVq8oLvIe3Q673r1+zP7pHxDfVB+e79yAKnBxsMERANE0EVtRYlF5kWAhVSEwUS6RCXD9MNPAznCvcIEganAtf/hv1E+sb1EPEd7iPt1uyP7Nrs+e6b8rL2kPqQ/tQCxwZACeYJswkXCfIHYAVgAfD85PiK9WHyVO/u7L3r2uvs7KXuYvEr9dT5tv5vA/0HWAxTEHITmBXDFv8WNxanFOgSnhGeEHIP5Q1EDLQKrQjRBasC8v9G/br5+vSM8NPt5Oya7Dnsv+zv7qTy6fYO+1P/lAMjB2wJMgpFCtgJVghlBRIBlPyg+Dj1GPID76TsgeuP68Dsre6Q8V/11PmE/ikD2gdcDFIQNBMyFVsW0xZWFuMUYBMYEiER0g87DtgMWQs3CQoGiQKh/9b8KPl39PrvQu0h7KnrjOta7PTu4/JJ97H7GwCCBDAIZAojCwALKApUCAwFsgAq/Bv4pPRR8WfuLOw564HrxOz37gLyA/aW+nT/UAQLCWYNAhGaE2cVfRaYFqgV0BMQEtYQ7g/YDnQNLAzKCqcInAVSAnn/w/z3+Dn0y+8R7STs3usI7AvtwO/Z81r40PwwAXQF8AjwCnoLDAv9CfsHswRAAJH7W/eh81jwZe1d65nqA+t37L3uGfJz9l/7PADfBGAJvQ1vEQwUshV4FoMWiRXOEysSGBEvEPkOmw1mDCsL3ghaBaoBsP7x+/333fJs7hLsx+vo6wXsUe188E/1HvpU/nsCmAYsCtgLnQuKCh0JDQd9A5v+u/m09YfyoO/c7Bbrv+rH66jtI/Cm8zP4Qv0gAnwGmQq1DhgSUBRRFYIVOhUFFCESKBD2DnYOzg3IDIsLSgp9CLMFggKK/6X80vj385Lv8+w27A/sJ+xV7W7wFPXQ+Sn+YgLABmkKHQzeC5oKKwkMBz8DE/7D+Jj0fvGF7t3rLOpa6v/rMO708Kf0rfkk//ADDAjeC78PEhPcFGwVTxWyFFATGhEhDx0Otw0pDRYMGQs4Cp8I4gV8AnP/ePyI+HXzuO4F7EHrTeuG69rsMPAa9UX62P5VA8cHfAsvDboMTAt6CfIG4gKD/T74AvS68MntN+vz6WbqM+ys7p3xs/Xj+lIADwXaCFoM6A/OElgUlxQXFFkT9hELEE8OcA18DVINtAzIC3gKlQihBU4CPf/s+6P3VvLG7XzrHuua60Ds9+2r8Zz2xftmAOgEVAl3DGwNTAxiCm4IpwU3AZr7O/ZN8mXvwey66vfpGOtf7RXwcPPN9y/9agKXBvkJNA15EOIS2hPGE1ETfxIAEScP2A2wDcgNaA2fDNgL2wqkCDIFfwFD/rb62vVK8PnrIOoS6ovqoete7gnzkvjH/ZACawfmC7IO4w5DDQILZQi0BFn/aflR9G7wXO2A6s7oFukR6wHuC/Hn9PX5m//YBMsIDAwpD8MRQhNhE7QS7RGlELoOyQy8Cx4M1wwwDR4NtwywC0IJzAVHAur+7Ppx9YrvQete6WLpFOqf66/uP/Op+Pf9PwNoCJgMyg6KDsoMhQqsB64DWP6a+L7z6e/i7HrqWukO6hXsx+7l8fj1M/vUAKgFWwmBDHEP9hFCE08TwRLbEXsQew69DDEMowwiDfgMgwzxC3wKmAezA+//fvw/+NLyjO1X6rfpcOqp6+Lt0vE198L8sgE7BoYKyg3qDrANLwtFCN8EaQDo+nv1JvH87afrDurW6WHrGO5G8cP0EvlH/nUDtwfZCo4NExDIEUcSvBH1EBIQng62DEQLIwsDDOgMSQ1ADbgM7wqoB9EDLQCA/Jf3hPEv7Dfpvehv6dTqme0y8hf4+/1oA30I9gyvD/wPJA5CC/kHtgNZ/mf4FPMi7zrsLuou6crpEOw979Dy3fae+/sA2QWdCVcMfw5sEI4RnBHYEMQPlQ70DCoLPwq8ChYMEA0zDQINewzlCrEHawM6/wf7CPZp8J/rSukv6TDq3evo7vXzHPr8//MERAkPDV4PWg9IDRAKdgYdAqb80fa18RnukOvB6Q3p/um17F/wUfSs+KD96wKFB+AKPg0qD7QQOhGwEH0PSA4PDaILaAoPCuQKLgxFDfANMw5+DfsK+AadAov+NPrE9PLuqOrW6PPoAuoc7ADwb/Vc+8QAuQV3CkYOAhA3D6QMYgmeBdsAOfuQ9e7wku0Z637pNenE6rHtRvEd9Wb5af6GA/kHSAujDXgPtBAlEckQ5A/QDlwNqAtXCg8KCwtkDGwN3Q2RDTQMOAkgBfUA+PyC+AXzjO0B6g7p9+m761LuefLv98/9LgPlBwUM2w6ND/EN3ApcB4wD9f6H+Rf0su/S7APr/+kl6sLrq+468hz2ifqM/5cEsgiHC34NEg83EG4QzQ/MDq8NbAwiC2gK0AoTDFcNIg6MDmgO5wyVCQ0FiwBK/GX3p/FD7P7oYuhP6TLrNO7m8uj44P5NBBUJKw3vD0sQcg47C2kHJwPo/QL4hfI27mfrmenP6Irp2Otg71fzc/cH/P4A2AW4CVYMJg5eDxsQHBBmD38OXg0QDLYK6glmCrYLLQ0WDkMOng2RCxII3QO//677yPYc8T/spumF6a/qkuye7zv06PmD/50EWwlqDc4Pvw+aDWIKzwagAnX9s/dZ8k7ukOvf6UrpDepX7JHvSPNe9+b73gCEBS8J1wvEDUsPMRBDENYPFA8cDtoMkQsdC8IL4AytDeoN2A0fDesKFAd/Akv+Ovpf9drvQ+sx6ZbpHutK7bPwvPWq+0EBIgaTCjoOIRB+D/EMnwntBYYBD/xM9mDxuO0s62zp5OgL6rnsPPD18xb44/wPAs0GWArdDLgO8w9sEPUPGg8iDucMXAv9CcYJ9gqtDPcNlQ6UDrwNSwtCB8cCd/4E+pv0xe6I6vHok+ki61TtFfF59oT8GQLqBm4L8Q5rEG0PmwxFCXYFwAAb+0T1ffDz7G/q6eio6Bzq5+xX8Eb0uPjS/QsDaQe5ChoN+w5bELoQORA5Dx0O0AwpC+0J4QkBC2AMKg2fDeMNRA31CukGfQJk/uv5k/QE7x/rpuno6SPrd+2b8TD3Af1UAioHqQvhDuoPpQ7qC5AIbQRp/7X5SfTq76bsZ+oX6Tzp6erY7W/xY/Xb+bz+lwPAB98KPQ0PDywQgRD/Dx8PBg6UDBML/AkFChoLagyGDUQOgg6bDeAK3gaIAlH+rvkE9F3uT+qu6PvoaOo37aLxXPdM/bQCtQcsDFcPUxARD18M2QiaBIT/4/mQ9Ajwf+wC6pnou+hQ6h3tpfCp9Ev5Pf4iA24H1AqFDY8PuxAREaIQ1w/IDiQNXQsyCmMKmgu/DIoNIQ5eDlYNYAo/Bh8CMP6F+cTzN+6a6j7pc+nF6ortBvJ89xT9egKtB0YMIA+3D2IO2At+CBsE0P42+fvzrO9Y7P/pvujX6GnqRe358DP1yPm3/pkD5wdUC+MN2Q8HEUYRsBCTD0kOnwzWCsQJ6Ak1C5YMmg1mDsEO7Q0WC+cGrwKe/uX55vPo7enpZ+jA6CPq9eym8Yn3jP36AhIIsQzTD60QQA9XDJ0IEwSw/vz4qfNA78/rSun25yroBOoe7dXw+PSG+Xb+WwOdBxMLzw3jDx0RThHFEOcPvg4EDQILuQnfCRQLVgxIDSkOsQ7NDfwKJgd5A93/C/vm9AjvJ+uV6WvpcOo97cDxNveN/MsBJwffC90Oaw8dDqYLHwilA1v+7Pjz83vv4Otn6UrosuhI6hbtt/Ds9J35bP47A4QH8QqfDZAP2BA5EcEQvA9JDo4MwQqoCcsJ8wpeDIgNiw4qD5UOGAw7CDIERwCj+5/1Tu+46q3opujj6Xvs0fBf9jD8uAEHB+gLPQ9DEBgPbgzUCEIE6P5b+Rn0hO+16/fop+fh55LpXewF8GL0Ivka/t4CHQevCoANrA8DEXERDBEXENIOLQ12C2wKeAp8C7YMxg3VDmoPng7kC/IHAQQaAE77RPUy7+Hq0+iI6LXpjewf8a/2VfzmAWMHUAyBD2QQUQ/IDA4JIgR//ur4x/NK74HrzOiG597nnumO7GPwtPRI+e/9jALLBkwKCg0SD2YQ3hBzEHYPKg6tDCwLHgouCk4Lzww5DnoPaxAVEKcNjgk1BWMBBv0L9zDwq+oR6N/nBOlS6y3vr/S/+qYAQwZhC00P7RA2ELMN/wlYBcz/8vll9HzvautW6LTm2eaV6InrN++L8274pv3AAiUHsAqEDcUPUBHFER0R5w99DtMMBwugCXIJjAojDJsN3w7rD94P1w0kCgMGHAKP/ZD38vCi69XoIujM6P/qDu+X9HD6AQBuBZkKiw5EEK0PZg36CXMFBgBV+vv0TfBD7CTpW+c757voc+sF70Dz+PcF/ewBVgYECu4MPQ+vEDURxxC7D4QOBw1xC0EKAgoEC6gMTQ6sD5AQcRBhDpkKRwYhArH95Pc18ZrreOjQ55Totep77rXzlvk5/7sE9gkIDgYQog+NDUYK7gWpAOn6bfWW8ITsZ+l25zvnj+gj66vu1/Kl96z8jgHsBY0JmAzkDl4Q+xC7EP4P0Q5IDasLZAopCv4KbQwSDpcPyBDLEPYOhQtYBzUDfv6M+O7xJuym6Gvn6ecE6r3t4/Kc+G3+OQSNCawNyA/DDxIO7Qp+BhIBUfvb9fLwuuxz6XrnJOdX6MrqSO6C8k/3a/xdAdsFkgmeDPoOexAlEdQQ2w93DtkMRwsBCrMJiAofDP4Nyg9KEccRaxAQDbIIQASC/5f5oPJX7Fjo4OYd57boAOwK8Rj3Sf0eA4cI9QygDzYQ3w4CDM4HkALM/BT38/GD7QPqvOfz5sjn8OkX7R7xvvXd+vX/mwSpCPYLvA6rELARqBHDEH4P7g1IDMMK2AkeCmYLOQ0eD7QQihHJEBoOKArkBWEB1vsX9Xvuwel85wjn7+eR6i3v9PT5+p0A9wXNCjUOkA+2DjgMeAiuAzX+evgd827us+pC6EXn0efE6cbsw/B79an62f+TBJUIygtXDjkQYRGVEdkQew/XDTAMnwqjCcEJEQsNDfMOkBCSEUsRHQ9PC/wGbgIU/W/2h+9c6o7npuYP5ybpi+1g84P5O/+lBN4J0Q2lDykP4gx8Cd0ESf9f+czzJu9e66boVeeE51LpNOwe8Mj00fn5/pADlQfuCqsN1A8bEXkR8RDTD3oO6wxjC1QKXAqQC1INSg8dEWkSXRIQEB0Mkgf4Aqn96/bP7w/qt+aE5eblJehc7PrxDvjk/acDCQk4DWsPWQ+QDVMK7QWqAP36o/XI8KbsjenY59LnRunh63Xv3fP8+EL+JgNgB8YKjQ2dD+EQSxHTEL0PNg5wDKEKVQk9CXQKfwy9DtIQYxLBEiQR1g24CSgFsP/K+ITxdOuD55HlLuW05lzqn++Y9aH7yAGnB10MFQ+6D58O/Qv4B7kC3/wM977xQ+3b6eTnXedQ6JTqC+548nL3mvyOAQAG0wn0DGUPLREXEhsSVxEZEH8OqwxBC9MKmAsMDZsORRDMEVISshDuDHcIGwRp/0v5GfLT66fnquUt5VPmsenB7qD0Y/oJALsFsAr/DSEPTg4BDGQIvgN2/hf5B/R177frQela6Ajp+erj7bHxQPY++00A5gTbCCYMwg60ELkR7hFqEVYQxQ6wDM8K7QlxCvkLww2nD1sRGhL4EPkNQQphBskBkvtM9NLtR+mb5ljl2OWm6ErtyfJf+DP+IwRlCegMSA7rDf4LtwgvBOn+mfly9OjvJ+ya6Yzozuhw6jntCvHN9dv6+f+uBLcISgwwD34RyxLyEl8SDxFQDyoNKwtOCp8K1wtbDQ8P7xDXEdwQzg3sCRQGggGP+330F+5q6Vvm5+Rl5WzoM+2W8gz4wv3OAywJxwxcDiEOWwwPCWkEK//k+fb0fPCi7PvpsOjs6JLqZO1H8dn1z/rM/1wEeAjtC8IO5xAREl8SxBGcEAMPCA1iC4oK4gocDLYNrA+MEYQSihGgDuMK7wZdAkv8G/Vb7iLprOXc4w3kn+YP63vwQPZc/JwCQAhVDIwO6w54DWwK+gXdAI/7WfaW8Xztmur+6NPoLerZ7KfwJ/Uc+jz/EwRvCBoMJA95EdgSGhNmEh0RSg8ZDRILAwozCkILxwykDqoQyBHXEOkNJwpmBvkB+Pvd9D3uO+ng5RPkWuQt5/zrpfFx94b9twNPCT8NIA8yD4ANPQqDBfD/V/oR9YXws+zw6aToz+iG6l7tJ/HB9cT65v+cBLIIMwwSD0MRfRKuEhUS6xBVD1YNfAuRCucKNwzZDZcPPxH6EdYQ1g38CQAGawFK+wj0TO1M6BnlYuOo44LmdetP8R33Ef1GAw8JMw0bDx0PiA14CuQFXQDD+pX1EvEk7TTqvej46Mfqt+2Q8Tf2ZfutAHUFlwklDSgQXBJMEyMTPBLkEAkPtQzICv4JhArHC0INBQ+jEBgRbg8hDJUIAQWAAEP6EfPI7DfoQ+Xi46Xk+efY7DjyxPe//SsE2Qm2DWUPEw8gDYMJrAQu/635lvTt7wPsQeki6Mfo5uo47mvyYfez/OkBpAbGCmIOORH5EpoTRxNYEvEQ9w68DPkKYAr+ClQM+A2dD8MQhxBsDhgLewepA8r+OvgL8c/qceb/41/j/uSx6KvtJvPt+F3/4wVYC78O4Q8wD9EM+wgABIL+JPkH9GDvcuvF6NHnn+jz6n3u4PLh9yr9bwJiB9ULnQ9yEg8UeBT/E9ISGhHPDogMMwsWC+sLEA2GDiUQGRFpEKwN7gkOBsABL/wz9UTurej05AnjK+Ov5SXqi+8h9Q37dQGtB44MOA+6D1oOWgsAB7EBJfzG9tjxg+0H6g7o5OeR6bTs0/Cw9Qb7jQDUBXgKgw7CEfcT4xR2FEoTjhFrDxcNAAsSCksKZwv5DLIOaBDwEKQPqQwCCUEFjQBF+t/y7OvD5orjReI+43vmhOsu8Rj3eP0+BHUKqg5sEAgQ/Q16Co8Fv//d+Xv0oO91617oE+fY51Hq+e1d8nz3E/2WAp4H9Au0D6cSXRS9FAYU1RI5ERUPxAwhC+EKvgsZDY4ODBAHEXkQJw69ChUH7QJP/V72S+986VXl1uJ24pHk1ugk7rfzw/lTANMG2gu1DnAPSw6ZC1AHFQJ+/P/2B/KU7THqTugj6MLpzOzr8L315Po3AEEF3QnxDTYRkxOgFIEUkRP/EQUQpQ26C+cKNgtJDIENBw+CEAIRng+FDO8IOwWqAGL6BvNI7Dnn9+N54ibjReb26krwB/Zf/B4DCAn+DMkOpg7mDIMJywR8/xr6/PRG8EXsm+l36A3pN+ux7i3zI/hf/ZICngdFDBgQ+BKdFA0VgRQnE1wRJw/rDFkL8Qq9Cx8Nqw4aENoQORDaDXEKvgZ4Asj8lfVV7onoueSw4oviqOTY6DLu+PM1+tkAJAf6C6gOTA8SDjYL9QbMAWT8+PbK8UPt8Ok+6CXotOmr7M3wp/Xy+nIA0QXDCtYO8BH8E/cU6hT/E3USVxDoDeILEAuYC7AMxQ3iDvwPcRAvDwsMIQgkBHr/Uvkx8r/rJeeH5HfjP+Qr58HrOvEG90T9nwPxCG4MuA1bDaALaQgFBLf+Svk09MPvS+zd6droY+lx69HuG/Md+In9+QIWCIcMUhBDEyMV5BWPFYQUvxJWEPQNcAw4DN0Mtw2JDmgPug+NDtgLbgj+BN4AJfs29KDtuuiG5cbjwOPs5QjqJ+/F9ND6GgHLBt0KCQ1dDRwMXQlJBWkAJvsO9oHxvu0a66rpx+l364buevIB9xr8bgGnBkwLJw9BEl8UZhVGFVAUzhKjECkOIAx1CxgMFQ31DaIOOg8SDzUNBgpyBqcCvv0a9x/we+rc5tzkJeR65QLp9u1f8+D42f6iBE0J+gukDM8LdQnfBVMBavyC98ryxO7M6zfq8ukB62/tJfHF9c/6/P83BTkKjQ7/EV0UsRXlFREVeRNLEdkOuQyoC9oLrAxuDSAO3Q4pD90NtQrKBvYCsf7m+P3xs+tz50HlZ+Qo5ebnS+yG8Qn3Bf0kA1AIvQs3DR0Ndws5CNoD4v7K+bH0+e9Z7BPqJOl16V7r7O6e89P4Jf6cA/AIqg1mERcUvhVAFrwVYRRbEq8P/wxbCy8L/Au+DFMNFA6XDtcNUgsMCOgESAHV+9H0Ru6h6dHmCuWQ5EfmCOro7in07vkjAMgFyQnsC4cMnQsVCTMFjACZ+5L20fH/7ZPrauqG6hbsVO+887H42v0fA2sI7AxxEOgSdhQcFWMU1RLLEIMONgxzCj8KSAuvDLQNfw5vD3sPeg3DCccF5AHh/PL1ue596bXmSuWt5Pbl3ulK79n0N/oVAAEGgArNDCINSgwwCnkGqAF1/Gb3lPJO7kfrsemD6aTqGe0A8bD1y/ooAJgFwArcDugRERRLFXMVahSuEoYQGw4TDBwLcQs1DLwMUw08DsoOgA1jCtAGjAOh/7j5zvL77GbpWuf05fnlJug27PLw0fVO+yMBJQZKCbUKBgtBCv8HTATT/0T77vbb8mjv2+xt61frtuyS73Pz9vf6/DcCXAfgC5kPlxKvFJsVZBVRFKwSdhAFDkkM2AtZDMAMvwzhDBENgQxECs8GZQPp/137UvU07+vqqOiL5yPnUui163TwafUw+lD/pAS/CNAK8gr+CTIIGQXFANz7Xvd08wPwQO2S63jrvOwi72HyffZd+3kAjAUDCugNOxGwEzUVYRWVFCITKBH5DvwMDgwrDJIM0QzkDDQN/wwqC/oHlASWAbz93vdD8VHs7Om56KfnsudJ6tzutPMI+Lz8JQLJBlEJ1AmHCcYIjwaeAtP9bvm/9TXy5+5t7Gjr5uuB7UXwDPS8+AP+QQNBCLUMbxBxE1kVIxa1FVkUbRIGEPYNuAxyDIYMWQxmDLYMvwxACzgIDAU/At7+gfkX8/ntO+vf6YDo++fH6eHtqPLw9lP7XwAqBVEIewmLCfwIKwfDA1L/APsq927z4+8B7Yjrl+vk7EvvtvIh90P8fQGJBgoL+g5HEpQUzBXWFfsUXxMZEesOfA0jDTANzQxgDGYMsAzXCw8JkwWoAgEAyPuJ9XPvnesR6grpA+iO6JzrYPD/9CT5uv3TAhUHPAmSCQUJ5gd5BZcBEv3U+DT1y/HE7qLs9+vV7M7uw/GY9U/6hP+jBIQJzA19EUMU1BU8FpkVRRQwEsMPrQ18DC4MEQzqC+kLDQyCC38JeAZ3A6EAvPwX9x3x0ezE6rXpvuj26I7rCPDa9Bv5pf2VAuYGUQnKCZEJlgheBocC7f3X+fn1Q/Ki7hDsO+uz62Dt9O++83r4nv37AiUI1QyJECATuRR1FVIVMBQ/Et4P6w34DPcMSg06DSUNPQ0dDbkLpAgWBbsBG/4l+QTzz+2c6gPp/efD56npc+0Z8qn2RPtaAMgEuwcFCUwJ9wg5ByUELgAb/C34IfSl8AHuiOwy7OvsE+9j8pX2TvtSAJcFUgpwDssRUhTpFSQWdBXoE9ERrw8TDqMNmQ1MDakMQww0DAsLIwhTBBkBT/5E+pb0/+6K60XquelX6TfqMu288X/28/p2/84DMwcSCX8JCAmiBxEFcQFD/RX5MPWu8cTuxOzu61/sDu7Q8Gr0yfjH/f4CIgirDHcQiBOOFYwWeRZ9FboTbxFmD0UO8w2XDb8M/QujCxELEwm6BWICWf+8+4r21/Co7IbqiOm46CLp2ute8Ff1yvlN/t8CwwYNCacJcwlBCPQFgAJo/lv6LPZF8vfuzuzS67rryOwa763y//ag+7cADgY2C6IPDBODFfcWWBeXFtoUbBIVEJgO9g2EDZsMyguXC38LEwrMBisDOgBa/Rf5avNc7krr7Okw6SLpw+ol7pPyR/cU/NsA2AStB1wJCQqkCcoHzwRBAUn9Cvms9AbxU+5l7FfraOsf7RXwyPMa+P38dgLLB60MyBDvEywWUBebF7UWohQeEjgQfA/uDs0NfAziC8ELkgq1Bz0EcgHq/hb7wPWi8CXtO+vo6SLp4elh7C3wafTr+LL9IAKuBQEIUQmnCbgIsga5AxYA9Pua97/zivAO7jzsWesF7PPt6/CS9Pj4Pv7AAxYJzA3iEU8Vsxf1GL4YNxfnFKYSIhHwD5kO7wyjC/cK8Am/B5wE0gF9/3r88/et8rvulexq627qHOqm6+ruLvOI9937LADVA4sGOwgwCSMJogcRBc0BTf5q+mD2yPLz79ztJOxT6wzsXe608XT14vkW/70EGwrRDuYSFxY2GAEZiRgWFw8VKRPWEaMQ7w4MDdgLUwsOCiQHZgOKAF3+BPv69bfwdO0M7PbqCeo36qzsi/Cs9Mz44vwOAX4ECwerCBwJRwg1BpcDlwD6/Of4/vTv8ZPviO0x7B7spO0i8C/zA/fH+zsBmgaEC+YPtRO3FooYIRlFGFUWPBS4ErkRLhAZDl4Mkwu9CkYIqgSQAb7/hv08+dzzm++c7ZbsXOuZ6pPrr+6m8qb2lPqK/nACnAXSB8sIighxB48F+QJh/yT7K/fr80fxuO6U7KrrOewW7qjwBfRF+B79QAJgB3cMBxGpFEgXyxgnGTIYTxaVFGkTLRIkEMANMQw7C6sJtQYuA5wAn/61+zL3avIh71PtKewB67/qXuyX74TzS/cT+wL/5AI2BjoI4whxCF8HngXWAiH/9/om9/bzCvFO7kPskuto7Fru9vBM9Hz4hP32AlwIaQ23ETEVshcIGR0Z1Bf/FWUUGROkEX0Pdw0iDAkLDwnHBbQCjwCF/gX77fVg8Y/uD+2164/qCOtV7dXwb/QV+D78gABIBPoGnQgjCY4INgcTBSkCSP78+Rf21fL87y/tRevv6ibsbO4s8eX0l/kK/6IE8AkJD0sTZxY2GOgYrxhSF4IV3ROKEgURBA9RDVAMUAv0CH0FkQK1AIL+Sfrp9KHwTO7U7CDr/um56mft//CY9H34xfz8AK0EigdkCeIJFQmEB2YFigKG/uL5tPVC8lfvmeyg6k7qe+vr7fjw1PSv+S3/GwXDCt0P3hOvFq8YmBksGWoXJhV8EzwSrxCfDtwM+QvhCnoIJgWHAuwAiP4o+sv0x/C17irtVesv6inr8+1m8cf0Y/iv/BgB4QSAB/gIYgnFCFgH7gSIAV/9H/la9drxk+7P61vqpOoz7JTuo/HE9c36SwDeBUMLVhBoFBMXbhjMGFMY5BbzFCMTqREvEHcOFw03DP8KlAh4BTYDmQHY/uv5WfSY8JnusOxH6hnpsury7TbxUvRT+IH9XQK+BfAHfAlMCooJVQdgBOYABf2/+Kr0FPHo7XnrM+q26mvsye7k8QH2LvukAB0GcAtuEGcUwxb7F3QYJRi0FpAUzBKZEVIQow4rDWcMegsnCeMFOwNlAaD+sfkJ9PDvxe366+Xp8+h16rftQ/Gn9L/4vv2UAkIGmwgQCnoKnQmQB5sECQHU/Hj4Z/Sx8HTt9er56a3qfuwE7zfylfbF+ysBhwbOC7wQeBSeFn4XtRdMF/kVFRRaEiYRCBDGDrcN4Qy3C3wJwwaJBHoCGf/T+Wr0jfAn7uXrjumy6DTqNO1u8O/zlPjr/a4CCwaKCIEKUAthCtcHtwQ9ATn9vPhY9JTwcO0c6+zpU+r/623um/HD9c36VQD5BXULZhAWFEoWjhdRGDgYyhaZFK0SbhE3EI4OKg1sDJgLcwlEBr4D+gFz/8r6RvU88e7uC+256nrpoOpv7Yvwn/OX94L8PgHXBHIHcwl9CvsJDwhgBSkCUf4M+tr1+/Fu7pXrDOor6o/rs+2v8NH0xPkC/1sE8wldD4YT+BUmF9UX1heYFnsUfRI8ERgQkw4/DY8M7AspCogHWwXFAzwBfPzp9rvyNPDj7SHrgulI6rjsj++E8qD2vPuGABsEvgb/CEoKBQpFCKUFjQK//nf6MPYw8pjusOsf6hjqTOtq7WXwefRp+bH+MATkCTUPLRNZFXYWMxdiF1QWAxSyEUQQUQ8YDr8MIAz5CxELtQgTBmwEswIk/535ZPQT8fHuqux06gvqxOud7qnxRfX5+fD+BQPtBRgIpQkKCvAIlwZlA53/ifuK97Dz9+/U7OLqhepc6wrtoe9g8+f3nvySAf4GkgwbEc8TGhX5FagWOhaHFHIS4RDTD4YONw2UDIMM5wviCWsHjwXTA7gA3Pvj9hrzRfCs7XXrxOrQ6+Pti/Af9MT4nv2jAa0EFAfTCIAJzgjsBh4EgwB//Hb4q/Qc8f7t4OsN62vryewa75Ly2vaD+1wAYAV8Cg0PZhJTFFAVyRWUFWAUhxLUELsP3A7WDQkNzAyJDA4LcQgJBl4EMQId/sr4MPQY8anuOOyy6hnrLO3W7+Ly3faY+y4AyANrBlUIWAlGCewHeQUQAhL+FvpB9oHyDu+B7FTrWutM7CzuP/FH9bz5Sv4jA00ILA3vEDETeRQ0FWgVqhTwEicR+Q83D08OTg3hDNoMKAwRCmoHhwXDA3QAL/vG9Q7yju8l7d/qJeqf6z7uTvHt9Hb5X/6YAswFGwilCRcKJAn4BrsDyv+K+1f3SPN071nsguoi6uXqguxR70/zHvgK/d8B9QYjDLYQlxO6FP0UDRW2FDsTBRE/D3oOHA6ADQQNIg00DfcLhgk+B48F+wIy/mb4wvO/8BXuPOuD6Rnqieyi7/3yNfc3/AMBvARPBwYJ9wnTCVIIYwVzATD9Gfk29VPx2+1/64PqyOrb6wLuYfGx9Xv6V/94BKoJbg4FEhoUHxV+FVYVTxROEjAQ2Q5IDtMNHA2sDLIMRAx5Ct0HtQXMA6YAmPsX9hDye+827Q3rR+qo64/u8fGZ9QP61f4gA0cGWAirCQcKEwmPBtEClf5j+nD2cfKE7oLrDOol6h3r3ezS7yb0Ofkf/tYCwgfkDEcR4BPRFP8UEBWAFLwSRBBoDs8Nkw0NDaEM7AxdDV8MBAqmB/AFngMU/yL57/N58N3tPeuA6dDpBew67/nyhfe6/IMBSAXrB8YJugo1CjoI3ASpACr8p/dq81Tv7+vA6STp1Olo6xvu/PHO9sr7rgCxBdAKiQ/cEpYUHxUXFZ4UTBNKEU8PFw6gDV8NPw2dDSkOvg3YC10JdQeBBcQB7Pvo9ZbxoO7M60zpgegf6iPtrfAI9VX6yP8XBA4HTwnvCloL4wnNBsMCM/6F+eX0pPAG7VHqCukS6Vbqquwk8LT0oPmL/mwDmAi1Da4R6hOvFOYU2hQYFEkSABBMDo8Nbg1aDXkN+Q0vDlMNTQsqCT4HcwTD/7H5Q/RG8BvtRuph6J3ouOoE7iryQPfh/OkB2wXTCAYLFAyAC1oJ6QV3AW78iPcs8zjvlevj6OLnm+h16kPtF/Hf9R/7TACHBc0KfA/ZEosUQRVqFQoV0hOYEV8P9w2PDW0NGw1LDecNKw7eDEgKBAgJBg8D1/2Z95nyLO+D7P3poehs6evrY++h8874P/7SAjkG3QjqCr0L4ApPCIwECwAx+4D2CfLv7WrqKui154zoh+qF7avxz/Yh/IwB5QYtDM8Q2hNhFbUVhxXiFEMTCBHwDuMNnA1JDRUNaA02DvYN3ws+CWsH0QUWAuP7vvXD8XLvv+zy6Q/p0eoc7n/xdfV5+sv/NQQWBz4JswoCC78J3gb1Ajr+Rfmt9Jbw+Ozf6Rfo+edN6aTr9e5a83f4u/3ZAgUIFQ1UEeoT3hQZFRYVjRToEnoQgg7QDc0NmA0tDU4N5A1gDR8LUQh3Br8E6gDn+hL1qfG+71DtwOr+6TDs5+9t8xH3f/uJALgEYwcLCfwJGwqjCK4FrgEQ/Zn4VfRY8Ivsg+kP6E3o3OkA7A3vXfOY+Aj+BQP/B+kMLRHIE4UUuBTJFG0UxRIwEIQOJw5YDuANHA1LDcAN+gxoCrkHRgZdBB0A5Pm09CXyifAT7n3rMeu57WXxwfQw+Iv8PgEXBWwH2gjCCakJAQiRBD0AzvuX95HzSu9y68bo6OeW6O7pD+xJ79DzIvlR/lMDQggSDSkRfRNwFLQUvhRLFH8SNRCgDk4OZw7NDUgNew3MDbYMEwrdB3YGPgRc/zD5zvSP8rTwue1f69Drmu4K8gr1uvhz/QwCcAWDByAJIArDCaQHGgQEAMz7j/dN8x3vdesJ6RXod+jE6fzrau/X8+H4+P0IAzAI5gywEPsSBhSLFJMU+BNEEhsQtQ5HDkoOzA1NDX0NmA14DN8JfwcGBsoDU/9q+f300PIY8ZbuWOyu7HDvwfLI9TP5vf02AnIFZwfMCMUJbQlOB74Do/9s+y/38PK97jzr3+gL6HPox+ke7H3v/fPy+Oj99AIJCOwMlRC1EtITeRTBFOwTDhIGEOQOlg4aDmUNKw2qDaYN9QtiCZQHcwbtA+T+Pfl19YfzavF57rHsf+0n8Arz1fWk+UH+gwJpBWwHPQkoCpQJKQfGA/T/rPtg9+ny1+5W69Ho2+cc6I/p4etD77Tzjfi2/e4CLQjmDFkQcRKLE0kUhRSzE7YRgg9bDjQOJQ5nDc0MHw18DX8M6gmQBxEG2QOD/7v5fPVw88fxVu8n7ZXtR/B683T2uvkV/kICUQViB+II7QlWCQMHoQPK/9r7dfcK89buNuu46Ifn1+cr6Vbrl+7o8iX4YP2FArMHggxkEJUSthNUFIwU2hPIEawPew49Du4NCw23DDwNfw0SDHgJqwexBooEx/8k+ov2AfUu8yLw2O1u7ivx8vNL9mX5sv37AfYE7QaKCJQJMgkXB+QDLgAU/Mr3l/OR7+/rEemf593nOulq60TuNvIb91f8tgGxBlALEw+UERsTxxP1E08TuBHND2EOCg7pDWsN7gwPDaIN4QxxCuIHgQYaBVMBnfvJ9rX0uPN68cvuPu628A70x/Z6+TX9nQHYBMMGKAg/CSwJ+QadA/v/Qfwl+I7zU+/X61zpEujm5xfpPutE7iTyu/bi+x0BMAa6CmwO/hCPEnQTtBMjE4oRlg86DtcNwg0kDZkM0gx9DSYN8AqBCBMH1QWgAiv9Wvjj9aj0fPKG73PuP/CH82b2z/gz/EsA6gOEBhwILQnyCDIHagTrACj9yfhE9CzwnOzi6QDorOe36NrqwO1I8dL12/osADcFxAmoDWIQIxIEE1oT8hJ4EXcP1w1SDTkNzAxoDLIMbQ0wDV8LQQkaCOAGowNO/nr55vY79bnyoO9k7tzvePIk9fD3xvvq/ygDwAUCCOAJ7wnjB/EEvgFb/uL53PRG8HHsj+l45+Pm7ucB6unsd/Al9Yj65v/uBHMJfw1uECES4hIhE+sSiBFND0sNpAziDMcMRQweDA8NoQ2VDI4KxQiEB9QERgBj+973ufUc8zPwje4r73Hx4PPo9rj61/5mAjUF6gfiCUcKpQj3BRIDg//9+tn1Q/Fp7Rvqq+e35oPnUem66xnvqfMX+Wz+PgPvB1AMzA/XEbUSHRPyEvQRABANDiMN1QyXDBIMJQwtDaYNtAx3CsUI4AfWBbcBZvyO+GT2RvSZ8UfvKu/q8HXzefYA+vD9VQEdBNcGLAkTCtAIVAagA3MANvwm95Ly7e7J6/voUeew54HpF+z77qfyafef/OQBrQbwCk4OiBAAErgSwhKzEcEP3w27DIsMYAwHDPsLrwyfDS4NYAtfCTYI2wZmA0/+uvk695/1G/Nm8GvvBPHH83X2afnl/IgAYQOdBboH5ghZCP4FKQNZALL8GfhQ85jvx+w46n7oUej/6YnsHO9h8rP2yPu9AAQFDwmHDCAPsRBMEYoRBBHND0MOBw2rDIIMdgx2DPoM1g21DT0M/AlQCOQGJwSo/8D6qfcC9jT0t/EV8Arxv/Oa9ir5L/zb/xwDSAW3BscHzAcrBloDRQD+/P74kvS28OjtpOuh6dvo6ul57EfvA/KQ9RP6G/+UA2cH6AqzDaYPixD2EPUQBBBzDu0MigzXDNYMnQzRDO4NdQ4hDZUKbgg5ByIF/wDo+y/4fPYA9dny/PAs8WTzPvYY+Q/8af9ZAogESQZ6B5oHCgaRA+YAyv3N+Sv1R/GO7knsHOqx6HHp3uvQ7p3xvvT/+Lz9YwJ8BhQKGQ0FDwsQmhDKED0Qtw4XDWEMggygDEsMbwxqDTsOdw36CtUItQczBqgCdf1Z+Tn3EfYm9OTxa/Hp8qn1Wfga+2v+XQHdA30FuwZLB0UGOQRjAW/+xPp19qHyku9W7RLrbOli6Snr/u2s8JXzMPe/+6MABQXkCOoLMQ6lD5AQORH6EMwPBg7mDPoMMg0YDa4MCQ3yDbYN0gsMCUoHDwZXA8z+6vko9wz2xvTr8pjxiPIg9SL4Avuu/XwAzQLFBFkG6wYtBgIEiwEF/9P7zfd882rwQu457FzqjOnc6lHt9++Z8sb1JfrO/k8DQgeMCjoN6Q4xEAQRNBFwEOYO/A30DTsOBg55DbUNeg5QDh4M+gjzBtYFmQMP/wn6IfdS9nT1gfPz8aryN/Ui+Kz6SP05AJwCTgSABUUG/gUZBJEB7/4t/Jb4ifRc8RfvUe1s607qIus47bnv5vGU9Hj4Bf2bAXEF3wjTCycOGxBNEdsRWREoEDQP0Q79DogOxg12Db0Nzw3lC94IOAbfBFoDqf8X+5z3kvY79sf0UPNM86f1mvj0+kT9of8LAqwDtASIBU0FyAMfAY7+MvwJ+Sr1ivFT7+/tdew360frPe3J7yfyhPTN9zr8qwCtBNoHqAojDQ4PtxB5EVgRXRA/DxQPOw8bDzkOUQ1sDYYNYwx3CUgGfQQEA3MAPvxF+KL2O/a09WH0xPNP9f/39/pV/Xn/mQEwA7kEngWUBQIEQwHX/nT8q/nc9fnxiO8U7vjsrutY68LsPu/48Yf0h/dX+5f/xgNvB1IKogxpDhgQcBGlEc4Qaw/FDvgO6g5NDkkNHQ1kDWMM6gmcBpEEZQMjAYX9SvkT96/2Zfau9b/0kvWn91z6P/1h/0cBWAJnA9oEbwWfBOMBC//s/Jb6dvdg8y7wY+4k7RTsQOsa7C3uxfB480D28vkT/mkCUgaQCScMBw7eD1IRFBKLEQcQAA+9DgQPdQ5FDcAMowxCDA4K7gahBFQD+QGv/rH68vcw91/3rfbF9az1Nffq+Zz8/f6OALUB8QJOBGMFyQSbAtj/ff1m+1v4pfQt8ePuk+1L7HnrqOt47Tvw9/LX9fz4//xyAYsF+AhPC0YNAA+GEJcRIRHvD6gOQA6sDmgOrA3HDJsMjgwFC1YIhgXLA1UCjv/h+6D4R/ck98f2JPa99c32E/ng+4f+TwCcAakCFwQ/BRUFOANkAAr+xfsX+Y71B/Ko7xfuGe0w7B3sd+2273byFvUe+Nn79v8wBJ8HYgp3DDYO7Q/mEAkR+Q+3DjMOMA5dDqgN3AyvDIYMpAsFCTkGXATeArkA/fzC+SD44/e394r2EPbO9uD4dft0/VL/owDmAVQDTgSiBDID5QCx/m78JPqz9jXzmPDS7untwOxa7F7tXO8k8o70XffU+r7+5QIwBiIJfQt8DUwPOBCTEAAQCQ9iDgoOEQ6tDQ4NzQyPDNMLtwntBroECwM3AR7+ufqt+N/3ovfc9hX2kPYn+H36qfyK/nsA2gEmAxMEaQT2A/ABmv8X/aT6C/iW9MLxbu/97RrtZOwY7Zfu9PCA8xH2o/mC/cABfwVhCAcLCA36DkkQuBBfECQPPQ7VDfMNsQ3BDCQM3QvAC0UKiwfxBC0DFAKz/4H8qPl8+Jz4Cvg796z2uff0+Rn8Ov6J/9wAEQJQA4AEMAS7AiwA7P0X/JL5Yfay8iDwn+6F7bXsduyX7cbvXPIl9Sr48/sNAAUEgQcpCm8MMQ7FD7IQhhCODzAOqg2nDYsN8AwhDCoMJQwYC6EIsgURBNUC2ACD/R76wPiE+Hf4e/eB9j73Dvmq+7n9Qf+yAOYBbgN8BJ4EYQP5AL/+q/xj+kv3rPPP8PPu7u3z7FHs8eza7rbxX/Qn93D6kv7hAmUGNgk4C08NGA9AEIcQmw+FDn4NKg0wDaYMAQx7C7sLrAvyCVAH1ATTA7wCBgBm/Hv5BvlF+aH4MPd09iD4pvr3/GL+XP/8AJACBQRfBIED3QGW/6n9UfuI+Ev1H/Im8J7ujO2/7LnsUO6D8Bzzu/XF+N/88QDVBMEH+QlZDBcOhA/tD2IPlg5RDeUMxgyIDDMMdgvdCxkMPAsPCRoGvQSgA7sBdf7O+mv5E/no+OX3vvZj9wf5VftO/bP+UgCNAfcCEgQ+BIoDZwEo/+r8bPqj9zT0Y/FI78/t/eyU7HHtUu+o8W30Rvf4+gX/sgIKBnMI3wrfDFAOZg9bDwQP4Q31DNEMhgxYDH4LRgu/C4YLJQpXB10FgQRiA9oAAP1w+rr5o/nR+DX3rfbA9+n58/t8/fD+ZQACAkgDPQQdBNYCzQBj/lP8nvmU9mvzwvAN74Dt5Owb7WHuhfCa8l71lviT/KEA2wPOBkQJsAuNDZkOHw/lDl8Ogg3BDHIMIwzbC5ELtwv4CwEL0ghMBvcEUwR8Agj/VvvY+RD68vlk+HP2lfar+Hb7PP36/Qv/mQC9AiIENgQkAxoBcP+v/Zz7zfg39WjyWvA972vuvO0h7mbvrvFC9Az3U/rX/WYBjwRZB+MJ9wt7DWEO2A7XDiMOLQ1pDEQMHQysC1ALUguLC2IKFgiwBVkEsAN1ARP+8/r5+Vb6oPn693n2Ofdx+Xj72Pye/TD/FQHaAt4DmAOlAt8AHf83/a/6tfdQ9MzxF/Dr7i3uue2v7ofw/fKV9TT4uvtS/9UCrAUACIoKaAzrDYEOjg5yDocN1wwNDKYLaQvECugKNQs/C/wJPgd2BZkEswMyAQn9hfrN+Qz6V/kd92z2bffi+SP8Gf1x/qf/eQH6AncDgwP2ATgAL/42/Ej6VPdt9KPx7u8k767u2O6S7zjxlvMw9lv5w/w3AIUDAwabCOcK1QwoDk0OSQ7MDU0N4gxBDNILJAvICisLcgvpCr8IDgbABAYEyQKF/7D7+fnI+Rb6qfi79n/2Cfiv+lD8bP22/joAGgJHA98DdQPoAQAA4v31+535sfbE81Lx3O/l7pTu/e4+8BXyFfS+9vb50v1SARkEpAbwCIkLZw11DrAOMg7hDSINqgwrDFkLxAoxCr0KMQtWCiIIWAU+BLoDJwLH/t76X/mk+cv5eviF9oH2hPge+wz95/31/moAMAK2AwgEQwNYAXf/5v0W/KH5TfZt81vxOfB17w/vlu+v8IzymvRV98X6Gv5vAREE1wacCdwLmQ1TDr0OiA73DSsNdwzzCyELYwr3CY8KxAqRCRQHxwQvBIMDcAG8/ZH6zvn7+Yb52veF9iP3EvlI++78/f1W/6QAQwK2A+UDAQPqADb/3f3b+zP5wPVI86HxSfBx7//u4+9e8RTzQfXs94v76f7bAasERgcQCvELUg1SDqAOkA5rDbYMXQzgC0YLCgowCs0KsAoOCRQG3ARUBGEDcQBJ/G36FfpD+sb4Yfb49Ur38/nD+5L84v1z/6oBjgM4BPgDagL3AMD/C/7I+0T4OPU1883x6fCH7zjv7O9/8bHznvV4+JH7/f5EAhcF/wdOCjoMgA1fDtQOdA57DZwMIgzYCxsLRQpOCpsKaApbCMgFYQSrA5ECMf+G+4/5b/ms+Tb4W/bs9Yb3T/ps/H39fv7W/x0CKgTdBDEEIgKpAHP/9f15+5D3e/R38pXx0fB87zrvBPAb8n30n/Zf+W/8EABlAyMGogicCm4MuQ16DncOiw2VDPULBAyXC5gK9wklCi8Ldwo0CIEF/QPOA90Bcf5q+pb46vij+Hr3ivW29QH41Po3/Rr+Rv8FAUADJAU5BRAEGwKBAEr/Tv1M+rf2zPM98j3xXvCE72LvpfCP8uz0WPcl+o396AAsBNAGNwk8C7cMxA0lDiEOaQ2kDAQMpgszC2IKMgpyCtcKyQlqB18FOARkA8EA2PyH+Wz4lvjX9zH29fQd9rH4Vvsl/U3+BgAFAh8ESAUKBcsD+AFuAN/+iPx5+Rn2h/Mb8gDxP/CP7/XvhfFP8671E/hB+8b+1QHaBEwH5wnnCwoNug2vDaAN8gw2DOILfQsYC1YKZwohCwgLUwlJBn0E4gO7Anv/wPo4+O33nfjD95j1RPXl9jz6pfza/Rf/YQDHAoUETwW8BL8CJAF///r9d/vT97z0mPLS8TnxVPAT8Mrwn/IJ9UP30vmZ/Lz/FQPOBWQINAq3CxINzA0uDkoNWQy1C44LhguKCgIKIQriCokKTwjYBTkEggNfAYX9uvkN+Cr43Pdx9jL17/VU+AP7wvw0/sr/hAFOA2AE3wTwAyYCWgDJ/jz9bfr49hX0gvL/8WDxpfCv8NjxI/Sd9tf4XPv3/SkBRATmBi0JYQqvC9oMtA3hDY0MqQtkC7ULqAtRCrwJ/AmVCrQJ5Aa4BJsD1wInAOP75/j890H4RfeH9f70f/Zx+ZT7IP1s/j0AawLOA7gEKwTfAiUBZv8X/q37yvi89d7zXPOx8hbyivFP8kL0MvYY+Pz5r/zn/8QCRAVTBzAJAwtDDBkNKA2SDAYMgQuLCxELOwrFCeYJnQrYCeIHigUcBFMDDAGI/df5MPgA+Jv3a/Yv9e/1A/ij+nf8x/1n/wABzgLAAwgEVwPYAUgAl/4D/br6JvjC9Uz0m/P/8r/y3vIA9J31Vfdy+cr7u/6NAeQDMwYmCCcKrwtoDOIMjwxZDP4LjAtrC2MKxwmDCaUJtAnTB7EF3wP4AvsBzv5K+5r4BPhD+Dj3B/aH9Tv36Pnh+2n9V/7y/6kB6QKPA9kC1AFaABn/xP23+7T5cvcf9i31WPT186/zoPS59RT3rvif+oz9PADNAtsE0wYPCd8KNAypDKgMagwYDM8LbAulCqwJHgkTCSUJEAj2BegDsALwAfH/yPy1+T/4HfjQ9+D2Dfan9pj4BvvG/Aj+Nf+DABQC2gLoAvgBtACk/1j+Af0A+w75ivdY9qL14/Sf9Ob0mvXX9lf4UvrH/DH/vwEoBHoGnAj9CUQLCAyDDGAMqwtLC8kKeAqaCd4IxAiNCPoHEwZOBBADJQKhAJz9B/s++dX4dvhR97n2vPZO+Dv6vPsP/fj9df/jAOUBLgJ9Aa0Avv/b/qb95/s2+rL4xPft9kL22vXi9Zj2Z/e4+Db6Pfyb/rkABgMABSUH9ghCClMLtQvzC6oLNgvsCksKiAmdCA8IEgiYB0MGawThAlICLgEV/2v8MPqF+fH4UPhc9+H28PdW+Sb7ZPxq/ab+rv/hAEUBJgF4AIX/x/6z/ZH8OPv/+QX5LPiP9yv3Hvdy9xL4+fhK+vH7BP4tAFoCXQQqBgcIgAmnCjsLQAseC6oKVwroCUEJqQj8B70Hbgd5BgkFLQMFAhgBlf92/dT6evkS+eH4afh097f33viG+uz7nPyZ/Yz+o/9mAJAAhwDh/zH/XP5x/Yz8Q/sd+h35hPga+Mj36vdk+GT5ePrC+279Z/+yAYkDMgWrBk0I9QnHChILvAq2CrsKUQqkCWQIvgdnBxAHOwZzBP0C6QFSATkAJP7++2L6+vmw+fL48/eE91v4vPkR++n7p/yz/dj+5v9xAHIACQBj/8z+I/44/Rb84foO+n75DvnF+LT4PPn3+dj65/s9/Sj/CQHcAm8E5AWGB8IIyQk3Cj4KNQrgCaEJDQlDCKQHKQf6BlQGAwWWA3kC6QHXAOX+u/wj+5H6Cvo3+VL49veS+IH5kPp++2b8VP0t/v/+lf/r/7f/SP/S/jr+g/2F/Mj7KPuM+vH5Yvlx+dv5h/o7+wr8WP3o/sgAjgIfBIgFrgbqB/EIpwnICW4JPwkPCcQIBggnB8IGiAYeBvIElAOgAusBHAGL/8b9PPwp+4T6tvkP+YL4XPjd+J/5qfqI+1n8Xf1F/iP/iv+Z/7z/ff/+/hn+Mv3B/C38lfvN+jv6TPqD+hP7nftb/GT9k/4yAM4BawO/BNIFBQcECMUIAAnzCOYImggmCIMHAAe8Bk8GwQUJBT0EaANUAl0BTwD8/l/9rfuo+g/6dvm6+CP4afg/+R768Pq0+9H83/2F/hL/dv/n/9f/PP+s/iP+yP0P/TL8qfs6+xX76Pol++j7uvyt/Zz+DQDPAVcDlARvBWkGPwe2B+MHzwe1B1EH0AZgBgwGzgVzBSwF2ARfBIUDiwLRAR4BNgCf/vf8w/sR+5X6uPkT+cn49/iZ+Vb6bPtZ/B/91v2b/pn/HgAjALD/NP/e/lP+pf3W/Dr8vvtj+1b7pvtM/AH9z/3S/hIAcQGwAtYD5ASsBTYGiAbJBvEGvwZqBgoGygV9BQAFxAS1BL4EUgRuA64CGgK6AeAAk/9F/vv8GvxG+6T6Mfql+Vr5Y/kh+jv7D/yk/CT9+f3y/oj/uP+U/1j/A/+E/hn+qv03/a38Pvw5/IH88vxZ/fL9z/7q//8A6gHmAs0DswRMBasF6AXjBdoFnwViBQMFeAQOBMADzQPfA8EDZAPDAkwC7QGbAfYAyP92/kz9o/wp/Hz7vPol+iD6ffoH+6P7Ofzr/IP9Iv7H/lT/qv+E/0D/Av/a/qD+Ev6V/TD9Cf0C/Q39cP31/Zz+TP8JAAsBFALmAooDCASUBOUE5gTcBLYEpQRRBN4DlwNiA08DHQMTAxsD7wKMAukBkgFKAbsAvf+R/sn9L/2e/PP7b/tC+zj7U/un+1D8CP2B/d39W/4D/4v/p/+J/2b/Q/8D/6D+VP4i/uf9of19/az9Ff52/t3+a/8nAPMAlgExAs4CXgO/A8wD1APeA9gDrwNhAzYDAgPDAooCgALIAuQCnwIVAqMBjgFeAcwA3f/s/jj+oP0m/bP8Ufz++8/7BfyT/D39pv3k/UD+2P54/7z/tf+V/4H/Yv8e/+X+q/5v/jL+Cf4j/lL+g/60/hL/rP9BAL4AIwGdARgCcwK1At8C9wLmAsQCyQLoAt8CjQI2AjUCgQK0ApACNwLtAasBZwENAZ4AAAAf/0z+zP2l/XL9+vyN/H385fxc/ar95/0d/n7+zv4q/4H/nv+d/2j/Z/+B/4P/Xf8M//D+8/78/u7+0f7h/hT/aP+5/wwAbgDgAGEByQEiAlQCagJ3AnUCfgJnAjgC9AG6Ac8BAgIoAhACygGpAYwBcAEeAZ4AFwBu/9b+Wf4F/tH9eP0z/R/9Xv3G/QH+MP5i/rT+D/9M/4j/pf+u/6L/l/+o/6j/lv9u/0//TP85/yT/FP8p/1r/f/+z/+//QwCVANEAGwFZAZABpwGpAbsByAHRAb4BogGmAbIBwwHSAdsB1AGbAU0BDwHvAMQAVgC1/yL/0f6x/oX+Pf75/eL9+/0o/l3+gP6f/sD+8f47/37/qP+q/67/xP/f//L/4f/Q/7z/ov+F/13/Sv9E/0n/X/9//7T/4f8PAFAAoAD8ACUBLgE1AVcBhwGNAXoBXgFYAWQBaQF2AXsBbQE4AfUA0gDIAMMAdQD9/4z/S/8+/xv/4v6X/lv+Xf58/q/+yP7L/uz+LP+E/73/yv/O/9b/7f/y/+b/2//T/87/uP+k/5n/lP+L/4D/hP+a/7v/yP/O/+P/FQBfAJAAogChALEA5wAaATYBKwEZASMBRgFqAW0BUAEsAQUB7QDRAKAAXwAJAL3/fP9H/xv/5P6//q/+u/7Z/u/+Dv8x/2v/rf/Z//b/9/8HABgAHQASAO7/0f+6/6z/of+P/3n/Vf88/zr/VP96/4b/gf+B/6r/9/87AF0AYgByAJ4A1gD4AAQBAwEMARcBEQECAe0A5wDSAKMAbwA7ACwAIAD9/8T/hf90/3z/gf91/1n/Xf93/5//u//Q//f/FwAvACcADQAMAA0ABQDa/6P/gf93/4T/eP9h/1H/Uf9t/47/rP+8/8D/yv/m/xMANABCAEEARgBWAF0AYwBhAHEAgwB+AGYARQA8ADkANAArABwAGQAVAB0AJwA3AEMAQwA7AC4AMwBDAE4AQwAWAPL/8P8OACIACADV/6f/nv+t/67/l/93/1//W/9m/3n/kf+e/6f/sv/T/wMAKgA8ADsARABOAFAARAAtAB4ABADn/8v/uf+//8T/yP/K/8//5v/7/w0AFwAdACsAMQAyAC8AOgBYAGkAagBZAFcAbQCAAIMAbwBWAD8ALwAiABMADAD3/9v/vv+1/8T/0f/U/8P/tv/A/9z///8JAAYA//8CABYAJQAjAAgA5//Z/9r/2//H/6n/kv+K/4z/kf+g/7z/1f/j/+7/CAApADYAKwAhACkANAAtABUACwAaACsAMAAzAEEAUABVAFUAYABwAG0AUgBDAFkAawBeADMAFQAkADkAOwAcAAEA/f8AAAUA///1/+P/zv/J/8n/yv+6/6v/of+a/5f/lP+b/57/lf+K/5D/sP/O/9b/0P/Z//f/CgAHAPD/3//m/+3/5P/K/77/x//c//D/AgAaADAAPgBFAEwAVgBhAGoAcAB+AIkAkQCYAJcAkACAAHEAaABRAC8ABwDw/+j/2v/A/6f/pP+y/7v/tv+l/6n/uP/F/8n/wf++/7v/uv+2/7X/v//E/8b/xf/L/9f/3v/g/97/3//e/9r/2v/f/+T/4v/n/+z/9/8EAA8AHQAjACUAJgA1AFoAfACMAIgAiwCdALUAvQCyAJkAdQBZAEMAMwAkAP3/0f+x/67/vf+//7b/pP+d/6z/vP/E/7//t/+3/8T/1v/i/9//3f/g/+///f/8//T/8f/7/wEA/f/r/9P/yP/D/8L/vP+p/5//qv/O//f/CQATABwAMwBOAF0AaABpAGMAWQBaAHIAhACCAF4APAA5AEMARgAwAA0A+f/3//r/+P/2/+//7P/q/+v/5//a/9X/z//O/9D/zf/R/9T/5v/7/woAEwATABYAEwAJAP3/5v/L/7P/pf+h/5//m/+b/6j/xf/d//T/DgAuAD4ANwAyAEMAXABeAEMAMQBBAGAAaQBbAE8AUQBZAFcASQA/ADsALQASAAQABAACAPz/6v/f/+D/4P/c/8r/wP+//8b/z//M/8j/x//V/+r/8v/t/+f/8f/x/+b/2P/S/9b/0//J/8f/1v/q/+//6//x/wkAJAAqABoAAgAAAA4AEgAHAPn/+/8HABQAGwAiAC0ANQAzACkAHAATAA0AAQDz/+7/9f8GABUAHAAgACQAJAAaAAUA9P/o/+D/1P/G/8D/w//L/8//z//O/83/1f/T/8//0//f//H///8NABwALAA8AD4AOwA3ADEAJgAYAAsAAADz/+X/3//m//L/+//8//z/+v8BAAkACgAEAP7/+//+/wwAIAAwADsAQgBQAF0AYQBVAEIAKgATAAQA+f/v/+X/2v/Y/+P/7v/t/+b/4//m/+r/6v/h/93/4f/m/+v/8/8BABAAGQAjAC4AOAA2ACcAFgAMAAUA9//m/9f/0P/W/+D/7f/s/+X/4v/m/+//6//g/9j/2f/l//D//P8DAAcADwAcAC0ANwA5ADYALQAhABcAFgAVAAoA+P/x//b/9f/q/97/2v/d/9j/z//L/9P/3//s//v/CQAcACoAMAA4ADkAMAAZAAIA8v/g/83/wP/B/83/2//v/wYAGAAeAB4AHwAeABAA///2//7/AAD5//b/BgAdACQAHgAZACIAKQAfABEACQAJAAoADQAPABcAJAAoACAAFQAMAAMA/f/v/9v/y//B/8T/xv/I/8//3P/t//X/+f/+/wcAFAAXABMACQAFAP3/+P/1/+//6//n/+z/9P/9/wAA/v/+////BwAQABUAGQAaACAAKgA0ADcALAAcAAgA/f/8//3/AgD+//j//f8JABAAAgDu/93/1v/d/+f/9/8IABgAJgAuADQANQAvABQA8v/b/9D/yP+7/8H/0//m//n/CgAjACoAIgAfACMAIgAPAP3/AQAKAA0AAgD7/wMACQAFAP7//f////T/5v/l//j/CwAVABwAKQAxAC0AIQAUAAwAAQD1/+T/zv+7/7H/tv/A/8j/z//c//H//v/2/+j/5//r/+r/5f/o//b/CQAfAC0AOAA+ADcAKwAaAAkA9P/f/9X/0P/V/+L//P8RABsAHAAbABsACgDr/83/yP/d//j/EwAkACkALQA4AEcAOQAPAOX/3P/z/wQABQABAAsAHQAkABsADAD8/+T/0v/S/+X/8f/0//7/EQAeABMA/f/v/+3/8//8/xEALwBDAEQAPAA6ADQAHAD0/8//xP/A/67/n/+s/+b/KgBPAFAASABRAFIAPgAnABcAEwAIAPz//v8NAB8AIAAcABcACgD3/+f/6/8AABEADQAVADUAUQBDAAEAuP+K/4D/hP+O/63/4/8iAE8AbwCPAJgAYwD//7T/pf+o/47/gv+6/x8AbACPAKsAugCCAAkAqP+V/47/Yv9B/3X/7v9KAGwAdgCUAJwAWADx/7j/vv+x/4j/i//r/2cAkwB4AFwAaABSAPH/kP+A/7f/2//U/+H/EgA1ABYA3f/G/8L/nP9Y/z//cP/H/wQAHQAzAFgAcQBXABgA6//s/wIA9P/D/7j/6v8aABQA/v////3/y/+Q/6f/+/8tACQAJwBlAJsAeQAdAOP/2//A/4T/b/+i/+P/BQAnAGgAiwBdAAwA7/8AAOz/rP+H/63/9/8lAC8AOABBADEAEAD2/9//tv+Q/57/6P8oADEAJwA3AEwAQgAkAA0A8f/F/6L/uP8DAE4AcwBvAF4ASgAlAPj/uP97/1f/YP+R/8n/DABdAKMAogBfABcA8//m/8b/pP+v/+L/EQA6AHwArQCIACQA2v/e/+n/x/+j/7n/AAA7AGEAiQCYAE8Ayv+K/7v/DAAYAOf/1P8LAGIAhAB2AFAA9v92/xv/Kf+C/8b/0f/u/1EAugDZAKYAYAAiAMn/X/8t/2//1f/5/wcAWADPANgAVADY/9P/AADd/4P/gP/e/ykANgA9AHUAlQBPAOb/xP/g/97/t//E/xoAWQA2APP/4//w/+b/zf/K/9T/xP+0/9P/GwBNAE8AQgAvAAIAwf+O/4j/oP+x/7X/0P/1/xAAKgBBAE8AMQDu/57/Zv94/7//DwAyADIAPABeAIIAegBeADgAAwC3/3L/hv/T/wMADQAlAGgAfwBGAA0AFwAVALD/PP83/5f/yv+y/8v/OQCIAFIA5v/I/+X/6f/F/7L/4v8WABEA+/8NAEMARwAJAOr/AwAcAP7/2/8BAEMAQQD8/8v/7f8QAPv/4v8HAD4AMQAGABwAagB0AAoAt//C//3/HgAjADUARwBeAGwAcABnACgAzP+L/5D/zP8CAAEAzv+n/7P/2P/x/wcAFQAHANT/mf+5/xkAQgAdAOb/4//x/+z/+P8oAGAAZgA8ABUAGwAjAAIAx/+1//j/OwA9ABkACwAeACEALABOAGEAPgDz/9X//v8dAAAAyf+z/8H/5v8pAHAAkQB9AEoAIAAAAM3/nf+b/8v/7P/k/9X/1/8AAC4APAAZAN3/tP+a/4b/hv+4/w8APQBIAGEAbgAsAKX/ZP+u/xkAKwD8/wUAPABXAGAAhgCpAFkAn/8t/4b/HQBJACsAMwBvAHcAPgAPABAA9f+O/2T/wf86AF4ARQBYAIQAcwAlAOj/4f/X/6P/aP9h/67/EgArAPn/4/8gADkA3P97/6H/EgAZALf/lP/T/wAA2P+3//D/HgDn/7T/7P8cANj/jv++/x4AMAD//wwAXwBuADoAUACqALcATAC1/2v/eP+J/3j/f//P/woAFQAYACcAOwAmAPv/2v/R/8b/xv///zUARQAwABYAFwASAPT/w//I//z/EQDz/8X/xv/E/6L/oP/r/1kAfwBPABsAAwD7/+7/8//o/67/iv+9/zAAnQDVAMcAgQAbAMr/uP/K/9v/9/8gACUAAgAJAE0AdwBFAN//rv+y/7D/rP/i/zcARQAyAEIAaQBXAO//tP/C/8b/sf/D/yUAawBrAFYALQDx/5z/Xv9p/7v/GABPAGgAawBfAEYAEAC0/1z/Pv9r/8b/AAAdAFEAiQCbAJYAiwA5AKH/OP9K/63/BgBEAGwAaQAnAPn/LgByAEgA3v+u/8//6f/M/8z/GgBbAD4ABAAAAAYA8P/X/+X//f/f/8L/9P9OAF8AIgD3/woAJAD8/83/1f/e/9X/0f8CAEsAZQBNAAAA2//i/+P/1P+l/33/hP+v/7z/mv+a//z/hAC+AJkAaABYAC8A1f+q/87/5P+6/7n/KgCbAJAAIwDm/+r/t/9G/wv/Uv+///f/JABmALgA3gDOAMUAowAxAKT/Uf9i/7P/8P/k/7D/sf/l//n/6v/7/zkAPAD0/+H/IwA7AOH/qv/5/1kARgDi/7X/y//I/43/hP/g/zcAVAA/ACIA+//b//7/PwBgAF0AdACcAI0AUgA0AAcAjv8K/+P+J/9v/5z/CgCyACwBLQHeAJ8AUgDT/0H/AP8l/2n/tP8cALUAGgEGAYkA9P+L/0X/NP9A/1b/av+m/0sAAAFBAdwAMgDD/5L/df9m/4//4P8rAGwAmgDQALEAIgCl/2H/kf/n/w4AIgAnAFMAdABaADwABAC3/37/hP+9//P/FABFAIwAlwBfACIAKQBNACcAnf8M/y3/4P9xAK4AxQDIAG4Awv9m/5n/AAAdANr/tf+4/8D/5/8rAIsAswBtAOH/U/8i/0f/mv/8/ysAOABEAF8AcgA9AOr/v/+7/9z/8//z/+T/v/+5/8n/9P9JAKEAowAoAI//UP9x/5X/vv/m////CAAWAEEAcQDFAN0AgADm/4P/uf/8/wMA4P/1/ywA/f/O//L/SAAuAIX/If9l/9X/3P/d/y8AdwA6AMv/2v8UAPr/qv+F/8b/BwAUAC0AUQCIAJgAUQDb/2j/Yf+m/+b/HgBcAJAAhwBcADwACQCn/1T/T/9h/2b/iv/6/4IAtwCMAGMAVABIAAUAnf+Z/8n/5/8MAB0ANwBMAEYASAAaANH/lP+G/4X/Wv+2/2kAuwCeADwA6v+i/zz/Cv8//7L/GQBHAFAAKwD8/y0AYwBVAAgAbv9A/6D/JQCiANkA0ACaAFIA4v9e/xL/OP97/53/AgCVABAB/gCcAHQAKgCr/0H/Vf+T/4D/lv/y/1IAdQBkAHcAiABGANb/of+n/6f/sP/n/xcAKQBQAIIArACpAEoAuP9P/1n/s/8dACoABQADADUAdAArAPj/9v/Z/7L/av+b/+7/5v/6/0UAlgB8AB4AGQAXAOr/6/8oAGsATAAAABsAUQAyAP//+f/+/+f/wP/K/wAAIgD9/8z/4v8UAHoAtABjANH/Wv9o/4z/n//g/zgApwCtAEMACQAQAEUAKgCq/1v/hf/i//j/7f9eAPQA/QBuAIH/NP+D/5b/kf+g/83/8v8IAGUAvgCLACcA6//5/8P/Xv+V/wcAVwBWADgAVwBrACAAv/+A/5D/6f8QABMAKgB2AC4Alv+i/8b/7f/E/6n/4//Q/+3/BAByAL0AUgDh/3X/Nv9Z/9j/IwB/AG0AAwBDAEIAPADn/3X/if9t/6n/CQBPAH8ABwDP//X/v/+l/6z/yv+//57/+v94AKsAnQDVANUA5/86/yv/ZP+h/5H/8v87APX/AQBXAF4APQDz//n/w/8h/5j/YwDXAJkAyP+o/7T/5/9/AMQArQAPAG3/Lv8U/3z/GQC4ANwAVwAAAAgA3P9P/zr/lf+1/5H/l//S/8f/PwAkAX8B2wCr/3L/2f+//3f/2P9iAKQA5ADHAIoA0/9l/4v/Zf9m/2r/1f9XAC4AIQCwAN0AQwB+/wn/kv/x/8//EwDGAD8B2wC3AO8AswD7/1H/TP99/zn/6/6M/zUAawCjAIIAEQA7//L+ov8pAG8A5f/L/xQAIgC9ANAAKgHBAHj/EP84/xkAnQCxANIAjgAVAJ7/U/9t/5L/nf/B/7//8v9nAGoAPQAQANP/7f+x/1f/fP9//7H/7f+SAEIBMgEXAbgAeAAOAHz/sf/x/xkA2f+W//T/1f+3/7v/3P8zANP/h/+W/5b/oP+5/xEAgwB1ADAAUQBMADkAtv9l/ygAyADuAL8AMQCX/4P/qf+z/1n/Ov/F/woAz//C/y8AngBrALn/sP/N/xMAcgBvAHIA0/+r/xIADwDi/+D/bwB3ADoAEADL/z4ARgA2AMv/Uv92/0//o//i/xgAMQANABYA+f/d/8L/8v+W/0//xv9jAN8AhQBFAH0AVgD2/7n/+P9TAO7/0v8EAP3/+f+P/6b/DADc/67/1v9HAHsAOgC6/3r/xf/1/zgAYgBXABsA2P8PAD4APwATADkAZwAHAO7/7//6/8r/LP+J//H/GQBKAOP/JAAAAND/9f9+/73/GwBVADIAk/+t//n/2P+9/yYAhQBYAMn/T/+P/yoAjAAKAFv/e//v/00AIQC9/7r/7//q/x4AOAA/AFYAGADS/x7/7v51/yIAiABOAFsApwDnAH4A///8/xAAPADs/7f/AAAxAEoARgA7AJf/1v4n/x4AigAiAAwAbACFAMn/eP9NAMQAhgBu/13/DwDP/8P/EAD1ACEBSwC0/zP/Jv9D/2T/OQDoAFwA5v8RAIAAmQAaABAAIQD2//D/v//S/wIAxP+L/0L/wf+eANAAswD4/77/2f+U/2z/lP9RANMAgwDj/73/CwCVAGAAEADW/xv/RP9P//P/RwB6//z/eACGAC4AKgAqAcUAcv/s/j//wP++/0AAPAEjAfP/H/96//T/tv+q/0AATwDB/2D/BgD7AJQAt//G/yEAyf8P/xD/AACEADYAJwCcABQBygD1/3L/BP9F//7/CQBiADYAhgC4AJD/g/8cAMEAhADJ/3T/av+V/8r/dwCiAIcAKwAJABkAwP+8/5P/lf+J/zj/4P+YAJkAmQDt/63/QACWANQAcADl//b+cv6i/mv/zwAIAT0BhQB5/+///v9QAHIAngDHANL/8P6R/kP/cQCbAIcADQG5ALj/MP+c/0sADwDt/xQA6P+E/8n/fACdAAsAx/8JAN7/sv+I/0//gv+n/xsAbQAiAFMAcQBzAE8AhP+U/8D/mP8GABQAaAC9AE0Arv/C/lj/YAC5ANMANABHALL/Lv90/7f/6ABFAfUANgBy/3H/vP8HACIA/v9z/4H/m/8iAKsAEQBIAIoAHwAW/4T+x/+KAHUAEAACAAcBBQFGABEAYQB6AK//Mf89/2//nv+y/+L/4P93ADIBwABNAFkAjgDZ/53+V/7//gYAsQA7AfwAHgCo/7T/9f+P/0f/of/N/4f/nf9dABUBkgDq/zgANQAgAMn/9P9JAHP/Q/9R/3v/QQBkAMUAnACP/+7+/v46AE0BygHEAGb/w/5y/vX/xgD1AMAA7v/H/x3/M//P/zsAYACF/0z/7//0/ygA7gCeAPP/Yf+q/2MALACNAJsAugDq/5n+g/+7/6n/0v/o/4YALACg/+7/fwBRAEoAPQCz/3v/aP+LAGIB4gBcAHj/6f6+/iz/OwBfAKX/a/8aAOUAWQFbAd4Adf9o/sH+5v/tALIAXQAVAKr/PP8M/6L/x/8HAHkArQBHAHX/0v8zADEAEAAZAJEAeQBdAHYAUAACAEL//P7H/xUACwDA/9//XQBjAHsAhgBxAK3/Sf/B/1EApgBoAOr/vv8KALn/n/+6/4X/sf/n/18AfQAUAJr/Lf+l/wwAEAAiAA8AOgAeAOv/DwBCADMAhv///pr/rwCdAAoA6v8uAPb/Pf+q/7QA7ACy/xX/4f+EAIIA5/+2/wsAzv+Y/9z/wv9kAH0AjQBZAFj/oP8q/27/8f/ZADQCYQGcAJj/Af+6/lT/MQA4AJsABgCkAFkAEv+m/xUACAFVAGD/vv+L/7j/kv8wADEBRwF+AE3/XP/g/zoAZABAAPf/b//3/pH/bACbABsBRgGSAIz/zv4X/87/3//B/9j/uf/L/ygAqACCAN//wP++/7z/iP/q/+cAyAD4/6X/4v/j/9j/BQCSAGsAKP/E/mP/ggABAcUAawDB/zb/Hf8MABUB4gAnACv/rv4g/xYAawFiAUYAxv5X/mP/KQDZADoBaQFtAAT/gv7T/s7/OgCoAOIA8ACKALf/Mf8I/+X/SQCyAGkAxP+2/+H+FP9B//P/2gCvAL4AvgCdALX/Of8C/1z/uv+2/+8AzgAwALn/g/+nAAwBEQEnACT/z/7Z/uP/tABPAc0Avv/J/yIA7f9o/2v/a//x/ov+Vv//ACUCHQLpANv/uv4o/sD+GgCnARMCGgHH/4H/yv8UAL3/kf/0/3z/G/+H/zcAgQADAKH/y//B/+P/1v/j/9r/Z/+g/9T/RQCMAAgAp//j/z0ALwAAACwAAgH2AMz/Av9o/zcAYwDhAG0BZwFeALL+if4n/8D/YQCDAEkAsf+s/ygANgCu/yP/Pv+M/9L/ZAAOAcwAJABb/5b/PADa/xAAGwCTAJ4Apf/U/14A4gCzAKT/6f7d/p//HQGjATAB4P/B/qH+pf5r/y4AIwFRAXUAT//9/ggA0QDrAKP/Kf9v/63/AgAWAA8B8QBOAJP/VP9AAEIARwBZ//7+R/8Q/+3/UADQALcA/f+W/5r/2/+J/+H/9//R/+H/6/+BAMMAawBFALsA4wAIAFv/Lv9d/zYAhwCZAJMAGwA1ACEAOQBLAOz/xv9b/4P/dP+e/8YASgFRASsA4v4A/3r/7P92AJ0AtABlAOD/z/8U/+j+mv9GAH4Al/9a/5j/5P+rAOkA7QAAAMX+x/4C/0//2/8qARcCOgEIAFf/aP8w/xL/IwDbAMYANQCH/zr/n//3/zQAVACs/2H/UP+0/0MAdQB5AAAAxf8s/0//bgABATYBbgCe/5n/uv8nAH4AIADC/5n/5v9WABIAJwA3AP3/o/+8/2YALgBeALsADQCG/9D/igDCAKP/Sf5R/2oAUgDPADcB6wHVACf/Pf8n/1//K/80AGcBswDDAIUAGABs/+j+tf+kAGYAc/+O/9b/+P/e/9j/ev9B/xwARgBKAP7/RAAQATkAbf8x/5f/MADt/xYAWgA2AAQAXv92/7P/FQCXAA4Ao/9t/6//QQDSAN0AWQC2/07/tP9z/2P/DAC1AEYA5f4B/+z/7gA1Af8A/ABc/7v9jv1V/sr/oQBCAXEB/wAXAHL/fP8p/0//o/9jAJMBCwGg/0L/XP+Z/7//YQCJAZsBJwHw/7b+l/7p/goAhQDE/3EARAEYATIAAf9j/9n/5v+f/wEAZwDp//H/x/8tAAgABQCTAKEAigDG/zUAXAD9/wAAiv/c/3z/Jf8OAPwAkAEIAfH/8f5f/g7/wf+fAEABPwBBAKj/T/+bAIYA+QAVAJL+yf4Z/zQAlQCMAF8ALQBBAKL/O/+n/zAAvf8bAIAACQDJ/5n/1f99//X+4P85ATIBowALAGgAjgDQ/+n/Tv8m/9v/NADKADEAwf8yAKL/8P9DAFIAxQDY/6//2f++/3MAmwBtAG7/i/4v/1//+/9KAHcAswFIAVwAP/+K/tr+8/6i/4gAMQEXAaQAAgDR/0r/Kv9lAKkAnACa/yn//v/D/8L/yf+XAHYBsQAFADj/Ov8AAOX/x//Q/xIAWACQANAAXgC1/0X/5P64/hX/KQC4AJwA2gAsAfUAnf+1/tz+T//o/3cAQQHaAPj/3f/y/wAAr//8//AA4gDl/07/Fv9j/3b/qv+nAK8AmQD1/5b/fAByAJEA8/+t/hf/G/+a/3cAsQDQAPX/AwDU/+7/6/9x/3sAJgAFAO3/gv/i//T/QgD0/+j/OAA8AGgABQDf/7YAWgA//8f/0v9l/8b/cgApAZQA+P+D/7H/LgAY/5P/4/80//D/eQAsATQB+P9H/2D/xf57/rX/xQAYAWoAyP8fAJQAyQCQAL3/kP6R/qP/VACeAK8APwF9AS4A8f4B/yz/a//p/1oAEwHrAGMAw/8o/4r/j//8/yUAn/8LAPL/pP+3/ykAEgF1AQsB1/+Z/hn+qv7T/zQAuwCHARwBlgAuACEANAAuACAAlP+W/7//RwAoAFz/1/+1ANoAWwCK/67+Jf/m/yQAvACVAA0ACgDr/4P/q/9IAD8A7P/m/ysAdAAQAJT/sv+C/yr/EQBNAaMBqgAh/yT/V/+D/zYAvABMAa0ABgC3/37/zP/K/67/Tv+c/6cAUQDd/yoAKwCOAKz/cf9OAPP/CgDg/2kARgBm/+r/aADtAF0APACTAPb/Cf9F/1AAAgAGAI3/sP+7AEAAnQBhAPP/SACn/3r/H/9N/y0ABgDP/1MAEwGxALr/Nv/2/j3/yf/0/2EARgAGACsAKv8X/4kAEwFkAOP/of/a/z8A2/8mAA8AYP+T/0EA0QAHAEj/Sf8K/+L+OP+WAPIBjwF0AM3/Lv7x/Wn/kwCqAQMBSwDY/+/+2v5u/xEA4P9E/xL/bv/CAKIBewHXADz/dv4o/hL/9wDcAU4Byv9+/43/mv/K/8kAaQFnAMn/2P/7/+L//f/R/43/R/8+/9f/KgATAYIBNAFPAMv+9f4M/xH/0P9QAMMAtAAUAPL/qQAsAFQAVgCD/3f/5/7Z/wUBRAEmAVIAvP81/6v/GACm/woA+//n/4UAegDgALYAHQB9ALv/8/0o/pP/xAD+AOMA4wB//0n/df+L/zIA9/8dAMEAvACKAIoACf8V/gP/CwDGAKQADgG4AU8A3f7h/l7/xv/c/9EAGQEcAf8AtP92/xz/8v6n////mwATARsAbP8EABMAwf+A/+P/zQDPALD/Pf8XAP3/yv/VAM0AFQCy/yv/Wv+l////2wDeABQA9v+kALEAUv+S/h7/nP8UAC4AVQFKARYAbf+N/oH/b/+i/94AnwDGAD0AMv9E/1X/KgBeAf4AdwAW/5L+Ov8SAA4BsQB/ACQAwP/6/t7+r/9qAAEAXP/EAFQBiQF0ACv/9f7W/Xv+zf8pAWwBxwARARQAfP/g/jL/LwCO/w8APwAbAFYACwCx/73/FACy/zcANwDH/1wAkf84/xEADQAqAAoA0f++/8D/AADx/5YA7v92//YAxgAPAPD/s/9R/5H+xv6O/2sArADvAJ8BcQCj/+L/Yv9m/5P/a/8LANP/6P+tAHQAsAC3AGcA7P+4/6z/R//4/9MA5wAuAGP/fwCiACP/Wv+W/8//VgDPABkBMgAJ//7+x/9CAGAA2v+0//3/PACOAMIA8wB4/9v+m//I/8IAQAEKAaEALACb/4P/IP+7/lAAbABQANYASAASANP/rP9F/6b/UgCIACEAjv4o/yYA0f+6ALkBgAE2AJr+6f0x/mr+6f8PAlkC6gFKAV0AFf9N/mX+ff7w/sv/JwFsAvoBOAAl/wf+jf0eAGgBTAEVASMAOQB8/rX9G/9ZAAYBnwA2AaIAjv8N/+b+lv9J/4n/lgDhAHwALwBjAGAALQC0/5b/+P8/AEoAqP95/5H/gf8KAFsAbAF9AWAA0f+T/ir/LgDFACsBMgAwAIj/Tv+R/4T/+QBzAfQAGABm/6H/df8q/4v/BgBlAJIAvf8xALwAQwCuAFoAFwCi/4L/p/9B//r+2v7n/5MADwF+AfQAfP9c//T/Tf+Z/9n/EwAtAEX/gv8IAVUBDwB9/7T/R//b/vv/BwE5AR4AUv/w/xr/f//d/+r/yAA3AHMADgAyADYAcP/B/rz+4v+W/w4BRQHRAMsAdv5j/n3+Xv95AKcAHgEyADoAGwCI//j/d/+4//oArv8A/1cAtwBKAMD/t/+S/8r/TQAdANEAyQBNAN0A1/9K/0wAIAB1/zz/Lf/I/zj/qf9oAbgBdQFLAAgAYv8y/rb+c//c/8P/5wBzAqcBjQCX/4n+o/7x/s//4QDpAFUAbwDEACcAoP9m/9j/QADp/+H/ggB6ADf/Vv/J/7H/QwCTAA4BZQHyANT/If9f/vf9N//t/7AAjAGMAcoA3f9I/z3+z/4PAHcAjQAyAGkAt/+L/kX/8wAzAYkA8P+f/4H/iP4o/14BoQGRAF0ATwBs/+L+Sv/W/04AnQDb/0IAxwDq/hP/dP/Q/r4BYwFj/z8AnP9k//L+Kv8rAK4A7QCSAHkBrQBa/iL+3v45/9n/VQCYARkCdgDh/2kAqgBjAIr/Qv82/wn/qP9pAPYBRAKdANL/Qf7v/fD+Rf+BACQBXgFtAd8Avv/G/R7+xv4O//8AVQGXAbEB2P+z/pT+Y//FAE4BxgA8AJv/Xv+t/mv/qQHmASYChQFOABb/Ev5N/m7/RwAwACMBzwCR/3r/5f9g/3z/JQGSAIf/5f4A/pz+1f72/nsBOAPxARMALv/i/mL+//4HAEUBCwH4/pT/VQATAYkBbQCTAC8Ai/+Z/gD/NwDx/5X/Xf/4ALcAOgBaATMAKf8Y/mj+sf///xwAhQDWALz/j/8kAQcBj//U/sb9hf/4AA8AjwEYAqYAYP9O/iv+NP/uADEBaQHUARAAYf9K/3T+gf/x/3f/tAFQAbP/0P/8/j8AiQC6AGoB5v+4/p/9Fv6R/1UA0wHNAQIBuQDV/4H/aP6C/rb/PgB8AO8A0gEDAPv+P/9D/xcAHQDUAPgAff99/tX+P/8Z/1QATgGMAXgBuAAKANf+I/64/uT/JwA+ACMBmwFfAfUAvv+f/lf+Fv7G/wgBmwEqAmsAEP+G/qH+rv94AGsA0P/m/rH+ewDCAVYB6ADYAOb/Uf6D/n//MACsACUAIQE6AQAAyADQAA8ASf7p/bX+ef9MAZsBEgK9ARAAS/8v/7X+2f5z/2X//ADWAeAASwALAJT/Hv9n/yT/Jv9SAE8A4f85/5v+rf9BAOwAjwEiAYoAOf8v/yL/9/7S/woA+P/W/+//3P+K/yv/pP8RAFcABQEjAdoAr/74/Rz/ef8UAMwAOgJGAsEA5/8E/2z+8P2a/hMA9v9kACABGwHVAHMABABF/07/Zv8HADwBqQDh/wEApP+uAPoAsf/A/xMAjv/I/o3/CABCAJMBPAFsAFkAZf/D/8f/uP7Q//MAPAEeAf0A0//k/s7+hv7O/+//z/9QAL4ARwGiAPL/tP4R//D/IgACATMAGQCfAAsARAAAAP/+cv9bAGoA8/+1/3L/pf9gAKb/aACPAAf/+v8rAIQAWAEkAREBNwCZ/+3+iP9CAIX/ef/7/tb+EADaAP8AfwH5AOr/DgB1AJ3/Z/5z/vH+ev/k/8YANAE8AXkAF/8E/6v+cP/rALkAcwAyAK3/lf+W/yMAEQCi/wMABAALABIAjQAmAccACABZ/w7/Bf+K/u7/wwHuAewB9wBYAHv/zf67/sr+UP96/6IArAF/AVkAxP9n/1n/h//G/i4ANQGoANv/wv7I/0kAggC5AIEAEAF1/1D+u/6h/mr/fwCfAXQC4wEnAG7+LP7K/lz/bgD+AEgBrwAoAIkAXv+z/uz+CQD9ADkA8f9p/wD/GP/X/wMBvgAdAG8AVgDe/7L/Qf+W/yz/J/+RAFUBIAEo/8H+7f6y/gAADwAkAUIBpv4//4r/OACnAUoAbgBcAH3/Df+S/z8AVwC8AE7/iP9s/+D+ewDIANYAwv+j/uz+gf/zAEgBIgCW/4b/9P8ZAScBGAGCAMr+Dv6u/qcAfAF/AccA0P7n/jD/j/8YAVABigBRAKD/4v6J/yn/Fv+BAGUAIwDaAG0A6AD4AUoBfwBG/47+nv5e/jn/TQEgAuoA/f+K/6f/6v/v/2cB9wAG/4f+5f6fAM4AxwAIASwA2f/X/9oAgQC2/xn/1f5k/wH/WQDaAN7/4//l/10AhAC5/wEAUgHhAIQAyf8z/t/+//67/pAABwK+Am8Bdv9f/37/A//h/eH+NADCAIQBnQDIAAsAQf7E/iAAjgHcAbgARAA9/w/+mf5p/2ABFwFFAC0At/4o/7r+y/6I/zcApwHjAAoAN/88/5P/fv78/40BFAFLAGr/PQBjAKj/AQAiAB4Aq/+Y/l3+IP+t/48AmgFPAVEAqv/j/mj//f9D/3wAEQHJ/6P/fgAlAXAAhgBLAK//TQBr/6X/Fv9Y/bH/YwGxAeIBsgB//+/+O/+W/8//EQBoAX0BYACR/0X/uP9I/oT+TgCFAUACuQArANb/7/2t/YP+QQD5ARwCLQGwAO8AXf9h/z7/0v4qAWEAkACxAeT/8/6o/l//UADiAFgAOv/p/0QAigCzAQsBBQBOAHL/tv+YAKD/i/57/oj/KAHxAVABwgBaAIj/0P2k/iABIwAMAJr/f/7h/6H/9/8+AfsA2QCqAAYAr/4l/8j/3/7R/6P/+/69/w0AeQFhAZoAQgCT/qH+1P5w/yQB7AAsADv/A/+//w0BvgGmAEUASQAXAMj/A//9/gb/U/6w/+UAWwE4Ao//9v64/nL+tAEaAZMA1QDy/nT9Ff1G/3UBOANVAkEAbQAv/jf9xP5j/3YAoAADAMoAJwJYAKb+DP8g/ub+CAA5AV8DAAI6AF7/8P50/8r+t/9KAIr/qACiAFkARAAG/2f/OwAQAPv/pf+U/yUAFAFsAQYBjwAbAHf/z/7c/zkAIQAyAQUA+P9v/5T9z/9KADYApQE1ALn/PwDR/z//6v/sAKgAewHw/9X+pgAy/5z/yAH9/0X/RQDO/wIAdABr/x3/+P83AO0AMAI7AIP+mv8P/sX+nAHBAGIAegAOAK0ABgGJ/oL9xv/F/i3/bwHmAOwB+gFa/7v/sv8Y/cz9tv9vANEBtAENABn/Mv4u/04BPQE5AaYAbP8a/6P+3v+rAH//Wf4J/2UAiv+CAO8AQwDpAGT/uP+IAO7/vv+3/3MAzP4q/1sA5P8aAcP/pf7j/wABBwIrAi4Bwf6S/Qb+Tf9YAfoAkQBfAeoAQQAoALr/8P4y/5L+Zv6l/5r/FAHgATkAZ/8W/3b/z//CAN8Av/8MAIj/HwANAVr/X/4P/lX+BwAOAtEC8wG8APf+Wv7q/4gAzv/K/y8AqACCAO0AKQIKAb7+Qf3O/VkAeQGmAEUAjwDj/8b/XACu/7f/BABQ/4j/vv+D/1QBRQKQAM//FgCd/uv9nP/d/4IB9AGtAIIB6/4K/Uz+1v86AaQBbAHn/w4BegCn/qX/2v3r/mgBtQDQAMoAO//I/k4AWwDQ/6r/awAxAbD/FP+l/g3+p/8eAeYB3gGgAGgAJgBQ/7X+pv6T//v/CgFhAawAqwDp/g/+hP8xAEwA+ABiAWYBxABH/kP9pf1//cT/AwK4AnECHwH2/6L+hf5x/o3/KgHh//7/6v9g/1YAN/9O/yMAPABTADwATgFkAKP/v/7Y/U3/nv+AAOcAVQAzALkA+QGTAc0AsP/B/vP9ef3e/gMAVQA/AZQC+wEvASwBHwAU/1P9fvy6/VwAQAJtAlcCoQDF/7f+dP2d/mP/KAA0AdoBsAFQAAL/kf7D/mT/XQHYAvABFwCv/vL9av6M/4AASgHUAMsARAHkAEUAkf4u/uH+Yf80AJ//agCtAUYBowDl/zT/cf4I/yUAJwEtAa//ov86APP/Zf8p/5P/l/+2/7YAdQH2AJ3/mf90/3b/jgCyAN0AQwA6/xb/kP/G/9f/aAArALYAtAAN/83+V//i//MAKgKMAUr/nv5o/rH++ADGAfcALAAm/4D/s/+D/p3+bv8SABsAFAB8AfoB/QB0/8j+uf54/kP/QQCOAfcB7gDIAKYAkv/j/iT+t/53AMoAMwHrACoAMf/m/nL/K/8UACEATQABAdUALAE3AW8Aof5E/53+Mf7nADYAsACFACP/mACZAAUAj/+UAGkBCAGaAPL90/3Q/kT/jgDIAAMCzgFMAEUAG/8v/u7+Kf+2/zkABwD1/+b/sf/IALkC2gFUAHL/Av5t/vr/XQCTAOsA8P+c//P/W/+q/90ANQEkALT+RP3D/nwBwAAtAWcAmP5+/xQAnQH8AdIA2P/t/oz+Ff4y/9n/BgDiAAoA2ACIAUgAOADe/4b/D/8e/4b/AwA8AFv/3/8/ADYAFwDh/6AADAFEAd8Axv9j/jT+Zf9IANsAwwAfAI7/hQA8AVsBmQG8/6f+4v11/dj+RgBqAcoBaQFv/xn/qv+y/wsBqAACAFf/M/4u/6kAYAFUAZsAjv8c/0P/A/+P/2wAnADOAJIAWgAkAKL/6/8VAE3/Bv+Y/wIBdwEQAUcA8v3e/cr+PP+jAIIBdAHcAFIAfv9V/zf/+f4dAJIB6AG2ADP/rv7u/hb/kv9sALUAdQB6AIz/SP9LACAAFQAwAD4AdgDx/8z+4P6c/9P/HgG4AbkAg/9+/kr+M//3/9L/wwCzAQIBnwD7/1v/jv/u/qj+AADyAEoBYwE9ABb/0v4D/6L/TgDaACIBjABn/2P/o//5/yQA6P9oARcCKQG//13+kv7e/pz+hv/1AOoBEALCAJ//kv6S/sv/RAEjAi8BDwAU/9z+tf5G/7IAawHxAFv/Z//K/z4A7P/j/iL/hv8SAPD/9ADWARIBTgDm/hj/2P/RABQBxv+G/wH/1f5k/zcAzwCaAEYAaf9R/8P/BQAHAQwBJAB+AHoAPgD6/wr/Ev8LAJwA3/+r/+r/jP/J/+f/5P/1/2j/LP+i//7/vgCoAWYBEgHAAKv/Ff/p/k7/NQBQAIT/xv9DAP3/4f8//1r/ff+J/2QAkgC2AF4A+/91APz//P9BAGUA5wDZ/1j/Qf9C/+L/YADZAHoANwBX/+L+hv8AAMUAogB7AGsAyP9c/4f/cP8t/5X/8f/bAC0BxwDp/+L+5/4z/+b/8P9jAAMBuAC7AA4Aaf/o/kn+vf4kAI4BCQIkAVz/ZP75/jYAFgEDAWgA3v/8/pz+m//KAKAB2QFmAVMAYP/W/kr+F/8x/03/4wDBABEB3gAzABgA4/6L/9f/nwAeAaUAQAE0AAMAr/9A/2H/bv/6/3YAOwH8/yv/Z/9a/6P/i//K/xUAnwBWAB0AqQBlAEMAbv9V/yMAz/8DACIApgA6AToAwf8x/0b/If9k/jn/sf8XAPv/uf+LAOEAugB7ADcAJwDx/57///9+AD0BpgCN/6b/Zf/b/z//Df8dAM3/uv81/3L/AgDS/9j/zP/XAOoAZgCK/zv/ewBMAPv/AQDj/7r/4f8wAPv/1f/a//j/cACFAP3/3/+O//v+Cv9W/8f/gABsAH8AkgDd/03/Ev+p/yYAxwCEAFEAtgAhAM//Vv8dAOMA7gAlAEf/kwAKAZoBGQGU/9z+Df7d/h0ABAF8AEj/Af9U/90AtQBn/7j/1v9DAOAAcgDuADsB/gCyAOP/xv92/6L/1P/A/8X/Of8KAKAAJABw/2H/6f/3/1UA8v/i/2QAfwB1AHgArQA9AJn/MP9G/8r/NwBxAPEApADO/2n/Fv9e/8z/TQCwAMkAtwAaAJv/cv+e/xYARQBqAFwAEQDm/3b/k/9CACQA2P8SACgAYAAgAM3/3/83AMAAWgDt/2D/Hv8+/4H/IwBZAJkAVgASABkACQDM/8v//f+V/3T/aP/f/0kASQCRADgApv/y/uT+Vv/B/zcAsP/Z/44AMACs/+7/qADnAHEApf+f/xsALQAkAAoAvf+T/3H/qf8zABcA1v9l/4X/PgCWAJcA7P/N/9z/mv+k/+7/vgAhAaEAEQDG/0T/Dv9N/+b/swCsAHcAbAAPAJz/Y/8j/3//cQCUAGcAWwAOAPX/9v/W/+n/sv+y//f/LwAVAOL/RgApAD8ASAAKADsA/v/W/xUAWAAHAAMAHACK/8j/LwD+/yMAgwAJAAoAhgAMAAgA+f+I/2//ff+Z/wQAhwCOAM4AUACO/6P/4f86ACEABwBTADEAf/8F/0n/CwCvAJgAEwC//3//ef/c/2UAtwCmAD4A3f/p//b/1/+1/8b/2v/e/+r/wv/X/8L/nf/A/+j/PwBeAFoADwCf/3r/eP/Y/1MApQBSALH/sf/d/8T/wf8AACoALABDAFMATQBFAOj/mv+x/+T/PgCcANkA0gBVAKb/k/+1/2L/Uv+h/wgAaQCaAH4APwAZAJf/L/+R/yEAigCfAFoA5P91/3T/lv/F/yQAIwDG/xQAuACXACAA1/+V/5X/x/8OALQA9ACNADAADgAiAOX/g/+S//X/HgDB/2//mP/9/0UAbwBhAFsAOgDo/7P/oP8fALcAsACPAIwA+P+K/8b/pP/J//z/0P8rAHsAOwDe/9H/2f/c//f//v8dAEgAgABTAAoAUAA5AN3/7f8vAEcALQDx/8j/xf+z/9H/BQAiAGIAhgBUAC4AKAAeAEEAYgBEABIA3v+u/9f/CQDf/63/jP+j/9b/nP9e/6T///8JAN//3v82AFEAAgD0/wEA3f+1/6D/1f8EANr/Z/8P/yX/Wv+r/zAAggBiABIAxv+K/7T/IQBTAHoAXwAJAAUAVwC6AKoAUgDn/5v/oP/o/ykAEwAFANr/mv+l/8b/9f8xAFoARgAKALn/mv+q/7T/5//+//r/AAAEABAACADs/+H//f/7/yUAXQA2ACQAKAANAOD/q/+V/7H/8v8fACoAFQDu/+L/5f/0/xcAFgDn/+z/IgAQAOb//v8VAA8ADQDi/73/2v/7//n/6/8FABUAEQA0AE4AYgBeADIAGgBOAHQAMwD2/wkAKwAUAPf/8P/n//P/7v/G/9v/BgAdACkAHgA3AC0A4P/N/wsAJAAsADsAGgAXAPv/rP+2/+v/5v/W//P/IQBNACwAz//A/8//zv///0wAigCJAEcA7f+g/5r/1/8EACMAQgBEACkAAQDM/73/6f/5/xgAZACVAHkAAACZ/5T/1/8QACcAQwBdAEIA8//E/7T/u//u/08AjgBlAAwAov96/4L/of/8/3YAngBLAOP/kv+p/9f/0P/e/xUARgAWABMAMgAgAP7/0v/W//r/UABcAC8AKADU/5L/c/95/9L/CwAKABIAQwAbAMv/t/+8/+//FQAqAFMAUgAIAKL/eP+Y/6//w//q/x0AHQDg/6D/Tv88/57/BwBKAHQAcQBDAPz/zv/w/xkAHwAmADgAPgAIAL//lf+0//L/7v/s////JAAnAPv/8/8MAEUAYwBdAGAAKwD2/+r/6f/s//D/EQANAPb/9//N/5j/iP+2//X/MwBaACMA/v8DAP//BAAiADAALgAoABIAKwAyAA0A8P/k//D/DwA6ADcAHAABAOj/yP/A/+v/9f8HACQAGAAdADIAJgAPAA8AHQAsACUA9P/v/wYA8P/3/y4ASgBCADIAFQD+////7P/m/w4AGwARABwAGAAHAO//3f/T/9D/9/8SABwADQDc/6r/jv+2/+H/AQAOABcALQAcAAkA+//5/wsAEQAlADAAIgASAPL/3v/g/+T/7f8GABEAGgA3AC8AHgAXAP7/7//y//T///8jAEEARQA3AP//1//V/87/v/+v/8v//f8pAD8AIQD0/8j/vP/R/wYASQBkAFwAEgCy/6D/xf/i/+f/4f/x/wMA8f/v/yAAQQAqAAYA/P8HACAAKwBBAFIANAD+/+3//f8BAAsADwAHAPj/4//j/+//+/8mAEUAJQAIAAgA+v/2/xMAGAAFAP3/5//R/8n/0//K/7H/sv+z/9f/BAAcADIAFwD6/wUAJQAmACgAPwAoABgAAADV/97/9v/7//T/+P/9/+v/yv/C//H/FwAXABsADgAHAB4AFgD5//v/CQAQAAgA7v/h/+T/2P+9/7X/zf/n/+r/9P8FAAUA7//R/9D/7v8HAPj/7P/h/+n/FQAcABcABwDq//3/DwAYAA4A5//o/xwAUgBOABwA8f/z/wwACQAYACAA9//5/xsALQAWAN//wP/T/wQAJAA5ACwAAQD2/wQAIgBHAF0APgD1/73/qv+q/7r/5/8XADgAKAD1//D/EwARAPv/AwAjADkAGwDd/9j/7P/q/yIAjACSAEcADwDs/9P/of+M/wAAuQDtAHsA0v9m/27/uv8qAGQAKQAIADkAVgAGALL/y//Q/7z/0f8FACMA/v/O/+j/JQDu/3L/eP/+/1IAFwCu/6T/7/8YAPb/3//+/zMALQDw/+z/GwAzACgAHgAPAND/h/+U/wgAVAAnAPn/+v8jAB0A3v/U/wEAHQAYAAEA5v/a/+H/3v/c//f/FgAPAOz/6P8GAPj/rf9+/6T/4//x/wgAMgAvAAgA5//y//3/5P+5/7z/5v8DAB4ALgAbAPj/9/8qAEIAFwDj//X/JwAlAAIA/f8GAAAA+f8JACsAMQAVAA0AGQAeABEAAAAHACIAKAATAAMAAAD+/+3/3v/2/xkAGgD0/9P/1//j/+T/7f8FAPT/0f/r/yYAPgAWAPb/CAAuADgAKQAYAPz/4f/e//D/CwAIAAQAIAAzACoAEgALABIAFAAcACEALQBHAE0AMwARAPv/9/8HAA4ADgAdACYAJAAHAOT/3f/2/wIA9f/1/wIAFgAuADIAGADy/+X/7v/x/+f/2v/b/+7/9f/8/xAAFwADAPr/CgASAAoAAQD6//7/AADz/+r/7P/+/x4AMgAoAAAA4//o//H/7f/l//H/EQAlAB4ADwAMAAoA+P/d/9L/1v/e/+3/+v/p/9f/4v/u/+f/7P8EABgACADg/9r//P8CANv/zP/j//T/8P/r/wMAFQD7/9D/xv/b/9r/0f/e//r/DAAOABYADgD4/+H/3//9/wQA5f/Q/9z/9/8IABIAEQAMAAwABgAKACAALQAkABoAHAArACgAEwAJAA4AEgABAPH/9v8AAAAA8f/c/9T/3//p//D/+//4//3/FgApACkAEAD5/xAANgBAABUA6f/p/wEAIQAbAAMAEwAmABIA+v///wAA8//4/w4AHQAMAOn/4v/2/wMAHwA7ACIA9//v/wgAGgAJAPb/AAAZACUAJgAkAB8AFwAWABAA9f/k//z/GwAaAP7/5v/2/yIAKQASAAIA+//7/wwAMwBOADYA8f/I/+P/AAADAAoAIwAzABYA9f8DABIA+P/w/woAEwAFAP//GgAxACgADgDz/+//3f/K/+f/DQAGAOb/4/8BABQACAD1//7/DwACAOX/3f/u//f/9f///xgAIQANAPr/+f/8/+7/3//g/+j/5P/j//j/DAAIAPb/3//k//b/AQAVABMA9P/e/9v/4P/t//3/BgD8/+z/9P8EAAoAAQDz/+7/7//s/+H/2v/k/wAAGQAXAAUA6v/X/+D//P8LAA4AFQAUAAwACgALAAEA6v/s/xMAOAAuAAsAAAAJAAsABgAAAAEADAANAA8AEwAOAA4ADAANABoAGgAHAPT/8//9/wAA+v8AABoAHgALAPn/+P/5/+3/6/8AAAwA+//1/wYABQD2/+3//v8YABEA8f/Z/+L/AQAaAB4AEADz/9H/2v8JACQAJAASAPr/7f/w/wIAEgAYAA8A9f/z/wUAAgD0//P/9f/y//b/BgAYAAQA3P/Q/+X/9P/y//3/EQAYAAoA9P/t/+3/6v/m/+T/6f/v//H/+f/5//H/8f/2/wMAEAAAAOX/6f8AAAcA///+/wUACQADAP//AgAEAAUACAAXACMACwDl/9v/8P8JABUAFgASAAMA8//v//L/8//v////IwAfAOj/zP/m//j/4f/d//3/FQARAO7/3//2/wEA9v/v//z/AAD4//L/AAADAOn/3P/v/wYACwADAPz/8P/p//n/DQD//9X/zf/r/w0AGwATAA4AAwDy//H/AwAOAAUA9v/x//P/+/8FAAoAAgD4//v////9/wAAAAAAAAYACAAEAP7///8OABYAFgAXABIABQD9/wYAGgAcAAYA9f/9/wAA/P/6/wEAEAAPAA0ACQAGAAoAAAABABIAFgAYACIAJQAaAAoA/////xcANAA9AC4ADwAAAAgACgAFAAcADQAPAAMA8f/r/wAAGgAaAAUA/f8DAAMAAQABAAwAEwAJAP///v/y/+r//P8cACcAGwAYABYAAADj/97/9/8JAAcACAAeACIAAQDo/wAAJAAzADAAHQAHAAAACAAQABwAIgAUAAEAAAAQABoAIgAkAAwA+/8IACEAKgAaABIAGAAbABcADgAVACAAFwD6/+//AQAWABgADwABAO7/4//p/wcAJQAdAPr/2f/P/+z/CgARAP//4//V/9X/6P8CABwAIgALAPj/4v/X//P/GAAZAPX/4P/9/xgAGQAKAP3/7v/j//n/GAAfAAkA8v/h/9L/5P8KAB0AEgDz/9v/3P/n//T/AwASAAAA3P/J/9r/AgAcABAA9P/q//P/+P/1//L/+f8VAB0AAgDs//H/CAAEAOz/4v/i//L///8BAPz/7f/p/+T/4f/p/wEAGgAiABQA9//v/wMAEwAQAAkADQAGAP3//P/8/wkAEQADAOT/3v8FABQA/P/b/8L/xP/2/ygAGgD5//D/8P/5/wEACwANAPv/4//m/w0ADQDY/8P/7/8iACoAHwAKAO7/1//n/xAAFgD9/+//+v8GAAUAAgD4/+v/9v8GAAkAAQABAAQA+v/7/wcAEwAQAPv/+f8AAPz/+f/3////AwAEAAkACAAGAAsADgADAPj///8MABAAAwDz//r/EgAaAAsAAAALACMAKAASAPn/9v/8//P/8v8DAAwACwAEAO7/3//t/w4AGQAEAOD/1P8AACYACwDT/87//v8dACcAGQAEAPL/7//7//T/+f8MAA8ACwAHAA0A/P/g/+j/DgArAA8A3//V/wAAMAAvAAsA5//Y/+b/AQALAPn/5P/s//P/7//5/w0AFwANAP3/8f/v/wQAFwAQAAMA8f/v//n/DgA1ADQAGQDr/8r/2f8BACMAFwDl/7//zv8AAA8ABAACAAAABQD+//3/AQAUAC0AFADx/+P//P8VABQAGwArACUA+//i/+z/9v/6/wEADwAGAOz/4f/0//r/6P/r/wgAEgD4/+L/9P8CAPz/8v/5//P/5f/v/woAFgALAAAA7//f//f/FQAOAPT/+v8ZABoA/f/i/+L/7//y//f/CAAYABsAHAAWAPX/z//e/wwAKgAvACQAFQADAAMACQADAAsAFwAaABkAJAA3ABgA5f/Y//r/IgArACUAGgATAAkA6v/g//L/EAATAAIADwAiABcA1f+n/9H/EwAzACoABgDZ/8f/5P8IABMAAwADAAUA6f/V/+v/CgAEAPT/AQAPABAAEwAWAPv/0f/e/xgAPgArAAgAAgAIAP//AgAhAEIAQwAfAP7/9P/8//v/BAAlAEAAPQAMAN3/1/8BADYALwD5/+D/+f8TAAYA8P/w//v/AQABAP//+//z/+z/5v/b/+P/EgArAAAAvP+q/9P//P8OABQAJgAlAOb/lf+L//H/TABLABQA9f/z/9L/wv/m/xwAQgA3AAwA4f/S/+r/AgALABAAIAAZAOX/uv/J/wsANQAmAAsA8v/n//r/FAAIAOL/6/8gADcAHQD8//H/7P/n//r/GAAjABYA///n/8v/y////zUANQAAANL/1P/y/xIAGgAPAAcABAD8//H/+v8JAAgAFAAaAAQA4v/a//n/FAAhAAoA5//i//f/EAAQAPH/zf/b/x0ASwA1APf/wf/F/wEAPgBDAA8A4P/u/wsABADs//f/HgAlAA4A/f/t/9P/1P8KAEMAPQD6/9j/6/8DAAUA7P/i/wAAJgAlAAUA5//V/9z//f8VABsAGQAKAPf/5//e/+T/AwAlAB8A///q//P/EgAmABgA9f/p////GAA3ADsABgDJ/9P/KABUABoAx//P/ycAUwAsAO3/2P/5/yoATgA/AAIAyf+8/+//RQCAAGYA+P+o/8n/GAA3ACYADQADAAgA+v/Y/8//+P8+AE4ABACv/8P/JgBOAAkAwf/U/wwAHgAFAOv/4v/h/+7/+P/n/+X/AAAHAN//xP/v/y4ALADp/8P/9f8gAPj/2v8ZAFUAJwDI/7f///9AADQA9f/K/+j/IQAkAOz/y/8CAD4AIgDX/8v/BwAoABcACQABAPr/AgAIAO7/1//t/xMAHQD9/9f/0v/g/+L/1//k/woAFQDp/6n/rP/5/yQACADV/9P//f8SAO//vP/O/w8AMgANANT/1/8CABwADADq/+L/9/8XACgAHAAFAPL/9v/9//j/DwA5ACoA7f/b/w8APwAaAM3/tf/7/1UAXAAHALX/uf/9/yIADwADAB4AJgDy/73/zP8fAE8AGwDJ/7T/9/9LAD4A3/+I/5r/FwCCAHcA8P93/4T/4P81AHkAbwDR/yr/af9mAOsAUABj/yz/yf99AKYAIQBr/1P//f+XAHsADwDk/+b/6f8GADgAFADA/wsA1gDUAJv/1v7K/yUB1wBp/yD/YgAmARcAzv4b/3oANwHFAOn/R/8m/7b/tgAvAYAAUP/Z/qL/xQAXATEA7v69/igAnQEOAeb+3P1u/7cB9wEaAIP+0P43AMAANgAKAHEARQBO/xv/WQA2AWEAPv+J/4oAaABy/37/ggC6AMf/b/9hAO8A+f/4/pb/+gAyARYAOP+N/1gAgAD9/5n/5f9uAFEAtv+D//j/LwC6/37/EQCUAC8Agf9Q/2n/nv9kAD0BuwD5/iX+iv9vAWIBqP+Q/j3/pgAtAXMAVP/n/oT/nAAeAXEARv/o/sT/xgDUABUAa/97/y8A0QCuAOD/Z//4//QABAEEAFX/1P/YAAIBCAAz/5P/pwD6ABQAPP9r/x8AbAAfALX/jP+o/wYAUwApAJv/TP+2/4UAwwATADT/Iv8FAN0AwADw/3z/3v87APH/lP/c/18AXwD2/6H/k/+1//L/OQA8AOv/of+3/x8ATwABALn/7/94AKoALgCS/5T/SgDlAKYA6f+f//b/SQA7AAoA7P+7/7T/9v8oAOb/h/+U/+z/DgDR/6L/6f9DABAAiP90/ykAxgBtAJP/Xf8aAK4AWwDL/8f/CQAIAOH/7v8mADMAAADO/7r/vf/D/+L/OwB/AC0Adv9V/xUAnAAWAFv/jP9UALgAhAD+/zz/9f7f/0wBdAEMANT+Av8kAOsAlACh/zj/5P+5AIQAlP8d/5//bgClAEgA3f+o/8X/KQByAEoA3f/g/2AAjAAKAJP/3P+MAMMAXgDa/67/x//o/xYAWgCLAE0AmP8Y/27/bwAJAYAAb////pD/UgCUAGgA5/9C//v+vP/sAPoAqv93/vf+gAAzAZAAgP/3/kX/FADCAKoA2v9f/5H/4//j/+X/MABOAAYArv9v/4D//f+PAJ4A5P87/2n/DAB2AFoAFwDo/8n/6v8oAEwAWABPADMA1/+a//P/kgDWAFkAlv9O/7z/bgCdACkAsv+G/7//GgAoANP/TP9L/+b/ZAA4AJf/P/+k/zsARgCw/1v/CgDbAIQATv/R/tD/IwE0AfT/5/5H/5QAQAGuAKH/+P4u/x8A9ADrABQAVf8u/3X/7P9kAKUAfgAIAI7/aP/y/8UAFgF0AIT/av81AB0BMQFLAG3/Xv9QADEBwwCJ/+3+y///ANMAz/85/3b/AQAoABoAAwD6/wwAuv8//2j/RQDYAEQAY/8z/8X/XQBRAL//Pv+W/2oAlgD5/2P/gf/r/9r/4P8yAGIAMACR/1X/u/+GAOMA7//1/mL/nAAhAVkAm//e/2sAwAC+AIAAUgAqAIUAKwEhAXsAvv/O/50AEgFEAVIBwABw/3H+av+YAVIC+gAe/03+wv6Z/4wAGgGZADD/x/35/fz/uwH5APr98Pst/TkA+QHzAJD+9vwj/bX+aQAXAUcA0v4n/pX+iv92ALEA7/+m/kr+if/mADsBRAAv/1D/6f+SAC0BqQFqAfv/kP9OAUAD9gKyAM3/gAFKAy8DqgEgAe8BJQJ8AQcBqAFcAr8BbgCM/6//qgBYAeMARf+G/Wj97P6CAKkA0/6V/Mb7IP1Z/+//qP7p/Dr8TP2l/v/+QP53/Zv9uf2H/er9Nv8IAO3+G/2x/Av+wf+VAIUA9f9l/1f/3v/dANYBYAIjAt4BcwJKA74DXwNMA7sD+QM7BMcEwQUnBScCYQBoAtwFsgU2AXj+2/9xAtkCxAAE/4/9nPy1/XP/5f8c/vf7w/v/+3P8v/y5/Ar9t/wg/H378Puf/QH+fPzg+lv7gv3N/l7+LP1e/LP8rf2v/iP/PP9x/5//3P8PAKQAWwEzAiIDuQMIBPkD9wNZBC4FTQZfBnYFmgWjBwUJGwYfAYEAVAWRCVIHwgB0/T7/uwI7A0QA+P0u/Un98fyD/Hf9zf10/Nf6LPoH+7P7w/t6/JT8E/yh+if6OfzS/Xb95voF+mL8f/48/oP84ft0/N/8bP22/pL/vP99/1//vv8nAH4B+QLqA+AEzAQCBKUDXwU5CPcHmQV9BbEIiAuECdkDbQDRAiYJTgsoBSr+av02AokE7wGA/wf+Q/y3+gv8Vv9e/8771fjq+Ov60fty+x/7c/uw+676CvoC+yb9Tv3T+kn52frc/V3+uvx6+5r7C/yO/PH9gv8RACf/dv7l/sP/nACuAaIDiwW9BZgEiAMVBIAGYQiKCDsHigeVCn4LNQdyAbIBNgjPC4UHOAFi/woCTwPcAaEAVf/e/fD7EfxN/mj+GfyC+Vz5ffvI+0r6QPlg+rL8kvsh+UT5i/ux/Fn6OvkN+4L8LfxV++T7UfxC+yH72vy3/nP/Fv/v/vf+8P7k/0IB9ALFBHAFiwWJBGcEegajCIQJ3we8B14LOQ37CLUB3gB6CH4NjQnUARP/fgJABPgCHwFv/+b9Uft3+2X+qv+D/Zv4XPdk+lD8PPuB+FH5s/sn+x35kPgx+4T8YPpv+En55vvu/Bb8rvt5+wn7C/tg/FH+/P7M/uH+IP/k/jr/4gAlA8oEIgUnBaoEWQT0BcIIjwomCTIHrwmeDY8MhgV7AOAE+AvvCysFGwABAlQE/gLGAOH/fv/M/O/6nPz6/mP+TPpb9834rfsh/Hb5CPgU+hn74fmV+BT6DPxD+jD4+fjN+/H8cvuj+hL7Xvuj+xb8GP0+/nr+Bv9d/4H/pP+M/wsBJAPQBL0FjwV6Be0FcAdyCUoJAQgyCnsO8w1wBpQA+QSaDKoM7AUFAb4CSwVRBLEBo//P/rb9bvzJ/GD+if5W+3L37vdp+xv8V/ne98b5+fr6+Kn39vlN/CT76vdb9/35d/yu/Cb7G/pK+sf75vwn/QH99Pw2/oP/DgCK//z+7P+RAasD9AXNBisGEAUCBgkJpwl8CBcJig2nEDcLmgK5AO0HEA+2DOgExABoAokFagQLAVP/g/7I/Tr8iPzc/qf+3Po795H3MPrA+hv6Tvq3+cf3zvVH9xv7YfwS+lX2H/bJ+a38gfxo+pL5hfqC+8f8X/3//C/9kv5aAHYAbv+m/3wA1QG6BJsHjghaBoMFGAjqCbsJawmvDF8QWQ33Ba0BsgVuDfUN5gc6Av0BLAUgBDsB+/+k/6f+z/vx+1L+xP6v/F/43PYZ+Ov5Yfum+qn5c/dq9TL2fvmr/HT7evde9XX35PpM/Hf7lPq3+jr75ftG/F78xfwG/tv/5AAuAGL/XP/GAOgD4waICEMHugVhB+gJfAo4CS8LkxCMELwIJwGmAm8LlQ+eCxYFvAEPA30DfgJPAVIAXv9d/C/7nv0iAKD+DfnI9VX3s/lB+8D6wvmE+Dj1hfQV95X7Lf3E+C71wvUq+dP7Z/sW+/T6Qvon+8j7ZPwN/c79w//q/33/of+d/5cA8wKzBrcIUgfoBYAHSArlCgAKBwseD6EQJQscA50BRgktELkN2QUqApcDrAM5AYsAewL7AFv8UPqw/HcAT/+X+vL2U/ZZ+Uj7bvow+bX3rfaS9YD2svox/Hr4jPSC9aL5qftB+h36Afvw+in7xfqW+zD8Av1k/zoACwAd/1n++P8nA/QGQAlhB6kFwwaRCWsLSAo2DNAQsxFNC84BNQEoCr0RsQ9jBv4AWgJvBI8EXQJxAAj+Pvti/IH/owHk/pz4C/Xg9Rn6uPzU+iD4i/aY9WL1zPap+mX7VvdW9NX1x/n2+iH6Ffp9+nT6+Pr5+477DPtW/Ij/OgEsAFb/6f7T/5gCqgbPCY8I1AVOBq4JiQxIDAsMLg/7EEQMugN1AbQJuRFeD+kFoQD3AlYFpwNGAQUAPP4e/HT8IP+HADj+P/nK9Qj2wflb/HL62vdo9sH2gPbJ9u35tvrl95L03PUm+nL7Tvr8+a76nfqS+hD7lvtT+y/8Zf9eAdMAKv/2/dL+7AGdBmkK0Ag8BXoFTAn6DNcLeAtQD2oRzgygA9UABQmaETgQoAdKAc8CZgSMAtQBLgH1/3z8fPuR//8APv57+dH1gfag+H/7n/xc+QT3O/X39Nn2/vhk+8n4+fTR9aH4+vob+oH53/o8+oL6Qvv3+hT7vfs1/+IBegAb/0/+4/7DAXAFpQlSCewFGwbUCSUN4ws8CnMOZRIvDtYEwwCBCEURNxCXBy4BwAIdBXIE5gEWABT/p/yA/Ez+dQCr/wf6J/bi9UL5Fvwn+rz45ffh9rv13PWI+Uz7A/iz9OD1H/nc+uv5BPq4+r/5S/pG+0X7yfp/+0v/sgF2APf+KP7D/mkBPQVkCTgJAwZbBtsJHA25C94JWg5GE6EQ5gUR/+MFihBeEsAJDwIoAvgDqQOHAa8Ar/+i/MT7vv2IAA8Acfq49fL0Bfg5/Gn7avkE+Nb1bPWy9Vn51fs6+I/1T/Y/+fH6Qfl/+Sz6nvls+2z8vfv9+dD5F/4XAegA5/+o/qL+QgAYBKoIcgmuBnEG/AkSDYcMHwplDRwTHRF0B/7/WwWKENcRXwliAqUC1wV6BBcBagAf/zv9zfuG/FwAAQBP+8f2H/Ul+ar7rflV+EP3Nvdy9hX2oPno+oL33fQO9pn5/Pqq+On4A/ol+tH7j/sF+3j5evk3/joBQwFL/9r8w/2iAM0EOwmqCEsGCwYPCYANTgwLC4gOgBMrEyII9v+WBAQPTBMhCzUDOQOpBQkGkgEZ/53+jPw9/DD9egDUANf6zfWc9Ff4E/zt+Tf4cfc+9rn1MfXe+Pj6CfjE9Sv2B/kc+o74SPhk+Tv6HPzw/BH7FvmZ+NL8LgFnAUcA0P3n/XwADwSBCFEJdwdgBpMIsw0ND0MMIQzHEAwUJw3hAsYCVgyKEnYMSwPsAYkGlAdzAqv9jvxR/cP8ZPzs/isATfxz9kn0wfjb+6b5GveE9R73B/dA9kD4KfnT+LX2O/bN+CX66fgH+G34xPrO/Nf7b/p/+FL5hP2pAIQBIf+m/Kz9xgDZBKAI6giwB1UGpQelDFYORg3QDB4QUxRED1wEMgHqCFISRBDDBQQCaATiBugDVv7w/Xr9Qvxr/OT9rgD6/U/3S/Ti9Uz7lvzl+GX2PvT79Ov1xfcS+2H5tvZY9cv2QvqS+Tz48ffC+NT7Zvxe++f5rvhZ++r+TgFfASj+zPyg/ukCMQgVClkJBgcfB3kLkg7RDdILlw4FFNkRVwgcAhwHHBApD7AHKQMxBdMHRwPj/tX9Zf7I/cj6HP0cACv+Yvms9O/2ivqS+jb5cfbu9bj1Y/Vc94D5bfmy9zX24vZF+Rv5n/gt+In4rfpQ+3r7vPp1+fX6Ev6AALAAJP7y/Lz+/QHhBrsJwAnxB6oGNQrVDcsNNgwLDkoUFBRTCpwBQQQUD8URYgqXA70DGAc9BNP/kf6K/nn95/qx/HUAP/8J+rP0O/WA+tf7OPo59yv1AvY49S33ifns+Bj4MvZa97X5e/kH+XP3LPfP+WP7Xvw4+zj5LPqX/Nr/yACo/of8Jf2qAHQFUAkKCuEI1AZiCCsN4w1FDHENPhOxFc8MrQJxA60MlBJiDcUEewPrBXsFmgGS/p//RP6v+ov7/P61APL7BvWV9Wz5yfuA+ur2Z/ZF9sn1q/YV+AT5ofjm9tL2CPlH+Qz5bvdA96L5G/qd+0f7kvkJ+oj7Lv9BAP/9wvxY/Ej/ugSJCHcKMwj3BQ4IrgvnDqoNAA0EEtIUgA+5BTQDuAsIEvQNIwZlA/AGJgeeAoH/Lf7w/Sv8tPtj/8QAkfyy9Q30Yfml/Pz6Zfd+9Vv2W/aX9lj3pvcW+Hj3gvfA+JD5IPn+9t/1Efjd+vD7O/tX+W/5T/sj/hQAKv6t/HT8fv6uA6wHSQpnCJ0FHgjwCzIP5A09DPcQ9xOSEUYJ9AObCR8Puw57CGQEbgcRB+wCR/9L/j//JPws+4z+HAAM/r/2X/Q1+B37vvvo91L24/Yw9lL2c/ag98z4A/jm9wr5Pvnd+Cz3bPbi9975u/v0+/X59/iZ+jv9j//d/nb9JP2Z/bgBQwYJCV4J6wbPB3ML3Q1QDk4MPQ/BFMkSDgvGAzEHaA/QDk8J4gS0Bi0IMQOW/2j+iv5z/fv6o/2DAHn+0/hi85P2lvvs+wz6TPaD9mz2dvV496X3ZPgq+Ej3j/kC+lf5lPf89Ov2qfmg+3f8ZPqi+fX52fvF/jH/Ev7J/G/9yQAZBXcIeAknCFoHLAqiDSoOuAwhDk8TixRpDTEFYgXvC9oPhgsqBrkGXAc9BeL/I/34/mD9O/ww/T3/6v+/+f/02vU7+VX8y/r598r2qvWm9Rf3rfc/+EL4ePeA+Zf50Pil9zP18vaZ+Lb6xPwk+7L5G/nu+qX+VP9W/m39wvwBABsE0gcUCjQIlAf1CH4MVg+7DSQO0BG6E5cP2gYIBRILEg/3DOAG8gXgB4gFIQFV/cv9Uf58/Br9/v46/xL7HvWC9a758PtU+z/4b/dk9gT1hvZ296X47Pd196T5x/nV+HD2WfXB9lH4bPqW+6T7GfoA+YH6gv1m/zb+2/xy/SsAqwPDBisJiQlWCPMI0wsYDusNFw45EjIVxRDeB+IDGAlZD3gO4wjBBegGVwZoAYf9Tv2K/m/9wfzH/jf/sPvU9ez0ffin+937EfmZ90j2bfXb9fD2rviO+Mv3gfgN+fD4Uvev9TD2iffe+Xf7bvsO+sH4SvpP/Yr/0f5i/Q39nv5/AogGGAprCpMImggpC3EO/Q4XDogQURTsEUMKfgT3Bz4PkA4LCTcFfQYICPIC/v3b/D399f2W/Lv9af8S/Bb4EvWM99j7IPuC+dP29fUb9ov1tfdx+PL3gPej90P57fgx9yD2xfUa90P5y/qc+1f69/g7+s38dP/T/rX84fzK/p0ClgalCfEK6AjlB+QKQg7SD2YOpQ9XFPUSJgxyBAgGhg5DENsLfgX3BNgGQQNT/wD9GP4s/hr8m/1E/tr8K/jg9ND3z/r4+1f5W/b+9Yf1Tva991v4ZfgG9yb3e/hG+Nb3lvZD9gn3IPhk+kr7o/pN+e75vPwQ/yH/P/0p/cr+/gFDBkUJTAuJCRsI1QrsDSEQIw65Dp4TRhOqDYcFbAYVDroPWgsHBUIFqgfIBAIAaPzT/LP9vPyD/Q7+FP0z+TT1EvcM+rb7mfk79gv2r/UW9wz4vPd/94z2v/fW+DH4Q/co9v/1Hvc/+Dn6Wft4+sz5rfnq+2r+pP73/XD9UP9+AgoGYQm+Cp0JLghhChgOiA93DocPExTeE5gMKQUwBu0NChEeDC0G+QROBnYEkv9x/eT9r/0d/fj8W/5B/VH4c/Vk9ob6lvzm+S73EPVV9aP2fPdX+Mn3Ofcj95733fdp92H2vfWQ9gD4Q/o9+3n6Rvk6+a77U/7s/rD+V/6X/ywCBwX5CEwKewmBCEYK+A4vEIoOpg5dEvIT6Q0CBnIGkw1rEYsM/QRUBP0GYgZFAXP8uPzN/aj93/3+/QT9xfgX9eT2sfqs/OD53/W29G71jff391z31/bC9rr31PfU9yf3Evae9ef1qvfT+TD7sfpY+Wz52Pt//oz+0/29/br/uAKsBWUJywq3CUQIYwnvDSIQYA/zD7QSTxQ8Dr4FmwUuDLwRjQ0SBioFlAacBbn/rvtA/VP+0P7f/b/99PxZ+E316vVO+sH8PfrJ9gf1EfYN9+X2YPbT9r73ivg1+Bn3ZfbX9Tn2yvbM91j5YPpn+gf6T/rR+5/9Ev5r/qj+6f9AAgEF0QjDCuoJrgjbCSgOWBAED2sPrBJyFGEOyQWjBaQM8RFUDf4FMgQ/Bg8GXgA8/BL8qP1p/v79bP7t/Fb4C/Uh9pf66fzj+YP2x/Qf9jz41/ce9132L/dZ+Df4u/e69pj1wfXX9n74E/rt+Zv5QPkx+nb88v0g/mT9pf2F/64C2AUbCbQKTwmFCI0JOg1gD7gOvBA6FM4U9A2fBFoEeQw8EqoO5wZgBJIGCAW4/9X78vtk/pr+x/46/6v8IPhN9CT2vftn/aL6efYN9ff27fdf93z2DPd1+DT5HPiL9pT1U/Xl9sv3xfhh+Vj5lflE+Tr6tPvr/Pn9iP4Q/y4AfAE/BKYH+Qk+CqEI1wl8DdQPbA8NDygSOhQ5D/QGvwT+CokRuQ52ByUEiQWIBVEAjvwf/dL+Y/4X/eP9mv3w+c71+fWZ+lb9a/t/9x/1GvZj99j36vfo90n4vvcp9xP3uvY59vf1f/b39575Pvqf+XD4+fiw+/L9p/7e/ZX9P/+9AToFAwiQCY4JhwgMCv4M9A4bDzgP6BKQFFwPJAf3A14KkhC8DqoIEAXcBZoEyP9z/HH8mP4q/1j+UP6s/I35MvYw9v/5rvxB/B35V/b29d72L/d49wP49fiO+d73ePaQ9aX1lvb29h/4pPkI+p75mvjE+Nr6r/xc/hL/C//K/7kATAPTBn4JNwoRCdAJNA2jDz8Pag5GETgUdBA4CBwEdwlQEF0PCwlFBBIFKAUPAer8svsB/jT/df4D/j/8yfmN9+b2lPmp+/T7XfrF9qj1TPbR9/34Vfgz+Ev4y/dA9yP2l/Ux9sj2SPij+bH51vi+96b4Wfu8/ZL+Nv7L/a/+iwBvA4cHGgr4CZ8IzAg7DAYP5Q4hDyUSJxWJEbcHogK4B8UPGRFCCisFkgVcBYQBdfvy+YX9ZQAMAeP+k/s3+V73q/eu+Sn7YfyM+474KPY49YD2Xfi/+Nn4Lvma+Jr3afV09KD1rvZ/+Ib5DPqL+QD4Wfge+pj8V/6Y/sf+dP+lAP0CzQW7CLwJ4QifCW0MQw8uDzkOvRBbFG8SKQqKA0YGcw7hEKILWgVHBIoFzQKi/b/6Svwv/zIAF/+Y/D/6jPg0+Cz5hfow/B/8dvlK9pD0VPab+Ab5jvj691f4+Pco9sX0qvTD9WH3CPlj+hf6XPjN9w76Gv2n/g/+g/34/v8AzwNMBrYIDQr0COEI7goZDrYPBQ8vEXgU2hIbC+8CgQSdDP0Qtw0QB1cEHgQHAiz+bPt8/Cf/MgCc/z790fqT+Iz3uPjI+vn83Pwb+nL2o/Sl9cP3vfiI+L74VPiL9/v1zfQY9az14val+PD5Hfrp+Cb4CfrM/Mj+6P4G/rr+WADYAsYFaQhACs0J+AidCrsNYQ+oDlwPVhMIFGsNWASkAnMKrRDEDooHhQOIBNwDe//W+sz6aP7SALEAbv46+9v4iveE+Ov6ofw7/ZX6GfcQ9XP1vfeT+FP4DPgA+DH4Fffh9LPzBPRl9lD5zvpL+m74sPd4+cH8pf7O/g3+bf6IAKACRQU7CAwKSwrsCJkJEQ1KDywPEg89EqwU2Q+oBW4BDgikEMoQTwj+Au0DHQWiARL77vmC/SoBswFf/lb7Gvls+Iz5xvoW/Gz81Prm9431P/XC9ob4ZPj49y330Pbs9gT1TPR49B/2qvhN+Ur5Q/g++Eb69Pzb/hH/9/3//b3/RQL+BDgHnAmjCisKUAoDDPkNDg5gDg8SThXKEcEHEgH/BekODhFVCs0DbAT/BbwCE/w9+er8MQEfApP/5/vS+eD4VPnj+u77vfwv+zz4r/Wj9GT2Bfig+AL45vbS9lv2MPXf85XzQ/V992j5wvmx+PL3vfjT+5v+i/+m/vH9Y//PAS4Eewb9CNYKlgrrCVALzg3UDoAOyBCEFF4TpwrWAbYDJQ3+EcUM2gRMA5IF9wNb/oX6pvzCAIIBaP9k/AD78/ly+ZH6Evwg/Wn7bvjG9fD0XPae9xz4mvf59pb2z/UM9Rz0CPTr9Bn2rfdk+Jj4nvhW+eD7J/5B/2n+gf2g/t4AAQS5BmEJ6gr6CR0JSgpLDcsOdQ7VDyYUhxTWDBQDdAHHCrsRIw/pB+YDvAU6BED+D/vL/NQBDgPj/7r9QPsO+pX5JPoo/bD9PfwA+ar17vRT9fv2C/gw+Lf3TvYs9ezzDfNA82T0X/bm9474PfjI9+z35/nS/CP/8P/Z/pz+0/8YAiMFGQiJCi4LyAlYCp4Mag6tDl8OpRL4FCwQawYvAVoInxCZEAYJOAMBBeMFYAIk/cv7wP+NAbMAUf6J/Pj7Mfp2+gH8hf0E/ZD5lvZR9dL1yPby9h73ePcK99v1BPSL8qLyqPNg9eP2s/cP+Bz4VPim+eT7+f1F//n+v/6O/zkBdwSaBzcK/ApTCXMJiAsfDtMO2w2aEU4VrRKLCCQAugTWDgMTwwzTBCcEzgWTA/39tft1/5UCHwI8/3L86Ptr+mj6zPvQ/fX+cPsN9xj0VPTK9rz3yffd9z33Nfap83/xI/JB81H1rvaU94j43vc296n3DvrI/QwA3v+6/t3+XQDeAqMFMQjGCq4KQgoCC60MkA7QDUwPERM7FEkO8AQuA1QKPxGDDiAH7gMWBt0GLQGZ/DT9ogD4Ac/+aP2q/Vf9M/wj+nr7mf0w/QT69vXN9O/1MPc19yr3xPZk9lT1+vKA8k/yk/Mv9fv1n/cF+DX4gvhj+S38sf5T/6/+//0+//QB3gQXCGIKmQpnCesIMwtQDtQOXA+cETkU/BCwB1YCJQY+D5gRhwuLBVIEoQUTA57+2P0HANMBcgDy/db9d/1c/Pf6OvtR/o3+dfuM9o3z7/TC9iD4+/c+99H2HfUz8/Dx6PEw83v08PUc9/v39/d39y34vPqb/mcAjv/W/ar9HgBYA4UGZwn+Cq4KmAnOCXsMlA74DjoQ5hKvE8kN2ATuAnsJphBZDzYIuQTJBewFEgG8/Kv9JQHNARj/0/1G/nf+6/va+YD7J/6I/gX6L/XX83j1tveZ9/n2z/aM9gD1avJb8fjxYvNa9Jb11fcj+fD4svdr+MD75P4gAOT+UP6I/wACOAXSByYKOwoKCRAJjQuwDnAP6A5cECATzRB1CWIDAgYNDi0QpAv7BfYEYQY2A9D+0/0lAKQCSAC4/e39kf5l/o373PrV/NT9FPx296/00vR69nP3A/f49o32APWU8hHx6/GK84307PQF9oz3e/jv9+T3X/q8/RsAeP8A/l7+hgCzA2QGYAhgCpkK/QmJChUMbw4+DgYP2hENE5IP4wZ2A1wICA94D8IIDwXHBZcGEQOM/Z39dgB5AqMABv5m/jr+E/0d+377vP21/Yf6N/ZQ9Dr1dval9rP23fZ29mz03vH08NXxRfNI9GT17vak+KH4wvcu+Jj6QP7P/4L/E/95/5kB3AOQBn0JrgpmCp0JEgvIDX8OEQ4/D9USgBLdC3gEngQ2DEwQqgxBBtgEbwcBBqkANP38/r8CbAKJ/+T9n/3x/Xj85ftU/eX9T/wv+ET1L/UU9qT2j/Zm9pz2fPUp86fxj/Hc8pbz7fMF9cD2Gvgk+PX3QflR/N3+if8I/4/+rf+VAYcEKAiACv4Kxwm9CbsLlg3aDZcO/hFeFOQQ7wdBA0cHHA7iD0UKnwYbB1AHzgMe/v79UgFqAz8CD//3/Z/96Pw1/PL8jf4Y/iT7IfeT9Tb1O/Wi9er10Pei9zn1OfJB8F3xbfJC80701vWn9yz4kPe395L5dfzc/kL/0/7P/g4ArgKBBT0I2wnZCa8JbQpuDJwNpg10D3YSghPlDdMFOATTCQYQbA6fCBEGFgdcBw0Dy/4R/6IBzgL+AB3/BP9m/h/9Pvwm/Z7+df1q+vv2XvVK9VP1DvbU9o/3bfbu8+fxAvGy8TzyKvN+9Cb2sve395j3NPif+qH9KP/w/gv+a/5yAHEDqwZ+CaoKxAnaCDEKEw2CDogOJBAFE44Sqgv4AzEE7guhEYIOGQicBQIHzgX5AIj+RQBxA5oCGwBd/4L/nP53+zH7CP5HAGD+DPk+9U/0S/Xb9XH2j/e/9/31g/Jk8Knw6vEU85zzFPUG9573Kvc59rb3TfvF/l4AHP/T/eP9/v+pA7sHdgpTCuMI1gj6CyUOzw0zDd8PXBSwET8JbwOoBroOBhD/Cv0GhAefCFYEGv+y/gwCIgToAfX/8P/0/9P9wPpW+1/+XQAG/ob42vQX9Dn1BvYu9tX2Ovfh9d/yoPBo8Pfx+PKD82j1V/dI+NH2vvUA+FT88f8RAED+fv2+/mgBqwQTCHYKaQpSCaEJNgxxDugNAA6bEI8TnxBcCEgEnAdODlYPiQpvB+sGBAdoA2L/2f/wAS0DbAHY/+H/J/9G/VX7Dfx6/q//CP0m+Ij0xvMu9Wj22/YW95j2nPQx8mzwB/GG8ijzMfQ89fT2L/fv9Yz2WvnQ/WQAqP/1/QH9nf7dAcMFlgkqC4UK9QiqCTIMxA2NDpcP5hIbE2cNRwZaBFYKNw/1Db8JSQeJB4cFQgFc/7MATgN9AyoBwP+n/rr9jPzw+7P9uP9+//D7sfas80j01/W/9uf2Mvfj9kX0K/Hf7yrx5fKw8zL0m/Xs9oP27fXn9nn6gP73/1T/sv1Q/RT/OwJnBvgJMgslCjsJOAqsDCQOuA5CEVATrxHoCocE9AWeCz4P5QyFCEMHdAZZBFIBGwDMAQQDSgJjAAj/gP4B/ir95/yX/rn/1/6R+rv1efRZ9Rj3GPe79h/3Bvb/84Dx0/Dl8b3ynfOk9MH1SvaS9bX1H/gi/IL/1v8c/ob8Ef3b/w0EUgj3CsMKpwhjCLQKGA5YD4wP2RF3E3QQyginA5cGUg0xEKMM5QfBBRUFmAMrAeoANALYAuMBXv9W/hL+yP10/Sn9wv72/1T+tfm59HLzQvVs9/v3Vfdu9sv0vfLj8PXwNvI88y70wvTc9c71APW99cT4kP1XAKr/gf01/N/9LQEGBfkIEAsUC5IJXgngCzMOZw8rEK0SHhQAD8cGCgMaCEkPQg90Cl8GLAb3BVkCov/f/4QCZwN8AVH/KP7n/RH9lPw0/W7/cQCA/RP4APQ29JL2TPfG9tf2//at9QrykO9s8Gjy+fMo9Jn0oPVG9dX0FPbS+Wf+4v+d/sj8tPxj/7UCZQZcCcQKawpCCbkKbg1vD4EPZxCoE3MTXg0HBYsDdQokEJ0OoghEBekFDAXjAen/tgD4AnsCaQDx/jz+3v3S/H38Uv6AAAQA8PsA9njzPvX998L4C/fY9VX1B/Th8ZXwAvGn8u7zjvRx9UX1UvQR9F32vfvO/ysA1f2A+0n8cv+TA7oH2wreC8gK8AleC1cO8g7bDnMRMhWqFKILswJTA+oLIBJgDkkHlwR2BYwEpQDq/v0AawOOAuD/iP41/qj9svvS++b+ewFuAF/67PSc89n1Nvim95v2Kfbg9Rv0L/Hj78/wsPLt82j0B/Uk9Y/0pfQp9/T7j/+k/0j9Evya/eUAiAQQCBwLkwulCo8K0AwqD+IOOA8QE+0WEhMpCHQB7wVhD3sRHgtIBbQEQAb6A9v/9f7vAAoDzwFu/0T+iv0X/Wb8iv0wAAUB+P0A+DP0vvQx9+v3jvYm9qL2Fvbj8orvju+k8Z3zifOK86P0JPXj9C/1pPiF/b//lP5G/Ir8Jv8cAogFLgkpDB8MPQqVCk0N8Q/oD6cQ2BR/FmMQLwXGADoI7hBHEbkJFASfBFYF5gIY/+X+wAE/A+QB9f5F/ZP8wvya/SH/0AAxAI38afff81P0v/YO+OP3vfar9TX0pfEV8PLvavFM8/zzivSW9Er0bvQf9nL6jP7Q/zf+Vfz9/G//1gKCBjgKowzUC7kKbgsCDk0PYw+tEt8W7BVQDAYCRQIRC5YRkw6gBycF9wXJBGAAhv0v/yQCIwO3Abb/wf0l/Gb7g/xt/3oBqAAQ/GD2WvP680n2i/ep9333mvbJ84Xw+u4S8DjygPOE9GH1dPWK9PrzffZk+0z/0v/e/Zj8gP08AOoDRwi5C3kMPwuqCu4M5g7NDj4PVRPgFysU3giFAKgDVA0fEYEM0QbjBWIGOwOJ/gj99v/7AkYD5AGJ/4b9cPv3+nP90gCCAl//Xvmo9KjzfvW39gj3Lfel93P25vKi77nuZ/Bx8tXzJPXN9Rv1rvMf9Az4JP3S/wD/Uf05/fH+gAFgBIEI1AvUDOMLdgtNDbsO9g5sEMMUIxc3EVwGKAHRBuEOHQ9ZCcEFbwewB0YCjPzl+6AAZAT8A4kBqP7Q/Hr7f/vL/ZsAmgGC/sH4GvT68sr0XvZG98D3qvf+9QnyCu9w7nHwAfOV9PT1MPY49e3zpvQA+fP9UwBk/5T9mP0+/wsCmQXSCaIMTAw5C4cLlg2DDsQOSxKmFq0VXAwnAiQCMArwD1ANtAeTBnsHBQU8/zr8yf4hA94E7AINAF39svuo+zf9LQD2AYwAxfsU9krzT/SK9mz3iPef9yv3TPQc8Eruqe+o8oX0DPVT9dX0CPQJ9M/2w/to//7/IP4J/c39tP8TAzQHiQvpDF0Lbgq+C0MO1g7JD+UTNxcSE58HPAAbBBINRRBXC44GMAZgBh0DP/6D/fYAYQRTBEEByP2B+1b70vyw/2sCowLm/kr4h/Of85P2V/i69xH3lve39iPzXO/r7pjxIfTt9P/0CPWM9MDzrfSr+Jj96/+d/p38uvwM/9kBqwROCEULEAzHClwKRgwbDj8PhBHBFXYWbQ5gA+7/RwexD4UPRQkBBX8FVwWdAQ7+qv6eAlUEqwJc/7H8EfyK/Lr+nwEGAw8Bh/sZ9gn06PVI+I/4i/fQ9qn2C/X28drvJPCE8nr08/Sq9A70w/Nc9BP3fvsE/y3/w/yK+x79qgCAA/cFPwmWC6ELGQqECjkNMg9QENYSHxYdFGgKvwDtAFgKJxGODosHzgOlBAkErQBc/oP/8QLpAxoCD/8i/Ej7W/zR/0UDagOP/wL5XvTD82z2Bfn5+MX3mvYE9hT05fAz70nwoPPF9aP1qfSW89rzbfXF+ED9uP9O/xb9QfwL/vsAZATeB1YLXQyrCnQJ0Qo0DtUPgBB+E3kVbBG2BiP/AgNPDK0QWwyRBusEbgR9Adj9Sf7VAQ8EEgOnALX+qfw8+4v8nQAOBN8CHP0E9zX01vQ194n4mfjy94j25PRX8jLwBvCt8Vj0qPWE9Xb0c/P782X2Afv4/iMAaP7x+5H8Sv/vAiIG5giDC8MLlArhCY4LMA6eD8QRtBQZFQgODQM7/9EFPw9tEAQKMgVuBBUETgAf/Rn/VQPOBBgC1v77/O374PsI/j4CQwT/AG/6/PTI84z1zPdg+YT5VvjM9f/yHfGL8GXxL/MA9bD1zvRn8/3yzPSy+PL8mv9c/2H9B/w5/YYAGQSfB0oKvQv/ClYJ7wkrDIAOShBGE4EWwROLCZv/OgAUCi0RxA63B5IErASjApb+4PyIAJEEhATsAbD+V/xn+rn6Of+sBI0Fs//X9y/zr/PK9hv59fnR+L/2rvSz8nPxsPBJ8YzzrPXc9R/0nfJE81z2EPpF/Sr/uv5w/W38pv0FAboEeAj9CpsLkArdCSILDw1tDjUQaBTaFp0R4wbf/9YDcAwoD2kL3gZKBqEFVwGW/c79ywEEBKQCawAL/hL8nvpS/D4BjgTmApv8DPfb9Gf1FfcB+Of4cvgP9wf1ZvLm8JbwUPK+9ED1Z/Qi80XzD/Vx95n6b/3L/v39w/wf/Rf/PwJFBe8IoQt3CzUKtwnzC2kO0g7sEMQU/hV6D1EEJQD2BfcN0Q6+CUgGzgVpBLz/3/wM/+UC+gNgAYb+Af23+6n7HP74ASgESQFn+8n2vfSP9Q/3jvi8+cL4PPZE86Txr/E18kPzIPSv9Fj0evPA8yf1Mfhn+6b9vf7I/Rf9Xf18//gCugaHCv0LKQuJCREKvww2DkMPCRIyFt0VgQylAcb/9AcJEM8O+AjgBcYFNQPG/bf7Lf+TA4cD2gC+/lT9kvti+g/9eQLfBCkBA/r29Kv0j/ZI+Bb5OPmG+F/2t/PX8RTxc/HG8qX0uvUU9XLzxPK09IP4o/z3/hz/A/7N/H/9fP8KA3wHDwuiDCMLmAlTCooMag7RD3wTHRdwFL8J/f8hARsKWxCZDSAIrQadBVkBefsx+0MBgQVLBAIAPv0q/Nb6EvvI/vkDMATN/q34yPVU9sj2Qff8+D36Mvlv9frxUvEP8tHykvPp9Kn10vQB8+jyCvav+cX81P3P/dH9d/1q/tYAjAR5CCILwAvyCqoKYQu9DKIN5Q8+FZoXRxJKB2UADAWNDEUOSwoIBxMIOga0/2v6tvvwAbQEmAJb/2r9A/wU+qP6Vf7MAvwCQf74+Gv1avVm9rD3X/k4+b/36vRs8vDxzPG08hv0AfUo9c/zJfNL9AT3G/qj/DH+Rv4P/sz9Bv/vAVwFggmnC0MLeQodCicMeA3ODbEQAhXJFmsQ4QWqAYIGGA0aDe8I/AaKBw4FrP46+wP+pgJxA3cATv69/Sr8X/pE+5T/dwPWAW/83Pc89iL3l/fb9834//jZ9wf11vI68mfyRvMV9CL1/fS681nzhvTo9/r6+vwl/lT+if6w/u//rAJXBv4JaAviCgQKxgrSDEoN7g2AEXgWXBbVDJwCpAFHCV0PrgwlB7sFpgbIA1j9kPom/hgDmwNnAJX9q/sr+tf5YvxtAYEDhAAl+/j2WvZP9yr4JvmY+Wf5aveM9Jby7vG38unzd/UC9g71m/Mr82/1yvgz/Af+PP7W/Vz9qP65AG4DuwazCQ4MpwtUCpwK+QtgDcoOtRLeFiIVZwubAeEBxwneDw0N0AYWBQUF4gLm/bj7IP88AjACLgCR/hn9YvqB+Hf7ngFCBLsAqPls9fj1nvfr+Bn54/hA+Pr17vPX8l7yPPK/8tH0j/ZH9sXzf/Kh9LP44vyu/sn+F/48/UX+4gCtBFUIFAqFCkcK6QpEDHUMvwznDmQUzRfWEj4I5gBZBLoMtQ+/C1YGQAX5BFgBZf2+/F4AxgLyAXIASf66+6z4H/mm/vUDrgNU/VL3xfVB98H4ovi0+M341/f79a/zmPIz8nryUfQ69s72zPQU8lbyBvYc+1T+t/7c/Uf9K/4AAH8C/wRzBwEKIQtXC/cKKwtHDDoNZRDXFHcWOBHeBpUBqAVNDSgPtQlBBTAF2wUpAm38cPsA/7sC1QK4AIr+u/s5+VP5y/2+AsoCzv0i+FP2Ifep9273fPdl+En4Y/bb8xryKfGr8ZPz9PUp91f1PPNz82D2F/v7/dT+1P5u/nn/zQBnAvAEjgc2CnMLlAtyC9ILRQxQDcoQoBRLFfsOjQXeAqwH7w28DeQHBAWGBTsF4gDY+yj8OQA+A38Cw//j/Dz6A/nH+qv/7QLaAGD73fZ19pz3o/c692/3rvhh+Of19/IP8VfxoPK19FT2P/bl9LPzLfWm+Ef8bP7F/hX/Zv9GAGMB5QLoBasIqQojC9cKowsIDFQMrw0qEUsVExRCDFUEUANUCTEOTAw4B9kEXwVhBDsARfyv/CYAlQLWAgQAFfz8+Gf4tPxYAWUCBf+L+Ub3Jvdd90D3+vY4+OH4xfcs9S3yrPDC8NjyePUV95v2xvRn9D32wPng/GL+Kf+w/30AMQG8AVwD/gUtCQ0LHQsOCw8LZAsMDJEOXhNzFbsQJwgTA0cGdwx4DbIJCQb+BYcGGgMt/i78Vf7GAeACJAEm/tn6Bvmg+lb+VwHAAK38K/lN9wH3Ivfa9r/3pfhn+H72evNk8efwI/Jd9Gv25Pbt9c/0EfWJ95P6T/37/o7/KQBCALAA0AECBKUHUwruCjoKEgoUC/QL1wyLD/8TeRRADkUF6QGuB+cNGQ5pCZMFFgZmBVgBPP2k/F4AWANTA+sAMv34+ZP4+Pq1/4sCjQAQ+/32Gfbw9mn3X/cu+Ab5OPhb9VvyuvA/8RPzS/U+9zH38/Ub9e318vgU/G7+/v95AKEAdQDkAOcCKAYjCTUKLAoMCgkLZQsCCx4NVRHhFL4RmwhJA2sF3QsYDtsJHQdlB9YHFQTR/UT8OP/DAjgDFwGt/iT8APrl+RP9WgA5ALf8afgA98j2T/Z79u72oPiQ+PH1aPNN8XPxkvLQ88f1pvaF9t718fUT+PH6of1Z/zkAcwBPANQAJgIJBQwIwwlECroJLQoKCyILDwzsDtsSKRPYDHwF6QO2CDkNGgxgCMsGEgcZBZQAyv3x/tABhAIrAbH/iv0c+9D5gPuj//kAXP5V+mr3+PZ99lH2bPfE+Ez5Cfca9E7y6fGO8l/zDvWs9h73lPYE9i73t/mQ/CP/cADnAIwAVQB0AccDAQcrCQEKAgrkCV4KMgqKCmoNpRHdE4gPcAe8A1oGdQtYDBIJrwdOCGIHzAKX/eb87P9RAlMC3QDG/mP8YPpx+pn9QwDb//T8Ovk89/b1WfWM9mz4f/kZ+FL1aPOq8iTyXfLK8xn2rfcV90b2jvZO+B77q/3t//cAfADf/yQAaQKzBSEISwmeCSIKvQqqChQKqAvFDwQTihGICg4FdwUyCQMMiAquCDcIgAZlA5v/Uf7u/+QAbwFxAVAAE/7R+gv6nfyN/woAuP2S+ir4XPZI9RT2Cfhr+Yb4N/Yo9PDyEvK/8ffyFvXK9hf3vPb89hL4vvlB/NL+jAC/AAoAggBSAhsFVwecCGUJ8wmvCn8KCAoRC4wOIRPREqUMwQUUBAgJzAwADD4JuwcKCHgF9QAg/nn+ywAVAk0CjwHY/g/7U/mX+8b/IQFi/on6RPhL94r2MfZi9035I/n19jf0h/Lt8X/xGPIq9Dj2A/eY9nX2rPdu+Wj7wP3G/5cAcQBFAKYBcgTLBmEIjgjICDkKMAsSC5EKPgwaEZgTnQ8PCFMEuQd0DIcMNQm5B0QI9AZrAmX+jf6kAK4BcgElATcAM/0y+mb6/f2PAB7/vfsv+RT45/bT9YP2o/j9+BL3OfRh8gvyW/GS8U/zePU89yz3aPbr9iT4kvpt/Zn/FAHlAFsAIQGHAxsGuAfvBzsI5wnZCs4KsgpuDCARBhPxDlIIsATUByYMhgyKCjsJLAktB3sClP55/igAuAHVAt0COgH3/CD52/mb/XoAmP8r/Nz5cfgz9y/2VfbR94n4ZPcd9SHzh/G98AzxLfML9kL39Pb19V32b/j2+sb9pf+qAA4B1gC6AW0DgQVVByIIEAmLCoML6wowCn4LJBBKE/8PWwn8BGsHfwxGDfcKpggGCEwHhwP//1v/AQBVAeMBDAJnAYT93fml+bD8FABp/1T8kPnk9x73RfaK9oL3z/eS9n/0AfOr8e/w7fDH8pX1GPfy9uX1YfZw+Cn72f2e/+cAUwE9AQoCYgNvBVMHLwhqCZgKOQvCCv8J6QupEBkTlg+dCLYECQj8DN4NXQvSCHkITAcsA2z/Sv6J/60BuAL1AjUBmfy4+L34lvxgAMv/X/wk+Vz3q/bQ9bz1qPZP98n2FfUR80zxB/AO8FXyePWc9673s/Ya9+f4k/sF/rj/MgHXASICtgLlA/EFqgeiCHgJtQqqC2IL5AqWDOkQvRLQDhYIPAVLCRQOOw6XCrgHvQfvBqoDz/9q/gAAHAKlAyUDHgCy+2T4s/nj/WYA+P7F+r73B/ep9sz1SfWa9Xn2RvZ19Hny8+947kXvHvI59jf4f/d79tX2Ivkz/Gv+MQDCAYsCWAMrBCYFvQZKB+8Hywm/Cy0NDAwvCzwOcRLsEvkM9AWDBbkKAQ8kDoYKeQimBzYFbQH4/u7+dgCyAZYCRQLC/gL6Wve0+Y7+HACC/S35e/bt9UP17PSE9W72k/bi9Kjy2/BO76HuB/Bk88v2OfiK9yX3JfhJ+ij9wP8rAtMD+QPnA1QEzwWjB6YIjwnRCgQMGQwKDPMNkBEQE7gOKwirBT4JMw5hDhgL+QjgCBgIJwRg/5f98P6cAa4DXQMEALz6Y/dG+Rz+jgAc/pn5EffN9l726fQ89Fr1zvZl9rjzafC/7cPsYO4Y8uj1Z/fb9lz2gPfD+QD8oP7DAaAErQXJBEEEcAVzB+IIkgnmCv4MyA0EDfYMkw/EErcR+AseB2MHAQsLDdcLhwpGCqEIVQRv//X8jv0m//8AoQKlAZL9i/j89qD61/5v/3X80vjz9qb1O/T380L10/a69pP0jvGB7grsBOxK7470gfjK+Ev3Z/bP9/b6X/4dAvkELQbiBQYFfgUCByEIXgnLC7UODBBUDn4Mbg4tEu4SWw4sCP8GKwotDQ8NoQr8CEkHUAT/AL7+Ff4j/vP+6gAqArL/dfoc98H4Q/1E/xD9UvmK9ir1Y/Q+9Pj0aPVu9LPyBvE37+3sPOvD7KTxkfbE+Cj4evd4+MH66/1sAaEEbwZ6BiYGpgbBB7oI+QmnDNkP5hAWD3sNhg8aE+ISpw2kCL4IJgxzDUQLIAnyCIcImAVRAWn+fP2O/a7+jgAhAWD+vvm39+T5Ev0+/WP6hvcv9oD1o/QO9CX0FfQ78wDyfvBt7h7sb+si7ubyu/Yp+Cv4kPjN+a77tv7BAlEGxgctB5UGdgfdCOIJ+QoYDcQP0xBaEJIQ6xHvEVwOzAnrCBMMfg7SDGAJ+gdeCCUHZgPW/7b+Tv+3/8v/e//l/SX76/iy+Wb8b/2p+yf4mfXQ9Jn0CfWE9Rn1ovMy8VDvWO4D7SDsN+2O8BH1jPeo95z3Yfgb+zf/LgMVBm4GSwVmBU0H8QlqC8sLFg1DD0YQpQ91D1ERZBPJERkNuQnGCU4LHQuwCYsJ7AmFCNUE6gDl/mj+if5W/00Am/98/NP4kvdx+dL7Q/yZ+uv3YPWH87TyG/O084nzqvJa8ZbvQu2N61vsIvDs9Dz4XPky+Rv5GPrO/AgBUgXhBy8IkQdlBw8IQQnXCmIN5Q/4ENQQ5RDcEbcRlQ54CisJLwtMDXYMkgnpB5wHyAZ7BHYByf+Q/7///f9T/0z9sfry+KP55/v+/Kv7bPgh9W/zYPOH9LH1V/VQ84rwZu537T3txu2m78ny6vWh97b3e/eF+GL70v8rBKoGvgaNBU8F5QZYCX8LjQ2MD3cQkQ8FDpQOMxFKEtYPwQvCCasKIgvKCXAIiwgrCa8HqwPR/wb+Qv7M//4A5QDI/vP6Ifja92v5DPsc++D5+fdy9fzymPGf8YvyRfP68q/xSe/w7Hbsru4W8yL3KfmI+X75aPpv/Dn/lgK0BasHdAh5CJAIHQlvCv4MoQ/OEHwQNxAvEToRCQ6OCdsHbwqrDS8N9wmCB98GGgZSA0kA3f95AegCqwJ4AF397fl49+f3sPpA/S/9F/oZ9nXzrvJl83z0yvT78wvyg++M7dLs3u3f8I70qfcD+YD42/eC+BT7a//qA/QGKgjNB4UHGwgCCbwKLQ1fD2YQ7A/gD/sQlxA9DecIpgfUCuYN/AxGCVQGAgbwBf0D7wGaAdMCcgOrAaH+rfuO+VX5xPqj/Ff9n/ud+Lf1ZvNl8pnyqvOd9OLzu/E17zLt8Oyb7t3xqfXt91H47vfg92T5X/xCAIcEcQdUCAQIXweKB4kIQAogDeoPNxFZEQMRPxCoDWcJVAexCY4NXQ4CC1oHaAZIBqMENwJOAYgCfgNfAkMAGf79+0z6pPnd+sn8xvxg+gH3b/S78x70n/SR9EXzMfE+7wDu8e387gjx2fN09gL4aPgO+Eb4U/pV/h8DmgawBx8HXwZ2BuwHjQp2DY4P9Q+KD4kPaQ/sDdgKbQhaCYUMaw4HDWwJcQYMBREEUAM6A8YDJwQdAwUBzf58/LT6N/o++/f8Qf1M+0n4c/Wk8xTzUPMS9C/0yPK08NvuIe6/7nrwVvNA9sz32vd39zP4uvqS/skCFAYvB6oG7wUmBokHVwmtC04OJxC5EDEQTQ/LDfYKUAiCCLwLpA6YDYQJWga0BegFxATfAkcCuwLFArEBrf+C/a771fq3+x/9AP3v+v73zvXG9Ab0mPN48+nyo/HU74rufO5I79TwJvOc9U734ffw9/T4Z/vT/ooCWQWpBtsGhQaJBjUHsQhlC2YOVxDaEI0Q8A8TDj0KrAbqBhgLag+lD+4L3QdVBb0DMgJjAXkCXwT7BHADHwBA/Fv5vfjQ+qD9Wf5N/MP4j/Vp8wLytvFp8vryRPId8Abuae2S7jfxafT39g34t/dI91/4efvw/0EEDAf1B1gHOga0BXAGDwkJDZMQHxJjEaIPqA3jCuUHBAfxCdkO9xA0DlgJwAVVBJ8D2gIiA34EMQXnA+MAnP07+/X5LvrB+wr9ovw5+hD3mvTc8rbxYPG38eXxIfGI71/uou4T8Ebyb/QK9h/39PdR+c77Gv9lAukERgbMBukG8gZmB6EI7ArkDWgQqxGTEfgPwgyWCPQFcwc0DCUQ1g8JDAYIdAVtA4gBPQFsA1EGkAY7A2j+VPpc+NL4Wvt8/nn/xPzQ91zzCPHR8NDxZvNT9Anz3e+97PjrNe7X8UD1l/dr+A34n/ei+Aj88ABwBWUISAleCHkG6ATBBZAJog5nElkTDhKuDywMvgewBDYGWgwoEncSWw0kB5sDlgI1ApYCUARIBiEG8AKR/ib7m/kC+vz7Ev4b/if79/YS9AfzwvJD8s7xifGY8NTuRu1r7bTv5/KC9bH2bvbV9Xn2ZPk6/hoDLwZRBzAHlgbcBWsFpQZQCi0PqhI6E50RPw9RDJ4IAgYdBwYMuhDfEMUMGAhFBacDDAJuASEDugVWBtADnv8N/CX64vkp+/r8p/07/Aj5ivXL8vfwRfCt8HHxefFL8MTuRu477yrxbPOD9TL3Rvg9+SP7VP4WAiMFvQZVB0oHsQYgBtQG6AluDuwRJBOBEoMQsAxTB8UD2gWgDHgSiRKmDQwIAARLAdb/2ACABM4HggdgA6L9FflC93H44Psj/5v/ofyZ9+HyCPBB72HwcvKZ847yxu9N7QHtBe9P8o/1pfc3+Pr3VvjB+vr+bwO1Bh8IAAj0BqoFXwWFB+wLpRBcEy0TKhHnDZMJrgUWBWMJxQ/dEisQOwoDBUMCWQG8AaADAQagBiQEkf80+7/4sfjZ+vP9rf8N/mT5RvQg8XnwYfG/8qPzPvM48YDu/Oza7dPwbfQk90b4Jvj+91n50fxaASQFEAdfBwoHlQZcBiAHkwlLDYoQ9RG2EQ4Q0AxgCBUFBQYlCy8Q1xDvDN4HrgRAA2kCUgKNA2oFsgUZA7b+j/pf+Pj4wPvI/o//vvyb99/ydfB/8MHxF/Oo88/yv/CO7uXtbu918qX16ff4+DL5jPke+07+JwJ1BXQHJAj5BxgHmQbLB9IKnw5hEWYS4xFlD8IK5AVFBOMHCg5wEbMP0wo1BnMD6wF1AZsCyAQGBo4EqAAL/NL4S/h++s39dv+Z/cL4pfOg8AXw8/BL8j3zF/Ny8Rnvte1z7jPxoPRI95v40PgB+Xn6nf2SAdwEoQYpBxEHpwZoBlMHKAocDjcRRRJUEd4OFwvrBvsEfgcuDXQRrBDJC6QG0AP1AsgCIANOBFwFngSNAVf9D/or+af6Tf3o/qj9qvnT9GDxVPAo8aryufNp86PxU+/u7ZLu8PDq83X2+/ef+BX5Rvrb/HAA3QNJBlUHRgeeBgYGtgZRCf8MFhCZEZ0RDhBgDIcHsQSmBkEMtxBoEFYM2gcLBasD4wIFA0sEbwXMBAgCKf7p+pf5bvrK/K7+Af5p+nf1k/Ex8AHxovK+82TzmfFs70Hu/u458cDzwfUe9xv4OPm3+uj80f/pAo0F+QYWB2AGuwVuBgsJqwy7DzMRCBGVD5AMXQhcBTwG5wqeD4cQcg0RCbEFqwN+AmQCugOLBd8FlgNU/xP7zfhO+fT7hv6u/qr7wfZp8j/wWPDH8Vbz7PMD8/rwB++N7vPvovKI9b/3D/nd+cf6c/wQ/zgCLgUnB7MH8wbcBQAGQQjhCycP4hAUEdgP7AzDCKMFRwZrCrYO0g9WDVUJyAVwA3QC6gJeBIwFEwW0Aib/oPue+dz57fsD/gL+Rvvw9vTyr/B+8LLxPPPp8+zy7vAs7+zufPAC85X1jffM+LT50fqs/Gf/awIMBb8GOAfMBioGagZYCHYLeA5SENMQFRCNDWcJwAV2BR8Jzg28D9kNDQppBskDOgL/AUAD9AR7BdQDPQAo/Ez56vj0+pL9Wv4R/Kf3YfP08L3w+fFx8wX0C/MN8VHvDe+N8BLzlvWF9674a/l2+mz8bv/JAoYFFAdIB48G1QU4BmIIpAuLDkEQpBCBD3kMUwiOBXQGUQoSDvoO6wycCUkGpgM0AnoCHASGBWIFUgPr/038y/lx+Rz7I/2C/WL7l/fp84fx2fCQ8ajyOfO48mnxTfAo8C/xGvM09fz2Sfhb+dn6L/0HAMwCywTRBRsG9gUXBhMHJwnQCyAOlA/6D/kOTAywCCwGswbjCSoNIQ6JDLYJ1AZaBKgCYwKDA/wEZgXrA9oANP1O+lz5evpf/Dn93/ux+Br1bPJG8WPxJfLV8uDyQfJm8QLxhvHg8rb0ofZr+C/6FPwu/lMAQQLJA+UEkAXgBU0GVwcxCWsLbA3zDosPdQ5xC90HLQanB+oKWg2WDSQM5QkeBz8EeAK5AowEEwbJBYgDAABk/O/5g/kD+/b8gP3f+6r4HPVy8iLxM/Em8g7zPfOO8qHxSvHZ8S/z3fSX9kn4LfpQ/Jn+rgAzAkUD+QOABAsF9gWYB7wJywtODUcOXg4HDUcKjgf2BgEJ5wtgDboM5grKCIAGWQQ2A8QDTwU0BncFIQP1/8X8ivoA+hH7jPzr/Jn7zfiO9enybfE/8fnx7vJ280vzivLL8bDxe/Ip9F720/hO+3v9M/9jAD0BDgL7AgIEFgVkBgUIxwlmC7IMlw1+DdwLOwlhB+YHUwquDGENWwxfCvAHgAXfA9kDNQW5BvwGXQU+Aor+kvt5+kL72/y9/fz8rvp+92b0RvKQ8R3yNPPg86Xzn/KJ8RzxqPEv80L1j/fN+cv7eP3F/s7/xwDxAUgDlQS5BeoGRwigCfIKPAxTDVoNlgsHCa4HvAgsC+MM8QzMCwYKyQeRBTsEhQTVBcQGagZ3BF0BAP6H++H6xPv3/D/9BfyN+Yf2yPMV8sLxaPI283Xz8/IO8lDxV/FI8uTz4vX99xr6LPz8/VH/PwAdATICggPYBBkGZAfHCC4KfQuuDFINlQx3Ck4I/QfSCSkMSg3VDF0LMwmUBmcEyAPzBKkGVAcxBlgDmP81/Gv6pvoc/F39TP2J+3z4HPVv8jDxevGp8tLzGfRC8+fx9/Ao8Wzyf/QH96b57fuR/aX+Xv8yAG4BEAO0BAcGHwcvCGwJ0wpSDG0NJA0gC6QI2AeZCVYM5g2JDcwLTQmfBpsEUAS/BWoHzwdNBmED1/+r/Pr6QPu5/Nr9jf19+0L45PRm8pTxN/J18zP0z/OG8hDxZfD68JTysvTw9gf50vo//GX9fP7C/1sBGQOQBJgFZAY6B2QI0gl6CyANww1sDLoJ7QeVCAYLBg1VDV8MrgpJCJ4FHwS5BG4GeAffBscEoAEK/lj7wvoQ/Jv9yP0//HL5MfaE8y7ybPJv8wb0rfOY8lbxmfDZ8BvyJvRp9o/4WvrG+xH9R/6d/ycBzAJABDIF4QWuBtQHSwn9CqkMcg1hDNQJ4QdXCLYK0AxQDZgMGgvICA4GVwTBBGMGagfpBhoFbwIq/zL83/qi+z79//3//Gn6Jvcu9G/yLfIA8/bzMPR68zfyMvEN8Qfy3fMm9nz4evr9+xr9Fv4s/3UA+gGLA9gE0AXABusHTwnLCjAM1wwIDNsJFQh/CKsKjAyqDKILQgp8CGsG8QQjBWwGFwdJBnEEAwIk/4P8Sfvy+1D9w/2O/BX6MveU9PDyrfJn8yb0JPRn82/yxfHi8d/yrvTw9i/5GvuC/HP9JP4I/2cALALaAwMFxwWTBqkH9whoCrQLPgx1C7UJeQjwCIIKqQulC/kKFAqlCLUGOAUTBb8F/AVDBdUD4gFk/+X8qPsY/BT9RP0b/PL5VvfV9Dfz5PJ+8wT01vMr84TyP/J38mbzFfVa9675ifvD/HL9Df4H/5kAagL4Aw8FzwWgBqYH9wh9CtMLUAxWC3kJQwiiCBUKSwuUCysLIwpWCBoGiQRzBDQFqgVBBf0D1AHq/kH8Mvvf+/T8KP0a/BX6c/fG9Pjyr/Jw8yT0NvTM80Pz1vLe8s/z0fVm+Mr6efxv/eT9T/43/8MAsQJlBIsFUAYJB/AHHAmSCrULows1CnsI3AeiCNYJmAraCqIKmgmfB4QFcQR5BOUE9ARuBC0D5wAH/sj7NPv5+9H8zfyv+5P55PZs9PbyxfJW8+TzJvQd9O7zwvPp8/H05PZO+Yb7Kf0o/qL+Bv/R/zgB8QKOBOQFFwdECFUJZgpRC4ILdwrGCOYHnAgRCvQK9QqCCpoJ0wegBVIEdwQ7BWMFpQRQA1ABtf5X/HL7MvxP/XP9Mvzk+Sj3lvTl8o3yO/MW9GT0G/Sh82LztPPP9M72V/mx+0j98v0P/kr+JP+7ALkCiwTbBcwGnQdoCDsJKwrsCtQKygmiCG8IPAklCm8KNgq+CacIzgYHBWgE2AQVBVsE7gJVAYD/ev0S/P/70fwv/UD8Vvoh+Pj1KfQ681zzEPSg9Kz0XfQQ9Cb0+/So9u74N/v8/AL+WP5s/t/+KQATAvsDlwXkBtoHdQjgCGAJ+AkYCmwJjgh8CE0JBwrvCUoJoQjPB5AGXgX/BEMFAAWzA/UBcwAj/779rvyE/Pr88fy6+8b5x/cW9sD0E/Q79Lj05PR69PnzCvTb9DH2y/eU+VP7qfxp/dr9b/6K/xcBxAJFBIAFiAZkBygIAAnwCZwKiQqnCZgIJgiOCEsJtgmGCb8IiwchBuYEPgQhBBwEnANoAsUADP+J/WX86vsR/Df8nfv7+eX3CPbE9Cv0FPRh9L703fTS9PL0gvWO9vz3qPlm+/X8E/69/jX/+f85AdUCcQS5BbMGkAdpCDAJ+AmvCs4K+AmqCP8HcAhdCasJ8QjIB64GuQXEBBcE8APbAzsD2AEpAJv+Qf0u/Kv71fv4+yj7OfkR95L10PSB9Hz0wvQe9TH17/TZ9HX12/a4+Kz6Zvyi/Uj+nP4d/0sADALTAzcFMAYPB+UHswiJCXsKLgv/CuEJtAhnCOoIXgksCYEIvwfsBtMFqwThA4MDOQOfApQBJQBe/pn8Yfv2+gT72voE+pv4BPeC9Vr0y/Pu83f08vQ09VD1kPUk9kr3AfkD+9L8EP7J/lX/EQAaAWwCBgSnBewGsQdVCEoJWQr0Cs4KQQrdCaYJUgnjCK4IngghCA4H6gU2BcIEEgQqA3EC4QHLAOz++PzS+2b7APte+qz56PjO91r2C/V69JH0xPTS9O70OfWP9Qb26vZp+D76A/x1/Yf+Qv/c/6QA4QFgA8kE6wXaBsMHnAhlCR8KugrcCkYKTAmTCH8IzAjkCGsIYQcpBhIFQwTEA3wDHANeAjABqP8C/oH8Y/u5+nP6Pvqn+XP47vav9QL10PTn9DD1ivXF9dX1A/a79hn40vmH+wn9Pf4W/7D/ZACFAf0ChwTmBfkGwQdNCMwIcQkuCpoKRApPCW8IQwiUCJcI2we1BrsFIgWkBBUEhAPzAikCBQG8/3f+Rf0i/EL7yfp8+t75yPiT94v20/Vw9XL1wPUe9mv2ovb59pb3jPjD+Sj7lPzO/cv+qf+SAIoBjAKqA+0ENwZOByQI0AhwCe8JAAqMCdoIWQgmCB8I8QdyB8IGCwZVBZEEygMeA4wC6wEPAfX/tv5x/UX8Svui+iz6svkJ+TD4Wvej9jH2+/X+9UH2qPYO9273APjx+Cj6WPt1/KH90v7S/5MAbgGKArUDuQSpBbAGrgdkCNYIKglPCfgIRAjDB8gH+wfTBy0HSQZhBaUEJgTJA1UDqgLiAQIB7v+R/jP9OPyk+yH7efq++f/4L/hW97n2ffaE9p72xPYQ94j3Evin+HD5dPqL+5P8h/2F/on/jQCFAWsCSAMmBB0FIQYaB/gHnwj9COcISAh2Bw4HUwfMB8QHIgc7BmkFsgQGBHUDHAPdAnACoAFtAAn/sv24/DX86fuA+9b6CPoq+Tv4Yvfy9gb3c/fj9yv4Yfiv+C352/nH+uH7BP0b/gz/1/98ABYB1wHaAhAEOQUiBs0GVQfLBwAI2AdvBxIH6gbfBrwGaQbjBUIFmgQRBK0DSwPEAgkCOwFqAIP/gP6M/cr8Ifx4+7b6BPp1+fT4bPj899b39fcm+FD4hPjT+FH5BPrk+sP7iPxK/SP+GP8AAN8AwQGlAoMDWwRDBTkGDQedB+oH6weHB+wGiAaUBsgGtAY0BpUFDgWNBPoDZwPiAl8CxAH9AAAAzP6J/X78xvs9+776Qvq5+Rv5Zvi892b3effN9y34i/j5+HT5APqr+nb7WfxF/Tj+J/8AALwAZQEaAvwCEQQXBe0FlQYhB4YHmAdUB+cGngagBsAGtwZNBqcF8gRZBOMDcgMGA6YCJAJOAScA9f7v/Rz9bvzR+zD7i/rj+Ub5wPha+Cf4L/hr+ML4Bfk8+ZH5LPoD+/j7+fzz/dL+mf9VAB0B/AH3AgEECAXxBZQG4Qb5BgEHCQfuBsEGqwa7BrwGYwa3BfgEYAT1A4oDEgN6ArwB0ADI/8b+4P0U/Wb80Ps++5z65/lG+d74qviX+IP4cPh6+L34M/nE+Wv6GvvF+2j8Ef3I/ZL+eP9vAGwBUQInAwEE4QSsBUkGuAYRB00HWgcrB+IGmgZWBhgG1gV7BQQFcgTPAx4DYAKcAd0AGABI/2X+cf2A/K/7Dvua+jr60Pla+fD4r/il+ML4A/ld+df5a/r4+nf7+fuV/FL9G/7z/uH/1QC0AXkCOQP6A6gEQAXeBYoGEAdCBx4H2gaLBjoG8wXYBdYFrAUjBVUEbwOXAtABFgFcAJ3/1f4I/jP9TPxp+6r6JPrF+WT5+fia+Gn4aPiL+MD4EvmN+TH64fqF+yX81fyd/Xz+aP9MABgBzgGFAlMDMAQJBcUFaQbxBkoHYwdLByYHBQfiBrcGcQYTBp0FGAV7BMYDBQNHAn8BpQC9/9H++v09/Y385PtD+7D6Jvqp+Uv5EPnx+O74D/lS+aX5Afp4+hD7vftu/BT9xf2J/lT/DgC7AHABOAIUA+0DrgRVBd0FTQabBskG1QbABpUGZQY2BggGxwVyBfsEYASkA9sCEQJDAWgAi//D/hL+Yv2o/PX7V/vN+k363PmK+VL5JvkM+Rn5U/mx+RX6e/rz+on7L/zM/Gj9F/7b/qb/cQA+AQcCwgJvAxwEywRtBeIFLwZUBmMGXQZMBkAGQgZABioG4wVbBaME4AMcA1QCiQHNACIAev+3/tz9Df1g/Mf7Ofuv+jD6uvlW+Rf5Bvkd+VP5o/kG+nD63fpF+637K/zH/H/9U/4z/wkA1wCpAXoCRgMFBKgEJAV1BaEFuAXKBeUFAQYYBh8GFwblBXoF2gQnBHgDzwIeAlsBjQDB/wH/UP6n/fz8Sfyg+xv7s/pV+gD6ufmR+Yf5ovnj+UT6svob+3776vts/Aj9uP1z/jn/BADTAKEBZwIgA80DdgQRBY8F7QUqBkoGVgZcBmMGYQZHBgAGlgUOBXMEvAP4AjICegHIABEAXf+l/uf9Ff1D/Iz7Avug+kT66vmh+X75efmS+c35KfqY+g77ifsT/K78T/39/cT+nf93AEgBCgLAAnEDHQS+BE8FxgUNBicGKQYpBi8GOAY0Bh0G2gVpBc8EIQRgA5ICvwH2AEEAl//e/hr+Vf2R/Nv7QfvG+mb6FfrJ+YX5VflG+WP5o/kE+nr6//qN+yH8vPxk/R3+4v6y/4kAXwExAvMCqgNMBNcEQgWNBcAF5wUCBgwG/QXXBaQFbAUjBb8EPASgA90C+AENAT0AiP/K/vr9Lf18/OP7U/vO+mL6Dfq/+Xz5UflJ+Vv5ifnW+UX6zvpq+xT8w/x2/Sz+6v6z/34ARwEEArsCdAMtBNYEagXfBTYGbQaFBoYGeAZZBiMG3QWOBSoFqQQEBE4DmQLkAR0BQgBk/47+yv0S/WX8x/s1+6z6Nfrc+aL5kPmW+av5zPn/+Uf6q/ov+8f7c/w5/RT+9v7J/4UAKwHNAXsCMgPpA5QEKgWnBQEGPAZfBm8GZgZQBi8GAwa8BU4FtAQGBFYDqwIBAlMBmwDc/xH/Qf57/cP8IfyN+wX7jPol+tj5oPmF+Yr5r/no+TT6lPoC+3n7/PuY/Er9AP60/mj/JADiAJMBOQLlApYDOQTBBCsFcQWdBacFogWcBZgFhgVZBQYFmgQZBIgD7QJYAskBMAF9AK7/1P4J/lX9vPw5/Mz7Z/sA+5/6TPoS+vH58PkO+kn6nfr5+mD7zftG/M/8eP06/gD/vP9pABcBxQF1AhsDtgNFBMUELgV7BbcF5wUGBgsG/wXoBb0FcwULBYkEBAR8A/UCYAK6AQEBOgB9/8r+I/6H/fb8dfz5+4X7H/vH+nv6TPpD+mX6pPrs+jj7jPv2+3P8Av2f/UX+9f6k/1AAAQG0AWUCCQOhAzQEvAQwBXkFoAWxBbwFwQW+BaoFegUkBbYEPATBAzwDqQIBAkoBiwDF/wH/RP6N/eD8TPzZ+4D7MvvY+nn6K/oD+vz5EfpA+on64Po8+6f7K/zI/HL9If7e/p3/UQDuAIEBHQLIAnYDEQSYBAoFYAWWBa0FswWwBZ0FaAUTBawEQgTHAzQDigLgAT4BlgDa/wj/Nf5r/an89/tc+9j6Yfr5+aP5a/lV+Vz5cfmW+dD5Hfp/+vL6c/sG/LT8ef1F/hb/4v+qAG4BIwLNAnADDQSaBBIFdgXEBQIGHgYfBg4G+wXfBagFRgW/BB8EdAPAAggCTQGQAM3/Cv9R/qD9+/xd/Mf7RvvY+n76OPoJ+vD58fkP+k76qvoY+5H7FPys/Fr9Gf7e/p3/WgAcAeYBsAJxAycEzQRoBfEFYQauBtIG1wbMBrsGmwZkBg4GoQUgBY4E4gMlA1wCjQG3AOT/Gf9R/on9vPz5+1778Pqh+lb6D/rU+bD5p/m8+fb5Tfqw+iD7q/tQ/Ar90v2b/mn/OgANAdUBjQI6A9cDbgQBBYkF/gVPBn4GjwaRBoMGZgYvBtwFZgXXBDIEfAO0AtwBAgEwAGH/k/7J/QT9Pvx++8/6PvrL+Wv5H/ns+Nf46PgT+Vf5sPkf+qf6TPsJ/Nf8q/15/kb/FwD1ANcBqgJtAxoEugRQBdMFPQaRBskG4AbaBrgGgAY9BucFdwXiBDgEeQOmAroBugDE/+L+CP4r/VH8ivvX+jb6q/k9+en4pPhr+Ez4Sfhj+Jf46/hf+fP5ovpg+yn89vzK/az+lP98AFsBLQLzArIDagQXBbEFMwaXBtUG8Qb3BuUGtAZhBvIFcQXnBEwEngPdAhICPgFgAH3/l/61/dX8Afw/+5H6+Pl5+Rb5z/in+KL4uvjn+Cr5gfnw+Xf6Efu/+4D8U/03/ij/JQAeAQUC3AKrA3oEQQXzBY0GEAd/B9AHBQgXCA8I7QewB2AH/gaDBuoFMAVgBIcDrQLSAfYAFQAt/0H+Xf2P/NX7LPuT+gn6mvlK+Rz5C/kX+Tv5d/nR+Ur62vpz+xP8wfyF/Vr+O/8lABAB7QG1AnUDQwQVBdQFdAbzBlkHngfBB74HpweBBzwH1AZZBtUFRwWXBMkD8QIhAkcBXQBp/3/+nf28/OT7JfuF+vj5dfkD+a34cfhL+D34Rvhr+Kz4B/l8+Qj6qPpf+yz8CP3n/cT+n/92AEgBEQLTAooDMgTKBE8FwwUkBnIGoQa3BrgGnwZsBh0GrwUqBYwE4gMsA3YCtAHjAAoALv9U/nr9pvzd+yj7h/r2+YD5IPnX+KL4ifiP+K344Pgg+XH51/lW+vD6oftj/Cz9/v3Y/rH/igBoAUgCIwPsA6QETgXoBWUGwgYGBz4Hagd/B3YHTwcRB7sGTgbPBT8FoQTxAzADZQKaAcsA8P8O/zP+Z/2l/OT7L/uV+hr6sPlY+SD5EPkc+Tr5avm5+Sv6sfpF++z7qvxz/Tn+//7T/7wAqgGVAnUDTAQNBa0FMganBhMHaweeB7MHsAeXB1oHCAecBhkGfwXPBBEEQgNiAm0BagBw/33+j/2k/MX78vow+nr55fh4+Cf47PfD97X3x/fv9yj4evjx+H/5HPrR+qH7g/xu/Vv+Tf9GAD4BJALzAqwDWQT3BIUF/gVoBsQGDgc7B0gHNQcBB6wGOgasBQsFUwSKA7EC0QHsAAIAHf9B/nL9sPz8+1n7x/pF+tb5gflP+T75R/lo+aP5+/l1+g77vvuA/FH9Mv4Z/wAA7ADfAc8CswOIBFEFBQahBiAHhQfaBx4IRwhTCEMIGQjVB3cH+QZmBr4FAgUvBEkDVAJaAVoAW/9g/nT9mPzP+xP7XPq0+Sn5vPhn+Cj4A/gC+Cb4Y/i2+CX5rflE+uj6m/ti/Cv98P20/nv/QAACAb4BdAIfA7cDNgSlBAgFVwWCBY0FfgVYBRYFuARFBMwDTQO9Ah0CcwHFABUAYP+q/v/9X/2+/CH8kfsd+776cfo2+hP6CfoT+jL6Z/qy+g/7efvx+3T8//yS/TD+2f6J/zkA7QCdAUUC3wJsA+0DZQTSBDEFgAW/BeoF/AX0BdQFoQVkBRsFyARnBPsDfgPuAlECrwEMAWIAr//9/lT+uv0q/aj8Nvzb+5b7YvtB+zP7OvtR+3X7qPvs+0H8pvwa/Z79Nv7c/oz/OQDeAHgBCQKTAhUDkAP9A1cEogTcBAQFFAUbBRQF9QS6BGsEDwSqAzcDrgIVAnkB1AAmAHb/zv4y/p39CP2A/A38qftT+wz73PrG+rv6tPq3+tT6AvtC+5j7BfyJ/CD9vv1f/gH/pv9MAPIAkQEpArkCQQO6AyIEgATXBB4FUAVrBXIFZAU+BfcEmQQlBKkDJgOdAgUCXAGsAAAAWf+3/hz+jP0K/Zj8NPzj+6X7dPtN+zf7NftI+3H7svsI/HL87fx5/Qv+oP43/9P/cAAKAZwBKwKyAjIDoQP6A0MEgwSxBMsE1gTVBMgEogRcBAQEoAM0A7kCLwKZAQABZADG/yX/gv7k/VT90PxW/O/7n/tf+yf7+frj+vL6Fvs/+2/7r/sB/GX82vxd/er9cv77/of/EgCWABABhgH7AW4C1wIvA3cDrAPRA+ED5APXA7UDfAMzA+QClgJBAuABcgH8AIEABACK/xb/qv4+/s79ZP0E/bT8bPw1/BL8A/wE/A78IPw2/Fb8hvzF/Bb9bP3K/S3+kf7z/lf/vP8kAIkA7ABKAaMB8AExAmkCmgLDAuoCCQMdAx4DDwP3AtkCswKFAk4CFALWAZABQwHxAJgAOADZ/4L/NP/r/qT+Yv4r/gX+6v3T/cD9tf21/cP94/0W/lP+lP7X/hj/Xf+r/wkAcwDeAE8BvwEpAoQC0gIWA1kDnQPiAyEEUgRtBHcEbgRdBEAEGQTnA64DbAMdA74CVgLhAWIB3ABcAOf/ff8Y/7P+S/7m/Yv9Ov3y/LP8e/xM/C/8Ifwd/CH8NPxb/JT84fw2/ZD97f1L/qn+B/9s/9r/RwCvAA8BbQHKARoCVQJ+ApgCpAKhAowCZQI3AvsBugFoAQsBpAA0AMj/Yv8E/63+Wf4F/rP9aP0h/dz8nfxu/Fb8SvxA/Dj8Rvxz/LX8AP1S/bj9KP6Z/gr/f//x/1cArwAHAWEBugEKAlYCnQLcAg8DMQNEA0QDMAMMA98CqgJoAhwCyAF2ASUB1wCDACoAzv9r/wz/tv5n/h3+3P2q/Yn9ev10/XX9f/2Y/cT9/f08/n3+v/4F/1T/rv8EAFkArQD9AEUBigHRARACPAJfAnUChQKKAoECaAJDAhYC4wGrAXIBMgHfAIIAJwDc/5v/W/8Z/9f+mv5i/jT+Ef7//f79BP4R/iP+P/5j/pP+zP4G/0f/jf/g/zIAewC8APkAOQF5AbQB7gEnAloCgQKeAq8CtAKqApQCcAJEAg8CzwGEATsB9wCyAGYADwC7/2n/GP/K/ov+Yv4//hj+4/2+/bX9xv3b/e/9Ef5D/nT+n/7P/g3/Uv+S/9D/GQBmAKoA3QAMAUQBfwG3AeUBCAIcAhsCDwL7Ad4BtwGJAVEBDwHDAHYALADl/5b/Q//0/rH+b/4p/uX9rf1//V39Rf03/TD9NP1A/VX9eP2n/dn9DP5F/n7+uv7//kv/mf/i/ysAcQC1APQAJQFKAWYBfQGRAZ0BpQGjAZkBgwFsAUsBHAHgAJoAVQAIALf/Zv8V/8v+jf5e/jj+Ef7m/b/9q/2j/aH9qP2z/cv94/3//Sj+YP6h/uj+M/+D/9r/MQCIANsAJQFsAbAB9wE6AnMCowLPAvYCEAMbAx0DEQP+At4CvAKTAmICJALjAZ0BWwEZAdAAhgBEAA4A3v+y/4r/aP9G/yL/A//1/gH/F/8t/0T/Y/+J/6//1/8HAEcAkQDaACUBbAGrAd0BBQIiAj0CVwJrAnYCeAJyAmECSQItAgYC1gGaAVsBDwHAAGwAGgDJ/3b/Kf/g/pz+Xf4c/uD9sf2N/Wv9Vf1J/T79Mv0p/Sz9P/1f/Yb9sv3k/SH+aP6s/u/+MP9y/7n//P84AGwAmgDGAO4AFgE3AU4BUgFNAUYBOgEjAfAArwBoACMA4/+g/1j/D//S/p3+a/47/g7+5P21/Yf9Zv1a/WD9Z/1x/Yf9rf3a/QL+Nf5y/rP++v5A/5D/3f8rAHoAzwAnAX4BzwEQAkUCaQJ/AowCkwKSAosCgwJ+AngCaQJPAiYC9gG/AX0BMgHiAI4APAD0/7j/hf9Y/yf/9f7P/rj+qP6S/n/+d/5+/pT+sf7R/vP+If9i/6z/9v89AH4AtgDtACkBXAGJAbcB4AEJAjICVAJpAmgCWwJAAhcC7AG6AX0BNwHzALoAfgA+APj/sP9k/xv/2/6j/nj+T/4p/g7+Af4E/gv+If5D/m/+n/7L/vv+Kf9X/3//p//a/w8ATwCQAM4ABgE3AWABfgGXAagBqAGaAYQBcQFdAUQBKwEKAeEArQB5AEsAGgDi/53/Xv8m///+1P6l/nv+XP5H/jX+Mv5A/k/+Vf5Y/mr+lP7G/u7+Gf9Q/47/xP/u/x8AWgCVAL8A4gALATMBUgFZAVMBSQE7ASQB/QDNAJwAagA4AP3/wf+J/1r/LP/6/s7+ov53/lT+O/4u/iP+Hv4c/iH+N/5Z/n3+nv7H/vH+I/9g/57/1/8NAE4AkgDSAAYBLgFPAWgBegGCAYYBkgGaAZkBiQGCAXcBXwE4AQcB2gCpAHAAMADt/7f/hP9T/yb/A//n/sn+rv6P/nP+Zv5f/mT+cf6K/qv+1/4M/0j/g/++//r/MwBwAKYAzwDyABQBQQFrAZIBtgHYAfMBAgIKAgcC9wHfAb8BmwFsATsBCAHYAKsAgABLABUA6P/B/5L/YP81/xP/9v7d/sn+uv62/rz+w/7a/gn/R/9+/6f/z//5/x8AOwBbAIQAsADaAAABJgFHAWcBgQGSAZwBnQGVAYEBaQFKARoB6QC6AJEAaAA+AA0A2f+g/2v/Pf8V//D+y/6i/nr+Wf5D/jv+Rf5P/lb+Xv5y/ov+pv7A/tz+/P4g/03/e/+n/9b/AAAnAEsAbACOALEAzADaAOIA6QDsAO4A5gDfAMgAqgCFAF0APgAaAOz/tv98/0b/Hf8F//T+8P75/gb/B//7/u3+4f7X/sf+uf7D/uf+HP9Q/4n/x//8/ywAYACVAMAA0QDUANcA4QDxAA0BLwFQAW0BgAGHAYABawFHARsB7gDBAJQAbQBUAEkARQA8ACwAFgD3/8v/m/9m/zL/Dv/4/vL+BP8j/0T/W/9v/4X/lf+i/7L/wv/N/9n/7f8GAC4AYgCaAMIA3wD3AAcBCAEBAe0AzgCzAJ8AkQCGAH0AdQBlAFYARQAiAO//sf91/zj///7V/sX+yv7T/t/+5f73/hf/Nv9T/2b/dP9//4z/oP+1/9H/8/8nAGgApwDkABcBQAFTAU4BQAEwAScBJAEmASIBGAEKAfYA4wDPAK8AgQA6APj/vv+L/2T/Qv8o/xD/9v7r/vP++v77/uv+zv7H/tX+7/4J/yn/Vv+C/7P/6v8qAGYAhwCMAIkAlwCxANMA9AAQAR4BJQEsATUBOwEnAfYAvACCAFIAJAD6/8X/jf9b/zD/Ev/0/t7+zf61/pP+av5O/kr+VP5o/oH+lf60/uX+Jv9q/6j/2/8JADsAewC8APMAKQFVAX0BoQHBAeMB/AELAggC9wHlAdEBtwGRAV0BHgHbAJkAYQA1AP3/wv+I/1T/JP/2/s3+ov59/lv+Q/48/j/+Sf5U/m/+kv65/uz+JP9a/47/u//p/xsAUACBAK4A1wAAASUBRAFfAXYBhAGLAYoBegFYATABCgHmAL8AkgBfACgA9f/D/47/Wf8f/+f+sf6A/lb+Of4o/ij+Mf4z/jL+Qv5m/pH+tP7P/vH+JP9v/7z///8/AH0AuwD+AEUBiQHAAeAB8QEAAgkCDgIWAiACIQISAvYB0AGpAXcBLwHYAIAALwDo/6v/eP9E/xD/2v6v/pD+dv5b/kH+Lv4i/h/+J/4//mX+kP68/u3+KP9k/6X/5v8kAFYAfwCjAMgA9QAeAUMBYAF8AZ4BtwHFAcIBrQGKAWMBOAENAecAtwB+AD0A9f+5/3r/Rf8F/8H+gf5E/hj+/f3t/d79zf28/bj90/3//Sv+Tf5x/p/+1v4V/1H/kv/a/x4AYwCsAP4ARQF1AZoBvgHjAQECEwIjAigCGwLzAcoBpgGHAWMBLgHyALAAbAAoAOr/q/9j/x7/5f7H/rv+sf6i/oz+e/5p/mL+af55/pD+rP7T/gb/T/+X/9X/CQA5AGwAoQDeAB4BWAGAAZkBrgHBAdYB5gHsAd4ByQGwAY4BZAE0AfYAqwBjACcA9f/M/5//a/8s//X+yP6a/nP+S/4p/gj+7v3o/fr9H/5E/mr+hf6w/uz+L/92/7P/6P8WAE8AkgDVABgBTgGBAagBxQHeAegB5QHOAaYBgQFmAVYBQgEsAQQByACAADMA9P/A/4f/Rf8A/9L+vP6r/pb+fP5l/k7+OP40/lf+if6y/s/+6f4Z/1b/lv/P/wkASAB6AKcA3AAdAU4BXwFcAV0BeAGdAbcBvgG7AaIBewFTATYBIgH8ALsAdQA9ABIA6P+z/2//Jf/e/qf+j/6L/nz+Wv4y/hn+E/4g/jv+Wf52/pf+vf7t/iT/Yv+X/8n//P8xAGYAnwDfAAwBKwFDAVYBbwGIAaEBqQGkAZYBfwFnAUcBHQHhAKIAbgBBAB0A+v/f/7f/iP9V/yH/+f7W/r7+sP6r/rL+vf7W/vj+IP89/1n/ef+U/7H/2P8FADYAZACUAMUA+QAjATsBRQFLAVABTAFFAT0BMAEZAf0A5ADGAKMAfABVADYAGwD4/8L/gv9P/y3/Ff/2/tf+xf7F/s3+2/7r/vn+BP8G/wP/FP89/2z/mP/C/+7/DwAqAD8ATQBdAGAAWQBeAHgAkwCZAH4AVAA5ACkAHAAPAAgA/v/n/9z/zv+7/6H/gv9z/3D/f/+K/4//of+w/7f/tP+7/9P///8uAGUAnAC6AMoAygDMANMAyQC1AKAApgCnAJ8AlACCAGEALQD//9n/vv+g/3P/S/8v/yT/Fv8Q/xL/F/8o/0L/Zf+I/6T/t//N/+7/DgA8AGsAqwD2ADoBaAF+AZQBqQG4AboBrgGnAaQBnwGEAUwBAAGwAGkANQD//8P/fv83//T+rf5q/jX+/v3P/bz9x/3k/Qz+Lv5K/lr+cv6S/sr+Jv+O//L/SACiAAQBZQGyAd0B9wEUAkgChQK5AtYC1QKyAncCMgLlAZABNwHYAIAALwDq/5H/HP+f/i3+3v2h/Wf9RP02/T79Tf1Z/Wb9ef2G/aH93v07/qT+EP9+/+v/WQC9AAMBQQGEAb8B9QEvAmYCkAKWAnICNQL/AckBgQEkAbgAVgAGALr/YP///pL+Gf69/YL9Z/1T/T/9Kv0n/UT9bP2b/cj9+/04/nz+4v5X/+H/aADQACIBeAHiAU8CpALZAgUDNwNiA3gDbgNBA/ACkQIvAtMBfgEaAaAAMADY/4j/J/+z/jv+0P2G/Vb9N/0x/UD9Vv1p/YP9rf31/UD+hP7T/jj/uf9PANUAPgGTAeYBOwKSAuoCMANoA5gDrwOhA3EDGwOrAj8C0gFsAQgBmwAsAMj/af/o/lD+w/1F/fH8x/yx/KH8nfyk/LH8z/wB/TL9Z/2j/e79XP75/qb/TQDRADIBlQEBAm0C0AIeA1QDfQOZA5EDcgNFA/YChAIKApYBJQG2ADwAwf9W/+X+XP7C/UD97vy//Jr8ffxq/G38jPy+/An9SP1j/XX9nv0X/sj+dv/8/2YA5AB4AQwCiQLgAiEDTQNnA3wDmgOzA5EDHgOAAvIBkwFFAeMAaADr/3f/A/95/ub9cf0S/br8fPyA/MH8+PwO/Rv9TP2l/eb9Cv5G/s/+lv9RAOkAdwECAnUC2gJPA9gDWASmBK0ElASNBH4EOAS5AxYDdQLnAXsBGQG4AEQAoP/e/iX+g/0N/bL8bvxW/Fz8cPyG/Ir8l/zI/BP9Uf2N/fb9kP47/+L/bwDwAHwB6QE9ArACTwPsAzgELgT5A8IDhgMpA7MCLAKUAfEAWQDt/67/b//o/jD+c/3Y/GL8Dfzr+/T7GPwz/ET8bfy7/B79ZP2R/b/9D/6i/mH/LQDpAH8B/AFoAtECQwPBAzQEegSIBGwELwTQA0oDowL8AWgBzAA5AMb/e/83/8b+Kv59/eT8b/wZ/PP7B/xI/Ib8rPzU/CT9nP0J/lT+kv7X/kn/6P+lAHEBKgLBAi8DmQMZBIwE1ATtBOAEugR6BAsEbgO6AgcCYQG+ACUAlv8a/63+G/6A/en8Wvzn+577jfug+9X7F/xS/J388fxD/Zb98v1R/sn+U//v/7EAdAExAt0CbwPtA1cEsATmBPsE7wS4BG0EBwRmA4wCrwHxAFQAw/8o/53+Hf6T/e78S/zQ+2r7Fvv2+ib7kPv++1P8pPwY/Yf90f3//Uj+2f6S/1MAFwHlAawCSAPPA1kE4gRNBXAFVwUrBQYFzgRcBLUD1wLoAQsBUQDD/0r/xP4j/mn9ofz4+437Qfv6+sv67vpZ+9b7Svyq/CH9nv3v/R/+Z/78/sz/pgCAAWACOAPfA1UEywRTBc0FBQbgBYwFNQXYBE0EhgOiArQB1QACAEz/z/5e/sP98Pz1+zD70Pqi+n/6cvql+hL7iPvv+1j86Pxw/bn98f1o/h3/6/+1AHgBaQJbA/0DXATHBE8FtAXUBbUFfAU2BaIEwwPTAvABBQEbAET/nP5A/tv9Nv1q/KT76/pK+vj5/vlX+tj6V/va+2z8/fxW/Y/91v1B/u3+tv+HAHABZQJAA/EDgwT5BF0FugX3BRoGLAYDBnIFkwSNA3kCawFmAJX/Cv+e/hn+df3d/Er8pPvo+kr6EvpI+sD6VPsC/MX8d/37/UT+iP7Z/h3/d/8KAPAA+wHqArkDWgTxBG8FngWeBb8F+AUMBtcFNgU2BAUDwAF4AFv/lv4I/q/9ZP34/I38AvxA+376EPoY+nL6IPv0+8z8k/0L/ir+Sv6P/tP+LP+h/1sAcwGDAmIDHwS6BDAFkwXYBQYGRwaaBrkGaQaKBQIEQAK7AGv/V/6L/ej8fPw9/AT8tfse+xn67PhQ+Lf4rPmo+uH7Tf22/qX/yv+t/6v/1P/z/yAAyADKAc4CmAN1BGoFAAYwBksGcQahBsgGvQYkBt8E9QLCABH//f0f/WH8+fux+zX7iPr5+an57fi99yL3D/j/+Yj7pvz+/Yb/0QArAdEAkwCVALQAGAEMAk0DQATRBGIFUgZMB+wHWwijCLAIxQilCEAH2APo/6z9D/3K/Mf7dfqV+mn7yvrC+Of2l/b19h32sPXc93L7xP1c/jj/PgHSArUCbQHRAI0B8wHSAVcCiQNVBaQG9AasB3gJDgslC/gJsgicCPkIkQYQALf5u/jF+mb7mvkt+PP5UPvb+Mz01/LB8rnxwfAg8x760QCHAv8BkwMCByAHwwJ2/6b/6gELA9QChgToByAKhQnmB2gI8gr3C+gJFQjRCIQK3wdZAFD6gPjF+H/3d/W29uT5+fpr+Br1PPRE9OPxoO7/79r21P5VA4oECwdFCn0LqQleBUMCAwGtACMB/wEzBbwJFwyxC9QKbwt+DEUKPQVjArMDyAV+AmL6lfWI9xL79/gs9Mb0avjI+L30JvG08pv0gvIT8QD2Q/+fBXgF1gRdCJEMJgzIBpQCVgK2Ag4CqAHOAlEG3wkpCvkJFgvSDFcMtQeOBEwFJwbbAuv63PXG9t33UvYd9P30bPkL+uP0IfJX87v0gPJB7v7xCPtnAjAGrAaWCTgNMw2XCncGswO9ApoBDgFPAisFEAmGCsIJwwqLDMsM4QkcBdcD0gRXA5T8A/TG8zz4pfce81nxmvZC++b3vfGM8PnzRPSP8LrwdfgQAsUFQAZUCWUO9g+qCi0FOQRBBAgDsQC3ANUEEgkOCpIJ5QmgC8sMBgr/BZEE+wUqBTX9j/VO9D739vhO9cbyhPVF+U33kPAN71rysfPE8bXxf/kIBJoH2QWKBgEL7g0iC1YGtARkBaoFgANeAv8EpwfxB9oGqgcCC+sM1ApHB8AGwggzCB8BAPgG9JD20vik863wkvUT+hX5w/K578vxxfE/7yDutfR3/+AELQb3B3YM/Q50C1QGsASNBKMDPAOkAtgEFQkHCq4JBAkdCsAMhQwxCoQH1ga9B04Dbflc9F31v/Yz9pPyufJ29+H4nvQ47yPwA/Ig7/zuLvRd/MwDaAZcB6oKkA4tDbQHkAU6BYAECwQPAz8EywfGCb0JuQgwCTcLxQveCqkIbwe4CNEFX/xo9JX0i/cP9orzkvOE9in5KvWf79ju3++h77rtA/En+9gCTwYwB00J8gwyDMkIrwUvBKYEpQRYBNEEBQcpCUIJbQgTCHcJNQwYDaoKZgkCC/kI5wAU97zxj/Pi9vz0iPEo9H75tfjp8dXttO6C7vns1+269JEA5Qf9B8oI2Ay7DxwMmgU3A9YDywRjBNIDWwaQCXIKWQkPCDEJjwtNDNoKGAoUDOQKNgQg+4bzafM99hL0QvHI8oD2oPdg8+fuae5X7k3tSe2d8j79wwRZB1kJ9gtGD0wOMQnBBYcE0wTFBCIEMAUfB1QInQhiCHIJ/Qv/DA4M9wrMC2QLuAVZ/PLyePG79Rn1IvKE8d30hfeL80nv7uwf7J3sk+se8Of7EAWfCCUKZgyzEAcRfgtvBnoDWQP3A2YDygR+BpYHZAjkByQIQAq0DLsMhwsbDJ8MvQq5Agv3RfHE8lr1HPSk8HjxmPV89bzwFO1P7IbsuOuB7Ez1yAHOCF4KMwtyDzMTwBCACmsFxQOdAwgDTwMvBRwIYQlqCAUIFwmWCyANpwuoCmALHAtHBmn7+/HA8Tv07vJM8ILv/fIC9ffw0uw17IDtqOza60DyFP60BxkLYwzADwcTARMTDj8IlwSmAqoBsAFKA2UFJghMCWsIHwlOCk8MUw0cC2IKHwt0CYAC5/bH8Afz7fTZ8iDv5u5X8/vzaO6+6s/rY+1d7C3ttfYmBEsMQw2pDfQS5RZpE+ELEwW8AqcBRP/o//YCPQdWCbgH6Ae2CcULPAx7CtQJGwsIDJMG2fuY9LTyhfNr8ofuTO3N8G3yDe/L6zrsi+6D7WrsjPKn/fgHSQyNDQwS4haXFlsRygpvBg4DDf/1/LH+4wKMBooHFgiHCXML2wwQDCkL5wpACzUJTAAM+JP0pfSc9fvw2Ozx7oPxG/DF60jqsexn7Rzso++X+UYEfQqqDHoQ2hUCF40TAw52CXUF4QBF/rD+GQGGAysF9QbICMAJDQvWDBwNeAxhC1IKyAUI/QH1OPH289n08u957c7uz/GC8NHrDesB7CntI+4c8vj8cQgSDsgP8RJIFwEZJxTFCwwG8gGj/h78OPw8AKgDfAXhBtgHQArTDDANmgsiC04M+wnbA8360fM69E71kPLy7gXuqe8c8M7tA+vJ62TtMu1H7y32pQGQCnINeBBTFOIXQRcyERAL8AW0Ae794fuf/aUApgOTBUQGrQiEC+wMWw0fDAUMJwxICL4Aafeu8h31nPQa8AnuE+5d8Afw7+sB63vspu2D7W7wkvqjBTsMKg+/EngXhxh6FYYPbAknBP/+xfv4+3f+1wDLA0AGWAfGCZgLxQw6DY0L5QoeCjsGfP5f9eHyMPU69IPwnO3E7XbwRvBn7GXrLu057gDvzfI1/YAIhQ7OEP0SyBf+GZ0VTA57BnEBzf1Q+rL6Zf2QAbgEEQU7B18K6gzbDSUM2graCoQK5QSG+pD01vOG9FHzl+5D7CnvPfEh7yDsPuxy7pzuju9a9twAWwqdDiwQmhQ9GSYZFxRfDOUF4QBT/OD5gPqs/QYBBQNVBQ0IKgvDDdQNOQyrCi4LvwmGAe/4yfOZ80n2rvJm7YntK/Bo8RvuU+vn7F/ure558Zz5fARpDCoPXhFrFjUZbxd1EdsJRwRD/yv8pPsE/Fv+/wCSA40GiQgaC7UNow3NCzcKQgpBB8j+qfVy8J/yM/VO8drs7+xr8QzzFO/D7KztiO/88JLzMPx9COkPHRHQEnUXFxs3GCIPMwbQAMz9j/pX+E/65P20AHIDmAWoCMkMQw5ZDJYKPwsXC4kGU/3m82XxEPV+9bPwru0a75bykfIb7o/sk+4L8EbxF/UF/ywK7A46ECUSpxa5GRMVhgxyBb8Azv1r+5H6Avwm/1EC5AMLBogJmwzgDTQMJgufC0IKhgTS+a7xIfKN9LPyi+4P7cfvvPG+71HtFe5k8O3wXvIj+RoE2Az0D6wRUBXJGGMYtRIGC0QE9/5z+/H5TvoW/EH/PAIiBIQGWgliDHINkQsFChoKKglWApL3kPGF8i31//KH7Ynsn/CZ8tTvcu3R7t3x8/Kv8yL7lgf/D5ER3BHDFeUZFhhaEH4HEAJO/hb6O/ik+Yr9EAECApgDNgc+CwUNzgtTCnYKVQvwB1z+efXv8nH0X/TA8Fbtnu7B8S/xmu6q7kLx6PLT8pb1mv6gCUsP6g+sEfoVsRguFp4O4AYDAvT9qvnR9735fP1RAM4BHARACLAMlg4ODfQL1gzPC1wFFvtU8yPytfMU8qzt2+t/7tjwou8E7uruwPH48jnzWPi1Ai4M+w/4EPQTGxikGTkVVg3cBoUB4PzR+Sb53fqB/UgAcAIZBUsJrAyYDXgM4AqNCgkJQQKn91fwYvDN8mXxu+zt6ibvzvJ18VfvvvAM9PP0kfXw+/4Gjg9PES4RrxQTGV8YrhGVCaEDxv4/+rX3TPji+o39JABnAzgHAwuBDScONA36Cw0L8wfu/6f19u/W8PTy8vBk7Mzr6u8m8u7vbO6z8Ab0XPUz97b+jwl3EHARdxH5FIkYxxbaDw4I3AIK/9X6hvhp+Tj8M/8mAYED7we8DJ4OZQ2VDOEMkgtABYH6lPIa8WvxVe+w6+Lq1e337+/ule5j8fnzQfSN9WX7zgRlDD0PkBC9EzcXcRf+EzwOYAfJAeH9EPvL+fP5B/w8/ycCQwU6Cc4NvA8GDmMMFwwACjwCR/ZA7ifuPPCf7aDog+he7SbxJPH07yfywPWU9iz4DP8OCf0PZBHIEUsVphn9GBQSIQpfBFH/3/oE+Aj3HPii+g/+ZQJzB00Miw/VEFoQag9LDn4JJf/98zXukO6L71TsDujg6M3txfA+8C3wbfIh9cn29/mTAaIKqw9LEJQR3hX0GCcXghDVCFwDgP8L/AT5AvjJ+Qb9sABQBKUIww0JES8RNhCcD00NbQVY+dnvSe0j7sHr+OYY5gHqO+6S767vvvHs9ET20/fe/QsHsQ03EA8SXxUUGaEZiBVID1UIeQFb/Gn5f/fO9pX4CPx6AHYFBQrYDqgSxhLOEGcPNAx+A4L31u6j7JftNOvr5SflA+qS7sHvju+m8ef0SvZV+Ez/hQkoEFURVhJIFpIaCRonFBkNZgYBAPP6APh79zz4kPmC/JgBXQdIDIQQFBOyEl8RTQ/HCc//7/MF7DrrIuxy6BHkY+Vo6jfule/68Af0BPce+Hz7fwQhDagQyxHFEzQXlRn0F24SsguWBKX9wPmV+E33OPfs+cz9XgLeB3sNPhI4FBcTPxHeDmIHyPps8GPslusn6YfkXOO150/sIe4X8HvzJ/aL9wr6MwBwCMMNyQ8UEpIVLRglGG4VHhDUCMMBuPwT+hf4nPbF93H7QQBEBe8KUxFfFa8VWRReEt0MgQH28znrFulF6MLjY99O4avnbe2i8D3yyvTZ91D6hP5QBhEObxFuEmEUNBiXG+EZtxKcCv4D4f3Q+Ev1zPLj8hL25PobAYkIJQ8nFH8X2xdSFlgTRwus/QHxOOrI6B7oGeR44KDjDuvi7ynx1PH787H3Cvsc//UFuwzHD88Q9hNYGBoa8BanD7EIdwOr/ln6EveR9bT2h/qp/yMF9wqlECUVJhd0FugTkg7sA5b2xewP6f/nj+Qp4KXgsObL7HrvsvAg86D2fvnP/BoD3wqsD5MRLxQIGOEauxktFBENQAbC/zf66fb99Jv0GfeE+yMBlgcdDpYTlRbhFnIV7hKSDE4AYPP465/pvec848bfi+Ie6UHtqu6g8DL0ofdb+ej8WgVcDm0S9xIEFS4ZkBtBGCsQhAhyAjb8RPcT9R318Pbv+dH9lQMiC4ERehWBF7QWvxSjEXQJzvyU8VHrxOh95iXiYt/44v/oCOyY7bLw7PTT+Of7cQCoCD8QmRIqExQWghnOGbEVaw5RB7MB9fsM9yL1FPVH9r351/7SBI4LphGvFXwXAhdLFGwPBAcW+13wz+pF6GblhOHl35njf+n27JvurPFb9lT6hf0/AqwJRRCBEhETPxUZGBYYXRNoDD8GLwH2+5P34fUk9uf3J/vD/28GMw5dFKQXqRhnF0YUoQ4pBCn3iO186FrlGeLe3ureOuTm6eXsaPA09Vb5ffwRAMIFiA0CE6YTBxRKFsoXvBYBEv0JVQIT/W74wvR/8/vz+fYq/D4ByQcDEYMY4RqvGW4XRhRrDroC/vT97IfpnOU34WjfnuEZ5oXpuetK8ML1lvhD+7gANAhgDjwRVhLhEwUWxRUtE/EOHAi5ABH7Ifgq97/2Hfc6+hIAIAZCDCwT+BjqGgsZHhXwEHYKn/7B8a7qJehj5e/g9t2i4HTm0Omt603wd/Y++iX9bAKFCukRpRP+EvAUWxdRFnMRdAsGBWn+ffgG9aT16vbW94D7FwJJCT4P2hSpGZsbOhkFFFUPngjM/HrwCukV52Xlo+Bc3Xjg0eYH6sLrdu/u9GX6Jf5yAxkMkBPOFXcVXRZUFxUWohELCmQCQ/xZ9xj1NfU79qj4LP22AugInRBXF78aBRtCGFkUqg/PBqL6nvDQ6rjmauOI4GnfNOJQ5oXp+ux88fj1Xfp2/w4FCQxzEu0UuBTRFLcVUxUXEdQIWgAW+6X3CvVA9OD1bfmp/dMCGgooE0YaZhw7G+kY1RWND3gEAPiR7g7pxOQt4UHf2d9B42bme+nr7eTyTvfM+lb/tgVnDe4SFxRrFFIV/hW+FJUPOghrAfD7dfcL9Y/1jPer+qn+7AOcC2MURhpuG+0Z6Rb9EmMM4gD39PTsUuhg5Png/9/34enlGOgv6rbvgvUx+cf75//VBmwOxxK2E4MUZRWeFKUSZw49B2gAsPot9nb0G/Wj92T7M/+TA2cLAxVGGqEagxn1FxAUPgtR/+L1L/DB6driBuAp4TvjyuT25ovruvHO9dT3U/zJAvwIUA5eEbkSAhTdFCIUXhHwC9YEsv5q+en0QvMA9XL4CPwlAEcGJQ8jGAgdtR0nHHQZ+BOGCUf8LPGN6nXlGOC73NbdvOKC5snoRe0t9JL5cPuq/ZcD6gsIEZgRhxKAFAIV6RIPD8EKNAW6/WT2GfP088b1V/g0/A8BfwfuDyEYOR3sHSEbbBfkEUMHGvpK8HfqXOVT4N7dP+Dh5OXm1ui27uf1M/qM+3f+cAV8DbQRxRIGFNsUyRNkEdcN/wjvAl/7w/Tb8Ufyy/Sf+Kz8xgAhB4sPwBfIHVQfpBw5GD8S0ggF/Rny4OkY5SHhJd313FbhI+Yb6ensd/MY+x4AsgGqBXsNLhPCE9sSVRPDE38R8wupBssCIP0q9kbysfJ39Qr5WPy4AHUHRg+QFv8bOB5aHWka3xPtB7z6yPAn6v7kId+y2/nd8OJO5qvpyfDT+AP+sP8CAqsIcg/FEV8RxxF3EgIR3Q03CpMGigFG+lj00/Ez8pH0EPi7/GsCrAn/EbIZOB4FH2seUhvLEnIF+/c477vp4uPH3WLc3t8W48TlKOqB8cX4+Psd/W4Bfgm4D08SThMMFNQUZxR7ERoN9QfEAMr4pvJK70XvyfEP9Tz5lv/gB+4QBhnFHSgfMB+RHOAUrAi5+1Dyg+xx5rHf19xN3+3iIeV46HDvhvf7+xz9DwEaCSgQdhKIEsQT5RSEE1EPgwqBBcH+lveH8sHwa/Gi87f3D/0fA0IKVRLTGbodWR5VHegZ7hEIBU74IfD36gTlJN9X3bHfpuP95mXrg/Jy+Rf9ev+8BNULHBHkEhgSJBLEEiURRg1QCF8CDvzr9jzzfPFz8sr00vdK/BsCHQkPEZIXKBtMHUcemhvAE8wH/vsw9KDuxucZ4RPezd554VDksugS8AT36fpP/hMFYQ1uEgoTAxKKElITRBFnDfQIXAMl/Wf3fvQ+9AP02vOn9Tf6n//ABTkNJRQoGXkbJBz2GywXzwvP/nH2CPE96j3iDt3N3VLhzeOf533v3vdn/FX/fgQWDCYSlxJdEIYQoBGIEFsNawnBBGf/J/oo9vD08PRv9Br1+/ef/NECLwqUEVwXvxpIHNQb1hfzDYsBMPiF8VvrQeRB33ffo+LG5WnpwPAA+Zz9pf/6AnoJ/A6bDwsO4w37DowOqwx5CggHvwFj+0L3P/bN9Tj1pvVI+KD8NQIsCTsRjxcXGtoaqho7FzkOAQJv947waut95Ijfr99h4gTmhuqO8Zb5Wf+fAe8DAwpAD1oQMQ8cDuANsQ3yC58IZwVVAMT5cfXx8//zlvS79Q/4bvxKAhUJRREZGLQajBuDG14YVRCJBIn5UvKi7DPlqN/Q35rioOUC6hDxa/kQ/5sAmwIiCKsNng5eDdUMSg23DWYMdQqgB9ICyvwB+Pv1RfXg9C/1yfYN+jT/MQZ7DgYVShi/GaMaBxkDEsAG/Ptw9OLtF+cK4iThqOOc5lbq4fBB+c7+gwDhAmwHIQzbDYoMFAykDHQM+wqmCZoHlwK2/AD41PWZ9TH1MvUF92b6lv4QBawNlhQUGM8ZphqTGbYT5wjf/bv1CO866PziQeHJ4gjlfuh17zf4X/9yAhIEbQcFDMcOSw4bDSgM0wqmCVkIgga6A37+2vjM9dD0uvQ39bT2uPh2/GsCdQmzEXkX9xifGZQZtRVgDDYBV/dn8CDrC+Wd4aXipeX16Nbu3fYa/i8DsgQ2Bt8JJg1+DXAMuQt3CokJyQgHBzEEMQDZ+oP2fvSD86LzLPWJ9/r6qgAnCNQPyxXwGPIZWhqhF04PTgSz+fXxh+yW5+jjXOOT5WLoJe1n9Bj8kAGoA7oEXgf6ChANZg1pDdsM5wu+Cv8IkAYxAnj8V/dT9OPymPLO8w720vnc/sgFfw0dExEWgRfPGIgXZBE3B038ZvXf72nq0+ZR5S7mWugW7Yfz2vpQAPQBGgRLB10KFAzTDCgM5goECyIKwQhvBgkCLP1f+Vf29PNA83HzBPWT+Gf9aQOZCjMQQROrFfYXLBmAFrENhAE1+EDyGO0h6fTmv+Yp6Hjqwu669Y/8DQDTAHUDLAeYCsYMLg19DS8NHQ1HDIMKwwb2AJD7Yvdj9BXyW/E18vf0rPjn/SUFnAzNEUUUjhbGGGkYTxLOB2T9P/ZT8VHsFun85znog+nc7HDzQfrN/o8A5wFZBaQI+QrEC44L0go6CgILcQrmB4ADnP7P+n73m/R08pXyhPR19/77tAIgCswPphJ1FDkX4RiXFXcMYwG8+AfziO1V6Rzor+hU6prsT/Ho94f9IQBoAcgDdAaYCPsJnwqvClgL9AsdDGAKewWeALv8Q/lh9YLyy/Fr8pz1i/nn/qcGXA2xEXgTXxWLFtQVuxAxBq/8sPXr8J7sVelH6GLp3uyw7zr0GPqc/j8BMAP2BYkI5wq9CxwLlgrwCnILkAovBzIC6P2g+l33qPOQ8RjykPT396X8xwJOCeYOwxE0FC0XZBiNFRUNIAPF+vf0kO+n6bTmj+Yg6W3sbPAW9uT67P6SAdkEvAgoC4wM0gt2C0EL3wrTCsQITgU2Af790vr49qvzr/Gh8vv0FPg6/UMDQAnCDXkRJxWlGAgZSRJnCCD/dPin85LuQ+ov6JHp7+vc72L19vlC/UP/FgFmA9MG6AjoCN8IEgkMCpcLvAvYCOEE9QB1/Rn6N/ai8mXxjfPV9pD7lQFeB8EMIBDJEt4VwBcKFfYMVQSJ/Df3kPL57FTpROer6Fzrdu+29Lz47fx7/yADOAcaCgUM+QvEC80LTAyfC+QJ+QaBAtj9IPpO90P0JPJz8lD1q/kh/kgCmwfXDAkQfBL0FKsWNRSwDBADbPvE9pTxX+x96dHo/elS7DvwAfW4+Yz95v/RAuIFNAhpCqALxAuIC+UL8wvXCUIGzAG+/Rf7TviN9fHz7/Sv9wn7ef+CAysIhAxxD6wRsBPAFP4QkwmkADj5E/V28R/tPOk56ObpYO0A8rH2BPuv/sMBsgSlB+IJAAsiC3YKHQreCtUKwgi8BHsAOv3g+nX4svWm9MX1GPgh+8b/hQW6CtcN1w90E1gWMBX4DjIGHv5z923yhu7r65jqj+lD6sLtk/Jv95H6V/2mALYD2wZxCfMLKA2xDeINfA05DSIKQQWBAGr8Cvmh9e3zl/P79Cv3e/puAJ8G1woADT4QrRM7Ff8TYQ75BvL+f/dy8lvvI+026vHoqOru7cDyKPei+un95AAUBHIHqgqWDEYMogvQCwUMJAs5CBUESAC4/Cz5ofaX9T/2QvfK+GH83wGZB/oKbA12EPYTmRWaEisMVwSN/JH1Y/Dp7O7qrOk76SbrEO/79On5G/2F/4MBNgVZCDMKIQsSC1wLVAsJC50JJgcABP//KPwK+Sn39PXs9Rv3o/ku/p4D0wh+DOkOihGWFFcVdxEOCq0BWfqT9N7v2OvV6d7p0eqD7BzwefXu+fj8IP/+Ab4FkwjvCaAKUgvhCxoMqgsZCt8GAQMj/yf8iPnA9sv1cfaO+Nb77v8BBeUJRQ0fDyYR1RPnE3IPnwcc/0r49PJl7trqCOp46lvrdO038dv2JPvT/UEA7QPMB1YJFQraCloLOgv9CpkK1Ai9BQMBeP1I+6f4Jvay9IL21vip+/7/XgWnC/gOlxAmEykW+RV9EPEHc/+7+IXy9uyA6GvnqOhh6d3rV/AJ9u36h/1nAE0EtwdyCQ8KegtxDJ4MpQzqCy4KvwaCAub+/Pue+H/1E/UW96/5bfxyAPEFAQviDVUPbREcFDoTPQ33BD79Tffk8d3swOmY6fnqs+y475b0zfk9/Tb/JwFpBKQGRAeRB6gHRQjCCI0JmgkSCDQFxQEd/6D84fmi98v3gPmk+4r+CQLKBiALWw5nEesTaRXvEmsMugMP+7j0PO906m/nX+fW6e7ssvAf9RT68f3F/0kBrwOXBngIUAgICGYJiAtyDBMLlQjGBaMC5P78+v732Paf9ln3l/q8/pYDCghLC8gOBRIAFGkUbRJGDIACzvj48kDvqOu06Frneeot7tvwIPUg+sD+XQB2ATQEZAe5CXUIeQfzCIcKjQrhCJwG6AMQAbz9MPuv+Wv45/dP+e78SgDTA7EHfgtpD9oR/RLtE6USHgx5AfL3QPIf7tHpZeag5kPquu2a8Ev11vuRAD0CXAMhBj8JKQoBCRoISQkkCrYJugjPBj4EdQBR/f361/j69t31yPdb+yT/GwOTB80MeRDTEowUoxUjFGoMXQHM99bxve0M6UTmROcN667uvvGD9h38v//hAFkCCAW+B8IIxgfGB8UI8QkwCloJwwepA77/p/xG+tP4+fY597v5mf02ATMFugocD5ERlBKKE1kU5BF+CY/9h/Sd78nr4Odu5SDneOve7u/xt/Y8/RUB7AGlA54GHArxCvMJ+wm8CpALwAo4CXsGPgLT/sv7hvn29pH0GPVM92n7Y//uA70JEQ50EbATeRbxF0QUOQv5/1f3pvHY7NXnZ+W25tfpL+308HP2pfu5/nAAoQJ5BlcJqgmSCesJDAvNC0QL5wkPB2UDKf/A+6L5Kfd99ef1+PiD/TsBDAV7CYYOZhLuE5MVJBaeEkoJO/2u9Mzv/usO59vj8uUz6kDuM/KF90/9YgAKArAD6QbmCdcJVQlLCWYKPgsUC7kJUwbqAvP+uPt2+QD3gPXf9f34Yv3EAVkGCAvGD4cT3RWGF1AX/hHtBj365vBA7M7oY+R14pDls+vB8OT0OvrC/wQDYAMGBAkHywnkCQEJfQnjC2YNDQxSCeYFGQIv/aj4BfaH9Ib09/Ws+bL+CwOwB2EMwhC0ExIVUhYvFZEPtwQc+SPygu3b6dfmw+aN6lHuMfFh9Jv5bP71/zIAyQFiBesHfQhmCPwJLwwIDP0KDQkZBg0CQv0u+lT4F/c69j/3l/svAFUE5wiFDcMRPxSMFZQWaxWBDtIBMvWg7XLpGObi4+fkp+mB7hjy+vY4/dQBwQL6AjwFJQijCY4ISAg1CnkLkQtwCoAIRAVoAFH7wvfY9Yj0EfQE9mD6x//QBXcLvBCSFDcWZxeLFzcUowpw/XHy/+ut6GTlL+SA593sa/H79Bb6+v4aAakA1QAXBAUHbwcWB6wIbQvgDJ8M7AqrCG4Eiv4D+lj3OfWT8yz01fcK/XMCmgfsDBASmxRJFVIWohV+EA8GVfow8t/suejw5c3mE+vQ7h/yFfaq+jT+3/6R/w8CYgV2B3gIAAooC6MLGgspCqMIPAV9AJz7TviB9l31svWg96H7ywCUBRkKWA45EmgUghVqFnYVhA+IA+b2S+4N6k/mKeOP5FTpmu4h8lL2TvwuADYB3AAmA0YHDAmkCT8KLAynDTcN6gu4CcAGdwGy+7X3TPWE8y3ycvN796L95wMkCbIOVxM/FqMXKBhkFrAOtwHe9G7tO+qa5h7kKeY47GnyT/Zp+hv/yAHGAGL/CwIBBpcHEgdEB5MJGwuGCsAIVAZUA6L+Z/pe+FD3Uvas9UP49v1UBK0J1A0yEnYV2BYkF1QWvhHJBpz41O306KLm6OOh4y3oE++39Nv4k/3eAAcBgf/r/4cDKgZsBqAGDAl6DCwO9Q3oC1MIUgPS/Q36Kfdu9DDywPJ597/9SwS0CZsONROmFQAXeBfGFaEOzQHi9N7ssulj5ynla+YE7L7y6Pfb+9T+5P+u/vr8r/7FAo0FrwZ6BwsKag0hDwoOHAoDBeP/kvvt+Cf25vOx8yn2AvwKAnsHyAxXEY8UrxVaFxAYohOBCIf5qe7R6SXnf+SM42Lnne0V9M35o/7tAUsBVP+A/2ACIAXrBTkGKQdOCqwN4w5SDfgIxwNV/xv8Bflk9ZPyiPIA9sb7JQIdCMINFRLrFG4XMRlIGMgQfgM99vHsaug25aPj7+VC6yry0/fp/PUAlAHj/3j95v1lAJACNwRsBSEIPAv8DVkPZQ1YCSkEyP9M/ID4CPWj8q3z2ffS/cME1wt4EjYWaRd/F10XtxPfCE36oO3V5ifk/eIR5FXote+S9r37RgCmAngCLQCV/tn/swKaBU4HOQmpC9sNhg8vDn0JXgOv/T35ivXs8iPxrPHF9bT7+QJHCgsRaRZdGDAYRxffFagPpAK99PDqlefd5uvlg+et7AP0MPnM/K3/MgBR/2/8ivvA/cUAMQTNBV0I7QsxDxIRxQ5JCpME4f9d/Hn4SPVo88r0XPi3/VAErQrEEAAU1BQbFS8VoRKoCKD6wu6u6C3n+OXV5gLrxfHl+KD9FwH1AWkAov2M+wL9oP9hApcEeQbuCUANvw/vDkEK8QQHALH8iflS9oj03vRK+L78HwJECC8O4xLVFO4VBRcgFmgPQwEK823qyub/5PDjx+Z07Vf15/pC/lMBOgGs/qH8GP3j/28CgwSeBrsJog3jD78P2QzFB4QCIP5l+s/24fPK8uD0bPl2/vkDRwo9EDIUQhaGF9kXdRQQCqn6Ie4P6OHly+RJ5fTq6fMT+4H+t/+kAA///vuU+i/8rgAfBPIFKQhWC4YO/w6IDKoHogLB/mX7WvlH9x72tfdt+7kAjQXiCfUNThH2EioT4xO+EsULaf7e8DnpT+Z45UnlYukR8qf5gv4XAVACTwHj/ZD7Qvya/3ECNATwBjMKTg38DrYNnQlkBNj/dPzq+Xz3wPW59UD4WfycAG4FMwpwDrERkROBFXoWsxL7B+753u7/6IzmKeWk5kvtr/Xz+1X/EQEVAXb+9foZ+rn8VgCdAtwEVggmDIkPdRDrDckIzwIS/qD6uffm9LDzkvW6+ST/AgSQCGQNJxGFE+0U5xbdFWINg//M8RLqBOe55IPkKukv8m/6/v7yAJkBYADD/ED6wPtv/3oCpQRgB9cKCA7KD4EOEgqdBHb/oPt3+Cz1l/N29D/4dP2KAoUHygshELES8ROdFXAVMhGEBu/4cO416ILlvOTY59LuTPYq/Bn/cAC0/8f86/lf+X/8lAC4BIAIZwuEDpkQzhDODS4IeQII/iX7Yvhe9pv1mvYs+av8JwGwBQgLdA/WEREUsxW8FKQML/+l8ufp6OU/5Gnm++yS9MT7kABdA5gD2gAo/Vf6kPpG/dwAQAREBwYKywyFDxgPtAqOBA3/m/vd+H/2QvXd9cv46Pz1ARoHPww/EckTgBRCFdYUKw/tAR3zn+my5tbmK+fF6jHyNvpJ/x8A1P8C/j77Bfmd+D/8HQFcBYIIHwvvDhAS3BJeDz4IlQH2/E36n/f49Gf0dfZT+lD+zQLGB7UMERBLEckSShQSErQHqvjV7YnpUOkv6WTq4O+A9zL+/QDxAHL/ivxB+ln5jvuy/ykD5QVXCDYMFRCxEUEP2gh5Asb9a/os90r0GPQc9+T7igDIBEQJjg1LEEYRuBLDFHMTQAq6+znw1uoe6ZvnrecN7RT1pvsx//D/9f5f/Kj5E/n++nX/BwRFBzgK6QxREMARsw4OCaoCWP5T+x34QfWq8+v1ZPr1/nUDtwdQDFoPThBrEPQRyhItDSEBVvSm7d/r/+k06DrqtfEm+T39//7v/qz9A/tH+db6+f6/A9IGEAn7CzIP7hAHD4MJIwMy/j77Rvmx9i/1OvYy+rL/zgPJBzgL/w3VD3EQOxKyEskO2wRl+CPwtutk6fvm8OZp7Az09vpL/jH/qv54/KX7y/ytAD4FQAi8CuAMkA8uEdIP8QpZBCX/cvux+Kb1cfMw8871LvsqANAE9AjVDOEPFxFkEzkVUxOKCvv8wvLt7SzsxunH597qGPJV+aT8Lf3l/DP79Piv+CX8bwF7BiEKygzfDyYSCBIuDnUHzQAZ/Ir5e/eH9df0lPb5+rD/ggOeBz8Maw8aEPwQyhNgFPQNAwEO9eHuhOtc6PXlyee97eTz8vjH+4r95P2K/GX8S/50AgcHtgrfDcoPahHnEZkPIgrMApz8Rfgh9fHykfKd9Gv4Yv0GAokGTguUDwkSVBKvEy4V4BGiB635BfCf6w7p3ebg5ibss/I794D5xvpl/Nb7z/o7/LYAcQYXCsIMJg93EcUSzxDcC8QEGP45+ej1rPPc8sj02fjC/esB+wX0Ci4P8RDpEC0TtxaeFCcKLvz38sLuE+px5evkr+m575Pzzvbu+Vz7lvpD+ZX7QAGsBooKYgy6DhUSuxNuEl4N/gbEACP7+/Zw8+zxxPLc9Un7BwHUBQ8JUwxrD3gRRhOaFa8WzBCOA8f19e2r6nfmhON+5UrsaPMQ9xz5vvpb+2P63vlO/dEDugnqDIkOwRDFE5QULBFbCpQCifwU+An1BvNJ8qf0XPms/y0F0QguDLYOpRAPEkgUWBY2EpwGFPiv7kDrJui65ATkZOn48Of1vPgZ+kP70fqd+q79FwNSCTsNhw9+ESMUOBagE0kMsQJK+4P2avNd8RfwcPKi913+VQS4CDANqg+AELMQwBK7FpEVEAxr/SDy8u2K62fn8OOX5lTtSvJ89G/1dfbf9vz3mvutAfwIbg6IEXsTpRV+F1QWJRBBBmH97ffE9Ivyt/Bk8YT2uv2lAzEHQwrBDacPOhAqEXwUrhXKDez/9vP37qfskufE42/lTu2f9E/2X/ab9nX3Lvgj+kT/NgX+CpMPBBP7FasXmxZNERgI5P4G+eD1FfOe8PXw3/W+/S4ErQdrCTkMiA/kEPUR4hJuEy8PWASW+FrwcO2I6ljmbeV06Rbxw/T98/TyXfPy9Yf4HP16Ay4KahAMFFsWSxetFuASPguMAtj7efhy9pP0ePMs9dr6QQFMBbAG1ggzDeoPixCYEXkU+hNHCVD6PfDx7NrrP+jF5Xfope409A31sPNm88/zbvY1+lIADgkkEL8UIxblFusXrhXpDs4D6PqV99L2LfZ09O70v/j4/h0EiAa5CGELnQ6lDzUQ8BIgFFsOfQB89EXvHu1J6mfmnOZV64zwIfPJ8rPyb/Oo9Kv3W/20BTsO/hO+Fu4XmBjBF/USHwqmAND5Evfb9cD0FvVM92P8WgEvBZIIGQvaDUEO/w0AD0QQRw+ZBj/65fAf7XjsqunC59nolOzK8IDyOfNy8/r0FPik/LUDWQwXFEcXMBc8FpgVLRPoDIYEPfye9272QfZi9sj2k/mm/ckBAgZMCUwNOxDzENUQ2BA1Ec4Ltv9t87vsMus26UHnqOf76WvtufA38xj0fPQx9o35n/8LCLgQGBcHGY0YUBewFRMRhgit/1P5XPdv9pT1mvYD+TD9hwA7BGcIiAtqDloOGA7PDp0O5wvFArP4S/GN7RjsY+kQ6bPqUO1/77nwavIT89rzwfYT/UoFpg2WFFwYNBn7FwQWBxLtCh8DwfxK+e73xfbq9kz4tvsmAEwDpgaUCXQMEw7lDfEOjA/hDT0HQvyE82fuPuzq6YLn0egs7AzwrPHK8X3y3PJj9Bv4yP8eCYEQ1xWnGAoaahk2FnAQ4QcTAK/6lvgi+P72Svdw+Q3+0wI+BiYJ8wpNDTIOLg6/DjUNPgh3//f2N/HC7Rzs7OnC6Ozqju+78o/xQe+W77fyvPbo+38DTwwSFMIYiRopGtAXphK5Cj8DuP7e+2T5sPfS95z6Lv6sATUE3AWtCOgKVgxZDfkOdRDeC6MCePm28oruhemn5ovnvurR79vyqvRg9Dvy0fFy84r5DAEtCJ8P7xT3GFwa2xgkFIwMBAVj/zn9r/vk+XP4Uvkt/ZsA7ANlBdAGyAm3CxoOcg8XEGUOXAaz+6LyDe/V7fTp9OdJ6r/vavTi9NbyQ/CK7vzuUvK/+RQEbQ2SFMEYoBo5GzMY1RE+CUYB1/08/S395fsF+wT9q/8tAu0DjgW6B70Ilgl6Cz0NBg6JClsBcvaP7h7tH+1X687qouzX8fD0x/Mh8VDu+u7w8Wf30v9TCYISIBeyGFEZXxiaFFgNZwWT/13+Cv/Y/ar7+voQ/mMBgwIDA/MEqAhHC8IM/Q3gDTgLBgRw+ZDwfuys69fqqurg7cbyBvZE9QvxzO1o7RrwafU1/KYFtQ9EF7wa/BnNF3sThA22B/UCUwFrAHX/cP6r/Z3+I/+k/24AWQJNBkYJfQsBDYENiQuHBSz+1PWu7tDqWOq67NLu8vF79jX5PfdP8QLtGe2V8P/1Cv2QBooROhpvHAgaQhbAEfoL+ARlAE/+BP6q/bf7xfyiAM4EHwaFBGAGBgv8DbQNzgsLCp0ETvw79dnvt+x76o7pVevm75/1kvfC9WnyyO+275vxQvam/HwEUw1FFXgazxoFGOMSsgzlB1kE8QEJ/1n9wf1w/jAAmwGSA8cEwQSIBjgI9gn1CawHTAT8/bH3IPOA8Cnvou127gvygfbP+YD5Jvb/8cHvcPB98974CAD/B9kOORTHFtEVyRLRDSQJ6gRGApMCpQEA/5z8kf0sAnwFhgc9CGsI2QnXCfUIzAZvA1D/9vdO8fftce6H8Lnvte+l8mf3t/lx9tDx0+6A8Pv0D/mF/gAGEw8XFSgX9xYqFHQQBAuyBZwBWv/q/7UAswB1AHMCtQXyBZQE+AMaBY8HYgl6CaAGJwJO/Qb3w/DD7IHrW+xb7mHySvfS+Sz5WfWC8N/uhvK++EP9EwJNCkUTSxh/GOIVNhLiDp4KDAVf/3f8of3E/sX/NwIDBoYJJQlBCP0HsgY2BRECXQDR/kH7lPYR8FvtB+5E71HxRvRT+YD7CPog9+/zSfNy81r17vlLAaUK/BGLFYgUJBKbEBkO1AmaBKYAQf7j/Zf/8gHwBGMG5gYBCKcIEwlUB/0DhgA+/0MArv3d903yau/x72bxBfOg9FP22fcc99b0wfPg8yD03vR3+F0AsAkfEYAVVBVqEpwP9g0RDFsHzQE5/9T/yAGIA24EqAQSBKgEzgWmBkUI5AaIA24AVP5r/Jv2iPEL73XuSvBO8oL2QPmn+LD2LvPK8ZryjvSQ9rL5LAHiCYcQXRSMFTYUghHQDjELBAa5AHf99vxM/nEBfwXIB+4Hhgi6CTgIDQQsATcB3AGD/8H5K/Q18ujyyvGB78fwUfUM+XH42vVS9Bzz4/L78/f2ZvxHA2MK9Q5TElwVeBWTEp0N+ghKBZUBnv7k/GX++wEFBXoHAwlECiIKxAcIBMgBNgLwAD38AvWj8OHxU/SW9IDyMvMd9r33yPYb8y/xofBX8efzQPpEA+MHBgvvDpoTaBaJFKAQfwpVBlMDcAAd/4j+3gA+A34FnQjzCiYLbwn+B1cFrQK8ARMB2Pvy8tLuxe5K8Bby8fHl8uH0PvfW92T0lPFn8Djxq/VT/NgDPQngDG0RHxXyFdcTcQ8vCw0I6gVLA9//3P7pAAcEPQZOCHIKeAq6B5UDzwGPAZD+jvkz9dLzsfOi8g/yC/Jc9Nz2hPdF97T1CvSe8KTuEPFp9sX92wTaCl4OARInFmEWFRH4ClkJ4AjxBnsD9f9dALEDZAc4CRUK9wpZCTIG7QIlAXv/D/s79gHz/fEm8pXy6/GE8PfxQ/XK9rf1LPQh86Hx4/Fv9Y/7cQPpClMQgBNrFekVoBMvDm8IAAUdA0gCjQFYA3oGgAcDB9AFrAY4CMoHUgQlAnYEqgTI/nf0i+/D7x/uq+vc6ebutvXf+Gv5m/RA8ujxtfDp8Jv0P/+aCIQPGBZdGf4ZkhdQE0cNDQgDBUMBqf2b/UIBUAVwBz4JmQuJC1AH7gKBAcQAUf78+IzzV/Kr9NH0O/Ap7SjvUfJy81r0qPTH81byCPIH9UT6KQEgBpMK7BCTFpUZvRZyE90Q7gyICCMCMv5w/UD/KwKbBFYJ3gt5CnsHIwbjBdUBev1V+Oz0qfPd8FDvA+6D8OPzR/S69CT1TPW+8WHuEe7b8Lv47gGNCQ4OthSeGlkZyhQdEDkNyAirBOoAqv6tAfYD1AS+BKcHbww8DAcJmAWxBMYCtf68+gD2AfJE7/zt7uxU7f7w7PFg8Qb0z/Rj8/XwyvAQ9KT4XwC3B14OlhW2Gh0cxhl7FU0QPApFBB0B+ACCAf4ByAGfATIEvwVlBVgDCAFXArQDxwMZ/3/2y/Cj7v3ucvBU8gb0L/XS9gL49vYH9NPvze1/8O336f9OBa4MJxUzGqcYghS9EfYN/whVA/sAtQKZBekG0AWFB9UKIQu0B+QCSAFVAcgARv2G9y/zU/Bl7xXuu+z67bTxOfV09dL06vPW8ozy6vJH9s38XgV3DggVthkOG+cX1BNGD9oKzwWiAD//MAFUAy0DuAJpBX0KTgsNB9QC6gGTA48BNfrm8gHwEvBV71ntfO8R9Kn16vRW8lXxpPDe767xu/QA+2ADFQvDEUgWwhjIF4YWIxVtEJwJeQN6AUYALv/t/74BcAToBcUGagcSB84EGwFf/tD9v/zy9+fwd+3x7SnuCe6W77Tz8vY4+OH3SvYd9bH0j/Vy+EH/vwiOEU4XrxqkGw0aRxZzD9kHAQFc/tr+vv5S/1oACgPYBSgHiwZGBJIDugOwART8mvXB8LPsduvs61LtGPEw9gf7JvvK+MP2evTB89n1NPt+APgFTAzsERcXIRruGZYVZBCSDBwHGAEx/aP76vnG+YD+vwShCcIKLwr7CTQJmQaQ/mf1EPBI7gjsUumb6tntb/Ee9Jb3J/qP+Ff2YPTj9DD51f0bAowIEhJoG18eChyPFx4SvQw2B8EBcvyb+Sr6Ef46AwoHUwmICkkMTAx5B0b/6PbT8DrtLOmD5onoVe+U9oP5U/p2+1b8LPrI9f/xH/HE9BT7hAMWDa4VWRvCHF8bjBfID+MGeABP/WD72vmP++b/swXYCvoNFQ9lDYwKhAUA/2n4x/HW7OLp+enR69DtxfBX9Kf4Fvuv+rv3DfM48XjzVPiS/O0BXwtjFZ8cNB7BG7IXFBJADG4GngFR/g37VfmS+zkBogbQB0kHjAngDAcLWAJg+NHyAvHu7ObmduQG6QXyKPiq+a/62ftr+1L4J/VX9Xv2c/lPAKMJwRKgFzga4RstG3IX0hBdCWYBqfrh9gD3rvudAKwERQi3DP0PDw0VByABc/v989rq++Qm4+/kZ+nc78j2wvyWAQID5f/4+XLzbu8v8JP1vf3bB5wUyh9WJIcifR1jFloMXQGD+Tj2M/ap+IX8LgP3Cj0QSBFKDnkL3Qa6/l/1huzS5i7jV+Pp5v/sMfaN/XsAhP9j/tH7APSm7NTq5PDO+/IGcRERGiAi/CWxIdwYDRC5CHf/gfdQ9Lj1ffoZAO4GGw0fEiUTYw3SBar/8fnJ7+TkxuAE433pY++S9KT50v6FAzUC/vy59s/x3O5777z2Kv8fB9UPlBoUIzsjux3KFB8MzgRP/Tf3svV++Wj/3ARHCowOYg/0DMgIoANj/Qr3P/CE6QnmNOdB6yDvWfOq+Mv8+/4R/fn27fDr7T7wh/Qc+6UFJxH8G9QhMSIgHg0YsxFWCHH+m/j1+A792gA2BGkHXArKC3kJIQW6AMX8Ifj38uDvr+wu6SnoresC8lP2KfoW/T3+Ff0t+ez1ffMw9Ij3dP0GCNwS7xoKHnwekByHFdIMLwUAACj80/kU+37+YgOsB/IJawtEC0QJCAW0/wD6pfCK5pzhKuP45lTp8O3s9gIBoAV7Ajz8X/i899/0pvGN9Y8Btw8qGUUe+yAmINsbDBSOCr0CqftM90r3xPoAAEUDCAhiDRcPLA3bB0EBC/cH7NrkvOGp4/flYOkJ8Mz5gAIfA6oAkf5l+8H1nfBn8Xr2yf7DCXYWJSE3JmslPx9PFvsL+f8L9iDyC/RO+bIAcQq3Ee0SKBAIC+oDxvnp7pbnWuQC5F7lfuii7134H/5RAKIAwAAB/Yv1O/B77zvz6fidAVENshluI8UlxCElGvsRlAgi/a31avT++Or/jAbODAgQExBHDIQFrv6V+EzySOph5b/l8ujI6yzu1/Om+skABgKG/dj4UvZW9bnxZ/C/9wEFLBNCHDUh6CJeITEcYRH+BZ791flx+P35jf+HBAwIfAlWCyULQAau/6j3W/GR7TDqt+af5SPpDO/y9En69v2+/qv9sfy4+Un14fMQ91b+jwedERgacB7+H9odjRfhD0EHK/7F96v2FfuKAH4FCQo4DewOLA11B93+Gvfh8ePrWOXv4Rvk2umE71v0G/mD/VAA6f2A+H/1o/Xx9jX5ewD8CpsURRsvH9EgBB7yF5cODAXy/lj7N/qR+6IAyQZmCt4LGgvRB1EBY/qT8wftcuhJ5RXlyOcg7fHxJfTx+Pz+LAEO/ir5O/g1+sr8jv49AAoIdRQ1HSQeRBtuGfYWNBH4Bwz/6/lK+tT9gwBGBDcIrAlmCEUF+gHd+6jyaOs6503mduYZ5jnp5+/S96D8+P2KAJEAXP3H+rz6CPzM+1YAhgpSFXAdGB/7Hp4dMBnsDxkDe/vA+Gj4Cvq1/gEFwwh+Co4KqQeaAHL48PG463blPeHZ4GXlMuxc8g74tP6CBawGewKb/Uz5d/an9k/8lwUQD4oYXSDTI6givByCE/QIpv7F9970hfbx+0sCawlNDjcOhAidAJ76AvQI67Xi9uAQ5ZfouOvF8Cr5oAEqBOUBx/3/+iz5JPaT9pj8IQalD8UXaB6+IFYexBhfElYLeAOs/Bj4jvi5/ZcDnAaLBrUGlQY2BGf+A/aU7uTpYOkv6c/nH+lF72f3uvtj/XH+N/6d/DD6ePiI+f39IQSPCrwSHxwMIKgeRhsXF0wQnwXq/MD4RPm4+6r9XAFLBpYK6wmGBDz/Avrs81fsJudb5UDkGeaw6fHvOfbP++kBigRnBZACzv0G+2z75f+kBUAO3RdrHtMglCDxHMITvwhA/wb4NPM18kn34/8jB0gKuAl4CJsEw/t18cLpPefJ5WTk1eaY7JjzwfhJ/mAEigXgAZn8OvpU+n36If0RBA4PzRmKH8YhmSBtG2sTGQm+//H2HfHN8mz54AHhBmAJ1gryCD4ElPs58pfq0eUV5crl9OhO7QDzvPqTAFICtwDq/5f/Nfxr+Hb4vf2uBbgNExaIHSIiwSF8HAgVlgskAPD0RfCN83L5uv3JAo8J6g2uC/sD/vvv9DLvx+nY5ajldeiY7NDw2PZl/XEB/QHhANv+3fry99X4Pf05BFINABgUIJQiPiD4GfUS4QrIABL4DvSj9rr6uv6QA74HzAm9B+oD4f1f9hfwFOpp53LmVuet6l7vY/XT+Qf/TQMNBJcBvf3g+477l/3sAXUJ3xPbG/IfUCAGHRgXHw3DAnf6+fS+8wb1f/qaAScHNwqaCYQGlAAL+RHxcekA5WHlZejs6wDvZfMk+54CyQX/Alj+K/2x/Bj7bfr8/lcJGBQgHF8g+CAzHngX8A2RBEH7hPMp8GDzo/tvAqkHhArqC70J0wFB+Q3wX+k55bDj0eZf6obvIPVj/KUCmwJLAS8Ajv+1/KT4O/ni/n4JBxP7GNQdUCA5H8kYNw6qA0/6iPUq9Xz3KvyOAd4GFwmmCG4G0gFH+6bzge5/6x/pcOeU5/vrn/J6+Jf8bf9PAjwCqf8r/Qz7JvwSAFoHKxAaFngajBwLHeQZJRFqB6T+7vc19IX0/fnNANcFHwi4CV8KJQbb/Yn0Yu5I6knmRuRg5X3rq/OZ+mb/UAOoBfUC3fzv9hb1A/jI/uUG7w5uGYEh7CNbIM8YoA9EBPL6BfQa8sv18/v5A0oJIQzDCzMJIQR3+xHzxutl5wHknuN454bsIfN89yD9bwRoBiICWfuc+ob9lP3t/fcCrw6NGRAe8x6tHSwakhLQB/n+pPgX9V31n/pXAm0HlQm9CLoFOAHN+Y3yGexX57HlNOe86xHuo+/E9Vj+RQNTAZX+Yv8KAHv+gfzF/ygJ9BFgF50Z3htOHJIXGhDsB44AyPmP9pz3J/tQAOoEqggBCR8HLANs/Hf0rusv5orkHuV05g3p3u96+dkBOgTSA2ADSQGY/bL5yvpwAEMI+hDEF/AdDyHHHkUYcA9UBxf/rPdv9Hv1z/qBAVoGCQgjB6QEBAHn+qjxC+pR5ifmvObE5lnqHfFv+Z3/uAIABswF6AG9/Yz7Of6UAnkIfREGGi8gViAvHC4XTQ+hBkz9I/Zp8wz1rvwoAzYGaQcWBxgGH/9m9brsd+eJ5bbjLeVK6rnwnPR2+Hf/SgVJBvIB+v7m/5EA0f8AAtcJAhTFGkYdGB7CHCIYTA++BBr71vTx88L23vwKA3IHvgl0CEYEEf4J9rPt5uYl4//jWOcg6pztq/R//YcC1gI7ATEBfAHc/w/+oP4MBaoN0RRaG90egR+EHPAV4QyeAfv3DfIs8pj4dQAjBg0J5wl4Bw0Cs/qp8f/opuT35EHm9OcH7M3y/vgR/cH/5ABNAZsAVf8j/zwAhwMLCXcQBRjVHBYfGR79GuQT9Ae9/ND1N/QW9pr61ABiBo4IfQePBMb/Nfnd7wHoAeUm5brlH+fn6wbz4voIAUYDsQI/AuYBt/89/RX9YQN8DLwSQBgaHCsf3R5yF+UNkwTn/bP4WfX095n+rAT1BvEGTgULAuD82vVN7qXoKui+55vmweYh6ubxzvgr/hQBagPzBVcEGwDN/d0A2AZhDMoSeRpLINwg+RvZFCQNmANf+f/y8vNO+a/+JQPGBcAGagXcACb6RvPS7YzqWugS53XmKeji7sj2f/yc/xgDZQYuBUkCEv9u/vUAFgW4DPYUNRxoHzUe2Rv/FTULL/6g9bbz8fVY+pj/aQRuB24HGwR8/tD3w/Bu6rXn1+cB6DzoiepA8BT3YPwVAMICTgRmBJsCCwFeAQAF2ApTEY0XkBxpH/4dARmTECgGivxg9gr1u/Yz+5UAAAWrBmwFlAHh+471z+736FbmKef/543pZO7W9Yj87f97AbUBNgKJAXD/Dv/qAAUHbw6qFSMcBh6ZHSca/hJlCaj/vfiP9WT3sfywAv4GLwhFBkgCS/4N+KbvFeom6Lvorug96BHsGvL498T6dvxDAWwDCQL1/zgA/QLCBHQIhQ80GNAeiyBRHoQZLhFyBgb9oPa99N71kvp2AdwFDwesBCUB1vvI9J/uLeoK6ALnbuhV7KjwqvNp9oP74wDlAuYAgwAkAroDFATTBRMNJRQ3GVMbqRtnGyQW9A3WBC/+ovnH9v34DP5oAo0DGwPXAYj+dflD8yruUevn6fbo9enf63fuPvJc+C3/rwHwAJ0AkgJEA9wBtwLKCMYR5heLG6Ydqx1jGYcQPQfG/735gPUv9lL8xAKjBdYF/ASlAk39mfUM7/DqH+lE6CXpYOzJ7j3xwfUH+8n+AP+G/4MB9gIOAhgBDQb3DYsUxxcDG6IekBwZFiANLQa/AEL66PbY+P/+AQOzAjkCxwFn/wD6tPQS8dHuXOwr6ZToT+pf7J7tqfH3+Lf+AQK/AgMFjQayBdoEUAfZDvgU+hieGgQbyhlZFDINaQWT/5f7Qvl4+i/+wwD4APX/Fv7w++X4E/X98IbtY+wW7JXsOuwh7IzvMvX8+9r+i/9iAgkGxAcHBSEFZgkxD0ET2BTEGJIaDxlGE/IKRgXR/xv7QPm7+x8ANAKKAccAWv8h/Aj4f/Lu7WDrpurD6tjp+OrY7h70J/mI+z79ZADRAm8DEwN0BWELKhDAE9QXrRtQHFoYzBJFDZgGuf4o+ez4mvwDAKcBbgICAzMBIvwT97jy4e+v7fTsge5X7vjtRO9981D3OPeN99f5Zf4pASQBDgNxB8gNvBHKFCIZ2hv0GrQWsRExDAIGVACj/KH7+fwu/24A+wDKAPr+bfsJ9zHzGvHn7xPuO+vz6Z7sQPGZ8170H/cz/Nv/z/+Q/3cBPwXzCCUMvRCrFjYbMht3GEQVShG+CuECqf0u/N78Rv5AACgCEwPxAdP99PeU88fw3e6V7nXuve5y70TxxfKk8g/0QveJ+mv8PP78APEDAgjXCtEN4RHTFccYmBhaFpUSKA6YCSAEJP8Y/TH+VwDCAaAB9ADR/3P8m/fj8+vxT/Ha723u1+5Q8H7xGvBV8ET0lfgb+5r76P17AZ0E1wblCPAN5BS5Gc0a+xjCFVoSAw5zCFICXP1Y/Pr9ggAQAjICQgG0/qH7aPiw9QzzWfD47jvvdu9M7kvt5u5n8/P2dPkM/CH/cwEXAVAB0QKrBisMmRGfF2kb9xvgGMQUzQ8DCBsAZPsx+/H8RP9AATICXgLTAEz9Efmb9fPyh/FY8Arv3+0F7hjwRPKK84L1U/lX/MP8jPzQ/nsCnQUaCRQO9BQiGtYa8he1FJ0R8gu0BHr/uf1v/joA/gECA/ECdgEc/sv5mPbl9KT0lvTw8mLwR+/D74rvxe5r8GT1qfqD/H/8M/63AcgDsQMzBqcMMRTvGJsZKRnEFyUUgQ0bBscA+PyV+tP68v2YAQQEYgPU/4f8Nvol+Av1CfK677XudO+77tntCe/i8t33jvrn++v8WP9JAbEBDQO2Br4MohLSF2YarhkHFzQSUgzQBhkCu/3Q+2H9xACaA7QDIAHr/cf7TPlx9rL0IPTb89zxiu9r7lDulu5v8FX1MvrX/N/8BP26/v7/gAFVBI4LChV7Gykd2xpJF8oRswrhAw//Pv29/Q8AjAPPBo8GQgJA/EH4n/b79Ijz4PPu9Qj22vKq7uLsZO528J7yaPYj/Mb/mf/V/r3/PAK/BJoJ/hH7GU4d2xqpFgES3wunBE/+3PsA/RcAsgOYBd4EcAFf/ab6Fvh69Y/0xfb6+Mb2+fEH7oPtz+6i7+bxGvev/bwATv/H/eb+wgD3AUYGzg5hGOAdYx17GZQUGw4ABRD9vflT+iT9NgEkBZ4HywbcAcb84viB9b/zbvQP9lb1pvLd71zvv/EJ8pfxUPXv+4/+CvzA+u38cQHrBMwHCg58Fosb+hngFeERSw1dB7MB3/6d/lH/vgBMA1wFEASU/7/7kPp7+uP5Jfmp+KX2T/JJ7TzrUO0P8KPypvfw/agAY/4i+8n6kvw9/jwCLApGFOsabxs6GeEV7RDtCPkA/Pz0+yn98P+DBHsIRQhoBMn/qf2D/Fz5XvY09RT1B/Nz79vtVO878zT1ivWE9+n5LPrV9/r3uPry/mAE9wr9E5YaRRsuF7UTIxEQC6UDOv4N/iQAagFmA/QGRQmEBUUAUv7g/c37yffn9D/z2/Gv7gbsW+4y86T14PVV99/5JPqz9xr23Pis/jMEQQpDEg0ZWBu+GEsUeBBgC4AEdf90/+YBJwPtBBEHqwe+BJP/Sfzn+wH9ePw1+gv3f/Lo7TXrT+tK7YPwg/Wi+4z+Xfyj+Ff4Avqy+g39MgMrDQ0WsBmGGYwYihY9D6oFHf8S/Zj99P4EApsGnQr6Ca4FFQLC/879FPue+FT2XfKs7Wrr/O0M8dzxxvTl+Vv8aPkp9d70HPgC+5/85wGNDLEVQRiIFxcYTBdhERgIfgDS/Xv+OwCxAlAGKgnvCD0G0AICABP9TPtJ+tL3yfPe707uVO4F747wI/QT+FX5s/cD9t/2Dfgv+Vb7qQHfCgQRLBQ+FnIY6xebElsLsQSRAdL/0v4lAdkFxwhkB2IFwQNyAWH+bfvG+tL56/WR78DrHezY7UPwTfQh+q/90vte96L1WfYO9mz2bfyJB88R9BckGqEa1BglE88KGAMe/sf78vwaAgoI9ArWCgwJhQahAaH7tPj9+Ej6hvd08jrvTu988APwpfHX9VH5EflB9kP1QfYP+Bb6qv4PBxoP0xQ/F3kXHxZGEioNDAeaAqL/Qv81ArgFegdUBigF5wP5AVH/LPx9/C3+ofyu9Unuhexd7efuFvDC8xn6tfxC+tD1D/UI9kr3VPutAqQM8RPYFosWFxYAFOINWQdXAs3/lf9qAi8GjwivCNAGuwS5AmL/Wfx0/P79Ev4c+nTz9e3d63Lspe7O8l/3lfqU+375wvUp9Gj1GPkh/p8FSQ43FSMZsRgiFt0RxQuBBK/+df3Z/wgEnAebCQQK/QgABhsCvP15+/n75Pvr+GjzcO+57SLvZPEv86T17PfB+Jn2s/TT82n1Dvoh/6kEOQuZErUX/BfZFGUQqQyOB1EBhf6BABwF+QfxCP4IIAjfBTEBgP3l/CH+oP3j+gn3uvLX7yDvu+9t8XL0UvbQ9uL2Jfbu9N/0avd3/C8CYgdbDbMTbhjBF+sSsQ20CWAFoADi/twBpQavCAQIFgc+BzwF+QD5/br9lP9A/jX4+fD/7VHvWfAL8czyYPYa+nj5VfWq8o70l/Yl92D7NwQaDg4UPxbIFuwVBxHSCGUDaQFCAfcBHwWqCbQLzQkyBqMDXAEb/of8G/+tASL+nvXe723vMfAu78jubPOT+fn5N/VO8xr2WPi297D4pv+MCIcOXhGpFJQXhhXTDqAH7gOdAVUAyAENBsYJjAkVB44FHwUEA4D/5P4rAEr+9/eQ8LLtOO+w8Y3yevTq+HH6zPYw8ULwLfNZ9gH57vxZBXIOdxNqFAwV3BR8D6IH+gHqAMgCnAS1BogJ0wpxCNAEogNVA/0BGwD8/8T/APwG9UnuZO2m8Kjy6PKJ9X/5fPno9fXx8fE29cX3bPnN/jwIaRB5FEUVjhTAEREMEQYZA5MC8AKQBCAHxwlqCh8IMwWLA/MB4v+Y/iL/Lv5z+RjzsO/I8BzyMfK58t718/gk9z7yZ/AN9JP41PqL/VAEQgxNEP8PBxCGEegQTAwiBnYDKgQ7BbIFXgd4CfQIzAaPBL4DMAPJAT8Amv6d+571gu8y7dXulPLW9M31zfYy95X1LfKK8WH0hfhD/B4ASgbIDVAToBOwEU8QIA5qCUYESgLiA/UGPggOCEcIFgiBBv4DzQF/AWYCuQHA/Xb3zvG57gDvV/DU8TX0uvZY94D1KfOZ8ufzs/Xq+Jj9EAQEC9cPIRI/EkoRnQ7sCjwHHQR7A9EFZghXCeMIEQgEBp8DJQIlArsD4AMrAcv73fXw7zzs5uzt7030g/dK+Xj5nPcR9BHxSfHd8zP4bP5cBlwOrBMSFRkUMBH5CzgGwwHGANYCUgZsCXILmgutCSUHYATtARMAJAD7AC//Z/k48invGvAo8gbzkPQx+Jj5sfY+8vnwBfMM9df2ZPsLBCUMeg/nEHMSFxJ3Dc0GGANSA3IFmwYgCCsKngpNCNMEjwNRA4kCRQJbA2sDSf6U9VPuyezt78HyQ/V++AH7h/lN9PPwnPHH9H32Ivjq/eUGzQ23D9IQgRGYDzkKQwQzAu4DZAbsBmUIiwpgCmsHPwT/Av8CzQKMAmwDaAJH/PHyvOy+7S3z8fY497j3I/k1+Gz0ifHn8Uv0D/ax+Oj+WwgKEEQS/RHUENsN6AdOAhABZANdBugHSQnQCt4KCgjRBKkC0wGWAT4B/gCL/gT5ZvIp8InytPQs9a711Pf9+D33ivMm8kP0cfb395r7ZQOYC7IPSBDrDx0PPwtjBRMCWQIzBK8FegcICmsLGQpuBpwDjQK0Ac0AOAGoAYj+3Pc58mvxfvP19BD1ffbL+Dz4wfTq8TzzRva+9/X4Qv54Bq8M2w4kD7IP9g0CCdEDXwLyAwwFcAXsBo4JZwoMCMoEnwPqAogBGwHDASgBbvw39XPwkvEE9lb3vPby95T5sPci81HxrfOO91j5VfstAZEItgySDH8M1QxsCwgHiANlBJkG4AY1BmIH5AihBwwEBwK6AigEpgQmBMoCJf9T+XbzmfFr84/1Afdr+Mr5G/ky9oPzcvPE9Sb4Gfpc/SQDnAhrC8wLcQsHCxsJ/gWbA+sD/AVnB8QHxAeqB2cGNAQIA80DxwTnA6QB2v+q/Z359PQN8xL1wvcv+N/2HPY19pv1dfSR9LH3g/t1/VD/XAMOCFMKtQqECk4KMwltBjUEOwQCBtcGqAa7Bi0HUwfuBSAENgPSAlMB6/8+//38G/lD9dTzT/UU+J74NPeQ9jX2CfUW9HX14viU/LD+nABCBDYITgo5CroJFgnZB/oFpQT+BFEGAAcUBxcH3QZiBUIDNAI4AhIDPwMvAnj/Tvsl92v0wfT59rv4c/iW9wX3X/az9W/1bfay+Mn7Sf7xAL0EsAgzCw0LFwksB7wFZATkA98EOwflCLcIMwepBckEVgPUAfwBqQO4A7wAvfyf+bT3hvYj9gT3wvjd+FP3SvY89h/2BPZQ99X5wPw//8UBowVlCX8KEQnzB2MH+QV7BFAE1AV9B8cHFwfLBpEG9QRpA/cCAQPqAg8Cov/k+yL4lPVf9Rf3rvjf+Kn4IvgG9/z17/VN95r4pPnc+yIAdQQfB5wI0wkgCsQIjgbpBCoFCAY5BlIGMgf8BzEHcQUjBMwD/gLmAWMB+gCt/n36LPd29vb3BPnY+KH4i/ip9//1QvVk9kX4Zvmu+pf9mwG6BOwGrAjECXQJWQeFBecE/gTrBFkF4Qb+B/oHagarBNYDUAMjAr0BigKTAe39kvka99b2l/cs+OP4CPrK+aP3x/XQ9ej2RvhB+cz6IP5FAm0FiQcrCTEJqQcYBukETwTOBBYGjAdYCMkHUQYtBU8ELwNSAucBBALuAVUALf0Z+sH3Q/Yx9qf3ZvnC+cr4u/d591n3Ivei93D5DfwW/6YCVAY5CdcJmQjMBo8FzwRHBKYEwwXOBrYGSQafBj0GfASbAg4CNwIMAvgAj/7R+zH5a/c99zr4HPk2+T/5FfmT+Lf3a/ea+F76lfsX/T8AzwPWBaYGdwcUCMkHhQZmBUoFWgXvBKYETQX3BWsFFQRYA2wDPwM4AjgBkAC8/k77DPgf9/r33PhZ+aH5EvrO+eT4U/iZ+HX5hvou/I7+RwG0A30FAQcZCAMI/wa0BcsENATCAwIE+AT+BQEGWQVxBBYDtAHWAI0AZQDv/1n+yvvb+Sn5Evkx+Xb5g/lE+Rf5GfmL+UL6Avsw/Lv9cv9CAYcDoQXVBiEHjgacBbgECQR1A48DWAQIBVgFXgX7BOYDhQJ1ATIBGQF/AEL/mP3M+zT6bvlt+QD6VPoE+rT5vPnm+dj5H/o4++D8pf4oAKoBawPkBKUFiAVUBSEFqQRXBEUEiASBBHoEogShBDgEAQPAATcBjAFrAUUAx/4p/W/73PlM+cP5f/qa+g/62Pku+mD6Vfq9+kf8Vv7r/xMBZgI2BFsFUwX6BN4EwQQSBHcDegPbAwQEyAPdAycE2gPjAuwBZwHjAO7/0v7u/Sb92vt2+j/64foi+5D6Ifp3+vD6Avvx+tP7lP32/vr/awFaA7oEIQX+BM4EvAQ4BF8D8wIhAzQD/AIPA34DkAPlAhYCwwGbAeQA0f/C/sb9rfyf+xD7GvtO+xz73/od+577yPvU+0/8Nf3+/a/+3P+NAQUDxQNJBLYEywQ6BFcD4ALdAtsCsQL/AowDoQMdA20CFQLGARoBQACM/+L+5v3q/In8hvw0/HL7CPs2+1z7VftU+6/7afwW/cL94/6MAMgBaQIvA/4DLASmAzYDJQM/AwkDvQIxA/EDAgRiA+wCzQJxAqoBxwBOAAkAJ//B/bn8W/wM/IX7L/sh+w371/rA+jb7/PuV/DL9RP6f/7wAlQGDApIDSAQaBIEDVwOEA3oDQANVA8YDAwShAxUDwQJhAqkB/ADQAKoAzP8q/rr8JPwQ/NH7avsm+x37FvsH+0r7wfse/IT8bP2h/qD/owDgAR0D9wNJBGIEaQQ1BIMD2QLMAh0DUwM0AykDRgMdA4MC0gFmAccAwP+W/qD96fxK/OH7sfuH+0z7IvtI+6P76Ps5/MD8cP0W/qT+hf+8AOABpQJGAxkEqgR+BOQDhwN8A0oD3gLCAgwDRwMaA6ICOwK7AQ4BIAAp/zr+R/11/On7yPu6+7f75ftJ/JX8nfyK/ML8H/1W/cX9xP4sAGYBbgIrA5MDtQNrA/kCywLjAssCjwKlAh8DiQN4A/8CPwJsAbUAVQAiAIf/mv7Z/Wr9Cf2G/DP8JvxL/Eb8/Pvr+1386vwm/ZT9d/5//yQAmwBiAUAC1QIVA20D2gPwA2YDvwKfAvACGQMHAwgDAAPkAqACOgKYAaQAbv8o/j39hvzH+1X7dPvJ+/f7Gfxw/Mr8CP1A/Wn9pv3q/Xz+dv/NACMC5gJIA6IDAgT+A4wDHAPrArYCgQKMAtACBQPbAl4C1gGcATMBEwCj/pP98/xG/J37evsI/JD8oPyq/Aj9cP1s/TL9K/2i/XT+Nv/9/xgBRAIMA3cD1gP4A6IDKQPJApACWwJaApICswKWAl4COAIBAm4BZwBM/3D+xP3P/NL7gPvt+178Ufxc/A/9+v0O/oL9VP3x/cj+Ff88/+T/GgEpArwCOgPdA0oE9AMSA5IChgJDAuMB4AEXAhAC5AGsATkBtAAuAHn/lP6G/Yz82/u4+wr8Yvys/AD9f/3F/bP9rf3d/SH+cv4k/xoAHwEhAhADvAPyA6MDDQOcAnECRgIGAgsCUwKCAlcCAgKhAe0A4v/V/iL+zP10/Q392Pz9/Cn9/vy7/OD8Xf3I/fb9DP5L/q3+Jv+Q/woA1ADMAZ4C+gIUAyoDNQMHA7ICcQJIAgQCrwFiASoBNQFYAVYBCQGrAAoA+P7h/Qv9bPz8+/X7Pvyx/En97P1p/rv+8/7w/rz+nf7k/pr/YwAlARsCMwMNBGYEZQQYBJQD1gLVAfgAsQDkANUAVgC8/2r/Zf9l/0f/9v6C/u79SP26/H/8k/zG/Bf90v3z/uz/bAB3AGoAcgB/AIQAowALAYgB5wEfAjMCOQJJAkQCBgKdAUsB/wCtAHkAQgD7/7X/p/+i/2H///6q/nz+Rf7q/ZX9d/2O/az9qv3Y/Yj+b/8WAG8AwwD0AAUBPQGqAQoCIQIMAvgBDgI0AkkCJwLOAWcB7gBMAJb/Ef/D/nr+VP6G/vb+ef++/5H/8/47/s/9r/3e/UH+uv4Z/37/OwArAbwBngEWAaQAjACkALUAuQD4AE0BTwESAQ8BZAFmAc4AGgDd/9f/nv8x/97+3v7k/s3+zf4w/7P/5P+p/z3/0P5w/h3+If64/lj/tf8yAAsBxAHyAeUBwAFoAeEAUwA1AIUA4gD0ANwACwFEAfcA/f/5/nf+Mv4F/i3+xP54/wUAawCbAHkA+v86/5T+Pf41/n7+N/9cAFkB0gHwAdkBfAHNABoAr/+c/8n/EAB8APsAPQEsAe0ArwBmAPP/V//B/nn+qf4Q/3b/xv8gAIcAqgB3ACEA9v/g/6H/S/8P//P+8f4P/3H/BQCrAD4BeAFiARkBvwBPAPP/6/8GADAAgQDnAAcBzgB6ABsApv87//D+pf5e/l7+z/6E/ygAlwDHAKMASQDP/1n/Ff89/63/GQCPAAABTQFKARgBzwBsAPD/V//c/rD++P6L/xwAeQDLABcBMAHvAGIAvf8Z/6j+ev6r/i3/uf8VAFUAsgArAYYBfQEFAUQAkP8a/93+zv4N/4r/BwB7AM8A/wAEAeIAcQDP/2T/Qf9V/4r/zP/2/wIAAgABAP3/4P+b/y//zf6U/qj+Mf8IAMUAFwESASoBdwGcAVQB0QB0AAcAeP8Q/yj/vP9AAH0AcQBCAOv/Z//0/rP+rP7L/vX+R//U/0EAXgA8AC4AKwAGANP/jv9o/2j/qv88AO0AewG6Ab8BjQEaAYoAAgC3/6v/uv/A/9f/DgAiAPT/oP9Q/wb/r/5Y/iz+SP61/mL/DgB2AKAAygDrANQAfAATAOL/7/8TACsATQCfAAcBNQEhAesAqgBjACUACgD2/9n/wf/X/w8AOwAxAAIA0f+K/zT/8/7p/gD/E/8d/yn/WP+l//j/PABrAI4AkwB2AFsATQBPAGkAmQDTAAwBUgGAAX8BPgGeANz/Qf/s/rT+kf6a/sj+If+L//b/XACqAKwAYgADAMH/u/++/5T/Vv9D/2T/sf8jAJIAwACuAIAAVABDADYAIgAHAO3/4v8DAGkA2gAQAfYAnwBBAAAAvf9i/+/+lf6C/tb+bv/7/2UApwDFALMAdgAyAAUA2/+d/1b/J/82/3j/2P8uAHAAjACBAFkAIADv/7z/g/9w/7b/LQCcAOMA9wDuAOQA2ACcADYAwP9R/xL/BP8e/2b/3v9SAIgAhQBhAEMABgCW/yT/6f7n/v3+N/+l/zAAxwBEAYYBcQH8AEcAif8A/8L+4P5f/wQAlADfAPMA8QDmALcANwB//+T+nv6z/gn/lP86ANsAVQGIAWQB8AA6AGr/xP52/oP+wf4o/6n/HwCBAMYA5wDRAHMAzf8c/63+mv7a/mb/AACKAAgBYAGKAY8BaQHaAAIAJ/+F/mP+m/7r/lD/+P/AAGoBzgHJAWMBtADk/yz/yP62/tD+Kv+s/0wA9ABsAZUBYwHyADgAYv+//nz+k/7Y/jz/pv8IAEsAeQCUAHYAKgDQ/5D/hf+k/9v/IgCIAPcAUwGSAZQBPgGsABIAmv9b/1T/Vf9Z/3n/of+2/8n/0P+q/1j/8/6k/o7+zv5H/9j/gwAgAYwBqAF/ARcBbwCh/+f+iv6W/u3+cP/+/4gAAgFWAXQBRgHRADUAr/9r/3L/wf8tAJUA2wD8AP4A5wCdAA8Aav/W/mv+UP6S/hj/sf87AJUAxgDsAPIAugBEAMD/Xv87/2T/xP9AALQACAExASYB8QCbACUAl/8i//T+CP86/2j/l//f/0AAhQCDAEQA9v+c/yj/xP6y/gT/gf///3IA4QA0AUYBGAG4ADAAkv8T/8/+0v4c/37/7P9/ABoBbgFhAQoBegDE/yT/xv6+/gz/f//z/2EAvQDtAMoAbADr/2L/2/5x/nD+xP5S/+P/YwDGAAYBKAENAc0AgAAyAPL/u/+R/3D/ev+3/wAASACVAN8AAwHnAJoAPADv/7j/iv9u/3f/of/c/xgAXgCfALAAhQBBAAUA1/+h/13/Lv87/4P/yv/1/wkAFwAZAPL/sP9y/1b/V/98/8j/FwBZAJgAwgDKAL8AtQCnAI8AdwBIABIA7v/S/7T/nf+V/4b/eP+A/6b/1P/e/8f/tv/E/9r/4f/W/7f/rP/C/9z/6P/x/wQAFwAyAFIAaABtAGIASgAiAP7/4P/j//H/8f/v/wIAPgCDAKAAeAAuAOz/rf96/2P/Zf91/5T/vf8AAFYAlwCQAFUAHgAAAPb/5P+r/2b/S/9k/5j/2P8bAEkAZAB8AJAArAC6ALMAegAiANX/rv+v/6j/kv+J/6r/+/9SAJIAvQDRAMIAhQA8AAsA5f+//5T/hf+2/xIAbgCgAJ4AcgBGAB8A8P+y/3f/UP8+/1D/g//K/xkAUABfAFUAUABYAD8A8/+S/1L/Uf91/6//8f9AAJEAxgDZANMAwgCKACEApP9B/x7/R/+M/7f/wP/X/xAAMQAXAN//t/+l/5n/iP+Q/9T/IABCAD0AQABkAIgAdAAbAMb/q/+s/7f/1P///zAAVwBrAG0AdwCJAG0AFgCo/07/LP8r/zX/O/9N/37/wv/9/wgACAASABwACwDg/8D/w//i//7/DgAsAFwAgwCEAGEAPAAuADsARwA6ACoAKwA3ADkANgBDAGEAggCPAH8AZgBIACsABQDR/6H/gv93/3j/e/+L/7D/3/8AAAAA8f/t/+v/4P/F/7f/yP/2/yoAVABwAH0AfAB2AF4APgAmAA0A7//P/9X//f83AGsAeAByAG8AbgBOAAkAu/92/0j/MP80/1z/l//P////LgBeAIIAfAA3AM7/c/9I/0j/a/+b/8z/CABYALIA8QAIAfcAzwCTAEgAAgDJ/5v/cf9L/0L/af+5/wwARABhAG8AgACSAIYAVQAMAMD/gP9j/2z/hv+e/7P/y//y/xsALwAfAO3/qf9p/07/aP+N/6n/vP/m/y8AhgDXAAUBAQHQAJMAawBRADkAGQD9//7/GAA+AGcAiwCWAH4AZQBfAHYAkgCYAIEAVgArAAwA/v/e/6H/V/8V//b+9/4O/yX/Pf9p/6P/5P8eADkAMAAFANn/r/+D/23/ef+l/9D/CABvAPEAVAFoAT0B9QCgAEkA/v/M/73/uf/I//f/RgCWAMIA1ADOAKoAXAD8/5//Tf/7/qv+iP6l/u7+Pv99/7D/5f8dAD8ANwAlAAoA0P+F/z7/H/8t/1z/lf/M/xcAcwDEAOsA2ACkAGcAMwACAM7/qf+k/8T/2v/j/wMATgCeALEAgQA7AAEA1P+e/2X/Of8r/0X/cf+u//j/SQCQAKkAkQBcACYA+f/N/6f/lf+i/9j/HgBiAI0AmwCbAIoAZwA7AA0A6//P/7T/sf/a/yQAVwBaAEIAFQDl/6X/a/9N/1H/bP+b/+j/QwCLALIApgB5AEsAIAD8/9j/yv/O/+j/HQBTAIgAqgCfAE8A1v9g/wX/z/6+/uP+R//S/2YAAgGNAdEBvAFXAb8AGwB///r+nP6E/rL+D/+Q/x0AlQDMALUAWQDg/0z/uP5P/hv+J/5t/uj+hv8vAMoALgFSAT0B8wCRAC0Ayv96/0v/S/9//+H/WwDNACIBSQE8ARUB0ABpAOj/af8Q/+H+4v4I/z7/h//U/yAAYQCVAK8AoABtAC0ABwACAAAAAAD//wAAAQABABQAPwBvAIgAjACaALMAzADGAJwAYAAXAM//mf9w/0f/Lf8n/0H/eP/B/wwARQBpAGQAQAAuAEEAaQB7AHUAgwCvAO4AIAE8AUIBKgEFAeAAwgCmAG0AEACi/z//+f7Q/rv+t/7H/u/+Iv9k/7P/+v8nACMA8f+3/5D/ZP8l/9/+y/7y/jT/f//M/yQAaQCJAHgASAAaAOr/oP9b/0L/Yf+r/wAAOwBUAGwAiQCVAIkAZwBJADgAJQAMABIATACkAAQBTwGDAcAB7AH0AdYBpwFsATIB+wC2AHYANAD4/9D/qP9+/0P/Gf8J/+b+sv6Q/pz+vv7M/sn+vP7A/uP+/v7//vj+8f4F/yj/R/9N/0v/Xv+B/53/mf+N/5H/jv9h/xn/8f4Z/3//8f8zAFIAjgDjAFIBsAHiAfcB9QH9ARICEQIeAlkCuwIEAyUDVAOKA6MDdgPxAkcCrAH6AAsADv8g/mj94vyG/G38ovwT/YP96/1H/nP+pf7o/gr/9/6//oL+Xf5A/hn+7v3l/fb99f3U/Zv9Y/1N/TL9EP3t/Nj85Pwn/Zf9Ef6q/lr/KgA4AWICdAN/BLMFxAaPBysIfgiHCEsIqQedBosFxwRnBGgE2QS7BZ0GIAf8BrgFkgMTAWr+dvsS+J/0ufEx8AjwtfAc8jD0yfZ1+az7Tv2r/sn/SwA9AC0AbgASAcMBJwJyArQCuAKIAhkCNgG8//r9dPxk+676hvoh+2b86/1o/0IBXgN1BSEHBwhnCH0IZQhJCDsISAhpCMoITAm+CREKugmNCEYGwgKX/sb67vfC9f/zsPJ48sTzG/Z8+Gn6/vsj/bP9g/3E/Fv8p/w7/R7+P/+8AMIC3ARbBroG/wVgBEkC8/8S/f35qPdA9gj27PZr+Ir6VP0mAC0CqgPTBHAFqwWUBScF1ATxBFcFDAYZBxYIIgl8CqYLYwx9DNQLfgo4CHYEtv9++zn4qPWW8zfycfI59Kb2CvlM+1v9uv42/+P+Lf5r/Zv8J/xj/Az9R/4rACUCtQNoBCAEXgNNAmMAev1p+vb3cvYB9mX2uvcp+g/9BwC1ArAE1gVXBmkG5wU3BaIEUwS4BG4FBAbKBh8ICwrfC9IM4wyPDNYLkwmlBUwBWP0W+h73KPQ+8nHyKPRu9t/4SPtr/ej+av/q/vf94Pzd+3/7rvsP/Bz9B/9FAf8CzAP1A9oDRgOYAdb+yvsV+Qn36vW99an2cPis+iz9vP8jAt4D3AT/BFsEewPeAu4CkgOCBK8FaQetCfMLwA34DqcPhQ9dDjIMHAn+BAAANft29w/1pPOr8uny7vTa93r6TPyr/Xv+YP4a/Tb7/PmY+a75Vfqy+7H9HQBpAjAEMQUyBTUEdQI8AHX9bPri90n2w/Ug9k33c/k3/AL/awFmAw4F5wXGBeEEwwPjAjUCEAKUArYDbAWFB/4JhgyPDusPlxBaELoOnwt0B50Cp/0u+dX1EvRw81/zJvRJ9lf5Ffyw/S7+NP7P/Yv8uvpM+cr4N/mB+nz86f6hARYE3wWwBj4GpQRYAp//Y/wF+T/2rPSU9KP1UffB+c78tf/8AZQDmQT8BLME3gPtAm4CcQL6AjsECwYLCAYKDww3DjUQSREzEXEQ1A7PCywHrQGo/NL4yvUE8yLxyfD38Uf0yfb2+LH61vuB/L38hPzi+2z7avvZ+9D8KP4UAJ8CzATPBckFSwViBJ4Cm//B+4v4g/ZN9fP0lfUd91n59/t2/uoADgNVBOUE9QSXBOUDbgNcA4oDDQTUBFkG6AigC74NUg+5EL4RixG1D20MQghTA6z9lPjy9JXyCfFM8P/wVPOX9o751fuk/bb+1P5C/mH9X/xv+9z6G/uF/Jf+AAFcAycFEQbLBXQERwJo//H7Xfh+9YLznPJE8xr1yvfd+sf9iQD0AqsELgXQBCIENgN7AiQCTgJQAzgFzgeNClkNKRDfEg4V1BXwFHwSwQ7YCQMEF/7g+Kj0KvGq7gzuou+P8lT1Wvce+cP6/Ps//Kz7/fp/+iP6R/p6+6j9BQBHAmkENgZ0B2MHwwUuA/P/Xfza+BD2e/QM9I/0ufXF95/6rv2nAP0CeQRCBYkFQgVyBJ0D9wL/AvADRQUIB3wJbwyPD04SPRQNFaMUmxK6DpwJqwOk/af4pfQJ8TDuAe1c7tjxYfV29wD5S/u2/av+zf1X/Or7Xfxx/G38s/1MABcDAwUHBsYGCgfVBfgCP/96+w74GPWv8kLxSvF38r305fds+/H+6QEWBDoFiAU0BUcEGAMQAugBAwMVBYYHOAqyDa8RUhXSFwsZLxmHF2oTSA2YBlAAmvpD9VLw/ezM64fs8O4m8hn1KPfG+Fv6kPvx+xz7O/pp+mf7vvxO/kcA1wJfBQUHsweDB0IG1gNSADr8N/jf9JHyUvE18fPxw/Pu9sf6lf6AAZUDLAUVBhEG8ASwA9kCfALnAvsDBQb9CHUMSBD7Ew0X/BiTGXkYxRTJDrQH+wBS+9b1dvCM7HXr2+xN7yPyKfWS+Jz7Sf3k/eX9Zf1Z/Ff7Mvsk/AL++/8HAl4EawaYB1UHrwX+Ann/Tfuf9m3ypu+L7iLvn/Db8l72A/vh/6MDAwZQB+QHzAeLBqwENQO3AkADUAT6BXgI/wshEAIUSxckGZgZqhhcFYwPJQj6AAz7Bvb58DTsLeqG67TuBvKU9Jb3KvvR/Wv+yf2B/ZH9Yv1G/HP7uvxS/wwCFgSVBbEG3QbKBTUDAQAW/Eb31/Kg71PuWe5Z73bx6PSm+SL+IQKLBc0HDQnECGsHzAVsBKIDPQMLBJcFqwcaC2AP+BN2F2EZYxpoGh4YDRKiCY0BFft29WXv8emH583o1+s37xLzlvdR/Hr/fABtACwA2/+e/gX99vt6/Ln+FgFJAwkFLwYKB30GbgTiADH8VPdj8r7ucuyb68Ls1u5q8g73+fv2AOIEpgfBCJwIvgftBWcEGwO3AmoDkwTlBlMK8Q50E+0WhRkuG/kb1RkSFF0MmQQl/hj46fGC7EbpPeme613vwPPX92T7Cf63/90A1wArAB//0v0d/eH86P0WALsC0AQ3BTUF0QTNA3MB1/yt9wTz1O/d7aHs3ew67knxPvWu+c/+GQMgBqsHUwh6CMoHtAZcBQ4F3QW8Bh0IbAoDDvsRRhWuF4cZtxodGVAUww3vBs0Aqvry85PtS+m759boMeww8Aj0nvcu+9H+ggFkArgBCQG0ACgAzf/+/2IBVgONBBAFgwX/BUoFaAIA/jD5L/W78WLu9uvS6qDrMO748aX2WPvV/3cDUAZRCOIIYwj8BqMFBgUiBQMGUAdXCdwL6g6SElMWzxmQG68aVRc+EkcMrQXv/jT4B/LY7B/pX+i06tTuw/LS9Qf5uvxJAPQB3QFbAfoAsQD//5T/JwCjAf8CgwPnAygEAwSFAh7/q/oY9oTy0u/U7WXsr+vd7Lfv9fOe+MT8sQAGBLUGMAiUCHIIqAcIB6gG6wYeCHgJTgvoDXURWhV5GIQa3Rp8GZoVFw/SB/0AFvsi9bPuOenG5nDoPOw78LzzK/dn+13/vAFRAggCywFdAdwASwBUAGcBvQLOA1IEawQjBPsCgQBS/IX3VfMt8DXulux/6+nrOO4R8lD2ePpD/ucBIwUiBxEINwgqCGoIqAjKCBAJ8gmaC/ANpRBBEwoWpxgMGnoZZRZIER8LwwSN/l74LfJV7JPoOeiU6gLuAPHa8533K/y9/1kBwQHMAQICyQEdASABOAJ5A5wDNQMTA3IDRQPcAM78dfjr9DDy/u997n/toe2r7tXwiPSi+IT8uP9SAosEWAbWB3sIqgibCGkIOgnWCpoMIg6hD9wRwxSfFxYZJhn1F4gUtg5vB6QAMfv69cjvzekB5/3nL+uO7pjxdPUZ+kv+9ABhAjUDbwM7A2gC0gETApAC/wLyApUCNgKyAeYA4f7D+/H3FfSQ8b3veO7X7f7tnO8b8kX1oPhZ/EIAIQNMBcUG8gcNCWMJggnDCZ0Kswu+DHkOcRC/EssUWRYVGL4YIReFEhAM0gUKAF769PPF7avpIegf6Xnrie5w8sz2Y/th/xMC2gPGBDQFtARdA2oCEAKJAlkC8QDA/zv/p/8S/6n8Zvk/9iz05fGy71XuBu4y74zwp/II9l36tv7RAZIE9gYnCakKqQqICqAK/ApyC+kL3gw/DmMQhxKqFAEXMhjDFxwVOxAkCvMD+P2491PxROta5/Xm/+jB64zuP/JS9+38KgE3A4cEtgUbBl4FAAQrA+wCcAIhAej/zP8MAJ3/Fv6i+x35mfbt84fxsu+j7k7uDO/88Ofzjfca+4D+iAEoBIQGMAgXCQwJ+Qi5CTAL1Ay2DccOyBB9Ez8W+RcfGWAZrRcAE+8LHwVI/+j5ifOG7Onn9+YF6djrne4r8rP26Pvy/6YCfwRPBWAFhQSDA8kCfAI8AocB4gA5APj/7v9H/4L9hPpe95/0qfL68B3vB+7g7T7vxvHu9IX4Afy1/+sCtgXXB+8I6Al9Cu0KOguHC2QMjQ0yD7UQcRLDFPEWuBhvGGIVIxAZCtYEl/97+THyyOvW6L3oI+qs6xjuh/Lb94b8b//hAVIEywXrBYMEXgMVA9oCKALkABcAwP+e/y7/mP2S+wX5UfYP9Mnx5e9v7vTtc+7d74Pyr/Wv+cj9EwEMBJMGCgn6CrkLsguGC1wMPw2PDdYNhA6WEBET/xR5FkcXoRYeE28Ndwd4AsD9bvcv8InqNujh6Kjquuy47+Hzo/gu/QIB9gOpBUwGLAa9BVkFXARMA3QClgHOAMX/G/99/kz9D/vQ9zD1D/M18Xvvvu007dftr+9a8sb1EPoX/tcB7ASbB1YKKwziDH8MIwxnDPUMfQ2CDVwOZhDlEmkVNBcPGPYWJxMiDekG4AFh/BP17+z85o/lOOdj6ZTrRe/k9N36h/+tAmMFnwccCKsGuATRA78DcQNOAv4AqQAUATcBjwDb/m78iPk29gLzSfBi7gTtNOxh7MXt2/D89HH5g/2+ALIDmAacCekL2AzuDO0Mxw2qDvoOSQ9UEHQSXhR/FQwWRBZCFRYR1Qq0BPD/WPvZ9Lft3+gz6Onpn+uT7ebwWfbw+/L/jQLEBOAGnAfrBmcFSgTvAz8DKALRAAAAq/8k//393vuH+Rz3qPRM8tzvOe527ajtje4B8L3yVvZ8+jX+FAEIBBMHCwoCDIwMpQw8DagOtw/PD7IPOxAWEicUdhUhFu8VBxSED54JZQRXAMX7mfTb7FToOuh26hnsg+2Z8O71mvuK/3ICFgUzB7EHcwYWBa0EyQQMBGoC+wA6ABcApP9R/mr8Dvpt96X0E/Ll7x3uCO2a7DXtJO818j/2jfqC/hYCeQXlCLcLSg18DSsNpQ1kDpkOCg64DRIPjhHKE/wU0hU1Fo8U+w+OCfADjP8G+inyXeqJ5kvn9en36xzut/JV+Vb/HgN8BbcHSwnuCJUGXgReA+sCEAJ8AFn/P/+o/3n/JP4a/JL5IveP9KTx8O7c7P7rRuye7fPvSfPH92L8hgAyBH0HqwoHDfsNsQ08DXANwQ2sDeYMgQyTDZoPpREaEzYUbxRsEqoNhwefAuj+DfrW8oTrA+gQ6QLsJu5b8Mf0xvooAGIDhAXbB2UJuggwBgIEPAPwAgYCawBv/1T/J/9O/pf8afrs9yz1V/KL71rt0+tT6zTsIu4p8SD12fnF/vwCZQYfCc8L6A1lDqENgAx+DFYNow0tDf0MgQ4nEWoTnxQhFQEVqxI6DXoG8gDa/Kr3N/B66Q/n/ejb69btvfAX9sD8cQGeA5AF8wdZCe8HyQSvAisCHwInAf7/r//c/9P/n/7G/Kz6G/hX9VbylO8c7XjrVuuH7AfvF/LD9WL6Jv9tA6gGXQneC6ANIw41DX8M9QzKDS4Osw2QDeUOOxEcExYUohQLFCQRaguYBGP/i/t59lDv+ujN5rXoqevI7cDwRfbI/IYB/wM8BuoIcgo4CXIGwARkBMYDGgJ1ABsAYQCq/539NftC+Wb3HPVl8prvP+2k62Hrmezl7tzxVvWv+Y3+KwMeB2EKRQ0jD48PCA9/DrIOwQ7xDXwM1wsNDfYOjBBbERMSfhKoEOoL0gUAAUL9KfgI8V/q7eds6cfrsu2Y8BH2tfzbAesEXwfxCesKcAmhBnAEXgMhAlAAxv5p/nz+6P2E/Kf6Bfkp96700/EP7wft2Ovw6yTtN+9W8kL2GPsmAKgEXAhGC70NOQ+YDxAPXg5SDjgOlg2LDDgMfw13D/gQcBGwEZwRhQ/BCnkESP9u+9D2d/Br6irou+mh7CjvDPIR9xX9AgL9BAYH9gi6CZsIFAauA2MCbQF6AJH/vv4O/jL9KPyQ+lz4w/UJ88bwle6X7KTrROx97lPxdvRk+Fz96gJmB2MKcgwPDoIP4A8iDwIOTg1ADf0MYgwJDPMMAQ/FEGkRGxE6EB8Oqgl/A8v9bfkM9UXviekQ583ooezp7/3yover/RUDNgbNBxQJmQl7CLUF/gJsAacABQAf/4L+F/5h/U/8wfrp+I721vMo8cnuN+2C7B7tDe/F8Sj1MfkZ/kwDxQf0CvYMjg6cD7oPEg8tDsMNqw1ODYwMTQxKDQYPdBDBEEUQNA+hDLAHbwEj/Ff4UPSB7pnoSua36D3t1vCb87b3lv1JA8gGfQiVCQUK2QgdBlADpQHyAGIAq//l/i/+cP2N/HT7ovnf9pvzsfCD7v/sS+y27KLurfFR9ab5rf4MBKEI2Qv+DWoPNhDuD+EOqw3qDKoMRAzeC9YL5QzwDt0Q6RHQEcgQMw5ZCcUCXvyU90nz1u0M6BXl8Obr67XwUPR1+Fn+RgT+BzAJPAkTCfMHWQUrAtT/+/4l/5T/kf8B//T9xPyi+/H5PPel80zw7O127O/rh+zS7mHyd/bJ+mr/cgT6CFcMbg6XDx8QuA+4DuENsQ3/DfMNbQ0CDXcN1g48EAARpRAbD/wL8Ab0AHb7Nffx8oztXuj55cPn7usB8KHz/fdw/XECvQV9B4QI5wjBBzQFewKiAPH/7P/+/6v/wv6V/WL8P/ug+fz2x/Or8FruIe397Anu+u/a8nv25voXACsFmQnWDO8OJxChEIYQzQ/WDuUNWQ0mDeUMnwzLDMMNLA8KELgPYw7jC6UHsAFd+zT2GfKz7ZPo8ORc5ZXpBO+M84P3ZvzYAWkGBwkPCiwK+AhtBj8D3ADW/zH/hf7C/VL9Af08/Bn7jvnG92b1ZvK27wbuye2z7mfw2/IW9jj67P7pA6cIWgzeDlcQHhF8EVARnhCmD9cOIw5MDVkMuwsODBgN6A3eDRANOgubBywCS/yf9/nzf+/B6UHlB+XI6KPtlPGX9Rj7TwESBp4IKgo5C9cKRgi6BEYCWAH/ACkAKf+A/t39s/zk+t74nvYD9PfwFO5o7DvsiO3j7xHz//Z/+44AogU3CqcNow97EHAQDxBfD5YOBg64Dd8N+Q3dDcINGA4LD7MPOA8yDTkKlQarAdz7lPbV8rrvgOvw5mrlEel570X0lfY4+Yb+wwR6CC4J7ggMCUIIrQV6Ar0AuAB/ACX/X/1g/On78Po0+ef2l/Qt8sTvNO707TTvOPF28yf2kPk2/oADigheDIMOvw99EEcRvRFyEawQkg/hDmUO7w1+DRQNVA2KDQgNdQsjCZkG8gIV/rP4P/TY8Nzsaejt5drnE+3V8bf0hfet/DkDvQdmCZUJxQlmCRkH6QNbAUAAbv/R/WL8jftD+4/6Avli95j1uvN78aXvPO/a72rxVfP19bn5NP4iA8MH3gv7DtMQsRHjEekRqxH0EPQP3A76DUcNmwxoDMcMJQ3MDEgLIQmLBvUCOP7z+LL0JvHX7C/obeVo56vscvFH9Nj2FPymAmQHBwkCCXoJHQn8Br0DYQHIAO7/P/5i/M77OPyO+8H5fvfR9U/0FfIM8DPvKPDJ8UTzbfXP+NT9QQP8B/gLBg87EU8SyBL4Er0SyxHuDyEO/gyPDDsM6gv6Cy0MGAwfC2AJIAffA0b/5vk59WHxNe1Y6BLlQeZi67fwr/MA9sz6EgIZCFIKDgqqCcMJeAh5BWACbgAs/2f9lfup+tL6sfpL+X/3+PXi9Hvz6vE/8c3xTPO99I72iPm5/X0Cigb+CdsMMA/lEK0RPBJfEu0RvxAoDzYOyQ2QDeMMIAz/C+wLNwtECY8GrQMAAEX7C/aT8c/tyOlF5nXl5Oht7qfyVPX2+Ez/xAU1CWYJ+AhaCaEItAXhAar/OP92/rn88/q8+jn7yvpT+W73C/Zl9GDy/vAB8ZXySPQU9rL4yfwVAukG+QoUDo8QbRIgEzwTtRLhEZgQ3w6fDb4MbgwsDPILEwziCxwLGgmABrADAABP+/v1W/F17Wnp0+XU5D3oJO6/8nD1ivhb/hYFzggYCSYINQjiB1IFzQFI/9H+f/4S/Z77EfuR+0r7+fk8+Fz2uvTY8tLxJPJQ8+n0pvaW+cH9gALeBngKrw1AEOQRixKjEocS+RGsEBIP1g0/DQ0NlgwVDLYLWQtZCjEIqQWpAuL+D/rK9MXwRO136ajlcuQC6LbtavIM9W34of7IBF0I1ggKCdsJGwk9Bm0CiAAMAN7+7Pwq+zj7ofvn+jX5Xfdq9jn1wvOp8qLyHvTp9Qz4u/pc/p4CnAY2CgUNWw/0EMERERLHETQRSRB7D9MORQ7fDUQNvQw8DJ4LagooCFUFHgKC/gT6O/VA8evtFurF5eXj0+YZ7S/yTPRd9nr7wQLdB2sJTQmGCWEJMAcBBKsBzgC0/6P9+fts+777Mvvm+Zj4aPcF9uXzlPKb8tzzS/V49qj44ftGAKUEoAhkDE0PpRHwEsETExSAE1ASgBAjD0UOow3xDNQL6QovClgJ1gdpBY0C8v54+nr1AvG37SzqEuZ343zlzOvj8RD10/aO+8gCEwgqCeIH5wdXCNYGJAOt/6L+Iv4N/ZH7Fvuz+2f7SvrC+Lr3l/aI9Avz0fJh9Gv2W/jr+v794QGoBUcJzwyED28RDhImEgwSmxEgEQYQ+A4GDmUNJQ1vDK0LjAooCQgH6QOqADH9dvkD9WDwtux76WLmnOQp5rfr/fHk9Rz4nfvZAS8H9gghCDsHSAfZBc4C8/+3/nL+5fw1+4H6NPu8+3L6uPj89qv1NvT/8j3zpfTr9hr5svsN/wcDPgf4ChoOShCgETMSYRIVEk4RKxDJDqkNtAxBDNcLOguLCmwJLggVBisDKADc/GD5xPQL8Gvseune5mjkNuVY6hzxJvY4+HT7SgFXBw0K4wjdB5MHggZCA1r/3/2m/Qn9Zvuu+u77x/wc/Br6uPgW+Jb2kPQx8x/0WPaL+Ij61fyZAOQEQQkCDdkP3xHdEoATjhMSE+sRMBCWDjQNUQyVC8YK3AmqCGUHrwV9A7cAZv1s+bb05u+k6yTosuQ54uHiDOig73r1tPi1+1IBrQfzClgKZQiKB1kGegPN/579s/3B/QL9Lvyo/J79Hv14+0j5ofd59bbyE/FU8ezzsvaB+fn8ggEkBwgMNBA3ExoVzhXuFJYT3BEqEEAOIQy8Ci8KgQrFCo8KJQpQCe0HQAWJAYX9CfkY9NfudOo251XkR+LT4hbou+/C9eH4f/vdALMGaAklCN0FkQUSBaYCy/6C/Cb9+/3Z/eP8Af0H/t39o/xl+oL4ovaB9GnzVfMB9WT3MfrF/XgB2QUYCjgOwRGuE7QUnhRIFHETvhH5D/cNqgyZC7YKIwpiCeUI1Af+BVUD3/+u/BX5pPS87+Pqiufu5AbjcONA51vuovSM+OT7ggCEBpkJSgmbB1IGnQWuAjf/4vyA/CT9ifxS/Lf8hv2v/VT83/oR+SD3//SV8+bzfPXF91P6ev11AfwFXApaDowRwxMZFUkV0xS1E0ESrBCxDvsMZgtjCtcJ/gg/COcGPQX0Atf/nfxk+K/zrO5S6nbnYOQX4jbi9OZk73L2qPo8/f4BOAhzC8IKpQfxBcQExAHT/ez6T/ug/Ar9JP22/Vz/qv9Q/jn88/nQ9/f0wfIp8l7z+PXb+I784ADGBRQLERA3FIAWCxd3FlIVxxOQESMP7Qw3C/4JQwkxCawJ6wlZCYEHpwRaAWL9/fiL8/rtLekf5VHifeAj4vXn2++e9gv6Pv0ZAssHxgpMCc0G3wTpA6oBgv4h/Y39KP8r/6n+vv4M/w7/2/wt+iv3ePR58jbxXvLL9EX4qvsm/5oDOwgrDe4QlhPrFO4UgBRqE3kSPBHTD38OHw15DO8LpAssCxkKrggpBgMDWf9U+yH3IPJ17YXpluYm5Cfie+Pe6M7wF/e9+Q/8IwC0BdMI4wfNBVEE0gM8Aqf/UP5d/n3/wv9t/2D/zP4G/kz8NPoB+C71WvPQ8lz0Kvf4+Yf9AgHxBOUIlgxrEN4SDhTDEyIT2BIgEj8R8A8HD28OjA2gDJ8L2wriCRUIiQVFAt7+dPtd96/ynO1P6SzmXuOe4VTiR+cy7+T16/mM/BEB4wZ9CrAKQwhNBpMEQQLT/9P9xv0S/lD+jP7d/lv/u/5J/d76Kvix9azzJfOj80H1+/eW+w0AYASZCOYMKRG5FBYW4hXFFMsTEhOuESoQTw70DBwMgQtEC0cKOQmiB4wF/QJR/6D7QPd58qDtZunh5mTkv+J8413olfDx9m/6ofzZANAG8Am5CR8HZQVaBBcC6v8l/hv+Jf6A/bT99/1//mD9ZPu7+cX3Q/ZI9OXzI/VY93D6Yf1HAfQEvwiSDAMQ+xLqE+QTbBMmEwMT/RG9EFMPGA5XDdAMjgzaCyYKEwg/BWQCAP8E+/f26fGZ7cPp++aq5Gbi4uMe6SrxK/dc+dT7KgBtBm8JNAjSBRoE+wOQAsEAqf9f/8D/fv+z/zUA4/+Z/oH8TvoF+BX11vJe8snzi/Y0+Z78dACkBD0JQA0VEUwTAhT3E20TTBNeEioR/w/XDnUO7g3uDcQNuQxBC80IJwZ7Aj3+P/rU9S/x4evk5wLmreTB4zvkRemp8WT4ffuv/I0ASwZECTYIGQUEBAME0ALEABv/rv+NAKoA4QAAAR8Bn/8G/U76k/dk9WXzzvKN82b1bfgx/IYAmwSWCKUMYhDnEr8ThhMfE6wS5hHdEKAPjw6tDToNNA0IDVkM0AoaCdYGGQS2AJH8C/iV8rbt2unK5vDjbOHW4p7of/AN9kH4UvtnAC8GgQiBBzcGOwVyBBsCIQB5/4T/of8o/5v/FQD+/7/+ifwx+jT3u/QU8+Dyl/Pv9Kj3VPui/3cDXwe+C9QPpRKDE68TtBN/E8cSXhEdEOIOwA0eDbYMqQzxC7UKMwkBB0QEZwBA/K33d/Ks7ZDpgeZP4wTh2eJV6d/xc/fc+TD96ALPCJkKdggDBn8EbgM4Abn+k/34/FH9xv3p/jAA5v8I/y39NfuW+Fb1Y/Nu8jfzxPQ49w/7OP8QBM8Ilw2vEfAT+hT5FJYUfBOBEWoPlg00DB4LrwpJCw4MWwzjC7QKtwg8Bb8AL/wr96jxuutp5/PkwOKx4cvjxetw9YL7ff0l/60E8wn2CkIH9wKAAbsAav/C/X79wf7w/80AgwEfAu4BNwBI/Vb58PRu8YzvzO8L8Svz3Pbf+3UBRQb9CoAPIxOkFCMUVBNOEhkRCw90DYkM1QuPC7sLYA2ODkwOIA2BC5sJowV4AHH7nPZu8UDrwOaJ5FrjPuOJ5TXtpvY0/cT/MwG/BbAJIQq2BpAC1gAO/3P90fvV+1z9Xv7u//0A+wGJAZf/V/2z+fP1K/JX8FzwGvFX89P2VPyZAUsGCAuGD1cTzhS8FOgTbhJ1EGsOJg2DDJ0LLguWC+YMEw7kDZsNSQy4CXQFDgCK+/j1XfAP64DnxOUx49Dic+Zx7834Df1a/hkAPQUlCZMINgW1ATMAwP49/Sb9DP5p//r/UABjAZYB5QDK/pX76Pd+883w2O9m8AryOfRs+Df9GQLxBoELLRDSEpATFhP3ESoRzw/xDh8O6Qz+C0sLOQwbDWkNYg21DMYLqwiFBGIAzPtt9tzvJuvI6MPmheTR49HorvH0+ET8g/1wAXgGLgk/CA0F4wLhAED/XP5M/h//Wv+u/z8AIgFUAQIAYf2S+ej1EvOf8dDwZ/A+8QL0i/hP/ewBhgYwCzUPkhF+ElESchEZEM4OHg56DXoMaQt6C5cMwA0rDgsO/Q0KDaYKvQbtAR382vRi7jjqO+iO5Vri7uJ86cHzAfvv/Q8AQgRoCQcLeAlZBnIDjQBk/Qr8X/y3/Wv+mv6A/3wAGwEJAFH9iflu9XnyuPDY76Xvp/Dc82b4HP2yAXQGmQuSD8YRYBIWElAR0A+pDuwNrA0aDXwMlwweDcMN4g0rDskNIAwtCT0F3QDH+hr02u7i66Lp0uVl40/mte5093z71PyD/7QE6AjwCJIGuQODAUv/F/2f/DH9Wf7N/uv+rv87AHoArf5c+133yPPt8XHw3e8R8CvyIvZm+ib/0APmCGANJBB+EWIRlBA4D8sNFQ2KDCYMBgxWDGkN/Q1CDrAO3Q5NDo8L7QcLBOv+l/iI8Snt9+rT6IHm3uXP6oPyPvno/DH/IQOgBjwIHQfOBMECUwAC/oP8Xvya/fv+FACvAJAALQDo/sH8YflV9STy2O/w7ubuJPAN8+32y/vYAM8FeQoCDooQahG0EEYPpQ0CDYoMDQzOC9oLqAzvDFINVQ6ZDzYQkA72C9sIbQS1/Wn1ku/e7NLqI+eM4yjlRey/9Gr62/y+/6sDjQeNCeQI2wZyAxgA2P1t/XT+Wv+y/2f/lP8zAI0APf/i+6f3+fPI8TTwBe+M7snv4vL69p/7VQBJBdQJZw2KD98PAw97DWYM/Av4CwwMFAycDC0N+Q0UD6QQ5BE5Ee0OjAu7BwECC/qO8v7tjOxP6h3nZeYH647zMPqQ/Xn/oAJtBgcIVQevBPUBM//i/Br8d/wu/qX/2gCyAQUCBgKAAHf9+viM9FLxOu/C7ePske158Mv0Zvnm/YsCkgexC1gO/g5pDlkNMAyYCwwL3wr0CsELvwyADVEOgg9mESESiRH0D5EN9AifALX3qPE979TsTugW5ffm6e049b75xPwUACgE/wa9B+gGnwTmAdP+u/ww/M/8jP7U/7kAAQEpASEBO//U+1D3tPNA8ezuS+2a7GzuwPGv9QD6aP52A9IHZQuqDToOuA1ODGsLKgsECzUL1QsUDQ8OYg5QDwwRvRLqEl8Reg/mCz0FJ/wz9JjwS+7Y6l7mheXh6lbyq/jS+7L+ewLjBQAIbwd1BW0Cl/+c/aH89Pxp/nwABAJfAiICAwLYALv9u/gF9Ajx3O4F7XLr/uvv7g3zhPe3+1cALwWJCcEM0w0sDdUL3grLCoMKaAoFC4gMSg7qDucP3BF8FJIVGRTlEbcOYgmTAAr3/PAs7pzrI+cs5AXnpe6M9vX6M/1+AMwEbwgUCQsH3wPnAAr/2v24/Xr+FQC+AZgC8wIgA9ECZwDA+2j2c/IG8NHtbusP6kjr0+5q8+X3Q/zwAKwF0AlRDOIMEwwaC+YK3wr8CmALuQynDtMPzRAyEpwU9RUdFSkTjhBWDNsDufmG8vrv9e7e6pPmmebI7Oj0t/n++8P98QDVA/UErAS0AtMA/P6i/rT/RgGxA0kFTgayBW8EVgPqAIX8zvVE8H7tbewy66DpTerz7UfzI/jp+6D/kQMHB04J7AmGCewIuggOCa0JTwvCDYEQrxG1EfQSkBUCGDAXRRRjEb0NSwde/SX1WPGY7+nrlOac5Y/qNfKF98D5ePz8/3kDYAVeBVkEGAJSANj/QABTARkCkwPDBJ4E1gOYAnQB+f1N+N/yM++n7Ynr2+n16XDsxvDj9Fz5qP2kASsF0wfpCVQKlgnxCPcIBQpyC1ANeg/AEIURmhLkFEkX9xaRFJsRpg66CeAAQfi28l3wke3D6LjmJeml71r10fgP/DT/mAI/BAsF2gSVA/wBjwB4AAsBIwJqA1MEOwREA0YCEAGi/hn6/vQO8XDuVuxy6j7qLOx472bzU/e2++//rQMdB1YJiQovCoUJmgkKCjcLSQxWDlgQYhFrEvQTqxaaF/YVLxMnEDwMiAQN+wD0ofBu7sbpluVQ5g3sJPNX91r66P33ATUFNgZKBigFgQPSAdQAFgF+AWsCQQPAA6YDPQPNAvoAA/2g9wHz7u9n7bXqxuhE6TjsT/CL9PP4m/37AdoF1QiUCtMKBwqfCeEJ7AoNDLsNvQ9GEa8SJRSKFqkXkRZAFMkRug7OB1H+6/XC8VDwIe0A6Sznk+r/8GX2VPru/Nb/AwJfA1oEEQRWA4cBbgCwAN0B3gO7BMgEvQPEAlUC5QCN/Rj4JPP+7w7uKewm6rHpkOs375fzy/e9+17/wQI0BtAIKArECRIJPQlBCgkM3Q0MEEIR1hEnE7sVoxiKGC8WeRPqEIsMygM/+unzFvFZ7hTpk+US5/LsSvMG9xX6S/3CALID2QRHBToEtwKIAfIABQIMAyYESAR5AzUDxgIoAvP+2vkA9WfxiO8P7abqZOk06k/tC/Eo9Sn5w/yaAE0EugfoCSEKJAo4CjALoQwMDhEQHBGWEVISZRTlF64Y8BY0FCgSlA/qBzH+3fWo8Rbv6emm5dbkpulL8BX1KvnG/D0BwQSLBgkH7AVTBE0CygBYANEA6wGZAogCQQJ8Aq0CSwFF/RP42vPW8E/uCOu96HboUer87bnxU/bD+jn/1wNQB2cKaguaC4cLKQvcC20MkA5tEAARoBElE00X6Rk5GdkWmBQLE+EMJQLq9wfyFvBt6znlR+K15b3tjvOG98r6Pf8PBCkGCAcLBqkE5wI0ARsBmwE/A1wEtgRsBOoD3gNKApf+yPhe8+Xvh+0y67To/Oey6UDtP/FF9UH5+/yOAKsDfAYoCBAJeQkJCmQLTA1NEAcThhTYFLsVrxjuGiwaqxarE+cROA2pA3P4APJ88FvuHelR5DTmYu2i9Jb4gvoU/hwCFwWGBUcE9wIrARIAa/8tABYC0AMDBb8EiwTzBJ8EBAIj/N/1Z/Fd7p3r/Ofg5WjmX+ms7e3x0vZl+wMAUAQkCPAKoQtpC+UKLQu3C8wMdg7ZDxkRXhJpFSQZ8BoKGtcXehZLExcL4P+x9sjyte/R6bzjkuJs6MTv6fT593D7igA3BBUG1wWKBOYC4wAWABAAOQHTAggEoQQuBHQEfAQgA6b+Nvgu857vJe256d3mjuaf6GXsZfC29E35hf2JAUsFLQgOCkYKaQrnCnoLDQ38DnsR4RIwEzIVVxiNG1YbhRhhFrQTng5cBOP5z/PG8C/ti+bv4n/lzuy88yz36vmj/RoCMgXfBfQEJgNSASkA/f8YAc0COATFBHAEogQ6BdYEowGn+4P1DPFA7mrrEegm5t3mGupi7nPyofbO+lP/lQOmBnkIEQnYCYgKKAsDDJ0NhxDaEuoTshQ6F/AaTBxvGqsX+RXHEnEK9v5w9uzyX/Am6p3jC+Nj6VjxcPUy92D6e/+nA/IE8QN7AiQBr/8V/5n/uAHqAzsFiAVmBUQGtwbnBLX/0PiD8yTwdu3G6Wbmf+Wn59/r0O/c8+D3jvxAAfIE4gcvCTwKkwrxCrsL6gxiD0MRNRJLEtcTsxffGgQbURinFosVZBFICJ/9UPfi80/vBugz47Dl4+tM8XTzavYe/GoBigQjBJsDJgPyAdIAx//SAAwCCwN9A5MDVATRBGwEuwH7/Jj3bPM38BztuukB5+7mCOmm7JbwbvTw+J79ZgKMBgIJkQoFC6wLfwzxDEQOhw+rEAwRvxGoFPIXjBnHGOIXGBjdFUEOswPE+1X4bvSw7LXkjeJw56rtjfED9CX4Qv45A+wFEgZDBbAD2wHXAF8A7AAiAS0BOwGuAToD3QOtAs/+Fvpn9lfzIfDr61jo/uY06ArrjO5U8nb2EPsyAGcFIQntCkwLgAsbDH4M3QyADU4OIQ+1D+MRhRWTGHgZwxgPGTUYHRNlCdb/kvrC9prw3ec745nlwOv38NLz7vd0/bwCqQUIBrQF/QN2Aez+p/1t/jz/qP+1/5wAGAP9BBMF1AEh/fT4UfUI8nrtLumN5pPmHumT7MLw3/Sk+bv+DQSdCBgL4guAC6gLiQtnC8ALUQxyDcUNIw+XElYWyRigGOEY7BmzF1cQhAXl/f35IPVv7dflQuTq5/bsZfHR9Yf7nADgA6UF1QagBjwEugAi/tn9Sv7R/s3+7/5kAN0B+gKQATr+ZPp79jXzUe+H6wXpF+hB6fPrz++E9LL47fxhAboFkQkFC0kL5QqGCs0KCguWDL8NJw7wDggRRxXsF6MYlRgNGa0Y0hKXCd8ABfzu947wGOlD5X3nz+uX7wH0T/kT/9YCqwQyBqgGagVYAiv/Bv4j/sr+Df/E/pP/6wBMApsBa/6r+rH2YvOL76jrS+lS6HDph+sD77fzPfjG/MMA9gRfCAoK1ArGCuAKkgquCiUM/g0AD6IOiw8jExEXJRjWFogWRRfMFPgMtAPC/ZP6mvUK7lroDegZ7L/wR/Qf+Ff8VwBxA7cE7ARsAxABqP6z/Pf8ZP4jAKAAKwAmAQsDuQMGAe/7QveT8xTwSexa6Qzoruj+6sXunfMV+B78r/+XAxIHzgh2CVwJrwmgCVkJHgrXC8wNIQ40DjgQRhR4F50XdheeGBMZChRLCgwCMv7V+230KOoN5CnmxO3W8gb1wva4++kCagd8CDEGBAR8AmIAjf73/BL9tv2n/fT9AP95AKQAvP2O+YT1VfKH7wHsL+nS5xXpiewU8cv18Pk8/sUCbgdkClkL1wpmCp8KAQqPCdwJvAtbDS8NIA4xEa4VlhebFqIWmxdBFrgOywQR/wH9QvkU8KLn5OYL7Zzz+vVP9137nQE3BsEGUAVqA4YBl/6G+876rvtf/WP92PwE/sz/XAEw/8v6PPZL8srvvuwe6rjoIukY7ITwq/XA+sz+zAKzBqYJWQvtCjMKlwnqCL0IkQgHCugL6AwADsoPlxMxFrUWnxb6Fn0W2xCTCM0BNP8U/Cv0oOsX6Fjs3PHR9FH2dfn8/kIDQgVrBaQE6QKo/zb8m/r7+j/8f/yv+877DP0A/97+yPt090Xzh/BN7vjrZ+of6unrDfD+9Fb60P6aAkoGuAjBCmALUwvYClEJPQj6B7YJBgy7DMYM8w2JEUcVaRYEFlgWthY/Ew4LDgN5/4j9j/el7jbpfetG8Qb16vZm+YL+mQJ1BNMEAASnAjP/X/z5+hP73fu3+xT8gfxB/cD9Mv2N+/D3hPOk75LtiOyd6z3rY+wl8BD1P/qp/k4CtQU1CJgK2QvqC74K3Aj3B/EHEwkUCmcKzAp/DAkQSxM1FVMWIBjGGO4UUw0uBpEDKQHq+Vrva+iC6g/w1fO49GH2mvvbAPoE1wbLBgwFCQGk/cX7VfsS+xD6Wvlx+Tj63Pvj/FD8bvnp9JTxde8h7mXs2+p461PuFfNh+Mn9oAKcBswJiAxtDkIOWQxQCUIH8ga3B7kIbwjBCDwL1A9kFCQW+xZoGDAZDRYnDq8GmQJt/8r4l+9O6mzr7u9L8371EPmu/dUBPwTSBRgGrQOX/9f7i/r9+Sj5t/jV+An6kfpo+1n8z/tG+ZP00vDH7krt8uvl6hjsuO+H9EH6o/+IBL0IjgvlDcsOFg7pC94IJweMBtkGegfcB3IJKQzrD2MTmhX2F4YZlBhUE/kLmAYEAx7+1fUJ7vbqg+yF72XyTPYZ++f+QwGsA4AGUQcSBNT+rPoZ+db4fPhw+Jn48vht+aT6UPw8/ET5svTv8Pzunu1M7PPrPO2/8MT1x/shAjsHIQuLDUYPEhDADuALJgi2BR4FZAUDBrUGJwlcDZIRvxQsFy8azhvXGHwRSgnnA6j/bPlO8T3rDepO7CHwjfQE+qb+jAHLAwcGCAhQBpEBbvx8+S75nvgU+Gn3l/fp+JT6j/ye/Kf6v/ay8hzwre7z7Z3sO+zH7QzyXPh3/uUDqwf9CrQNrw+oEEsPSAx0CHsGkQYsB1cHYgdgCbkMRxBgE+QWUxrRGU8UpQzNB0sFbADZ92vu8Olf6uTtFfJ39fr4pfvC/80EvAhfCW4FuwAB/TT7S/oA+Zr3J/az9X73fvqH/ML7D/jm9OLyzfFG8Hvt5esQ7FvvwPTw+hcB+AUICnINehAmEl8R+Q3ZCVgHmQbGBl0GxQYdCbQMkhCXE24XAhv8GtsVjw2mBxEEzP759c7sI+mU6uTtRPE+9dD6ev+gAjkFdAfRBywE+P74+lv52/jn91738vbT93X5uftX/QL88Pjn9DHyKfCV7ZzrqOoW7IXvc/QM+9cBlwfjC8QOgxHqEpURNw77Cb4HAQczBuAFKAZoCeENpBFBFScYsxoDGRUTZwwsB9ECM/vo8UvrEuo17ITuQPHb9bf7pQAaBJoGDgjpBiUDs/6C+5j5NfgW94n2NPe0+F77rv0h/uD7z/ef9E/y7u+d7K/pVukE7PTwCPdI/ToD3AioDbsRoRO6EmQPBAtwCFYHuwbLBRIFWQcrDGgRjhUtGPoatxuMFxIQSQhmA/b9BPXS6/XmI+nk7ZbxIPUb+owAewXfB6UItQdUBH//u/or+FT3t/az9gf3J/kk/H/+TP/x/IH53vXH8rvvBOxZ6YXokuoU70b19vtnAkAI8AzOEL0SpRK8D5oLYwibBlIGkwW/BdAHSAyPEUIVbRiIGo0aZxYGDw8IdQLn/Pr0ueyy6Knq1+/W8w/3AfvVANEFgwfcBjkEvgHv/dX5Zvdu9nT3z/eh+If6Wf39/+7/Mv0R+ST1s/GB7rnqF+i554vqR/B/9ir9QQMgCb4OXxK+E0wSxw58C7sIgwemBpIFkQZpCagOTRPqFWEY+BkDGsYUewuFAyz+rPl48qrqD+jl6nPwLPXy+P39lQLYBe4GhAYMBeMASfyc+H/33vfr92T4Pvnv+6z+3/+d/s76v/aC8iDv6etH6ZPo6un57X3zOvpQAXgHegzxDxgScRKkEGQNWQr7CJIIuwcwB/oIEQ0iEbcTpxU1GLEYnxSqDF8FdQHt/Dv1h+zY6F/rwO/x8qn1mPquAGIFwQY4BuEEmAJW/2H7Cvku+JD4GvlM+cv6Iv0Z/0/+n/rX9vHzXvEu7oDqQ+mh6lvuTfNS+Mr+IgXyCuAO+BA3ElsR/A7VCy4KkgncCLwHMwieC2sPjxJaFCkXkhizFDMNOQUwAYr9J/Zx7fXn7umB71Lza/Yu+mAAvgVgB2sH0QWsAxYAmvtW+bL4Bfmm+BD4mvlH/C/+bv0s+u32afS38YDul+uz6jTswO7f8iz4fP4XBS4Krg51EXcShhHoDjoNGwzvCtkI0QbRB6MLxw9ZEnkTwxXbFksTHQyGBGwAwPvX89brLOi865jwBPTj9kz7twGtBTkHhQbtBI8Cnf5I+xD57/gx+fr4KPl/+vD8Pv4z/Z36fvdY9BHx6e1S7PTrE+2E773zSvrcAAUHnQv3DkURhRHbEDQPaA3MC5QJTgihCP0KNA7XD+wR8xRCFxEVEQ2VBeYAx/1E9+DtsOiC6bPuxvKm9ez5W//GBKcHFwj9Bm0ExQDe/OH5IvkI+ab4Ffim+DX7jf3b/YL73vcs9Cfxg+4L7UHshOwf74LzN/qZANcGMwymD9URrBHcEFAPcw2CC+sIngf8B80Kdg4BESoTBRWGFsMToAzlBBv/3Prr80XsB+jw6bnvXPTi9wH8zQHtBqAIZgfNBLoBT/6z+pn4gfgr+bv5MPpj/MP+yP+w/nf7jveD8izu9esa63fr8+uy7h71EP0wBVgK5Q21EEUSwhJaEJMNQgutCTQIGwe8CIgMGRDrEcoTgxbMFusQhwdHAAr92via8OvoXOff7BXzCPe5+VH+XQT3B1EImwXuAjMAPP08+vv3ZfiM+cf6KPv4+5f9Ef4G/W750PSg8LHtGe3x7I/tXu9e8036WgGlB+wLCA9kEcwR2RC3Dg8NHQx5CscICwhPCjsOiBCGEtkTwBRUEYkJxwLk/Yv6hPNg6xToa+rT8JH1Ivle/QgC1AbtCBsI5wVDAvP+a/uq+Eb4R/g0+Wj55fnE+6H8tvyn+U71bfF47uvthu1Z7inw7/P6+VoA3AamCzgPIhF5EXQQtw58DWUMpgquCF8IYQqvDcEPkhG1E1sUvhCrCLwB6/2F+k/0i+wO6bbrL/Fh9uv5xv1LAm0FfAcdB20FsQJk/kn7CvmE+fv6APum+sb56Pr9+0z7jPjR80vw/+3Z7Rrv4vBV8yr26PoMATkH0AskDjEP7w5KDnIN9AyJDJIK5QitCNcLeA/5ECgSpBLqErAOagf7AIH8vPmR87XsFelF6/3xY/co+2L9/ADGBbUI9gjtBaoCKv8o/HP6dfnz+bP5BflR+Kn4jPoa+8n4wfNP7wruYO8c8X3xGPJQ9X37zQK3CFUMfA7qDwARcBDaDsYNlQzqCo8ITghyC+AOPBFvEboRXxH+DJQGDwCP/M340PEb64DoG+0y9N743vou/WAC8wcUChgIQARFATP/kPzr+X74FPnE+V35n/ij+ML5d/nF9iDyr+5F7o/vPfEU8kT0vPgf/3EF2wlqDcsP+xDoD/UNKg0XDbgMeAq1CB0KBA7AEZ4SuBJ5EnUP5AiwAM37A/mL9H7tAugW6mnxUPi3+439nAGLBpAJ2Ag5BU8Cov/v/Lf5yPf/+Hb65/oU+en3G/ky+vH4A/Qx71vt++0A8Lrx8vMD+Eb9LQMDCCIMyw8MESoQyw1oDPgMsgz8CnEI4wjjDI8QlRK1EhITlxEiDHwE8f1o+g72Xe8x6WzoeO0v9DT5uvzjAO8FkAnGCdgH+wQQAk/+9vkK93D2Hvjw+CL4r/fb+OL6qfo395LyX+/O7rbvX/C68c/04/ksANoFJAv+DqoRXhK4EOgOmA0PDT4LgwhhB4EJOg7qEVsTZxOhEjgPSgikAEz7F/dJ8TvqguZW6anwsPcJ/Pr/rgRpCRYL7wn7BnYDRwCx+873bfV79rD4BflA+F33nvi0+T/4//Nq77ntVe5c8KHyk/Xt+TT/ewWiCtoOkBGfEeEPag23DGsMDQvVCAkIwgrgDqMRkRKqEyUUmxBHCKr/g/qq9+Xy1et152Lp9/AQ+KL8z/+xAy8IFArvCKUFRgJ0/8r71fcD9gP3zvmU+i35BPhx+B76pvgx9ATvZexV7dLvmvKh9Sn6EQArBhwLGA+TEQoSJhBODXcL/QqhCu4I9weFCcANuxFRFKsVrRRlEDoIWgBr+1f4UvON69nmm+jq73r3sfxMAK0DmAf1CZUJ1gYcA5X/avue92r1/fV++G/5Y/j29tT3cPmb+Mv01e8N7ZztifDr88H2fPqQ/ycFvwr5DuYR6BGKD7oMHAvACyYLAQm2B4IJIQ7TEREUYRV3FKAQ+wgVART8Bvg480fsEehu6cbuWvYt/KcAIQSvBu0IiQiKBvoC5P4N+/T2PPXj9W/4iPlH+Fn32/eS+cX4GvVk8KXtG+5/8Obzu/f5+4cAogWpChwPoBGkEVUPKgyZChUKuAngCKEIxAp9DlMSExVtFr8V3hAwCBQAuvol98jx++qK58/oY+/49nr9fgIpBWAHPwhRCHAGZQJA/hX65/ZV9VH21vj6+Tz5L/hU+Ov4pffR81bv0ewJ7fLvJ/R7+I38sQCPBkAMxRCfEisRYA5lC60Kqwr3CYgItgcwCoMOdhLTFO0VexWUEE8IbQCt+1H4i/LG64vngOnj77r2dPx7APEDNwbsB1kIfgalAvz9CPrm9sv1XPbu93z4mPfq9vj38fkf+fL03u857prvqfKg9cT3dvskAEUG0Qs3D/8Q1A8BDsUMAgwgDIAKxghlCNwKtw/FEmgUgBQZE5MOcwefAOj74/cE8snrlejE69Lx1fdZ/L7/agNCBnAI/wcWBfAAN/1k+hP4XPbQ9c/2Wvcq99v2/vfF+Mf2BfO072HvJPHg81n2wfir/BECEAjHDG0PaRBsD98NKAxlC2YL+gkmCHoHwwqTD9sScBSoFI0TBQ8XCMkBMf2d+DDyWOte6Sbs0/EH9/z6W/+DAtAFVgffBsAEDQHN/S36OPfM9SX2rvfN91b29/V79xb5jPdK8xTwg+/R8er0Nvfh+YD9sgIJCOcLsg55DwMPIQ1RCzULWAuaChMI4wcpCw0QTxNdFNUUQhMCD8MHbwFj/Sj51fIa7KfpQ+yN8e/2ZvvN/hgCtASWBuMG3QTtARz+tfpe9yT1d/Wq9ub2ifV69eT2YPj59uLzWfG08PfyLfUp+ML6Gf66AlMHAQyfDqcP5g7jDIMLzQsKDKUKEAgQCHILJQ8WEoUTtxRGE/EN4QaWAVH+yPl28vnrROoB7b/yUfcW++r9EAGfBH8GpAacBFkBnP2b+XT24fXw9tH3T/a89H31Bfhl+Wf3mPOZ8APxEfPM9eX3Sfq4/UACswfmC0kOhQ62DR8McQvIC+cLiQo+CIYIBQwpEUQUDhWLFMcSYA50B8sBQv04+GXxReuR6vvtT/O396v6mv7wASkF8wZOBkYEjACC/Sr6RPey9cP1GfaO9fL0OvWa9yD4ivY188DxJvOn9LX2E/jG+pz+9AJ8B4wKsQxmDYoMNgxODDYNvgxXCuwIpAn6DU8RxxKnE9gTYBLADGwGawE9/Yf3UvBl61fr9e4r84D29PnR/Z4BwAQsBh4G9QNyAQn+R/rk98z2fffv9nr1m/R89a73j/e+9Kfx7vC48q30RfYJ+Af7Yv/tA+oH1goBDXENdwy0C+ULEA3CDNAKLglECt4O6xKQFJgU9xPHERsMRgXn/8b7qPaw773qMuvu7+70Ifg8+yD/OQMLBsMGqgVcA28AJ/2b+Qb3XvaX9s/2ffXL9D726ffz95X05/GB8cPyqPSO9dL3kfsNAHsE6wcxCzQNUw1pDEELdgv0C5ALsAnECIIL3w+oE7YUrhWdFTcSsAt/A57+SfoT9OTs1eia69PwovVO+en8VgH1BAYHKwewBVADxf+n+zH4kfay9mb3xvY79Rz1Cfe5+B73sPPk8HrwFvLP84/13Pd5+zAAwwQ5CbYM5w2lDR4MfQsXDLoM2gvpCN4HpAp/D3wT4hRcFe4UPBHKCocDCP8m+wL0puzc6PTrpPHa9Tr5BPzTAD8FqgcCCKwFGgN4/+f7lfgu9i/2EvaC9S/0+vSW99r4l/fs8zryuPJW9Ev13vU++Pr7pQAbBa0IZws3DWwNjQz3C4EMBg0iC4UI6AfZChUQbRPjFGUVVxSaEMUJwAMk/4P5Q/Iz6+ronevi8Or1cfmp/QACVAbMCKEI/AbPA2gAsPt49yD2tPZP97b1iPSv9ev3zviN9mDzfvGn8YzyePMU9Wb3kvtfAGUFagngC+MNzQ2VDZINvQ1QDboK9QgsCYMMYBBXErYTLBQqE1oOugfOAl/+SPmo8fDrPes87hnz5/VY+Xb9NQG7BDMGdQaoBCsCwP+F/H351fe191339/Ws9Hv1Wfe596T1IfO88unzKfUD9qf30Plj/bgBTQU6CH8JpQpsC3cLbQyoDNIMwQvwCU8LgQ5nEskTsBMzFCQS9wx3BXL/x/ti997wVuuD6sLuNPQQ+E37Uv4mAmAFxgYIBhcEKAJw/4j71vcD9773OPhe9lX0ZPVP9/L3JPVY8rTxmvLC9Ob1oPfh+Zf9OAKoBRYJagv+DDYNhQx3DVkOag7fCxUJGApWDdUQ/BGaEgQTzBAeDF4G8wHr/Tb4ivHS7MvsJPBn8zj25fi0/OYA+AOLBQEF+wN7Anr/+fs9+Zf45Phz94X1avQR9qf4h/ea9PTxH/Ic9PT0KPY99xj6yf6GAlEGugj6CpAMfwzlDB4NKQ6SDdEKEAlQCmoOAhHzEYwS7BLwEFgLNgXtAJz9FPhc8O7r0+yv8IX0qPaH+Rv9IwEkBKUEaQQpA20BoP4q++X4efj4+PX3H/aM9Zr3IPkz9zz0mvHu8SHzAvSo9Qf3y/oA/zkDsgd9CuoMaQ3nDOAMng0nD84NPQo7CFQKqA65EHgRFhLBEl8QaAq/BJQAZv2L9vrucOvb7KbxKfTA9nP53/0jAwUFvQWWBOsDgwJA/qL6zPcC+Fn4T/bK9Cj1ovhc+mf4E/Uo8+7z8vR69eb08fUY+S79qAFWBc8IUwu0DHwNGQ5ND/8P3g0tCm0IsgqaDjsQAxH0EfUSwhBbCo0E/QBY/rX3D+9g6/nsyPFG9Kb1Pvm2/dUCeQSmBBcF4wNTAhj+Z/qw+FT4+Pj/9hv11PX/95L5X/eu8z7yh/Ly80f09PSC97D6gf5UArMGhwonDc8Ngw3BDdcOQw+FDF4JQwihClEOPhDGEfQS8hJgDwYJVAQMAW/80PSt7Z/r7O0M8vL0XvfM+iX/hgO1BVwGRgUHAygAO/wx+cL3KvgE+BP2TfUF9+H5hvqj90L0nPJl82H0VfTm9NT2dvqU/uMCHAdTClsMHA0/DTsOmw8YD2wMAAn2CP0LVA8EEnASQhMdEpANSggfAzAAl/qy8lvt9ut374Py2/TB91r7PwBKA/cEFQXmAxQCcf+K/M35nfiz+DL48PZm9gf4+/mi+bv2VPOn8lvzCPSV9Gn1+vcK+1v/zQONB8kKLQzpDC8NIg5AD2AO2wscCVUJOAx3DwAR/RE4E6oR/wyrBsACsv/8+cjymewm7GHvsvKl9Rn4XfzRAPcDRAYVBugEtwJf//H7+vh8+KH4iPfO9XT1svco+t75mfaY86/yqfNn9O/0K/bX9zP7Uf/YAzcI/QrIDNwMMQ2lDn4P3Q6cC+8IOgl2DO0PdhFTEsASDxHNC10GWQLp/lD5bPFk7BzspO8a83f1ffh6/KIA2wOhBU4FJQQVAqj/gPy8+VT5Jfl4+On2fvai+FT6l/kg9mLz+/Kb83j0tPS09XX30vqM/68DowfnCawL6AzHDTUP5w9RD4gM6QlhCkUNag9GEPoQjRHZD3gKCQXCATv/P/p+8mPtpO1x8Mjzt/Vr+BX8nf9YA4UEBwU9BHcCAAAf/Oj5LfnD+fv4h/bF9YT3qvn1+Lj1KPNp8hvzGvST9A32U/jk++b/1gPaB5sKjwwBDTYN9w04DywPigxdCqoKkA1PEH8R8xIdE4cQpQoJBOAA6/2z+BXx8Oub7NfvrvNy9rP5Tv0HAeoDEwVMBRQEUAIc/337QvnU+MH5BfkI95D2gviV+rD5bvbP88Hy9fJc873zDfWC9wv7c//oA+kHFwv2DOwNDw7CDuMPQQ+VDIcJ8gnxDMwPShGkETgS0Q9mCqYE2ABK/jb43/A77LDsIvEs9Pr2ifkP/aABvwOTBewErAOvASn+OPvg+J35ZfqZ+ar3YveQ+fD6lPlS9V/ylfER8rjyHPP19CP4G/wkAXcFHQnhCy8N2A3EDZQOPQ8LDm4LZwl2CtYNghCVETwSZBIAECMKGwQJAEz8gPbr7xDsM+2/8GT0bfeO+q7+1AGWBIUF2wRFA7YA5P2Q+rn4MvkI+o/5W/g8+GT6Rvuh+eL1hPK28THxu/GP8nL01ves+8oAJAUWCS4Mxw1pDvANcg7qDgYOnwt3CaQKnQ1LEIYRbxK7EvQPFgrTA73/9Ps19rHvFuwx7Z/wNfSj9/76t/4eAngEmQXUBFUDFwHr/b/6p/gY+Wz6/fmp+Hz4E/o2+xH5kvWQ8vDwCvEa8W/ynfT996f8VAEkBt8JpQwWDlUOUg7GDlkPMw7BC2EKkQtrDrEQHBIrE3kS1Q5dCE0CJ/4q+mr0mO7A6+fsC/Hh9Pz4sft0/uAB4AP5BesE8wJ8ADL9rPuB+kn78PuW+lr5C/ll+tr6yfjb9Gvxze8J8K7xlfKp9IP3ovtmAacFZAmVC6IMfw2bDeUOGxD5DpAMvgrnC0oPohHbEgkTxREFDroHFAJ1/qr6pPSz7uTrDO6G8jv2EPkP+8/9ZAHvAxoFCQTRARkAVf2I+9b6n/vf/Ef7wPlH+Vf6zvrF95TzHvDa7svvAfHF8hD1afiK/bsCXgeCCnAMaQ2jDeoNxg6XD2kONQxkCt4LUA+tEdYSbhJdEZENbwemAS/9Q/mO8yzu+usY7nvy8PUK+cH70P4DAiMEggVPBEAC+v9W/cT73frF+3P8gfsJ+nL5vvoH+3H4LPSt8Jvvru+B8BDyUfQ3+J/80gFmBtEJhQxsDQoOOg66DocPaQ5pDO4K5QslDxYROhKDEmoRNA69B+gBP/3U+NHzKO4z7M3tavGD9Vv4x/vd/uQBXwTfBDAERQJCAN79x/uo+if7/vtJ+2/6wvmp+pT6Bfiq9Dzxze/W71LwDPKE9BX41/x5AUUGEgpwDPENMQ5TDh8P0A9xD5gNugtWDIcO7RBwEmASrBG4DXIH4gDA+574uvM0717sFe0F8er0YPmK/FH/1gF2Aw8F7QSPA1IBfv5S/GL7KfxS/bb8jvoY+T35kfki+Dz0vfCR7mzu9+9+8eT0UPiw/KIBmQW7CUcMLA7IDogO8g6YD3gPww05DDMMWA79EE0SZxMQEigO5Qd6AH/8Pvh9867ur+rx6z/vtfM5+DP75/5wAsEEZQaNBaUDyQHB/sX8cPuH+5v83ftu+r35DfqJ+hb56vRL8fnut+7f77bwE/Ok9h/7kADtBAEJrgyjDgcQ2g9BEAgRLRCHDu4LhwtWDWYPVRFbEgcSvw77BzwBxvy0+eb1OvAL7Ibrk+4V84/3FPsx/k8BygMtBu0F2QTKAsL/h/2H+yX8Ev1+/Gf6WPio+Fn5jvj+9Crxv+7/7WTvRfFn9BL4yfuvALIEFAnUDAMPHRBuD4EPAxDXDw0OzQueC6ENBhC7EesSzxItD00IWAF8/Kr5KvXZ76Pr2OoY7lfyR/ce+0b+tAFTBDMGVAbyBDcDAQDZ/E37sPtA/XD8Qvqc+MH47/mr+Hf1i/Gt7jTure6s8Enzhfa4+t3+zgOMCOwMrg9fEA0QAhD4EJwQ6A4lDCgLnAyNDh8RDRNnE1cQHwnoAWz9Ufqt9ozwjesw6tfriPCE9Ub6PP5EAW0EAAfPB7EGRwSXAF39Jfsc+1z8Y/uG+av3cvgx+rD5P/cB8wTwUe567nDwz/Lv9SL5ov0DA1wIHg0hEGURTBEdEZQRORH9DtULYApDC3YN+w8JEl8TAhCFCWYC2f1I/Gj3mfGI61npEOy+73T1pvlI/f4A3AMfBzUIUgcDBZEBI/4I/Cn8w/xd/Gj5m/d0+JL5wPkG9rbxte5X7T7u4+9c8qb1aPnc/SgDHAjgDAgQIhFZERQRmBEIEZkOsAuKCtoLYw7EEKYSuBPcEFkKLQM3/s77QfdD8IbqWehx6+bvq/Sk+TL9xwFdBZEI8gneBwsFOQHr/RH8Pvuy+/T6nfiW95H47fq7+iv3u/Jt74zut+4H8MTxffQO+O/8pALnB7IMjQ85EZkR4BFoElIRag7iCuAJqAv9Dd8PXRFsElcQawohBKn/DP329wLxsuv66a7sBvAM9Br4Gvz/ADUFMQi7CCEHMgWJAhr/Tvwm+6n7u/pZ+Ln2z/fh+Vr5Ffb58dbvR++i74zwEPLU9M34sP3SApIHwgs+D9gQbRGYEfsRjRGiDooLLwrcC3gO6g+lET8SexDLCnID9f6v+7T3OfFV68Lpp+sG8Iz07vj7/AgBKAWKCI4J7wdhBU4CN/+t/JL76/s9+7f4HPfa99/5ovn59QfyIu9g7rvuwe/P8XX0O/g5/eYCHgiNDEQPxhBKEXURRxInEWsOCgsRClUMpg7eEAgSlhImEOAJjAPO/tP73vbr79/qWekO7F7w5fRz+f78PQGFBbkIYQlEB50EwQH//oL89/sj/Fz7GflB97D48flx+br1O/EJ7xXuJO9s8OXxfPQX+M/91QPfCMUMog9REeERMBIlEqMRoA5LC7gKFwwnD5QQTRHvEf0OZgmqAsD9QPt59vPvwer/6BHsa/A89Rz6if2PAW4FtwgDCgwICQUeAlT/Lv1m/Gz8tvta+RT30/cm+XX4D/Vw8E/un+1Y7kbwPfKL9S/5Mv4+BFMJrQ09EEsRpBF5EeERHRFeDmYLcgq/DJ0PkhF0Ei8SCA/FCFkC+P0R+5L1vu666RXpq+zA8Kf1I/ol/l4CzQX2CG8JXgdvBDoBdP4j/Iz71/sd+9L4Ffcs+Gj5eviN9HPwv+497vjuRvB48tf1tPnE/lIEngkjDu4QAhLPEaARsxGxEBYO/Ap+CpcMZQ92ERwS6BGADiUI0wE+/Tr6OvWW7iTqZenp7KzxkPa1+y3/2wIHBqYIwQlLB0QEpACT/SX8ovuI/Hv71vgY94L3CvnH9+3zwu9e7RXtLu6B8F7zi/YN+gn/+QShCvAOCxGKETcREhGJEcUQFw5lC9kKUA00EAUSBRPuEeINFgd4ALv8f/kt9NDtU+nA6VvtGPKX9/P74f/tAsEFuggjCbkHWQSmANP9CvyQ/PP8xfv5+O/2j/dE+Pn2OvNJ7wbtj+zh7YXwovPb9oz6YP9PBQMLLA9+EagRNBE8EXIRFxFGDsgLewuKDdsQihJ2EwQSPw1gBqb/2vt/+EbzEO0B6YDpee1O8+j4gv1iAOgCkQZMCSgKTwcCA5//z/xB/Kb8C/3G+6b4Fffv99n4P/fJ8kzu6evE69Dt3fD08+P2cPr0/6EGjwxCEOAR0BFbEYURnRGiEIcNggrECkMNtBCyEgcT9xGhDMgFfv+f+5f4NfIG7GnokulZ7mXzEvmk/QoBhwS/B7oKtwqPB4kDlf8i/eT7Kfxm/M/6+/da9k734/cu9qLxm+2/69jrGe778Gr0lfdv+x0BpAeNDf8QORLTERIR8xDCELUPAA28CiALqA1TEXATLRRWEngMCAU5/qj6Uvd48Vrr6efb6fHu4vQF+5r/6AIcBbMHggoVCr4GkQGn/db7O/tE/I/8KvtN+F72bvc/+ED2uPEc7Uzrh+uq7Z3xTvXs+Eb8eAGACDkO1xE/El8RcxDBD+YPlg5RDDMKgArADTcR8BNpFDoSQQxABP39NPrY9ojwPOqJ5+Hpn++V9cv7zwAJBLcGJQlGC6wKpQbFAYL9FPuO+gv7lPsg+mH3R/Yy9+f39/VB8TTtKetg6xvudvFW9aL4WPxwAhMJWg9uEtASEBLKENgQRBCqDvQL3gkBC8UNXBHjE6QUahKVC04D5fyU+R325u+u6QXnw+kz7+D1VPxMAdkEmgauCZULyQoXB0wBEf6y+2z7MPzx+9v6v/d19hv3SPdD9XHwf+x06hnrwu2F8aX17fhI/bECkglRDwASpxIXEScQxQ9/D0AOZQsSCowL8A7mEXYTAhQ8EhgMdQOk/D/5Ivaz7zTpGOfM6uLwofZZ/JUB9QVmCCcKYQvyCY0GhgGM/XP7SvpP+2L7NvoJ+GH2k/eM9zL1j/Aq7MjqLutx7eHwlvT4+J79hgMmCm0PrBIPEyQS7xBfELsPtw0uC54JSgvqDbsQmxJYEwYSYwtIA6n8Z/lA9qzv7en+53rr+/BH9hD8TQGHBf0H8Qk3CwUKoQbeAXL+Hvwx+7f7nft7+rj3efZB91L39PQ08KjsWuu365DtjPC19OX4Hv2jAqsIZw6mEXQS3BGXECcQZQ/BDVULwgkfCxAOqhCOEuQSbBHzC8UD//0E+rn2c/Ao6i3pxets8Sj2ZfuMAWwFqQjaCacKmgnEBfMBCf7S+5r6vPo6+w/6/Per9oz3ovdO9bHwAu3F6wfs7O2t8F70Yfi+/HwC4AhDDmERCxKnERQRlhDGD60NOwsYClwL1A0qENsR6hKgEcwL+ANY/Q36tvaO8Ajr4OhV7InxsPZU/O4AMgWkB5sJ5QovCa8FRAE4/qH8vfsI/Cj87Pq8+Ej3zff691L1k/Bk7JXqQutL7XXwGfSK9z78EQLqCGgO+BDVEUIR/hCoENAP8w0cC8sJ6wrfDW4Q6xHSEpkRBQ1EBcb+CvtG97TxKOti6W3sh/Gv9tj63/8lBGkH2AmgClkJ7QXTAcL+ofxO+zH7Bfs6+mr4XPcS+Bn4uPXw8OnsJOvz6wHucvCa86/2mfvgAXEILQ6IEIoRQhHGEBsR3A/eDdYKbQk5C8ANCBB7EeISYRJjDfUEdP4/+xv4M/Ik62Xptew48iz3DPvg/9MDBge7CZsKLgnFBD8ACf6z/Eb8tPuK+zT7afly+GD4bvjl9ZDwCuwT6g/r8+ww72Dyg/bn+zYC1whzDnIRDBJIEb8QnhB9DyANJAotCaYKHg27D7sR1RMyE+INSwbs/+T8D/kh8pHrJumD7HLx/vWt+k//uwO5Bg0JUwolCX4FLQEo/lD8evs3+5P7MPuk+Z34g/ih+Dv2QfF57NrpXOpU7BfvWPI79kr7fwFACNgNKhHYEVgRtxBwEK4PTQ3NCrgJPguRDZ0P+BElFC0UsQ4mBsD/w/z4+fzylusP6QfstvGw9Rz6H/89A+8GwQiPCrEJggVOAcn9nvyT+876//qg+qL5X/if+PP4Rfdh8mPt4eqn6nPsJu4D8fL0lPkYAM8GEQ0KEdcRuRFTEVARphDcDfwKeQmOCg8N4g7zECATbBOHDwwIwAFp/r36WfT/7OzpSOxk8Lb05PjJ/YUCjwWhCIoK8gmlBpwBiP6q/Fv7o/oR+v75I/lO+Jr41Php93fzz+4I7GHrhuxt7rLwHPSQ+O7+/QUlDAQQ+hAKEf4QgREcEZ0OlQvQCQ8LRQ3lDl8QaRKdEzsQ5wirAR3+b/vO9ZruPeo17O/wg/WJ+V39uwEyBRMIPwpyCdIFPgEN/gb90fuF+lL6Z/pZ+i75nPhA+ST4a/T/7krr4OrO64jtpu8b81D4hv6hBakLfQ/FEJoQFBGIEQ8RcA4hC/EJ6AonDa8OMRDGEtcT5RDfCekCA/+d+6f1de6Q6jns//Bp9TD5AP3pACUFSQgWCgYJAwV1AZP+Af19+9r5Lfpi+kT6yPmk+Sz6bfgJ9ObumusH6/zrZu0p73Tyb/el/hEGpQseDwgQyRBWEWIRsRDlDdYKGAktCm8NCxBaEXMSOhM+EUcLqAO0/pX7ofbW79zqFuxm8TL2aflI/FoADAWJCNIJLQjxAwoAMf5Y/UH8A/pz+av6Pvsz+yv67vmz+F/0ee+m6/Dq8euj7JPu/PGj9wr/ogUoC0sOyQ/nEDIRMBHrD2wNugqKCb0KjQ2TD0kQ4BE4E2cSiQwgBLD+e/sO+MjxIez76yXwzvXF+bv8DwCyAywHxwiwBycEmQCK/kz9qvuc+ZD5vPpU+2r6I/lY+b74h/Uq8NnrG+tC7OLtNu/T8RP30/0PBYIKwQ2cD2EQMBEJEekPGw6aCyMKTQpcDDwP1BALEhQT6xFqDaQFiv+y/Dn5yvNo7RvsSPBG9Vj5uvtG/1MDIQbGBwQHmgTiANb9XPxS+yD6Uvn++bT6rPrA+TX5cPhS9eXwpews66zr6ew372PyVfd1/dwD6QkVDiEQqxBVEFIQDxCFDhYMzQkUCvEMhQ8EEQASPhM1EwwOIwbS/1r8+fkT9PbtL+wG7z31lPkN/SIAWQJRBZ0G2wanBEgARv0X+2T64vnP+Q77hPu++m/58fik+Fb2PPFJ7D7q0+o47TDvX/JH9yX9QQS4CUEOoRC5ELkQ1w+jD0EOqgsdCigK8gxcD88QTBLiE84TjA6lBvr/D/1o+qr06u6z7JDwxfWd+cv8Yf/DApkEfwVhBRwDAADR/EL7lfqy+Y75lfrH+7L7R/rw+Mv3N/UN8bfseOpn6izsDe/N8gz4pv0RBJkJZA3fD/wP/g9qD6kOGg7TC4AKsgr1DGkQphHKEg4UkhOwDykH5f+8/Fb6OPaT78vsbfBZ9lL7Sv34/rUBKgR5BdQEJgK6/hP8jvpQ+ov5gfn7+hX8afx7+pb4kvdx9SLya+1k6ivqIex77w3zvPdV/bIDYwl1DdQPZRBGEDAPGA7yDFsLcApqCrIMoA93EQ4TQBTrFA8RnAjMAKb8tvuq97nwQ+3D7732WfsQ/ST/lAE+BKoEawOjATf+L/t6+ff4jfl1+Yv6j/zc/Az8YPl+99b16fHF7b7pGel666buTPPF92r9XgN+CMYM8Q6PD8gOqA3kDHkM1wvsCiwLQA3CEBATCxToFPgUIhIpCjMBcvz++sb4mfLY7bnv8fXj+7f9lP7BAJACWwMZAnYAO/5K+2H59/j5+db6cfsS/Hb8rvuK+V/3yvTv8eHtZ+qq6ULrRe9K86v3/fwjAo8HfgsJDlcP4w4CDhcN1QyuDOQLdQvjDA4QzRIbFDgUbRQgEi8LtgLI/N/7VfqZ9BfvoO5b9eL7H/5y/gr/7wEfAyoCmAAV/hP8d/lj+Eb5sfop/JT8k/yp+xL6Jfgu9kLz7+4N6z/pJuvz7uny4vY9+6kAQwbKCr4N8A5wDjwNLAxKDN4MlwxLDBwNHRAbE50U/hSzFPsSpgxdBM/9f/vY+hX2PvFP76/zfvo6/eT+0/5vAKMBXgABAMb93Pug+dX3HPnC+nz8ef0A/Rn8VPod+C72IfMV72DrUOnQ6mPua/L/9mf7ggCUBaQJCw1jDhYOvgyVCyoMugySDBcMxgzMD8oSiBTkFHoU/xLNDUgGof9m/B77ePfd8sLw5/OK+f38SP6O/rr/YQDo/zP/n/3y+0X56ffv+Lb62/z0/L38A/xQ+vb4efYT9DzwJOxg6h/rxe5I8tj1/vnW/jkEZQiZC1QN4Q0XDfsLHQzqDNwNag0hDYEONhEnFNUUahTVElgPjglLAq/9rvv2+Rz2MfJo8q72x/vR/Tj+k/57/9j/eP5S/dT7OPqz+Lr3KvkB+638NP09/Fb7X/m891P15vGe7j7rC+ut7HLwJPU4+an+9AK1B0oLTg3UDn0NUAwIC3ALqAxxDFcM1gxJEJ8TYBUYFa4TOxK/DJkFh/4s+yX74vdI9NPxXfSe+n79+P52/mz+PP/P/f/8kPuO+Rr4p/av91P6O/wv/V/84vr3+Uf4g/aM83Pvg+zT6nvs8O+p8xH4HvxbAVgGKgrXDJMNaw1SDEkLXAs3DMcMkgxSDCAOpxFGFKsVlRQqE3UPaQgCAjH9C/2P+7r2wvIH8uL32fyj/j3+bv38/sD+t/2X+2H5Afg29nP27Pdd+un7/fto+3T6Y/nH9631afIH70bs8+tY7r/x2/XT+ZP+0gMyCHYLLg21DTYN+ws/C7ELXAxCDOULyww3EJIT6BTDFHUTXxLPDWIGPQD7/GT9xvrQ9c3ybPSv+iX+hf5m/Tn9VP5g/aj7Z/kl+Jv38Pb795n5uvvX/Cf8PPti+Rb4SPad82Hwvuxl60LshO9v84n3+PtVAN0ECQgYCwkNwQ04DSILDwtBDOwNiQ5ZDVMOwRCuE+gU2BMDE24Qwgu0BGT/O/5L/lP84PY29Nb1jfrL/Qv9hvwX/I78CvwG+i35Y/jl93H3bvdS+dP76/wi/KT5lfez9nz1hfNW8KbtGe1F7i7xrfSX+PH85wCeBKIHHwrUC2MM6gsUC3cLEQ32DkIPQQ50DpsQyBMeFe4TZhLsD0EMrQZQAdT/2v7H/P73mfS59pr6j/1z/Oj6e/ul/ML8iPpb+EH3F/fs9k33iPge+jD7I/p6+OD2Pvbx9f7zFPEQ7jrt9O7L8ef0x/em+0YAxwTnB6cJGQsbDL4MeAxtDHwNdg6zDsMNzQ0wEPISfxTEE3QS+RCPDXQIXQMmAWEA9f2l+YD2zPdF+xj9FfyP+vr62/vy+oX4RPbq9Tb2qfWl9X32I/nj+ur5Lfhg9rH2tfbX9Fzyme9474rwTPLR9EL3HvvV/n8CzwVLCIkKhgvqCwMMsgwTDiUPHg8pDgAOXw+cEfkS3hJoEoURUA8QCwkGIgP0AWQA2/za+N73jPk8++b6r/n1+WD7efug+Vb3YvaS9gD2UfV/9eP2bfgc+EH3ufYh9573n/b79AXz1fGj8RDyl/Of9XP4+fvj/0ME1QdGCnAL1QuADEANMA60DnAOlQ3rDEgNyg5tEEYRzRF4EVMQBQ1eCFQFxQMjAx8AsfuF+Qn6WvyB/A77b/q1+lv7OPqA+If39fYi9tD0zfSB9qL4CfnH96T2mvZ49133LfaG9EPz2PIR8/fzrvW79+T5b/x8/2YDzgb0CMQJCwoNC3kM/w2JDh8OKg21DMINDQ8tENEP8g7RDrYNQAwPCTAGzwSbAsEAuP1D/JT8Pfzp+236P/oh+1z7+vqx+a34B/jV9pj1E/VS9f31z/U29U31CvYR90v3s/YU9oT1QvU59Vf1K/Z99735dPwP/8kBYAQtB0sJcAoNC40LVgyrDF4MtwsFC8EK4AqjCzANlw4gDwAO9As/CvQIKwhwBr4D3wC2/oH+kv4p/vH8t/vy+xT8t/tU+qT41PfJ9u31HvUV9eb1LfYc9sT1Evbs9j33+PYd9nX1QPVD9Y/1V/ap9235Mvsz/dz/gwK7BM8FbwaxBz8JBwu2C6oLcgsvC5sL9AuwDBsNJA0ODawMcQxmCwoKcQgPB/kFaAT4AtUBMwF+ACH/pv27/ET8r/tv+jL5dvgE+Hr3hfYd9hH2Y/ZF9tj17fUQ9rn20vbB9rD2I/bZ9Xb17fUH91D4nvm/+j/8H/5NAEEC+gNeBbcGQAiPCb8KYQt9C68LqAutC4cLQwv1C4gMvgwcDOAKkQr+CToJqgekBVcE6QIKAiEBNQAy/2f99vss+xf71/ql+ST4QfcV9wD3kvaP9fj0vfQF9bL16/U/9in2DfZL9m32xPbn9sf2F/fq92/5pft//Yn/aQE9AzYFWwaKB3kIXwknCmsKfQqcCg8LbgvZC/ELQAz2DDANFQ3QC1UKUwlACGcHuAUhBAoDQwKiAWoAG//7/Tn9UfxD+/35yvji98v2GfbJ9fr1iPaY9kb26vXH9TX2cPY19sr1V/WL9Q72qPZV9wH47/hG+gP8Iv5LANcB9AKfA5ME+AVnB6YIIgllCbgJhQqCCyUMNwzhC+gLRAxoDMwLbQosCYgI9AdKBw4G6QQHBMQCfQHb/8f+Gf4d/Q38r/q9+Rr5cfjb91v3KPcm9xr31faO9j72BPbj9an1rPW+9fb1Vfap9j/3E/gi+VX6gPup/Bj+rv9OAcoC1QPXBM4F+AYgCMgI9wjXCPQIlAl9CjQLowvWCwYMRwxEDPMLEgu0CSoIhgYrBfkD1gK4AWQALv8k/pj9Zv0h/YD8cvuQ+uX5kPkS+WT40fdh92P3Tfcy9wX3wvaH9h32uvWY9cL1Kfa89mT3b/jL+WP7Df1//sv//AARAvwCsAMWBKkEQwUeBhIH4QfSCGsJEQqXCjIL5AtWDGAMnQt8Ck0JegjOB9kGjgVOBGcD2gITAtEAcv8I/hn9Ovya+yj7nfo4+qX5ePmN+dD5Dvrl+YD5zPgi+Jf3LPfW9oH2cfag9h/3vPdJ+OL4cPkq+g/7D/w6/Uv+U/9UAEsBXAKbAyAFnAadBxAIVgjMCGYJuwmWCW0JmAkrCp0KggruCTUJygibCEsIUQe+BRsE4wIVAh8B3v+D/pP9Dv2Z/DD8zfuO+xv7Wvqf+Ur5ffmi+Vz5zfhl+Hf4sPjI+J34Pfjq95v3lPfh92j4Fvmo+Wz6YPuj/A7+SP9iACUB7AHFAscD7wTUBZAGDweSBzYI6wh+CcgJvAl6CWkJeAlvCekI0gfbBj0G7gWZBdsEEwQ9A1cCYgFJAG//qv7H/aP8c/u5+pD6t/qZ+hf6efkf+Sb5GPnS+Eb4tfdh90X3e/fV9y74aPii+CH51fmL+vL6Lvt6+x/8Kf12/tz/KAFVAl0DbgSGBZwGcQcACEYIcAjNCC4JkQmnCZAJdwlaCVEJAQl0CKEHzwYpBnAFjgRKAx4CPgGeAPv/9v4S/mL96vxj/Kf7FfuY+hD6Vvmt+Gn4dfhz+DP4Afgi+If43vjV+Iz4R/g0+Gr4xfgu+a/5P/r/+vP7BP1D/nn/kACDAUACKQMyBE4FSAbnBnQHFQj3CN8JYApkCh4KAwomCjUKzAntCAgIWwfbBjoGWAV0BIQDmwK+Ae4APgBD/xD+5Pwi/N77afuf+sL5VPlx+aL5hfkg+bH4W/gz+CH4JvgW+MT3Y/cj92D3C/i6+Db5dfnJ+Wb6Xvto/HH9ff6h/w4BVQJ8A1YEHAUMBu4Gwwc5CH8Iwwg4CecJigoCCxsLBguzCjYKiAm/CPYH6ga6BW8EdwMIA7gCRQI1AeD/rv7C/Sn9Yvx1+2b6svl9+ZP53/nU+bv5T/nN+GT44vee9x/3ofY49gX2TPbP9nr3Efia+A75mvlQ+jP7RPxV/X3+xv8aAW8CfANgBFoFewbSB+kIjgmlCY0JzQl/CmQLsQteC6oKLQoSCtkJQwk+CCcHPwZUBVAEWQOIAu8B/ABy/5/9H/yB+1/7O/u2+iL64/ny+Rb68fmE+b34yPft9mD2XvaF9pr2h/aH9uv2dPfy9xP4A/gP+FL4AfkL+pH7YP1D/wsBogIYBD0F5wUnBn0GRwdWCCYJZQmiCWYKuAvnDC8NzwwlDI8LyQqjCW4IcgffBjEGOQUaBEcD3gJPAjsBbf+I/SL8XfsZ+8f6cvoh+g36OvpN+h76afll+Ff3hPYt9gz2//Xi9dj1Kfaj9jL3c/eR97b35fd5+D35e/ro+2L98v59AD8CvAO4BBsFagU6Bo0H2whkCYYJrQl9CrYLhAzHDGQM2As0CzsKSgmDCCoIxwfKBm4FMgS9A5gDCgOaAYX/pf1H/Kf7ePtr+z/7sPoc+sf50fnQ+S357PeM9sf1rfXk9QX29fX49Q32QvaB9tT2Pvd195P36fff+Hn6Qvy//Rb/lABeAv0D4QQkBTkF6AUqB4gIYQmWCbkJNApPC2IM7Qy4DPsLMgs/ClwJfwjxB5wH3gazBTkEbwN+A6YD7QLLAEj+Y/zQ+wn8Jvy3+7b66fmq+Qf6WPrM+WL4r/ag9YX16vUW9sn1UPU79cj1m/ZT9333Xvdn9wL4X/n1+nj8pv3B/kkAHQLoA/QELgUdBYAFzQZlCJIJvwlwCZYJjQoYDBINGQ04DP0K9gkUCZgIUgjkB9wGFgWhA0QD/AOMBHMD4gAA/ln8F/xw/Hj8ofuF+pr5lfk7+rT6NvqE+Kb2dPVW9aP1nPUq9bj07/TC9c32gfeZ9173SPfx9y/5pPrY+7X86/25/xkCBgTKBJMEWAQ9BfkGmggICX4IMQjoCKkKKwzJDG0MzQtwC/MKSQouCTUIjAftBjkGQgWwBIsEggTVAw0CwP+z/dX8yvzT/D38Dvtb+nH6IPs7+yH6TfiY9uD1ufWl9UD1v/Sf9Ov0mfVJ9sf2BfcJ9y33v/fb+FL6tvsA/U3+7P/KAVgDHwQBBOkDfATnBXkHOQhdCGoIPAmiCt4LkwybDFsMowuLCnwJ8wglCSIJUgiqBjsF+gRrBZUFRgTKATz/qv1+/cj9nv2I/CP7XvqA+uz6j/oy+V/39PVR9R/1FvXp9Jn0XPRX9O302/Wt9uv2qPar9nH3/fij+u/76/wZ/rj/nQEmA68DdgNYA0oEGQa9B0oI/AcVCDsJEAtZDJUMTwzrC74LEAsHCj4J0wjTCBcI3AatBT0FgAUrBegDuQHg/8D+Pv7x/Sv9XfyI+xP78PqT+t75q/iK97X2/fVE9YL0KPRD9Jn0vfTX9GH1UPZN97L3r/cJ+Bj5xfoy/Bn97v05/ykBvgJ4A00DOgMyBPYFrQccCLkHsQfiCAALfAy9DB4MkwuZC04LcAo6CYAIigg/CFgH/QVhBbgF2AWuBAwCrf9x/m7+aP6v/bn80vuC+zv70foE+s34jfde9oH14vSE9Hn0uPQY9Sz1FPUo9cz1x/Y490j3T/c2+Af6yftE/UP+l/8fAXACBgPQAtkCkANlBR4H6gfeB9wHHgnpClIMcAzVC8kLBQzsC74KOgm0CP4IUQljCMMGrAVzBYwFYARrAi4AzP6E/lP+Sv5Z/Wz8ivvr+r761vnF+D/3MfaX9fr0bfTw8zD0ovQH9fz0A/WL9TT2svbT9jf3dPgD+nL7k/zY/a3/bwGYApgCOQJKAm0DSAXKBo0HfwfNB/gIyQpTDOEMvAx8DBIMNAviCcUIywhDCVEJEgh8Bs4FJwZoBhYFlgIUANX+e/5c/vv9U/2t/O77Kftr+rX5Avka+PP2vPWM9MjzhfPo83b0pfSR9Jv0XPV39mP34PdE+Bz5WfqM+4j8t/1h/xQBKwJiAloC/AKnBLcG7AcECJYHzAf0CHQKogs2DLQMCg3TDIUL0AnxCCkJzAlACYcH0gVIBeMFGAYFBcUCfQAN/3b+X/4T/mb9c/xh+6f6FvqL+cn4xPe+9pD1lPTj88rzRfSd9K30VPRm9BP1//XW9kT3Evgc+V/6i/uU/O/9ff//ALABxQHGAZcCaARZBqcHvAdtB+MHSwkQCyEMdwy9DOUMnAw9C6kJAQlvCb8Jkgi6BpwFMwYaB6AGngQWAnkAw/9C/7f+8/0q/TL8RfvM+p76Tfox+cv3ZPZv9Y70wPNy85Hz0/Oj84LzB/SN9er2efd49+n3PfmE+oD7H/xO/RD/mQB1Aa4BWwK4A48FCgeGB20HQAcfCLQJNQv7CwMMkQwzDVwNIgw7CmAJbAl0CQoINAaUBWUGFgfyBZoDiwHqAK8A/f/g/tb9Pv2L/M77Mfu2+tz5a/ju9gH2lPXi9BX0jvOb89zzo/OY8yz0XvVy9rT2EfcY+Mj5IfvL+5X86P2x/8YAKgFZATIC+gO4BekGIQcRB4sHsQhJCloLyQs0DM4Mgw35DGwL6gkyCU8Jowg5B8cFqgV/BqwGewU5A8UBAAGSAMn/rf7r/Qj9U/yR+xn70fr4+bf4Svd29t/1IfUy9HrzbfNH81DzavMv9Gn1IPbJ9nT36fh/+nn7HPzR/D7+lv9fAKAACAFPAhYE7QX1BlYHigchCIkJrwpgC6ILGwwTDUwNUQxsClEJjgnZCdkIswaDBewF/wanBogEWwIVAesAfACM/6r+/v17/bH8Cfyh++76pfnp95r28/VV9Wv0YfMY82LzmfOb88HzxfTY9Xj2sfZd99/4bvp4+9776fyw/nUAYQFUAaUB2QLWBJAGJwcMBxIHLgjkCSoLhQt3CysMQw2pDVIMIQobCVAJkAkqCPoFFAXUBdMGDgbEA8ABHAEaAY0Abv9n/tj9Lf01/Gn70vpO+jH50vfS9h72YPU49EDz//Ix8zPzJPOy8x71f/YP90v3C/ii+Sz77vtS/En9EP+6AI4BsQEmAn4DWAUCB5kHigeFB00I9AkyC70LwwsuDAYNKw0TDFcKYwktCdEIXQe9BVcF2AUcBtUE3AKoAUYB8gD3/9n+V/4a/lP9AvzD+jj67Pnx+J73a/aq9SX1VvSn8zjz+fIC80zzOPR39WL2x/Yz9234C/pS++n7tPw6/goAWgGGAacBdwIiBOIFvAYSB14HKAhPCTYK0QpYCzEMPw2HDZAMAQv9Cb0Jcgn4B9sF3QRcBXcGAQbmA/IBQwG6AW0BHQC2/vD9rf0E/dn7y/pb+uH5DPnq99b2KPY39TL0O/N58kryZPIp81j0XPUE9p/27ffC+Tr7o/vU+8L8nP5cANQAsADmAGMCywTGBs4HzgetB2QI1gkRC0wL/wpiC7kMiw2UDGcKBQmPCQcKrgj/BWkEcAXSBkcGdwPbAH4AOAFTAQAAnf4r/gr+Zf0Z/Of6NvqN+YL4PfcZ9l31tPQc9JjzGfOp8pPyY/PQ9P71SvZ19k73F/nt+sz7Pvwl/RH/EQEOAvsBxQGbAn8EhAaPB4QHdwc9CNQJOQvSCzoMygxhDQgNkAs0CrsJwwndCLwG+AT7BCkGnQYyBeYCfwFQAU8BlgBG/zj+kf3l/Dn8ffvy+i/6Gfn89/z2JPYt9Tb0dPP28pHyZ/LS8vvzTvUQ9m/2N/e6+F36XfvI+4r8D/7y/1ABzQH+AaICDgT1BWsH9wfaB+sH8Ah2Cp8LAQw+DLYMBw1qDOcK3gmPCWAJCQjUBaoEBAXrBXIFdwOUAe4AGwHXAMz/mv7e/TX9dPys+/T6Sfo0+QT4PvfT9lb2RPUe9JrzhPNo8yXzV/NM9Iv1ePYU9x/4nPku+xL8pvzH/V3/FgHvASQCZAI5A+sEugbjBxUI2Qc6CJYJGwsCDDkMcwzrDNIMeQvGCdMIwQiFCOMGIAVYBM0ERwWDBCcD2gEfAWYAdv+1/lv+6/3X/Kf70vqo+nT6iPl0+Fz3ifai9Xn0u/NA8/XynPKW8o/zMvWz9nX37ve7+Cf6iPtr/DL9E/5+/9IAywGbAk4DggT4BUsHHwhcCI4IHAn4CbEKQwvrC8kMEQ3QCwcK5ghQCfYJwwhaBi8EKgRTBX8FLQQDAsYAUQAWAM//Wv/P/sb9b/yK+z379vr/+Xf4avf09pz2z/W79Aj0mfM38+3yVPNq9KX1NvZ29m73KPkV+yH8l/xp/e/+0gAEAlMCRQKkAv0DyQVIB9AHswf7Bw0JpwqxCwAMMwx7DIwMkQsQCicJAQm9CFIHVgVVBM4EiQUWBYQD7wEyAcsALgBk/5v+Gf5G/Vb8lfsI+4f6k/mS+Lv3EvdM9lD1kfQq9LnzG/PI8kjzhPS39XH2IPdd+Av6bfsy/OL8F/7G/yYB0AHpATgCNAPFBHAGUAeFB4kHTgjPCSwLyQulC9QLJgwgDCYLtwn9CI8I4QdcBtYEfQTWBOIE4gNwApIBKwGsANv/9v5d/tz9F/1K/Kv7JfuB+ob5oPjm9wf3LPZW9eH0iPTl81LzKvMS9FL1Svby9rP3K/mm+uD7pvx7/ej+VQByAdwBAwKxAu8DdwWDBs4G7wZ7B94ITAohC1YLfQvuCwYMXgvUCcAIWAgJCDkHkwWpBKMEFQXWBF4D8AEFAacAMQBz/9L+Yv7x/SH9Xvy9+2P70vq4+br4lve99uj1H/W59C30vfNu89jz9fQk9vf2bPc1+Ev5jPqM+3r8rv34/mAAYAEYAtECrQP0BC8G8gYJBwcHyQcqCVsKrgrUCmQLNgwlDJIKAgljCM0Ikwi6BqwEqQNFBPAEVwTjAmkBygCuAFkAzf/W/tL9D/1y/Db8ufvo+vX5GPmS+Oz3Ffcc9lf1xPQz9KDzefMa9BD1GvbX9rX3Dvlr+on7Hvza/Dj+5f9TAeABEAKYAuMDpgXcBjwH/AYhB1EI2wnpCvkK4gpZC9cLfQveCWQIKwiDCPsHBwY8BPMDwgTRBGADkQGtAMsAjQCp/4r+2v2f/Rb9R/xm+7/6S/rD+SP5SPgk9xn2bPUR9bv0CPRs84rzjfTo9c72dfdX+KX5//rZ+4L8gP0x/+4ABwJrAp0CmAM4BfMGwweBByMHlActCaAKKQvbCuAKnwvmC/MKDgn9BzwIXwgYB7MEIgNUAyoEEwSvAg4BRgAAAJv/+f5F/sH9N/1u/L37J/uw+kP6k/nj+PX35fYI9nL1QfXU9Dj0xvMf9EH1avZR9/H3CPlw+qn7gfwr/Xj+MgCvAXcCjALfAvYDrQVTB/EHvwdxBw0IhgnYCj0L/AowC3gLUgvzCWYIFwhqCD8IPwbTA8ACHwPUAz8DwAFcALP/hf84/9T+Sv7P/Q39Tvy2+//6jvrb+Tz5ifhz94D2nvVL9Sz17PSR9GT0x/R89WH2A/fR9/j4Q/pu+wn8+fyP/pYAbQINAxYDYwOcBHoGygcTCJ4HfgdUCNoJ8QomCyELUwvjC4ULGQqtCBUIawjQB9sFtgPUAoMD8gMeA4MBKwDW/47/1/4C/kT9DP2p/Av8TPuQ+in6wfla+Yj4YPcx9n/1R/UJ9Zz0/PMK9Mz0xfWa9i73MviY+eT63/uL/Lr9dv8xAVoCrQLrAq8DUQUOB/oH4QdbB8QHLgm2CkALzAq3CmML9wtmC7QJdghtCJoIkgdDBWEDIAPoA/ADpQK+ALL/oP+A/+7+0/34/If8K/yu++/6NvrB+Vv51fj49732yfVp9W31UvXD9Dz0X/Qz9Uv2BveS9174hvnH+s/73fw3/goAtwHNAjwDfgNwBPAFfAcmCPAHrQc8CLAJBQujC3ELUAuEC2ELoAo5CUEIFwjGB7kG1wR8A24DyAN8AwkCWABr/yj/1f4x/lv9y/x//Pb7dfvP+jf6zfkp+YP4fPdc9ov1MPU89Qv1lvRD9Jn0evV09j33+ff4+Br6KftJ/Hf97/6PAOgBBwOaAzsEQwWdBuEHKwjQB9UH2wguChoLHwslC78L6AtoC/MJyAiPCCMIHgc6BZwDXAOgA5QDoQInATAAy/9W/7v+2v0S/dP8S/y5+yD7a/oV+m/5ufgE+BX3WfbP9YP1fvU/9dX0wPQi9QD2zPZF9/b34fj6+Sz7MPya/XD/LQGVAkIDtwONBOAFeQdpCEwI3gclCG0JCAupC2wLQQttC6oLxQorCR4I3wfgB9MG9QSyA4QDDATTA3ECzABu/9b+bP7f/UH9jfwe/MT7e/sc+5H6A/pt+a74xfey9sz1ffVw9WL1B/WU9LL0X/VJ9g/3d/cZ+CH5bPrP+/38af4gAOcBQgPvA10EEQVXBqsHZQhTCCMIvAghCoULGQzTC6IL1AvBC8UKIQkFCMMHgwdPBpAEdgOtAxoEZwOyAdz/7f6V/hH+Of1b/O/73/u8+2/7z/oN+mz5ufgB+Av3AfZF9RT1TPVf9RX12PQb9dj1l/b69lj3Jvh3+dz6GfxW/f7+9QCmAsIDQQS5BI4FywYACKkIwQjXCKYJBAsqDGsM/gvIC9gLeQtMCq4IugeOB0MHOAaXBJMDggOBA8gCIAFk/2L+0P1O/az8Dfzq++b7svtL+536CPpU+YD4hPdk9nf18/Tz9Cj1UPUq9Rv1dPUc9sj2MPeZ93D4kvnb+jT8sP2j/40BRANTBMoETgUMBjYHTwjACKYI9wgECpkLlwyQDHoMdwxnDGgLcAnpBywH5wZSBvsE5wOcA7oDlwOZAvIAd/8e/h79jfzl+6r7avs0+0H7Dfva+kP6SvlU+Fb3Zfak9f70wPT09Cb1ZfWI9cn1Svar9ib3xveE+Jz50fpU/En+VgBNAr8DuASCBTYGHQcKCJcItgjbCIYJzgrbCzgMSQxrDLMMGQx5CsII5wfcB1QH8QWLBPcDOQQRBPECXAHT/8L+vf3S/Bz8k/tX+zX7Q/tV+xv7d/qs+bn42ffx9uH1KvXS9N30FvU19Wj1uPUf9pz2APd29x349vj8+UT74vzx/joB9wIpBNwEiQWtBs8HrQjnCNUISAlYCqMLfwyQDIEMtgyrDLQL6QljCN8Hpwe1BiUF3wPMAxEEmgM7An8AKP8O/gX9KfyR+zj78vq3+sv62/qQ+uf58/gC+C33VvaU9Rr19PQi9U/1a/Wp9QL2hPYT93b3+/el+Iz51vo+/A7+EQDxAXoDfwQ/BScGTAdbCC4JZwmLCScKKAtTDN0MyAyoDHwMAQz7CoQJcgi9B+IGxAV6BM4DqgNCA2QCEQHY/+T+vP2T/K/7NvsW+736Ufoc+hT67vlN+WX4mffz9kj20/Vv9Vv1bPVn9a718PVc9rX28vaG90b4Ivkg+k379Pzj/rwAkgIKBA8F2AWUBqYH7AiJCZkJ1AmbCgoM0wzYDKQMiAyeDOULaArgCMAHFweQBpcFcASpAywD9gItAqYAHP+N/Yz8svvi+qT6h/qO+mT6H/oc+vj5Rfkj+Az3UPYQ9pf1KfUh9Un10vUR9lj2yfYM94D37/ei+LP5yPoT/KD9gP+FATcDfARqBSEG5gb7B/UInwncCS0KTguGDFQNIg23DM4MtAzUC+AJMwiPB0QHcwbaBJUDMQMMA2MCDgGe/2v+Vv1b/Jb7Gvu3+mf6Lvoo+i/61/lR+Xj4pPf39lP25PWF9VL1VPWe9fH1OvZ59rj2Pfe690j4B/ny+VX7vfxE/iAA4wGeA+AEqwVyBl0HhghqCdIJCgqdCqELqQwDDcgMuwzbDI4MLQspCecHfgcZB+sFMQRFAxwDCwM2AqoALP/k/cP8wPsC+6H6YfoL+ub5A/oP+rf5+vgJ+EP3ofYA9or1PvVX9aj17vU49n/24vZo98z3M/i8+IH5q/ru+1j9AP/oAMgCXgRoBR8GDgcQCCsJwwnYCRwK0goNDOYM8Ay7DM8M7gw+DH4KsAi+B0QHYQbBBEsD2wLpApUChAESAMf+hf1S/GH7uPo9+r/5bfl2+bz56Pmh+fb4Ifhy98D2Jvap9U31VPWG9eD1O/aW9jH32/df+M34QPkQ+jH7a/z3/bv/qQGJA+kE1gWlBn0HdQhLCYwJjQnRCc8KMAwFDQUN2AzyDN4MuQufCRYIcwcGB9cF9gP7AgkDHQNgArIAMP8H/tn8tvvD+k76D/rG+Zb5vPnq+ef5ePmb+NT36vY+9sv1cPV99ZH1+vV+9tX2Rve59yn4kfj2+Hr5dfqj+wD9wf6ZALoCWgQ7BR4GogaJB44IJgm+CdUJZQqRC7wMYA0oDckMwgx0DPUK/gh9BxkHxAZEBZkDoQK6AsACrAEdAKr+fv0//An7SPoH+uT5hfl2+cH5+/nb+QT5T/ie99n2N/aR9Xf1ovXp9Vr2wPYh9373z/c8+K/4JfnS+cD6GPyo/Wz/SgElA7MExwV5BuQGlQd6CHMJ/wn/CVYKewv8DL0NTA21DIQMQAz8CrEIAQdsBiQGAwVBAzQCPQJiApUBBABc/gv98fvz+j36vvlw+Un5Uvmp+eb5tPkt+XH40/cl92z25/Wu9dr1QvaP9tD2GPeY91v40Pgj+Xr5Kfpc+638If7D/5sBUQO4BKgFXAYWB8gHoghRCbUJ+wlzCpwL1gxLDS8NwwyxDBsMQwpACLUGPAagBfgDfwLgAUMCRwIOAWn/Af7t/MH7hfqm+aH5jPmG+Zr5qvkR+uL5X/mo+L/3+/ZB9uP1+fVH9pr25/Zl9+/3gvjY+Of4PPmR+WX6Z/tu/AH+zf/xAdoDLgX4BXMGAwfZB7UINwlgCZ4JYArEC+0MUw1FDe8MvAyWC5YJpQdsBusF7gRYAw0C2gE6AhcC5wBO/9z9kfxP+zT6kPlL+R35D/lr+eT5Mfr3+Wf5yfgC+Dr3hvYn9kX2ePa+9hb3jPc5+KX46PgT+WX5CfrW+rT7t/xD/i4AYAIWBCAF4gV0BlkHUQgECWEJZAmtCbcKDgz2DB4N5AzsDKcMVgtBCU0HbQbrBawE7AKxAZYB6AFtASgAsP5U/ST84vrW+TT5+vj/+Db5afnR+Qv6BvrD+er4Dfg098v2u/ap9rX2+PZi9+b3Vvil+BH5SPnB+XT6Ovs9/FT9wP6zAK4CNQRJBdYFiQZcB0IIIwmVCc8JOgoZC0UMAA3dDNQM5AyFDCMLyQggB4wG2AVkBFYCGgE5AT0BoABS/wX+IP38+8r67/lb+SH5EPnh+CL5TPlv+a/5aPkM+VX4h/da9y33F/cl9xH3Zvfb9y34vPgO+W75G/q7+rX7sfyM/Qf/xAB3AgQEyASeBYsGgge5CGkJqAnBCSgKOgtYDKMMiQyPDMgMbwyhCnUIMAe1BtsF8QPLAeYAAAEKAWYAA/+//ab8svvj+i36bfnZ+JL40fhY+Yb5pPmd+ZD5R/ml+Az4t/d/91/3U/dY96b36/dh+OP4YPnn+Wf6IPv2+9f88f1+/y8BzALZA7IEuAXHBt8HvQhLCZoJyAlgCo0Lawy7DJIMpQz0DDAMJwr1B8MGdwaIBUoDRgGRAA0BRgE6AKv+Rf1S/J37n/qJ+dr4i/jO+CL5WPml+eT5Gvr9+Vf5m/gH+Kr3kvdv93v3pffN90D4v/g++a/5BvqH+kT78/vI/Oj9Z/8zAbEC4wPaBK0FmwaZB5sIdwmzCckJXQpjC40MzgyXDLAMkQzVC78JkAelBicGKQXzAuAAfwAZATMBEgAo/vr8ZPyD+4/6aPnW+PT4Avkq+WL5pvkr+jX6uPk2+aj4c/g8+PL3xPfL9/33Rfie+OH4Ufms+QH6mPo4++r7yvza/W3/GwFwApUDiASTBd0G8gevCDAJQQm4CYoKdwtYDGoMZwyjDIAMcgtjCUcHfQb9BbsErQK5AJQAJgEDAb//8P3i/Gv8lPt0+nT58/gY+Tz5Wfmb+cX5G/o1+tn5fPnc+HP4Qvj99wT49vcE+Er4mfgZ+ar5+vlT+sX6R/si/Of89f2D/wUBhAKfA4gExwX2BgII6ghWCaEJ8AlqCnMLLgxvDKMMlwxwDE0LJQmCB5kGBwaGBP4BcQBaAOoAqAAw/5f9ufwn/Gf7UvpL+f341PjT+Af5dPkj+nX6R/r4+Y/5PPn7+HT4OvgQ+Cz4bvh8+OD4XvnX+Uf6iPrH+mX7Afzc/P79Ov/xAGICngPOBNIFFQcnCNMISglZCZcJcwp+C0oMigyGDOAM0gxSC0EJVAeFBuIFuwNgAeT/HAD8AIMAGf+r/br8VfyV+1v6Vvmc+H342/ga+YX57Pkx+oj6Qfq4+Uv5yfib+Gv4SPha+En4fvgJ+a/5Rvp2+nH6z/ph+w/86/za/Uj/rAD1AWQDtgQEBh4HEwj6CFsJYAmoCXIKmQsUDCwMlAw0Df4M+wqoCGcHKgcuBqMDIgFPAPUAXQFWAH/+Uv3Q/FL8QfvR+eX4tfjt+Dj5Pvlw+Qf6afqE+g36jvk4+d74l/hn+GH4VPho+Jv4NPnh+Tf6ZPqF+v76qvsp/OT8B/5z//4AVAKwA/UECwYEBxII7whdCWkJiQmCCpULSgx9DLcMBw1cDG8KZQhmB+QGcgXCArkAeQAkARsBsf8W/lj95PwD/Ib6PPnW+Nf4xfid+Mj4bPk1+mv6KfrA+V/5PfnP+HX4VvhM+GD4gvjj+J/5P/qI+sb6/Ppu++z7WPw2/XT+3P9XAZYC6QNYBYsGowdyCP4IVQlnCc4JygrBC1sMpAzWDPkMDQwuCnsIZgeqBt0EYgLWALsAKwGZACj/8P1D/YL8Vvvz+SP57fjK+J34gPjv+NH5ZfpW+vD5jPmM+VP56fiW+Fb4kPih+MD4LPm9+UL6ifqi+vD6dfvI+3v8Wf2T/hEAQQGtAgwERgWJBnUHVggRCVAJkQkrChML/gtzDJIMIA38DLQLrwm2BycHUQZ2BFcC8gAiAWwBnABa/zX+UP1K/Kb6Vfnb+LT4sfhv+Gf4IPnY+UT6KPqo+Xb5MPnf+Jf4WfiB+L/4/PhN+Z759Ple+qX66Pow+2H76/uY/KD9/f5nAOQBPgOBBJ0FkwaMB3UIDglZCakJhQqUC2sM0gwtDZgNBQ0+Cw4JygdbB1IGGAQDAjIBcAGGAWwA//7I/aL8avsA+vr4lPhZ+Er4TPiE+Df55/kh+tX5QPnY+MD4f/hX+GT4iPjc+AH5TvnV+Tf6ePp6+n363/pc+wb86vwE/mP/0AA5ApkD1ATLBcUGpAeICDMJjgkpCgQL9gukDBsNog3VDb0MpgqeCJsHEQeZBYsD+gGHAagBOgEUAM3+ef0Z/Jj6M/mB+FP4Mfg7+F346vi4+fj57Pl3+ff4q/hJ+CP4V/i0+AH5Pflg+eD5XPqH+pD6Yvp2+uH6b/tU/HT9qP4XAIMB6wJDBDoFCwbSBqYHjggcCYUJXgqNC6AMQQ2KDeINiQ3+C9gJHAhPB2UGpgSvArcBwQHIAfQAkP8d/qT8Kfub+Y/4C/j39xH4Kfie+G35BfoG+oP59vjG+G/4Nvg6+JD4NPmN+cn5Ivp7+pj6gvpK+mL6o/oG+8/71/wz/qP/9wBCAm4DUwRGBSQGBAf1B5QIJgm/CZAK1AvPDFoNvA2NDccM6wr7CAAIWwccBhMEfQIzAnICrgFKAMH+ef03/Dn61vgx+A74QfgC+Er4K/m/+dn5VPnE+Kr4dPgj+B34Svjb+Hz5rfkD+kH6NfpW+iX6Sfqi+uv6pvtq/HL9tP4dAHwBwwKmA28EWgUlBk4HQgj2CIoJLwpUC3kMEQ14Da0NVQ0DDPQJkAj0BysHfwW6A8YCngIuAhEB1P96/gn9Fvtp+YD4M/gd+PT3G/iZ+Bb5PPkO+bn4dfgQ+Nr3xvf793f46/h9+er5RvpY+j36JvpH+qb6DvuK+yz8Jf1j/q///QAvAjYDQQQSBfkF3gbPB8YIYgkMCuYK6wu1DC0NmQ3gDRwNNAs9CSYIuweLBnAE3AJ3Am8CuAE8AAT/3/0u/DT6nvgR+Bf46fe39/D3gPg1+T351/hx+CP4Gfjm99/3K/jZ+Hf5z/kO+jz6avpN+jf6bPrX+l/73fub/L/9If+EAKUBuwK6A6MEeAU8BlwHaQgACX0JHwpWC1sMvwwQDYoNiA06DDQKmQhGCHAHrgXcA74C1QJSAhkB4f+f/h39F/su+Wn4R/gI+Nv37Pdf+PX4//jD+H745/fA94D3b/ff92b4Vfne+Rv6Tfpt+m76Wfpn+sn6T/u8+1n8cf3S/i4AWwEqAi8DDgQPBfkFtAaqB3MILAnMCaUKswuDDNAMIw1cDdsMKgsZCQ4Isge3BtIEVAPNAssC3wFWABT/pP0H/Ab6nfhn+Cb46/fE9/j3vfjo+LT4Tvj798/3ofdz94n3Qvjz+Ln5BPog+nn6gvqX+p36v/pA+7f7Qfw8/WX+rP/tAOUB/ALhA6UEdgUfBhgHHAj2CJYJNAolCxIMigyzDO8M8wzfC+UJNwiWBzsH5gVJBC0D7gJAAuIAmv9Z/gb95foc+UL4Fvgq+AP4Dfhy+L/4y/iK+Bn4Dvjp9733q/f198f4nfkX+kj6kPqy+sP6lPqp+i/7yvtc/PX8xP33/lYAgQGgAlQDCgTMBIUFZwYyB/8HuAhECQMK9Qq1CzoMcwysDDIMvQrtCNIHZAejBkIFrAM2A8kC7QGlAB7/N/6V/Lf6VfmZ+MX4n/hV+Hb43PgK+bj4Lvj49xn40veW98P3LfgA+XH5yPlj+nv6p/qX+sL6Vvug+wr8kPxy/XL+qP/OAPsB+QKQA10E/wTVBaUGaAckCLoIVQkdCt4KXQvGC/8L5wv2CnQJJQibBwYHAAaWBJEDOQOHAo0BHgDj/pH95/tX+k35//jo+Mn4svjh+PL4z/hs+EX4Lfjn95/3h/cP+Lb4a/nj+T/6gfqN+qL6zPo4+737OvzE/H39b/6M/5EAmwFpAhoDvQNbBDgF+QXSBoMHDAiCCBIJ2gl+CsoK/Qo1C/AK0wlDCEYH/wZ9Bi4F4wNUAy8DnwJfASMA4v5c/a/7YvrM+ZP5Zflz+ZX5lvlk+e/4s/hp+Bz42fe/9wr4jvhF+c/5J/pI+lL6b/qR+tz6QvvI+238IP34/er+8P/gAI0BFwK3AokDiQRJBQIGqQYyB64HBAi+CGcJ4QkcClkKfAoBCtkIogchB4YGuAWRBM8DtQNTA3UCGwHI/6P+W/37+wH7jvp2+lH6H/r7+ev5qfkz+dX4afhD+DL4Z/jd+Er5ufkP+kb6YPpb+mz6xPo1+7n7Qvzz/Kr9bP4V/87/jQAzAfcBsQKFAzoE4ASFBTMGqgYMB40HNwjNCAMJMwl3CacJFQngB8sGUQYLBlkFdgTAA2sD8gIcAg0B1f+l/nL9dfzN+0L7Dvv7+vL62/qA+ij6x/lv+S35Gfkf+Tj5d/nT+VT6jPqH+ob6mvrT+gT7RvvV+4z8Kv2+/VH+C//H/08A6gB/ATQC/QKuA2wEHAWwBTcGjwYJB5YHAAg/CFIIiwiHCB4IOQdtBg0GoAX+BPsDZAMaA7MC8QHeANf/yv62/d38YvwQ/M77jPuT+2/7I/vc+qL6lfo9+un57vlr+uj6HPst+0/7ivt9+1L7SPuT+//7Nvxw/Ob8jP0u/p/+9/5a/8n/NwDAAGIBEAK4AisDrgM3BN4EewXqBVgGuwY8B48HgAcSB5AGPAbeBV8F1wSYBH8EPwSpA/MCPwJXAVMAe//h/nD+9f2o/Zn9dP0U/Yv8JPzU+2377vqp+sr69voh+yj7M/tQ+0P7NfsW+wP7Hfto+9T7Kfx2/N38W/26/fH9Pv7X/n//BQBmAPoAvAFZAtUCPgPmA6EELgWVBQIGhAbqBgQHrwZLBgIG4gW3BWAFDAXBBHoEIQSQA+MCIgJVAZQA2/9V//j+uv5z/g/+qP1B/fL8l/w1/Mr7ePth+1/7Xftc+1T7Tvs9+yP7GPsi+1L7h/uu+8j7//tj/ND8L/13/eH9ZP7t/l3/2f96ABoBlwH4AWcCFAPMA1YE1QRQBcwFMwZbBkoGAQazBYoFYgUnBd0EmgRpBCUEsAMbA3oCtwH4AEAAq/88/9X+hv5E/vj9kP0b/cT8gfwi/K77XvtS+2v7cftl+2v7i/uR+3/7gfuv++37D/wp/FX8ovwW/Y79/v1j/r7+J/+g/x4ApAAYAYMB9wFvAgIDlAMgBJ4ECQV1BbIFwQWtBZUFbwUzBfcEygSnBHcEKATKA1cDwwIlAnIBzAAvAKn/Mv+//m/+E/7S/Yn9J/3H/Hn8SPwR/Nj7rfuk+6v7r/u7+937B/wQ/B38R/x3/KL8u/zm/Df9lP3f/Sb+if4B/3D/tf8OAHgA8QBSAZsBGwKuAkYDvQMOBHIE1wQjBTcFHQX5BN4EyAS2BJAEYQQuBOADfgMTA58CGAJ5AdMAOwCq/xz/jv4Z/s/9iv0y/dP8kfxi/ET8GPzs+9r70/vp+wH8G/w8/Gr8ofzS/AX9QP2E/cL98v0r/n3+5P5U/7L/GAB8ANIAJAFpAbgB/gE2AoEC5AI6A4YDzQMGBEAEUwROBDsEGwTpA5oDWwMyA/kCpQJWAh8C2wFqAdwAZgAKAI//+/6G/kX+If7Z/YD9R/01/SP95fyn/JP8m/yh/Ir8dfyF/K/83vwA/SP9Wf2T/bj91v0D/k/+nf7W/hr/c//q/04AoAD+AFgBqQHfAQACNQKCAr4C6wIiA2ADnQOtA6YDlQN1AzwD6gKhAmoCNQLkAZYBVgEnAfAAhQAkAMz/fv8p/83+hv5g/k/+Kf4G/u396P3p/cr9qP2R/ZH9mv2E/W39gv2p/cX9zv3x/Tj+aP52/n/+rP7y/h7/QP+L/wMAeQDGAP0AWgHDAQkCIwI5AnYCsQLUAugCCwNDA2QDUwMaA+oCswJnAgUCrQFtAS0B1gB/ADUA9P+o/1T/Df/P/qX+dP5I/jT+G/4D/uv93v3Y/eD97f35/QL+Af4I/hP+Fv4N/h3+S/5r/ob+rv7x/jD/R/9K/2P/mf/J//T/LgCOAOcAKAFsAbcBEwI9AkMCVgJ5Ap4CqQKtAsEC2wLOApYCTgIKAscBawEVAcQAeQA1ANn/e/82/wD/z/6O/lX+Rv5S/lX+Sf44/kP+W/5Y/l3+e/6p/tX+4P74/hv/Ov86/zT/QP9O/2X/df+a/8//9f8LABEADQARACIAPwBjAHYAmgDKAPUAGQEsAUABVAFcAV8BdAGCAY0BmQGbAZsBfAFRASUBAgHaAKAAXgArABIA7v/F/53/e/9p/0T/Iv8Q/wb/Cv8G//f+6v7g/uj+8P70/gT/Ev8g/yX/Fv8K/wf/Dv8b/yb/Nf9L/2D/dP+U/7P/0f/g/+7/EwA7AGUAjADAAAABKQFGAWABdAGRAZ0BiQF+AYgBjAGFAWgBSwEtAf8AxwCKAFYAHwDg/6H/fP9c/0H/Mf8d/x3/Dv///vr+AP8A//7+D/8l/0z/ZP+D/6//wv/F/7b/t//L/8P/ov+X/6P/vf/D/8L/xv/E/8D/rv+j/6r/tf/G/9n/8f8XAD4AXACAAKMAygDrAP0ADgEaASoBOgE+AUIBRAFBASsBEwHzAM4AowBvAEoAJwD3/9b/wf+4/7D/mf+O/4L/gP9+/2X/Yf9n/27/f/+R/6b/vP/G/8j/wP+r/5P/gf99/3z/cP97/4v/kP+K/3b/af9j/2H/Wv9X/2H/fv+X/6b/wP/a/wAAGQAsAFIAfAChALEAuADLAOAA6QDrAPYABAEIAfsA6ADQALcAmQBmADYAGgALAAMA9v/p/+T/2//L/7H/mf+Z/6b/rv/B/+H//f8ZACEAKQAhAA4A///o/9j/zv/N/9H/0f/K/8D/tP+h/4j/dP9s/2v/bf9o/3X/gf+F/4v/kv+m/7z/yf/e//z/FQAoADUAPwBRAFwAagByAH4AkQCYAJgAmACXAJQAiAB7AHUAcQBxAHMAeQB9AH0AgAB/AHgAbQBrAG8AcQBsAGoAbwB9AHUAXABLAEMANQAbAAQA+f/r/9b/uv+g/4L/aP9L/zH/Hv8a/xT/Ff8Z/xT/E/8Q/wj/DP8a/x//Nf9h/43/tP/O/+r/AgAPAB4AJAAxAEUAUwBbAGcAfQCKAJAAgwCAAIAAdgBpAGkAbwByAHsAggCPAJYAkACNAIsAhQB4AG4AbQByAGkAXwBaAFAAPgAsABYAAADz/+P/y/+2/6L/lP+H/3j/b/9s/2j/Y/9a/0//TP9E/zf/MP8y/0n/Yv98/5z/tv/I/9X/2v/a/97/6P/5/wwAIAA8AGQAfgCFAIkAiQCFAHgAbgBoAHEAgwCKAI8AkwCZAIwAeQBwAGgAZABcAGQAbwB4AG4AZwBiAFMASgA3ACcAIwApACEACgAAAPr/8P/U/8H/uP+u/5r/e/9j/07/O/8m/xj/Hv8t/0H/W/9y/4P/jf+J/43/jf+P/57/tv/T//v/IwA9AE4AUQBOAEkAQwBCAEQAWQBrAHgAgQCHAHkAagBlAFQAUABXAF0AYQBaAE4ARQAxABsADwAFAA8AGwAaAB0AHgAQAAoA9//2/wgACgAZACsAMAAtABgA8f/Z/73/nP+a/6L/wP/d/+7/9f/y/9f/qP+G/2z/XP9q/4n/qv/Q/+v/+v/y/9z/1P/Q/9L/6f8NADcAXABpAGwAagBfAFMATwBLAF4AcgB4AHkAVwA/ACAAAAD2/+7/8f8LAAYA6f/z/7j/if91/2z/kP+w/+X/LABDADMARgAEAOX/7v/A/wAASABqAKsA0QCrAH4ARgDN/6X/kf92/5f/wf8gAC0A/P8dAPz/Zf9E/zr/8v47/2f/n/81AGsAfACyAI8AUQBCAAgA+v8FACsAagCmANIA6wD5AMYAfgAbAMf/pf9e/1L/nf+b/6b/DACu/3X/uv8Y/wP/Vf8+/5j/5P8PAGwANwAmAG8A5//m/zkAQgCVAJUAuQAwAaAAYACcAND/zv/N/4b/8v85AGoAiQCeAEgA4v+L/zL/0v4l/4//iv9wAMYAtwDqALIAcgAvAEEABgDY/00ALgCf/+3/HACp/zEAXgCTAAABdQA7AKn/rf6R/WH9of2X/eP+RADNAGIBnAHAAOX/AP/i/bz9SP7t/jUAhAGjAh4DLgLTAc4ANP+o/p3+Ef8dAD0B0gGVAsIBlgDP/2b+4f3k/Xz+qv/xAI8B3AHKAdQApP/f/tX+mf4R/5EAPQGKATsCdAFlAMj/s/5U/lD+IP8dAM4AdAFoAacAr/+i/oL9dv2G/QX+EP+1/z8AmgBhALr/of81/9D+UP+N/8X/pwDaAAwBCwGnAAYBZwBLAKwAXACSAH0ANQA1APz/5//1//z/XgCJAHwAWAB9AAMAuf8RANL/AACdACMB8wA7Ae0AYAD0/4L/d/9V/zUAt/81AAcBuP85/9P/IP96/rv/OP9KADYA4/7c/5f+Ff4D/p/+Y/9K/1YAdwDlAGwAcQBVAHH/NgBi/9D+iP9mAJT/bAAWAjAA6AAnAD3/1f+g/jz/lv9m/1kAJwBoAO4BpQDaAJQBOQD3/6D/0v9TAPgAEgHlAeoCkQHHAHIBp/9f/vr/ev4U/18AV//MAMX/HQCy/1j+I////jT+iv6xAEn+Q/8tAdz+ygGc/6n+ugJg/R//6QGn/uYB+AG+AIoBKQB7/sz/Sv6U/igBcf9HAK8ADgBG/rb/rf4y/g0CBP4CAJgBLv5a/zn/uv7K/2cA8v+AAvwB4gEeAvL+/ADh/Rf9nwAAABwAWwJRBNn+4QHYAFj87QCW/FT+DgFz/lIAQQKzASkBWwMnAMv+jACB/DT8b/6Z/0AAxQGtBM4BZgKJAWf+If9P/uP9P/7nAAj/uP+t/xL/hgBz/8oB+gABA9f/ff8vAq/87f7w/0X/LQHc/1sAUAG+/Xr/bABx/Yb/HAIZ/5//9wI7/eH/AAEk/N7/iADO/igAZwAwAJIBYv7m/sT/bf4VAZH+SAAaBAAArgEUA8T+zf/FABr/uf5xAS0B8P8RA5UAo/6nAQoA4v1YAG0BLgFe/ywBVgFJ/uEA/P4K/z4ARP+2/5r/lQAu/m7+ZQDl/X//kALJ/ZAAJgF5/BABEP+l/oMB/P+NALIAc/91AHv/yv0CAGr/3/3E/noBe/+o/g8DLP9s/y4Ctv05/WMAmAEf/fEA4AGo/mICxPx+AbUBg//nA9T/vwMMAm3/2ACRAPIAWQA9AUkC2AFSAKMCEwHB/CUAPgFd/C3/RQAZ/h0Cmv+N/fICD/+o+gwDLv63+b0CZ/5m/LsAagC5/Zb/SQJn/eQAWQIWANT+gP5NAtb8cf7BAUn/TQJsAjgA1gHF/yv+hQBB//L9iwE/AS//ngEG/z8BkQAR/u8CAwKR/7b/RgGl/fj/zAH7+1wBuwNY/qD+OQI5AQf+3f/NAT0AfQBWASkAkP+E/23/3/6o/wAAagBxAWz+of9GA6P9j/6yAVj/h/+m/wYAgv9wAOD/WAC1/zn/FwFh/rD8cP/f/zv+hADo/60BegCx/iQCUfzp/5oCZvrlAmcC4/ynA2j+EQDGAKD/agMfAQsCxAFvAl8CWv+E/yMD1/7M/7QDl/oIBGgCz/qvAtr+uQCM/uP76wDu/fL9A/7P/pMBR/3u/qgBfv7f/mQBOv/B/bkAyP6d/8cA3/8ZAtoAngBgADQAaf9i/Qj/fwDD//3/5AAfACoAd/4w/Vf+0P5Y/m3/igEyAPcA4wHDADkAJAA7AWwAYQAwApIBQQPEAwcArQJGAZn+wwER/93/YwIxADEBsQFKAJL+Ov3e/4D9IvyoAisBAP5BAa4Bm/5g/FP/SQHM/Zj+ZwJIAQT/hAA7AJX/jQBYAFwA9P8wAFD/8f1S/s3/iP/S/p//M/8nAPP+Nf04/y8A1ADt/7j/SQFC/qv+BP55/WcC6f7jAWYE/wBGA2/+dP7A/0P86f8IAbwA1wMHAwgBYAJc/2X+9v8N/df/zgJDAzwCkgGiA93/Qf7t/qz9HAKRACYAoQQtAK8BPQDs/Dr/sf30AJ/+Uf/UAkb9MQAHAPf8gP93/PX9PP+X/ksBdv+NACMA2v7V/wn99f53//f8/f9JAOsAagFu/pf+lP1m/pX+ZP09ABIB8v/y/zoBLwH0/zcAUgDd/2UBUAHbATsDwwF+A48DmAGyAdsBKQIKAWsC+wO+ApsCxgGVAJQAtv/f/j0BVwDk/mEAw/67/tD8NPxa/lD9QP+C/zn/9/+R/WH+qf0h/FP+oP2j/tT/mP+CAEn/ff73/Br9O/6U/O/9ZQBFAAj/OP/R/6j9+Pyr/vP+nv+XAU4DGgK+AZQC0/+PAIoBtALSBLgCMgURB3oEZAJBAqUECQN9ARwEfwOjAhQDBgIzAfgAlQI5/5X8BwBy/oD9eP/6/fz+nv/G/Mj9r/yx+9j8g/vz/An+pP5E/kD9yv16/Iz7ePun/Dr+5/23/Y/+b/7K/CT8V/zb/E39b/0e/y8BGQJ/ANr/6gDv/9oAWwFHAVAEZAaqBW0FiQc5BogEpAS4A8QEEgXzBV8GlwZ0CJQFpAOsBL8BP/9hAD8ANwDbAasAIgCR/k79Xv1a+aP6Z/wR/G/8U/w7/1v8UPrd+zP59vkz+xj6ovuH/MX8Lfxc+iX7H/qs+Jz5rPpT/Bv9cP31/en9SP3V/Jn8bf5IAAUBgwRLBbUFCAcUBZwEcQWwBzYJ8AkVDAwLiwuFDEsJrQdlCA4KMAjqBDMHBwjbBKECpgFiAPz/DQD6/Wb8VP0Q/VP6sfj4+O/4C/k4+XT4JvnM+Rf5ZvjV98z33vc2+Jv3DvjT+Vj6uPl4+HD4mviY9/L3o/m4+k78W/1U/Sn+xv7r/nr/DQFEA0wFlQddCWgKTQqoCYIKVgvPDAgOvw0wD9oRPhC7DGEN4wu0BzYGZgdXCIQFBASPA/kABQBx/dr5YfrY+Vr4gfiS+F/4wvfT9nD16fUc9+X2TvfS9+/3evhj9/X1GfcO98n2uvfj99f4APkd+Hj4Ifn2+ND4F/oA+6j7Xv2T/kYA9wIWBPYDswQ3B24JBQpdC7MNLBAkEe4PLw+2EIsSzREcEKQO+Q7sD/ILeQbbBi8IcATfAY0BRwCt/yb9u/i391v4Lfc79r31e/bE9/H1kvRn9bL1hfYh9lL2xPfn90f4tvdn96D4VfiZ9nv3ifha98n2p/bs9zD5KPll+cT6SPv2+n38Av+MAf0CWgNhBVQIvAmKCp8L6gzWDT4OPA9fEqYVShX7EwMRCQ+yD18NowxODIIJEAjlBS8EZAPjACb+Gfuj+D/4Tfjf+Gn4sfc89mX05vX19hf2N/b39en1afeb9/33RfgO95/1q/Rw9jb4TPjo9gL29vYz9nv1C/fb95/4W/kv+hD9a/4Y/yAAGgFfA0kFYwhuC9UMJg0yDfAOwhDsEWITJxWSFoAUQBDGDcgNlg+mDTMJtAetB+gG6QNxAXL/gvxn+bv2lfds+jf6nPeM9Tn1+/UJ9hn2A/b09Sb2Ivac9pX34fc19iz1j/XA9kz4/vfM9vv1a/QF9IP1VPcJ+eH5XfrG+8v9gf6t/u//3gFaA+sEIgkUDXgNFAwuDNoOcxESEkITjBXNFfETkRAkDiEPfQ/JC9UHxwcVCdYHqAPr/1H+CPyY+Kb2A/iA+in6O/b787z1Bvc19hP1afUD9jD2IvaF9qP3QPd39cf0/vV+95/3rPaC9RL16/Ua9r32AvhX+HD5p/r6/Ij/EgCt/9f/VwKFBSkH0wm1DKANWQ05DXQPnxGmE50V8xTiEwMR3A0rDo0Opg2UCtEHbggcB3AELQEW//L++Pqw9xf4//nJ+9/49/T69JP1hPbd9Sv17PaP9mP1jvTu9e33kfbD9NHzOvUv+Df4sfbx9AX0jPQU9Tr3Z/p7/Lv82/uM/OP+xwD6AQ4DtgRtB4IKzwx0DWgNmA2rDmsQEBIeFMAVOBUiEoMNIwuSDNoNIQyjCJYGqwZhBTcCKP/v/EL7Fvmm9xL5XPvy+hP3lPM19Cj2DPf/9lj2OPb89Zv1p/X99aj1ufRn9On1vffQ94n24vSs87Pze/Ug+MX6LPzJ/PT8+v16ABMBIAFjAlgFYwmkC5gMdQ3LDTMO8g0TD4MSgxRBFpQUyw9ZDZwL1AuVDLQKigg7B9cGTwUqAaz9ivs2+jP5hviw+eD7s/qB9h30qPSC9zj4pvYp9lr23Pa69Q30GPTs9ID1n/W59TX3iPeO9ZDz1/LL9DX3uvi/+pr8Hf7Q/hf+d/4bAOsBpwQTB7sJhQzlDZcNjQwHDeMONRE1EyUVRxZ2FOkPGwvyCZILsQtCCQwHTgf3BwUFrf9P+/L53Pkk+WD5V/p8+/75gfbg9Mn1nPeL9yj28PXx9oP3XfYq9OHzG/RO9D31+vUx9wz3+/Sq89jzVfWF91P5PfsX/dz+7v8BAJ8AgAEIA0YFEAhFC7UNcA6yDTYNLQ7DDzYR+BIQFRUWRhOXDbUJwQlfC3EKzgavBdMGcwYOA/v8yfnF+A34Dvi4+Hr7D/wE+SL2+/Sm9r33m/ZX9mr26vcU+KX1n/Rn83LzfPSg9EH24fYa9hX1evNF9NL19fff+rn8iP61/1sANQHOAZMCVwT8BnEKbQ3FDoAOiw1TDhkQhxF2EpYTbBX3FGgQIwulCLUJdgoECP8F8wVFBsoDC/7G+UH4J/iv94z3+fk4/Aj7xfeG9Sb2/Pf09xL3Affm9zb47/Zx9Uv0B/Tv85309/WK9iz2//Qe9GL0c/V09/n5MvxX/lr/MABCAcsBhQI/A8kFjgnNDNcOxQ4lDnIOsg9yERUS8xJMFUEVoRH9C44IjwmNCq4IXQU4BPYEkwPp/vT5pfeZ97z3jvfM+Gf7Svtk+NX18/Vx+DD5Nvh797r3V/gL+AH34fX/9In0PfUL9pf24PVB9KXz1fNT9Wb3jvn7+1H9HP4O/yUAsQGAAtgC4gTBCKgMPg9rDxoORA5mD1IRTxL2Eq4VBRYVE38NuggoCUQKBAkABusDyATHAxP/Y/pM90T3E/c39gv4kfrf+6D5O/Yl9rj33fi2+Br4vPgc+cT4Afi19vH1j/Rs9G/1I/aB9hz15/O181D0G/Yr+Lf6y/yH/Y7+3P9mAWcCdAIYBBcHDQtaDo8PiA8UD+YPBxGzEdgS9xTwFiMVeQ/pCYEIDgp+CWUGoAOJAwcEwQAr+x/3gfbR9hz23/aU+bn7gPry9nz1OPey+Wr6wvlw+U/5JfmL+LD37Pb99V31fvVO9kv2OPUZ9LHzK/Rr9Yj3HfrJ+4P8Dv05/l8AAQIhAwwEvgUKCXIMCQ/HD2IPtQ8LETgSwhLqE/YVFxbSEVsL+QfgCPMJvAfcA8UCVAOqAc78kPc19ln2K/Zt9hn4a/uv+6P4EfYW9pj4jvrJ+rL6Ofqx+Tz5BfjM9yn3KPYx9g32ovZ79s30rfM680H0b/az+DH7TPzL/L/94/60AAUCWQP9BHgHYAtxDrQPWA/6DosQFBLuEgwUcxVNFk0TOw0UCWcIiwlcCHwEAgNdA3sCm/6x+Oz1kPXb9aD20/d3+m37QvnB9jP2cPjW+oj7AvsG+hz6Bfp6+Y/4Ovft9uL2TvcX9+P1yPQm86XyyPMU9gv5SvqJ+tP6Cvw4/qD/CQGcAmEErQawCTMNXA9yD54OiA8YEgMUkxS3FI4VBhVUENIKAgilCF8JIQZCA6wClAKKAKD6c/Y79YH1e/af9uH4MPu++oz4GvYC9/35G/xr/Az7XPpC+lX60fmk+Oj3OPec93T3mPaD9dHz8fKb8vDzCveB+br6w/rA+jr83/3E/8YBGANLBQUIvwvzDpMPHg/rDm0Q/xJMFCcVfxZOFsUSbQzgB8MHHglGCAEF6QKIAvIAo/zl92v1HfXs9b/2dvi5+vH6/fhe9jj2Fvn0+y/9evxG+636Qvrk+ZT5Cfn5+Bj5hfiT96z12vO48nHy+vNP9n74pPmY+Qv6Pvue/DL+zP+GAcEDqQb1CSgNoQ5WDjcOSw9REv8TghTPFZcWoBXiD4kJiAeTCHcJeQYnA9ACwwJCAG36gfWt9ET1OfZF91j5z/vd+tX38vV49xT7z/xz/Mv7kfu4+z/7Kfqs+Q/5yfiR+B34c/fS9fDzWPKD8k/0dvZU+Av5i/lY+pr7g/0g/8AAaQI9BK0HWwsQDuUOcQ5TD3gRVBMiFN0UjxbcFsoSSwwqCBoIWwnUB28E6wKxAj4Bhfx398T1mvUG9kL2tfdh+u76Pvnu9uH2mPkE/D39//wM/IX71vpx+iv6Kvo3+qv56/hm96v1QPQu8wfzzvNl9Tf3D/iX+E/5pfp2/NT9W/9RAZQD+QXqCMsL5g3NDiMP/BD9EgYUPhTwFJAWwxWtEM4K5gdqCKQIhwWxAk4ChwIWADL69vUj9cn1CfYm9kf43/oA+7v4A/dW+DL76/wV/ab8svxP/HL7nPoM+lj6L/r1+Qb5Mvc49QvzMfLE8kT0NvZx9xP4q/gn+WP6+/u9/R4AEwJxBPkG3gnhDDgOuA7sDw0SyRMTFEQUPhafF5oUrw0OCNEH6Qn8CMEEjQFhAYQBEv62+OX1oPXo9Xv1HfYZ+RD7GPqq9w/3wPnL/M/9Ef0s/N/72ftr+0H7dvtb+w/7pPnL97T1zPML8xbzBvRL9VP2OffX91T4Tvm0+jv8Ff4wAHACwwRBB0gK7QwfDioPrBD+EjUUtRNXFFUWWhdUEx8MCQi2CDYKbQeuAhoBiAL0Abb8zvbu9Pr1cPbn9av2bvnc+pP5oPck+BX7Sv1N/Un8HPxz/Fz8LvtJ+qT6efuY++35WffN9CbzdvKT8ubzwfVg9+T3xPc9+Ev56vqJ/Fn+BAFOA4YFEgj7CqkNtw6aD1YRbhNmFBgUDRXwFm0WIxGMCkQIxQkuCkkG3AHxALsBUgA++/H2sPXh9aX1Y/U797D5XPoX+fL3SPnD+yv9GP0t/Lf7ofvZ+9L7X/tc+2r7Evtf+ab2W/Qi89jyIfP189z1e/cP+Cv4h/jk+Rb7YfxR/ikBFgRrBjUJKwyBDhEPlw+HEdATkRRJFH0VaRf/FZkPIAmJB38JNwnbBFoBYwFFAlT/NflG9Rj1IvYH9uv10Pcd+lD6s/jB96z5lfwP/lH9t/t0+8D7K/yb+zD7vPsz/K/77vjR9cLz+/L18kHzo/SQ9rH3tPep94z4Ffot+6/8Ef/6AZsEvga+CR0N8A7MDi0PeREpFMcUYxTgFbEXoRVrDsUHygbyCEUIDARvAYICZQMR/8D3aPO58x72zPYX96/4ePp9+mT4efdC+ar8o/4g/p78sPvx+6r7I/vO+p37yPwf/Lf5PPae80Ty5/EO81H1efc++OT3zfea+Lz5yvp3/GH/fQIKBRsHPAqeDQQPAQ9cD/IRJRQqFDYUYRaSGAoVpwwZBp0GtQk6CP0CVQCzAs8Dff4o9m/yavQd99P2WPaI+C37Kvs8+PT2Svk2/dn+rf30+1L71fuM+/H6+fpa/Hr9L/zb+Ev1efMA8/zyX/Pi9Bb3gviX+GX4tfjd+Rz75Py5/6oCcgXeByILSA5HD90OMw99EZ4TzBNAFLAWhRiJFK0LiwX5BQYJcwfbAg0BKwOWA5T95PVl8iH0W/aM9hD3Gfkh+4n6Ffhv97j56/wk/jf9+ftQ+0P72vql+m/79vyD/bj7T/ht9azz2vKp8lXz4vUt+Eb5w/gt+N34A/rA+6H9bAAXA68FpAghDAoPjA8LD38PdRHDEiYT1BRFGCoZMRP4CZsEAwZHCA0GMwKiAQwELQOY/ET1ivLe81D1ffUV9zD6A/yq+qz3SvdU+V38lv0L/XX8rvt1+8j63/rU++P8Dv0u+4D47fU69Bzzv/KW8+H1FfgK+bz4j/h1+V760vvR/dMA6QNABvQIIQz/DooPCA+UD5cRMhOyE1cV5BfjF7wRxgh9BGQGyAhZBhoCVwFbAzsCxPt19WbzvPQn9Zz0d/YK+m/8qfr/9zD4Efu1/XT9Kvwx+277WPv8+mz7j/zL/c/8T/pz92L1PPQE85Hyz/Mr9iD4Svg0+A35X/pI+1n7z/zW/+UDfAd0Cr4NRQ+BD6MOWA93EW4SaxOOFVUZTxlQEpYIsQNiBVIH/gSYASECwgRVA+H7nPTr8VHzP/TJ9K73ZfsQ/Vb6b/fH99r68/yA/K37o/tn/P37JPsU+zP8c/2d/Pb5DffU9MHzCfMI84P0k/Yz+CP48Peu+PD5Nfva+wr+UwHCBJ8H3Ak6DXgPpA9MD7wPPRIhE5wT6xWkGGgYghByB9MDYQZUCK0EJwFVAeMD0gFs+l30rfIO9Fr0k/TA93j7vvxw+s33lfjj+lX81vtJ+0787/xy/Er7+vpL/Lf8gfvW+ET2PfUK9JXzV/OU9J32h/f+9xf4b/kU+yT8Df2d/l8BwQTcBycLTA4UEDoQcA84EJ0R1BI6FMsWuRliFyEP9gXNAuEFmAf6BJ4BhgEYA7UACPps9D/yPfPB8+z0afiA+4D81/kS+Nj4nPrF+2r7nfsv/Gj83/uA+yT8O/3L/MT6xPeM9bn0BfQz9Fz0mvUl9+33gviA+NT5MftD/IH9Tf9hApEFqwgVDEoPuxC9Dz0OBQ96EcET1BUYGIoZvhW1DGkEUgLGBf4GnwQBAmQCdQP6/gj4bvLH8Uvz8vMm9mr5Df0C/QD6sPcD+Cz6SfuP+4L7O/zI/L38kfxb/Iz8h/sN+Yj2jvSK9AL1SfW89T32e/eS91n3w/eU+ff7aP3p/ooAuQO+BqcJogySDogP1A43DzYRThOMFMQV+ReMGKYTTQp1AxsDkwaCB28EEALCAf8ASv0g95jzbPJm8pzzJPbO+gf96vsI+Vf3ovg1+jv7V/uQ+y/8Ufxk/Iz8u/yy/OX6UPjm9XX0v/Sk9NX0HPXe9Ub3gPc/+DP53Pp1/O/8h/4AAZsEGAj8CjMOnw+3D8MORQ8MEusTQxWJFikZUhl7EpQIAwITA80GSAZZA+gB8gLkAbb75/Rw8bjxmvKi8/D2c/tU/SL73/dQ90X58voQ+5H6YfvC/H799PxE/D/81fuX+v331fW29F/0S/Qf9Df19fYr+D742vfH+Jj6T/yS/VT/iALZBa0IHgvpDRUQ5A83D6cPnhJIFVwW6xdGGS4YShADBlcB0QMqCPsGjwKJAJcBmgD0+l/0TvFu8dPxPPPb9gP8O/5P+9H3Gfcx+fP6OfoB+k37g/3Q/pT9bvyo+zr7+/lV91f1R/Q79D/0kPTS9Ur3XPga+Nf3w/hy+jX8j/3o/4oDXwdfCsoM9g7VD60Pyg4DEC4TqBUGGFMZ8hmVFv0MagT0ACQE8wf3BS4DRwEJAa3+QPiI8wvxSvEY8rzzT/g1/C/9VPqA9xf4q/kA+1L6APqH+yj9j/6o/Yv8ovtN+iL5zvYu9UL0z/Mb9If08vW793b4d/gl+C35GvvI/MH+FQEEBZ8IlAteDvIPPRHaD0EPXREqFMkXMhhmGaIZQxQEDHcCRwFmBfkG/wTt/9L/nACj/bv3cPFa8Kfw2PEY9A74fPxx/Nr5Yvde+Ar7SvsR+hz5pvqW/YD+8/1S/O36Vvpi+JP2mvSU87Pz6PNd9Rn3SfiI+AH4M/gi+hv83/1y/+QBrgU0Cc8MsQ90EbMQ2Q7hDtkRbRZmGFwZHRqGGMASDghmAQQCrQXuBo0C0P/z/8z/3PsN9SDxQvAn8THydfUN+sf83PsC+A33z/hJ+wv8WPo0+lj7bP2e/mb93/tR+iL53PcZ9gH1JfTv89DzqPRg9rT3u/hT+H35Uvvh/Jv+l/8hA68GJgplDXIPlBFNEbYQvhG+E0AWzRbtFy0alRjbESMHYwFEA1sG1AUlAQH/wv9U/6P61vRb8a/wmfBN8ZX1j/pe/Un7M/j196f5Ifsx+iL5pvlt+0/9n/3i/M37JfrP+Jn2FvUb9I7zC/Q29Lf1Ufes+BP5xvjg+eX7//2X/w8B/gNRB+EKKQ5zEGASLxEaEdsRkBSKF24Xfhk1GaAW3w7sBDACNAPVBUMENgDv//P+/vwa+Cvz8/E88FTw4fFh9qH7APze+dn3f/je+un6QPoA+qP6t/yk/Ov8Zvxx+7/6ZPgY9zL1//M/8/zyGPTG9ZT35/hh+e/58/pE/Cn+hf/VAWIEsgfgC1sPqBLtEsoRahEqEvQUNBaMFzgapRp3FjIM/QJyAfMDAAYcA37/Pf89/vv7SPZQ8rjwYe868Lfycvgu/Lr7CPlz9wv57vr5+vX5m/n7+u78ff2f/WL8ZPvi+S74/fZp9YD0a/Mw8yT07/WG94D4s/ix+X/7af23/mz/owGzBLoIBAyDD2kSUROwEqUR/BLlFZ8X1hhFGr4ZGRX7CXYBUgArBPcGRAOq/wD+2P2I+kb1ivGT8OjwQPCm8i72fvpb+135+/il+eX7//ue+hH6hvo8/F/9bP0Y/U78H/v9+G/2nPSQ8z3z8PLJ83T1cPf8+Cj5l/o//Dr++/4U/9oAGATACIINABI/FPETNBGIEN8S2BVRGDUZrRufG6UU7whf/zP/OQTmBTUD6v5T/pX9x/nJ9B/wuO9H73TwmfPA96D7rPpv+Iz3XfmF/Bz9Z/vy+dP5O/zh/Qf+W/1q+2L6IviJ9on1XfTn88fycvNJ9R73wfho+TT7OP1A/tz+6f5wAeIEPgkEDuER5hQlFJwSOxIHFLsWZxcUGXgb7hotFLgHz//W/8IDJAXfAGb+f/29/Kj4ePKF77Tu6O8/8fDzEvjG+ob6ePiq98P5ePw2/Qr8YPo8+7/82/1z/ZT8ofym++D5Kvcm9e7zrfIC8sTyRvXR9wL5qfny+gr9jf7P/m7/MQEQBYQJdg4MEwsVoBSDEpcSwhSuFiAYlxnxGz4alBG4BXv/GwEnBbUE+v91/Z38Tfz6913yi+8v7m/v9fAm9FT4HPqY+Y73LPdK+pf91/73/O76S/tt/Ab9HPwv/OX8sfzE+kP3A/U485jxE/Ez8nj1Xfja+aX6q/tT/W7+Bv9W/5EB0gSYCRAPdhN3FmgVohP8EgoUnBYhGIQaghypGXERpwVl/0IACwMqBHsAHP4//D/67fbX8WjvBe7d7jHx+PPe96P5jPlT+H33W/pG/e3+yv1h+2z72fve/Mn8s/wX/Xr8o/rP9yr1BPNx8SPxlvI39QD4efnC+uf7jv0h/zj/DgAtAbMEHgqXD+8UTxY3FZYTGRNhFRMXTRkBHOQceBmqDskDzP7LAMEEewNNAL78jvve+dj15/FI7rztR+5o8OLzSveU+R35TfdD9yv6U/4oAL/9O/tO+gD8pv0L/pP+Jv7X/PT5yvao9HfzfvIn8r3yXfQn9kX4+/q+/cL/gP/O/lP+dgBMBW0LJhIkFr0W7hQZE9sTtRWBF6AZ9RvxHCIY3A08Axb/5gCCAzgDEAD5/SH74/cP83zu9+z17C3vZPFQ9JL3N/ky+aj3qveA+lj+s//S/aH7Svvk/FL+uP6n/oH9xPuH+Rj3pfWX8yry5fHc8mX1MvdL+fD6rvxY/lP//v9vAOMB6ARTCowQuxWnFxEXDhbFFQEWRRZSGL4bIh2YFyQMEAJW/wkCuQSrAq7+P/vA+MT2//IL8Int3+zZ7a/vK/Pl9vX5d/oJ+Zf4Bvs8/nX/ff2J+6z7FP2v/tD+4P4U/if8o/nk9jz12PPO8nrylvLQ89z10fg0/JD+3v+u/3f/1v+vAbYFWAsKEqQW3xfCFoAVKxbnFmMXFxlZG18cSBZKC2ECL//kAW8CbQDq/CD6CPk59s/yvu557J/sLe5f8PLy9vUF+Vv6v/la+k78cP/Z/zn+4/xo/J79bv6K/7j/h/42/Jz5s/ei9WDzhvE28Ynyr/Qd94n5dvuX/b3+ff+Z/3z/xwFtBbkLeRLiFqgYthZJFfcUiBUXFxEatB0THTEVyAgWAer/3gI0A+P/RP1r+r34ivW78Qnv3OyV7JbthO+e8+73Bfsn+wD5ZfkB/Fn/nv9//U38efz//f7+wf8XAKD+y/uR+Pb1lPQt84Hyi/KW8xT10fan+Ev7+/1h/ygAHf/p//0BTAbLDHAS1ha0F3sWOBaCFk8XHRgNGbYbtxn3EsQIgQFJASUCkgJe/5r8Zfpj95v00/Bf7uXsk+xR7pvwQPRd+KT6Cfsz+X35SfxN/+z/9P2b/L38Ev4e/zEACgBe/kz7Ivgx9oP0UfNv8o7yVPPc9Oj23fnM/IX+c/+t/uX+9f9+AiYIdg2dE9QWsxYfF9MVRBcyF0oXnBpkG48ahhBjBvQAbgCnA1cBfP9e/E76K/hg8wrwPO1L7E/tPu698Pb0lPjP+yL6f/i8+eP8nwB//239mvxs/e3+s/97/43/6v0O+4X4bfUr9NXy1/KE8xP0GfWD9gH5oPtu/vv/6wCeAZwBXQOOBqALgxIbFlUYRxhPF/EX1BbcFyoaJhvvFxgPPAbbAt8CyQJYANb7+vng+Mf3dfXu8IntoOvO6+7tzPAH9fj5CfxF+5r5efou/v//Af/d/Fv8sP3//lr/cv95//v9//uN+N31+POK8vfyofJS8+70mPdr+5L9n/5l/w//pv9RAMYCrgipDucUKRfUFmAWiBZKF14XmRe0Gccb0hdtDxwFygBxAYEBpQBf/ez7l/rz9030au9M7KLrlOx47ojw3PQZ+qn8sfsW+RP6HP1m/8z+w/zA/Jv9C//H/4H/5v4k/Rf7kfhP9UTzUvI18+vzzPQj9jX4EvvH/Pj+3v+4AHwBlAFYBPsHvw2KFCMX2hg8F18Wgxf7FZoYtBn8GSEW7wtBBT8BJwGGAc/+kP1O+9b42vbO8lvw+Ox460Dsae3G8aD2Bfy7/br7uflI+r/8Mv7g/Ub99P1a/kH/qf8UAFL/pfyS+XT2UPQ084TzGvSk9Nb0ufUt+OL6l/3a/2AA3QDlAM4BeQaBCrIQ1RT8FeoX6hWkF4YXLBchGagYqhndEmMKKARnAfwCawD8/Yv75/kW+bb1e/It79TrLOtT6zPu8/LY97X8ffxl+m35sPsv/87/Lf4c/Xv9a/5DALEAdAFe/9H7HPkh9pz1MPSU8+zyTfKw8zv2k/pG/VP/ov9+/tD+uf6mAmsHVwz4EuAU5BYuFvIV9Bc5F3wXZxjhGgwaMBOZCBkCMAA3/zb/Gv1V/VD89/ig9XbwPe2Q6/vqCu1b77Dz3fkS/TH9Mfpa+Qr8R/69/nn9nv2K/gAAVQChADcARf5a/Pj4u/Zj9PLyS/NN8zr0afUN9/j5t/tw/Q3/dv8AAUEBRAPuBr4L0xGCFSIXZRd8F0wXCxebFjsZMhs+GJEQigaKAs0BcwHK/9L8e/vb+d/3c/Wd8YftOuqq6TjsOPDM9Xb64vyN+2L5g/oj/Zf/w/5s/ZP9QP4IAIkA5QARAHH9h/v9+FH32fTF8iLywfFr82X18fjB+zn9l/6g/r//EQCXAM0DtQebDioU/RaJGL4WLBeMFpkVcxcpGRAb9hb2DbUGnwHQAJb/Mv3g/A77+/kV9+DzAPFf7BPqd+l87G3x2/Zk+8b8lvtb+mz7x/1+/77+/f3N/bT+LwARAS4Bj/8X/bL67Pjo9tn0yPJ88bTxJvNO9pX5efty/Zz+nv+qAAgAzwHmA3cIYA8DFKQY6RcaF0IWIBXgFoIX7hpCGsIUggxMBN4BTQBS/+/9DvwB+7T4FfaN84rvOuyY6c3prO2o8nb4/Pu+/IP73Poo/ET+xv42/hj+Vv5KAEAB0gHjANP+1vxm+nr44vWm8+jx7PHv8sv03feC+XD7y/zs/TYA0P9rAGYBTAPhCZwP4xWBGJkXyBa+FWAWbBeeGYYa7RgMEd0HQgNpARMDBwF//tr8rvmg+F31PPJU79Tqqemp6vXu+/Rj+Uf8h/vQ+db5Lfx9/s3+Ff5o/eD+NQDWAUgC+AAn/7D7yfkU+Cf2pPSV8ijy9/KZ9K/3xPnc+8j9pf7e/4z/3/9cAR8FLwvIEWMW8RfFF6kWARcOFucWDxkmGkQYERCNCLcDeQIyAtj/5/09+yL5MPf09L3yr+5D64LpUupi7vLzfvpz/f77oflU+VX8xv7w/v79WP19/jIA2QF9AhcBY/4d+075R/e29Rb0vvLj8hPzqPUL+D36O/yX/A/+NP9QAOQB4AK+BkwMRBKFF34YsBjuF7kWgBYOF98ZURo6FZkNYwZ3AyADwAEyAHn8h/n99kD1jPQ/8Z/tEOpO6Tjrue/09VH7xvzc+rn51fpC/nz/B/9C/hv+uv+CAdgCwAJYAFX9Ovtl+QL4j/Vc8zzynPHk8oP1o/gK+/37t/wD/vT+3/++AKQCuQciDggVcBiZGO8XARc7Fx4XoxjkGlkashTqC4IFmAMBA2UB6P6E+8f4+PUu9Qr0l/Ca7D3pYenO64bwCfco/Mv8avoP+dL7bf8BAFn/Pf6P/oT/fAHjA74DlwBy/Mn5gvgo9xD1xfMj87HyW/O/9WX4I/pY+tL7C/7g/gAAmgAGBP8IdA+yFY4YkRi7FtsWFhejGKcZbBq4GbQSbwurBW8EcwQgATf+pPrQ90X2nfQz80HwPuzj6Qjqtux28eD2//pR/Gr6IfrK/N3+CQB2/lr+IP/g/+4BOQI5AjEAXf0x+9n4BvZ68yzyKvJm86b0XPcz+Uj6QPsQ/Hb9C/5n/ksASATuCgkS5BbqGEgXMRYKFtgWPxipGdUbpxnQEuIJXQSJA6ADHQL2/qr7Xfjl9dbzlPKQ77Lrk+kG6tntrfKh+Kj8hfwp+kP53vtv/sT+Wv0D/WX+OwCDAo8D4gKM/4v7PvmQ99H1yPPm8hjzs/MK9Yf3D/qg+4L86PxB/W79RP48AasG/QwnE5UWrhf2FpYVXxacFk8Y8RqZHAUaAhErCCgDagLSAS4Aw/39+6v54fbG9Hnxo+6F6jDpbuqv7dbzcvnT/GD7I/m1+bz8j/7f/Zz8+Pzp/gkBBgP4A+gCzP9S/A75+faj9I/zh/PB85L1xfbm+Kn51fkI+2/8Lf6y/pP/0gHMBuYMNRPCFgMXExf6FVIWZha9F1cbsxvPFxkP7Qb5AxMDCAMxAQH++vps9zH2u/S78YbttemM6Wbr0u949Qv6nfvK+Yf4G/o2/bz+Av5U/fH97f+wAUgD8gKwACj+ovt3+kX48fUj9GXzA/TN9C/24PeB+d76n/wR/tT+PP6h/hYBQwZlDVoTkBcJGMYXDhc6FgkXwBeqGrYaCxapDi4HKwWYA8sBRP9q/B/7yPjT9nfzXu9265XpV+qg7HPwzfRB+cf6Lvrz+aP7tP3N/dz8Ef0U/xgBBwMsAyoC+/+h/bb8l/oq+Dv1bvOr8x/0ofXa9vP32viw+RX86P0h/87+k/5fAQkGQg5ZFG4XFBj3Fs0X4BbkFuEYIhu3GscU3gxxB+MEsQOdAsP/H/3++d33Zvb38sXuSOtG6iHrlOyO7530Kvid+bb5v/oS/df9sf09/f/9Pf/cAEcCxgLEAcD/N/8m/vH7jfjX9EHzO/K58qP0MfYV+EH5y/oy/Xb+RP55/eX9mQF8CKEPshVxF6YW2BaKFlUX3he6GfEcBBuPFNMLwgWxBI8DwAFf//T89fpA+C31fPGL7afqg+oe68vsRfDI9H75U/rY+Xv6Rvxl/qX9zvxk/Tz/4gE2A1sDXgJoANz+Dv1V+nb3xvSN8z/zbfPG9NL2Afml+pr7fPx2/Z79Iv4x/4YChghoD50VLxifGPMX+hZxFrgW8RlWHBQaqRLgCbsF3QSPBOACjf+s/Gr5x/Zi9NXwju0t64jqLOsC7SvxYfbE+b35Zvgl+R/8W/7C/cP8GP0l/7wB2AIEA18Bxv+I/tv84fp393705/KD8tXzsvVe+JD6Jfvc+zr8aP0Y/jH+/P/WAwQL4hEhFjMYGxiUF8EWJRZHGPMa9htPGFIQfAnfBIEDrQJOAWP/pfwA+uD2bfPW7qzr5ulC6gLsi+4380L3gfml+V75gPoQ/H38vvxc/ev+BgETAqYC8AGJAHP/Fv6T/GX6h/cf9UPzyfIN9Av2tvhg+mb7Yfy0/GH9F/0I/vgAFgZuDXoTDhelFyMXZRcUFzoWtBf2GsAcqBhjD+MHzANsAyQD6wAP/vf67PgU95nzr+6z6hTpd+oW7LbuNfOB99/5Ifmr+Cn6tfzN/Wf9Av3z/VcAUQKvA0ADagHf//n9qPvX+Mv12fRe9JH0kPXC9hT5gfqC+zf7afu1+0r9qP+zAU8HHwx6EjMWMBdfGRwYgBerFngY3xzFG/IVhw2iB/MF/gRvA0UAy/0G+zj5JfYD8UDtfumF6kzrZexv8PDzsPiH+Fn33Pck+eb7dPyr/Kv9i/96AR0DjQJIAf//lP4o/mX7oPg+9p/0vfQO9Uj2Cfgj+Wr6FPvC+8r8xPxH/S3+nwEJCCsPtRStFioXsRf9F2kXlRe6GWkcjhqNE+oL+wZcBmAFTgOHACL9y/r09xz19PBM7DnquepV7JLtOPCT9Lj32/eW9gv4uvpU/Eb8Gfym/ab/DwKVAzoDhgG3/4/+QP3a+lP42fYz9tP1tPUU9mL3SPmV+gj8APza+8r7Tfyo/woEOQrcED8VuRYjF2AXQBiJF0YXAhvnGxUZEhFaCrIIigYEBYsBzf7h/Bf6K/h99C7vJevS6VPrguy27arx2/X196L2a/aX+Gz7mvzy+wD93/0KAKUBfgI4A0sBoQBE/+X8M/rB9tv1v/Xc9ZX2lPez+MP5hvpn+/H8YfyM/KH9YP9fBRALjhEsFroVjReuF+UXQRgXFxMbxRrxFeoOcwjeB4IFmQP+AR0AdP0d+Xv2TPMQ7+rqyOmL6iTsqe+i9Gb52/h+9p71uvcO+2X7f/zb/Zv/GQG/AcUCZwKpADH/E/5M/KX6F/gc9/31lfRY9Tz2RvnE+qv7k/xG/OT8U/wC/qEAEAY9DX0SZxeGF8gX7RacFekWDhhcGyQaPBScDfoIcwcLBe4B1P+K/rT8Z/rb9qrynu2d6evo8en47CbxIPb6+D34r/Yy98357/oN+2/7ov1fAN4B+AKpAj0CdwFGALX+ofuZ+Dr2ZPWF9dj11fag9w/5yvlC+wX9K/1e/Yv8Af6uAfMG+Q5fFJQWwxb4FZ8WBBYEFr8YaBrgGNISfQzcCKQGbgQyAnsAf/4f/LX4qPVL8avsY+q46eXq0+xc8Rb39/nx+Mv25/Z7+DT6dPvS/YQArAFoAhYCAgKXAZYABQDf/XH7BPlC93j25/Xt9Rv2lPfh+Dn7xvzJ/En9Tfv4+7D9EgK+CpoQwxWoFqoWXxd3FWYUyRSfGFsbQxkaEisLwgcuBbEEOwJPABf9SvgX90j0MvIF77Pr2erB6dfsd/JK96f42/ZO9qL4KPsP/BH8wfta/Xz/cwLfBHoEMgOtAI/+uvxQ+rn4APe/9db1cPZN+L/5W/rm+hv7GPzg/AP9hv09//ED3QpnEZYVMRaTFTUVxxRYFUAXEhqcGScUmA3lCREJMgfWA6QAlf75/L/6Pfgo9GXv1+t16l/r9Ox78OP0Z/eO96z28vfR+XL6RPrO+h790P+xAb0CMQNBA7ICMAEQ/238t/mV9572ovYL9z73ofei+MX5cvuZ/MX8Zvyr/C7+9AEyBwYN+BHREsUTMBSdFJ8VQBV1GNUaGhmYE6ALqgcbBlEEyQL5AMj/e/3f+Tr2OvJ+7mXrSepy6lLth/IC97H5OPh091j4Efld+u75afth/u8AxQNoBM4DTQIUALj++f1P/N367Pje9qj2HvYr96b4mfio+qX7o/x0/rr8df0l/oAACgjRDPESfBVGFTUXjBSmE3YUcRajGaYVChAIDCII2QVtAscANAEj/1T8ofgC9Cfx3e077ALsrutX703zOPdT+XT4dvnO+cL5zvn3+Q79BgC8AX8DagOQA7AChQCy/1X90PuR+bj2APZU9fT2dfhR+QL7IfsL/Nf7ePu5/E/9gwA5BEsJlQ8uE3UVEhWjE+MSyhJGFWgZEho+FjEPzgi7BXwC3gB1AIcARQC5/Iz4JPTk79HsoerJ6ljtXvHG9Yr4+vj6+EX5v/mo+UH5hPsA/zoC3gMgA+sCEAJ+AesABf8M/lv7n/ha9tH0JPZS9wP5VPqs+g38T/yo+wn74Pol/gUCqAbaDBwR8RNPEyoSDRKjERwTthZqGlcanRQxDBUGDwKwAEkAOwAtAZn/nPz89y3zwO+D7JjqWOoB7XXyqfdi+kT6vvkh+sD6lPo6+tb7jv7dAbYDMAQJBK0CyQEFAD3+V/xz+uL43/Yh9rb2nPiN+Yj5Kfry+nr8dvxv/Jj9zP6tAmgHSA3DEaMS+hLVEcsROBJSFYEZgBrIF4gPGgmGA+gAswCl//0BZAHm/yz87vX98RbtyupE6obrafAg9c74G/pp+Uj5uPkM+s76s/vO/ccATwL3AsICiwIgA9kBSgBE/qf7u/n39tb1QPYx91j5Qfqe+nP7iPtj/BT8aPsG/W3/KwUEC6EPDBN6EsYRlRB0EJoSPBUhGQ4azRWTDYQF2QExAX8BaAGTAecAU/4a+rD0hfDA7M/q/Oou7TfyL/fY+gn7N/l0+AH5I/ri+hb8ZP4eARIDOgRiBMcD2wJ+AYT/3vwZ+jj4kPbX9Wv28fdc+rr7ePy3/Ir88fsM+/j68PzwAL0FJwzBEAYT8hLTEHcQPRCfEtQW0BjCF8ERHAtRBqACPgFsANoAKgGU/9z8kfhJ9PfvBu1V66Pr/+6n88j49/kw+cn42vjC+u36qfsU/Xv+3wD3AUkDiARFBBEDvgAT/mn8D/ps9yb2mvW99076QfuX/Oz7gPsU/Nb61vve+7b9eQNnCDIPFxIMEoQRrQ6yDu8PdhMJGbIZvRULDhAHMQTpAdgAdgBVANkAkP+G/Aj5j/O57rLqSOn97OrxJ/gG+xr62/gr+MT5vPpM+gr7J/0qAD8DSQTBBHYDewFXANj+AP7S+1j5uPdh9vb2Zvhk+n77G/uP+6X8Zv4a/jb8avtV/UMD7AnyDxoTXhMoEnUQIQ/SDzUTnRa5FlkRyQu+Bx8FtgKw/3z/FQA+AFT+ZvpW9qbxqe2p60rsa+/08/r33flP+cf4D/pH+0z7lvqd+9X+/gHmA6kDFAPhAkcCkwED/8z8HfrC98j2EPYn+Ef6qvsw+xP6W/sm/YX9j/zE+9P8jwB2BfMLWBAuEk8S7xBwEB4QbRFTFLcWaxWgEB0KowWNAtX/0v9mAAwCBAE5/XH46vJS7+TsJOx+7f/wsvW1+RD7Ivqz+dz5jvqP+rr6O/1DABoD/wMhA7gCVAIOAr4Alv54/G36PfjQ9rb2zvfg+aT6SfsG/HL8Cf2i/Fv8tfzw/egBnAfXDAARURI1Es8Qyw6yDgARDRWYFl4TSg1iCIsEVgLXAAUA+wB/AOf/N/zi9lXyXu4J7ULt2e9c9EX4+vla+bL3mviZ+v77OPwD/Eb+AAEnA2IDZQLgASIC2QGQAED+fPsu+d32rvYI+EX67fu4+w/7k/pT+1b8bfwS/IX8Z//4BAQLdA+pEH0QURClDzUPRRC2Ex4W7hTIEPILdQfRAywBx//b/58AyACr/nn6mfU+8TLuRO3y7RXxMPV0+GH5afjR+AX6dvux+4r7n/yT/t8ADwJiAoACsQLGAhsCJQAP/dr5gPeO9j33MPk8+wv8HPye+6z7+PsH/Nf7Ifua/H0AKAeXDQwRUBEeEDwPdg4bD/YQoxSyFU0TfQ46Cb0GZwTIAhkBNwD7/3D+uPsH+AL0svA379DuEfB18s/1F/gS+Pr39Pgw+5b8yfxz/L38Jf4uAPoBHQOwA20DlQJnABD+O/sg+VD4D/jh+ND5TfvX+3z7ePs3/Mr8mPyu+yD7dv2dAs4J2w45EaQRSxCdDs8N+g/VE3QWbRR7DzEKegdeBrYEqwMNAu4AUf/d/J75pPUQ8nnvKe4T76XyO/aA+AL4l/Yp9yz5X/u5+wv7kfvp/AL/ZQHyAsIDfAOaAmQBjf82/cf6F/jM9lD3Jvmz+1T8Evzx+9L82/1Z/Wz7+Pkx+zUAAwgcDpgRiRGyD3oN+QvYDUgSnBaTFo8RJwuHCNAHJAcnBQYDPALP/2P9EPpZ9zT1hPJd8Lrv+vF09U73cvaH9c71mfhq+338QPx6+yT8X/06/+oADQJMAjECFwFd/179Y/vQ+QX4CPh/+ZP7Yvyw+0L7yPtP/Qj+7PxH+5f7Ff9KBU4Lyg9qEUgQyw2eC4oM6RDmFV4XaROYDFIIpAYbBgwFDAR/A6cB0v6v+wr5hPbU86nwje/r8Az0g/b29vf28PY8+Lb5l/qT+mz6Fvt4/Fb+BwBSAdwBrQGFANn+f/1D/Fj6U/if9+P47fpN/GL8Zfz8/A7+Sv7n/Ln71/sP/1cEOQqnDkoRYRE4D9sMAgzVDw8UTRbbExkOMwqrB+8G2gWFBIADOwEx/s77IfqB+NT1wfGE74fvZvK59QL3P/fk9vH3ifnu+hD7Q/r4+TH7Vf1T/9YARQEwAav/p/6o/d/8IvxT+tD4vvcy+a773vyr/AP9Of4p/1T+P/x6/K/+eAM2CAQN3BFuExMRGw0EDIwPqxTUFWoTxA2ECecH3QZWBz0GUwSaAYD+//vX+XX39fTR8Z3vxfBs86/2/Pcl9zj2YPbY9z35Efnq+O75svtO/tf/nwB4AKL/9f6y/uX9m/yW+ob4wfe09/L5gPyM/Wf9n/0z/0QAqf77+5n7U/6xBK8KBg/bEeMRAhA7DUUMQQ8kE2gUJhNIDtwKLQn3B7IHogSyAmgACP6M/CH6jfiq9n7zwvCy70zxGvWO9iX3+fZI9zn5Yvkg+Vb4LvnZ+xX+P/+8/7b/HP8K/oj8KPyj+8z64/nv+LX5MvuP/D/9Mf3H/cf+Rf9e/6v+zf4ZAe0F1gu7D48RcRFsEC8P9A49EYEUuBU3E2ANLgh9BtsGNwchBbUCdAA9/rn7nvgS9hn00fJh8Ufx6PLK9T73R/aB9Xj24fgh+kz5OfjG+MT6Af0U/on+l/53/hf+s/wW+zj61flj+QH5v/nq+979wf5k/k3+PP9OAGwAuv/0AJkEBAqUDg4RuhHKEFAPrg4cEAITTBW3Ex0PogmmBZkE0ARwBE0DTAGQ/+j9UPom9xL08/EV8e/vGvGO80n2UPjV9z/37ffO+KP4ofdW95v5ofza/tb/j/5i/Xj8cfvP+jf6p/rB+r750vlO+7D9qP8AAPj/CgCqAB4BugCKAaoDhQcODMQQexRNFKMRJQ/eD9URDxMdEbINxAqKB0wHhgXxBXAFkgGl/5j6uPk0+Hj0HfOk77rxXPTk9Y73EPag9bn1Zvd4+NT4yPel93r5Efpk/Ov8FP65/yj+kPyl+Qf4zvio+Fn53fqN/HT/NgCU/3L/m/8mABoAmv/RAX0GUAuwD98Q6hHPEu8RlBBKDygQSRKzEnYQygw0CBQGXwUZBPECWwDO/lf98fqt+Ar2BfT68tHxy/Gr86T1d/dh9qv1gfal9zj54fcP95334vnM/IH9lP33/LL7GvqU+O/3wviy+eH63/ug/Ez+V/+7/ysAYAClAE0BVAKiBdAIqAs/D68QZBJaEtgRqRJqEsMTOxOzEIoNvgnwB/gFnAX+BHYDlgFO/kj8wfgS9sXznvGF8c/xtPNi9Ub2JvaJ9F/zl/TW9gP4Zvfe9qT42fo//P77W/uR+zD7O/qu+JX4mPlF+k36U/pH/RwBeAPPAkUA6/+QACwCFwQ9BiULDxBtEygUAhIuEbEQehDaEa4SMBQdEjINxggKBfUExgMBAur/1P2W/P35vveC9YrzkfGh8D7xCfO89Pz0w/Se9B32Yfev91b3FPf49z75IPuE/EP9zPw8+wb5avfo9675kvuo/E798/3g/h//N/+A/o3/mwIuBYEIxwkKDGwNCA70DwwRihNcFN8URBRBFLcThg9iClAFPwU0BYUEpANWAfz/ivzj+WH3WPX08wHxMO9y73HzdPZm9gP1//Nt9Rb2bPcR+Hn4aPne+Uz6fvqp+7L7lPoo+OX3lvlh++b8H/xu+//7Ev4WAEMBzAH8AjwDVgO+BTIJ8A36EGsRdxF0EDsRlxPBFDUX8BWxEpgO2gmmB9sEmQPJAq8CVgGR/zj87fdF9bPwT+9U7qDw9fSy9QL2VfSs8/j0KPWZ9Qf2I/Z2+Kn55PpC/BH8ivse+YP27/Zx+Vv8iP3o+9H75/zg/rz/j/61/v//fAKOBBkHAgtJDqcPjQ/nD6cRDBO8FMIWVxf1F0gU2Q7HCDMEdgVFBW4F4wNYAWD/DPqE9nHzvvF18RzwTvAm8UL0Bvc59sbzsPEj85r1OPde+AT5I/qg+qX6YPo++kj6efmy9yX32vmG/T//xv0D/HT8V/5xADsAmv/pALAE7giZC58OihHZEg8SVxF7EisV7xeBGDoWDRJUDvcKsgcdBSUDwgGTAB8Amv4X+3X3xfQt8iTvPe2R7kXyY/Ua9kn0I/QF9jv3OPX58sP0Wvij+5L8g/1E/cT79vgh9VT1mfiC/Vr+4/vV+6H9eP9z/5D+Mv6e/+MBPwYECzEOVxLhElYRfBBhDzETxBayGFcaNhZZEnMM/QUEBDkCAQQ0BO4BEQA8/Ab5M/WJ8Z/vPe4B7s7vM/Px9nz4jPZ+81nx2vEp9Ez2R/lv+7z8afyf+gD69Pg/+I33oPc1+jn9Gf/p/vL8hfyH/S7+Qf/F/9ICdgarCaUN4A+cEe8QbxAWEVUT4hZjGaMaOBe4EqMM2gfwBTUDbwJyAO7/YAA3/tj6fPW08cfvBu4/7fTu1/KF9kr3hfWH9JP0UPVZ9fX0fPYG+vr8Fv1O+4b5Uvlp+Lf33fiA+tv8Af2w/GH9LP4sABwA2P4I/80AMAWuCZkNwxAOEjMS+RGvEnMTnhXIF2sZQBk/EyYMhgU8AiACFwHBAUQCsQGO/6r5+PMr8OTtKO3v6znu2/Og+NX5xfY89GHzSvRz9Xr2o/eE+QX8mfxv/Fz7GPqS+LL2hPdC+ln9Sv8j/+/92vw+/af+5P9xAB8DdQdvC4wPChEgERAQPA+UES0UfRehG4kcahivD9QFqwHgAHcBZgKOAEsB2AB3/WP4TfGo7hrubO397UzwWfU8+WX4m/XW9JL1GvcI9+b1/fc3+tP8A/0b+m/5Cfiy+Nz5EvsN/uP+hP4p/Q78Z/zH/Yj+8/+gAcAEaAphDhQRKRDcDuAPjhG9FI0WqBjwGm8ZahOQCdkB4v8AAMAArABDARsCM//G+RDz3u6G7eXsiO2E70n0Avkt+vf3mfXx9cv3UvjZ96P3rfgI+3H8+PxQ+175yfi3+HX6Jvxb/pz/V/48/Rr8yfxr/mD/9QCyAmgHPQ7REvMSXw9jDcYOwxGDFeAYTRuNGswTZgmWAGP+6ABOAgcBCgCbAGz/U/pL883uke0F7pDuGPBG9F75fftf+db2jvY8+Dr4Kfch9//4yPyn/hn+QPuk+B34yvia+h39dP9BAD//7/y0+6r7KP3z/pAAyQPWCKkOoBEEEcUNKg1EDyoTVBfgGHIboxjsEKkGf/5w/rH/zACvAFoA6ACA/zn6bvMj7lzs+O1f7l3xdvZQ+vT7x/jF9p73Y/jk+Rv5Ufih+iv8Hf7o/PP5rvnI+EL6Kvzy/VYAmf9w/uT8Dfu2+zD9P/9EAjIFcgoZD3MQ+w8mDR4NHxCYE94X2hgMGS0Wpg1sBKj8IPxL/wABwwHGAMX/A/319svwGO1B7SDwxPEV9Gj3+vpW/LH5lff79ov4KPm7+Pr4qPp1/lz/mf3i+X73Zvi2+VP8u/7YAG4BWv+P/N76l/uQ/UwAiwKtBlAM4RCNElQPfAwpDdwQlBVwGIEZbhjXEVUH9f25+Uz92wFeA9ABp/4a/Vn5f/P77urtDvCe8XXyb/Wj+ev8lvxI+Tn3S/dD+Q76xvmt+tX8l/77/If5V/e69xH63/wg/zkBlgE+AC/9NfnG+NX69/7dAncGVwzBELQR1w+LDK0MChCCE0wYBBmGGDIU0ggtAEL6vfxiAT8B7wHa/yD+j/ro89nv8u0X797xhPNo9hX7//wH/JD4bPZA+Ob4ffmy+T76FP1G/n39pvqm95n3q/lz/IT/RAHRAOT+svuC+Yf52PtyACIEpQZoChkOlxDID8kMJg0sEFIUthcyGCsYthTbCjQAjfhR+lQBrAPVAzwAQP0y+oTz1u+47rLw0fPq85r1GvlS+xT9M/q99wz4Svg4+5X6Jfpq+0b8T/22+mP3l/an+Jf8SADnAM4Ac/8y/HT5zPeQ++gABwRgBiMJJw5NEX4QIw7aDC8PURO+FvoY7RjMFP0KjP+H+WD7HwCvAm4CngAl/mr5Z/Ri8Djv+PCl8uz0QPdR+m78ufqG+GL3JPgE+gX6m/oI+8z7+vxI++P4RfaO9hf6Vv0vAGkB/gAI/6T7Sfm3+Rj8DQA7A10GCAsID3gRGQ+cDBkNORAjFZsX9BkCGekSCghW/BP4N/vnAScFWQTGAHD8D/dx8fnvpPDK83D0UfT19oL5Wvxm+xb5nvik+Hv5ZfqR+r374fso+7r6F/g892r3vfk0/q0AiwIZAZz9F/qm9+v49Px+ASIFCQgBC9AO+g8PD2YOwA6pEdETIhZKGTUYYBExBQ772/le/Z0CgASHAi4ABfsa9h3yqvCs8ivzNPMh9cn4b/x5/Lv5q/g9+H35vPpS+jb7Cfuh+0/75vhR98v1Ivft+iz/CgJlAQr/oPyj+vP5qft4/nkBVgTpB98MKBDVEM4OwAzKDWoRvxWgGJYaExiHDycCwvfC98P8tANLBdYC+/8r+p71L/GQ7xfy9vLB8/L1Zfne/YH9YPly9yj3Tfll+1P6T/v9+hT6Nvr/9u/29PY9+Ob8bv/9AYIBcv4K/Ij5Bvkd/GT/5QNFB6cJSg4BEN4Q1w7WDCcPWBKqFp4ZUBpTFl0Lw/3n9u/4tv/vBEMEUQKK/qj5aPT27rjuGvHs85f21PjR/Pz9vvoS99f1jfhS+/f7T/tk+k/6IPpr+LX1bfTb9Q36cP6yAZQCdwBU/bj6zfkE+6/9NAFaBAUH5wsfEB8SYhADDcYNEBDDFOcYlhr9GeIRkgUP+rj1X/szAhIFSgRpAWX+j/hW8WruaO528Yv0Dfe1++r97/3C+Yn1EPZS+DD7zPo5+kj66/nd+HL2afXl9Yv4q/uy/mQAmwB8/xT9n/vZ+i38ZP52AYsFUAo/D94R7hD2DeoMIQ5lEqoWfBrNHGoXxgxG/rf1rffV/acEmQVuA7P/lvmS80XvWu6s8DjzlPW/+Qb9vv7Q+8T2EPVe9kH62vzP/HT7tPn097X2N/Ut9TX3ovlz/dH/SQGsABH+Nvyw+lP7Xv1GAB4DWQeNDIUR2hLCD1UOKA1YEKQUYhgxHc4ZnxGzA6H3BvcF+9QBGQRWAx4DvP7a93Txdu1A7vDwSfO2+M38m/8q/rD3k/Uk9uD5MPxB+x/7NPpD+aT3dvUM9BT1KPi4/GEAgwG8ABH+t/v0+k77GP3W/9ICnAa+CisPBhL4EAoODQ0VDigSRhYMGpYckRZfCyr9OfVi+Gv+AQQjBSADCAG3+hjz2e6o7DfwvfIq9kT8Kf+dABL7PvVI9Jb2T/sz/ef88vuU+dX2QvXA89/0V/eX+oH/YAHmAe7/ifxk+8D6APw9/tUAQwVBCWQNzBDsEAgP9QzZDasRzBWCGdobhhg4Du7/jfUz9gH9HQT4BW8DiAHv/Hj2fe+u62nux/FA9vb6y/40ASP9Zvex85f06fkr/bf9Nfxn+Wb3EfV788/0ZPYn+iz+/QBQA88AO/4L+0D5O/so/VIBLgTuB5EMfw8GEQwPOQ3sDEEPPRP+F+gbQBvJEnADlvdx9L36IwIyBQcGTwM5/433NfCa7cruKfIe9fz4Fv7eAH7+Yfmu9Br1EfgO+4f9/fxF/DH5zvXJ87XynvQC+O78bgEtA9IBHP8+/K76ifqp+wT/hAKPBtIKBw6aEFUP/gzbDCAOohIqFhwaPhywFXwJO/qK8+n3l/83Bm8GpARsAav60PJa7Q7tsfC29KH4kv0HAMH/a/vd9cT0N/ZJ+qP87/wX/Tv6FfcW9A3zmvTI9kj7HABlA3ADvABQ/cj6I/q7+kL95ADuBVAKUQ1AD0oPJw4UDScOrhDlFJ0YhBvpFzELc/0T9O/2Jv8eBXoHnQRmAa77xfNx7m/uE/Jk9pf4pvqf/jT+bvws+AT1nfdq+Pb7Sf14/Fj8pvfI9D3zLPNY9t75Uf4OA1cDdwFi/hH7dvs9+1v9uQD7A+UIcAvfDeQO7Q3EDf8NbRAwFLQXCht7GBMO6/9v9fH1jvxxAwcHQQaaA1P9XfUq7/zsL/Co9F349vuD/lv/n/w3+Bj2fvZX+GL6ofyy/YX8H/l99Tfz+vHa8xb4c/4eBAMFxQL2/ej6Jvr5+VX87v/qBN4I7QorDe0NOQ1gDA0N6Q/pE8AXwhrRGZ4RjwTQ9xD0x/klAvUH9QeBBbb/Eve370fs8u7A85D4Xfzv/uv/lP0U+cT0cfT89jX67Pwf/qj9F/pX9b7x1fAM8zT4hP6gAyYFngL4/kf7zfn7+Vb78P5wA/cHUQsODWwN6QwzC00MRQ9xE50YYhrYGlETgwY0+l3zTPhAALQGCgijBZYBmPqB8jPt2O2Y8QX3hPps/bf/Rf75+6X3MPVa9uj3S/v1/Lf8tvtk9z70RPJF8hP2rvqjAJAEYAQJArX9W/o5+cr5h/0qApgGegoaDGsNVA0eDJYM2w1cEcgUbRddGgYXKA1q/9n13vaX/DsDqwaCBj4E4P1q9drun+xn8Ir1a/l5/az/ZQD//Br4ofXz9NH2wfig+379KP3P+sn21vPL8bLymvYW/TgDUgWEA0T/Mfz6+fT53PvM/1oFzAgDDDkNVQ20DEALzQyWD+8S/BYPGtkZchJKBDX53PUI+jYByQQpB+gFBwHm+Qzxwe3h7qvymfdL+3j/rwDb/er5Y/bL9Yj2w/e3+vj81f3Y+/D3J/UF893ySfX++S8A7gMmBNkB5P0m+2L5uvl7/S0CYQeLCkIMkA2xDFELbgtUDUoRTRU1GdAbzhUpCuX8dfXa+Aj/tAUjB8UEMgKj++n0fe9z7gnyLPW7+PT81f8VAO/8U/jA9f/0cfag+Un8X/4f/Uz5uPVi80PzVvSv9y39kgLHBAYDG/+s+nn4yvhK/MMBhgZHCkwM/AyyDCILxQo3DUwQMBQbGKUa1xmaD68BbfjH9tj9BgMNBksH0QPB/0D3UPC87pLvUvQR+CX7Wf9e/yH9evmG9Rz2B/a591X7z/xE/uL6Rfcm9aDy8/I99bv6AgHkAyUDhgCI/JL5w/hI+ir/ygMLCCELRgw3DVwMcgvbCxkOMRIfFtkZWBtaFnEK5/3990n6KQDgBKQHxQaEAk37OvNO7yzvVPK59Vr46/zH/3r/ufuX9gz1ffTK9fn4vvtk/n/82/jl9V/zoPMj9Cb3WfwjAFgCTgFU/1T9Nvqs+fP7VwBpBUYIwwurDaoNBg2iC3INiQ8IE6YXaxqyGm4SoQUp+xP4Tf1oAnsFjQbIBB8Aafie8YXvevDu8i32k/ny/Xf/zv1F+nn23fSm9EP2Mfk5/Bf9kPtO+Er15fP28qT0XPiB/c8BTwI2Acv+5/sC+lr61f1WArMFDgmDDH0O/g5EDcgMHw6zD94TIBh9G20ZMA60Aiv7p/oA/6IBrgW6BsMDb/6c9QTxaO818JzzVPau+7r/Iv92/In4avan9T70/vUQ+UP7vPyB+oz4Y/aB8yPzFPT8+LX+aAE2AqYAY/6I+4X5JPtC/4IDgAcAC6MNJA8IDqAMRwy1DRkR1RSxGWsc8Bd7Cxb/lvmi+1AA4AJLBo4GtQKx+zDzVfAh8LPx0PSk9338Xf/w/Y771fjq9jz2JvRv9Tf5GPuk/Pb5H/jC9o3yO/L+8xP6nwCVAiYDyQAI/Tb6+vh5++4A+gQQCewLsg0UD5UM9gvTDB8PERNzFpgbrxthE/4Gufze+pb94wB/BHUGxgbMAeP4ivGQ7bXuR/H09K36f/8zAfn99/gD9nH0qfOR9Lr3J/yx/Sn8rvmT9lvzQfAa8ZH2cP1ZAlMDHALW/iL7t/hO+en9TgM/CEgLCQ1mDkMNvgtyCz8NohAHFG0Ztx0PGzoQhgJU+uP5e/0SAtsGDwqMCOX/zfQH7qLs6u7O8Rv3LP5EAjQBsPvC9k30ofP08yD21/pV/tv9GPob9/j0IfJV8HXyP/mZ/24CJQI9AE39G/qT+Oz6bwCLBd8ItwrLDAgO/QxnC9wLaA7qEToWmBqhHU4YTwyl/0H5y/xgANMD/gUPB8MFC/3u81nuM+0h73XyJ/iB/wMDxgB3+xn18/JU8iPzO/e5+lj+Iv64+ij4A/TZ8Ivvs/GQ+RYAlwN/AyIAJP1I+Uj4o/uuADQGngmCDP4OkQ6ODKsKAgtaDlkSDxgDHXwcfhWwCLz+8/qb+9v/YQOJB5MIpgP2+zLzhe7j7G7tY/LH+fEAEgNR/3365vWx8rzxH/MQ+An8BP5//c76/vcu8+DvV++P8wf7iQDJAzkDjgB0/Nz4yPjt+xMB2QUnCrwNNBChD5wMAQvHCzcPEhNSGL0d9hz7E58GkPww+s78mQDIBGcH1Qf7Am35GvJ27RPtGu8/81n7uQAIAuD+KPmE9eTydvIl9EH3r/un/d38Zfrh9ivzje/E73v0bftLATEDVQIC/2D7Bfk8+Wb9jgJpB5oLlg4UELYOSwx3CyoMJQ/NFJ4bFR9zGqsOGQOR/PD7aP6mAFwFlgg2BzABAfjv8R3tHOuB7nj1Uf64AoUBDv3z94j0bfIu8qX0x/kh/cj9gfwc+UL16O8s7rLxYPcB/oUBtAJ5AbH9cPoi+er6BP9BA10HRgzpD7AQmA7PC8cL1A2LEVMX4xyWHmUYfQvZAHr83/xj/3IBxQW/CKsFn/4a9TjvEO3w7HDxm/dw/s8BcP8F/EH4BPX88tHx3/Sq+YT8of0X+y34XvQf8MzvqvLM+Dr+QwFSArkAeP0y+kv5SPsJAN0EpwntDQAQ0A8GDdwLFg29D6YTQxgoHa8c0BRtCHn+0vuU/Q0BzAP9BnwIHATd+8/yyu2Z7P7tGvMQ+toA5wJL/3j5gvSL8i7yqfNS92n7mf3t+9f4P/Wc8XDv8+/89Bz78v90Ap4BEf8r+9v4uPnu/FUCXQd/CxAPKBBeD+UMigvMDVwQvxT1GbkdJxz9ECUFTv2s+7j+AQEtBdAHBgeHAe73//DY7FDs4u7s9BX9HgKeAX/8a/fd82fye/Kq9Bz5ZPyV/JH6zfeg9APxqu4b8Zj2Vfx+ALkBSQF2/i37qfmk+sH+wQMYCE0MhQ+dEPAO1wsGDI0OPRJZF2EbKx6wGSUOVwKo+lv8nQCXA5cGeQfzBkUAXfaP75Lr4uwf8aX3cv+FAtMAQ/sW9YPy+vG18pP1HPlN/GD8W/na9gDzn++d7gbxEvgh/tkBXQLc/1P9hfq0+QD8XwBhBQgJmAwpEJgRhQ/qDJIMsQ4/E/YXzxzTHUIXlwtm/7b6Mv3nABkEZQYYCL4G3/769K/tZerJ7Bny7fnEASMEzQC/+bDzkPGJ8azyfvbW+lb94Pxn+Gr0yfD07UjvefKN+fX//wFaAtD+ufud+QH55Px+AVYG3Ar0DWoQQRBsDcsLLwwlEL8VZxp6HvwcqxQCCBP9kfrH/XACPgZXCOcIIQVh/OXy++t56hPuhPTK/IcCMAMT/yT47PKi8CHx+fPA92f7mfya+uf29vIB8L/uYvD69PX6yv/cASkBCP7B+lH57Pov/5MDpgc9C3kO2BAREO8NfAyNDY4R0xVOG6EerxsXEqsE8vy0+x7+mAJRBVIITQjCAsL6PfF/7DDsve4Y9nX9JwPoAtX8NPcx8q3wJvL99AD5tPpP+7b5FvaO8iHvnu7p8B72e/yZAFgC6wCo/Yj6ZfnZ++z/OQSKCDoMng/pEKYPVA0nDEoOQxIfFzgcoB5JGhEPegIf+y77Sv8GBA4HZAgUBxkBOvjJ71nrvet78AH4cf/3ArMAlfuK9QLyQvFt8tz1ZPhc+pL65fcU9U7x0e6t71nyGvhh/c8AdwLY/wn9y/po+nz90gBrBesJMg14EFoQVg4fDcQMnQ8LE9IXyh27HREXCQs9APH8K/1wAMoDWAbWCL4FLP8K95nvi+yK7Ofx5vpWAUwDNv+g+C30RvF88W3zBvZU+TL6svlw94/zv+9a7R3vWfSN+mH/wQGCAUr/mfz9+vf70f5XAgEGCAovDo8QFRDnDeMMyQ26EAIV5BjUHL8bFBTDCJj+4vzk/oMBqwRgBtIHlQRE/ev1de6h6+zt2vPz/KMBLgHe/JD2MfR78t/xqvOR9Rn5Kfqq+EH2SfFO7rztj/Dm9uD7BQBVAZEAOf8y/AT78PsP/74DlweuC+oONxDOD8YNHA23Dt4RMxb4GU4cEBroED8F4v1r/Q8B4wNXBdkGewZnAyH8IvOc7RvsffAA+Mb+BgO2ADb78/U28r3x2/Gz8/n2Bfkk+kT4EvQN8Bbt0O0G8qn3kv28AFIBQwBF/dz6SPqc/DkBkQV8CfUMXw84EOUOBA3eDEgPgBP1F6QblxyxF0gNwQKn/af+/AGXBGsGtwfABrYBkvlh8c3sQ+1I8if6cQB9Anf/pfkJ9eXxQ/E98gT0Y/dO+Yj5K/eD8gHv8+zA7s/zRfk8/n0AzwCW/9384frt+rn9hAI0B9YKAw6aD7UPVA5eDEcN0g8PFAwZzhv7G1kVFwrtADL9cwD5A1sFtQa+BuYFCgFy+PTwpOym7kX1DvwpAScBgv0d+Yz0MPL68EfxEvTP9jr5GPnt9fzx7u0U7ZDvB/Tt+SD+7ACEAWT/uvxM+iT7Fv9QA9UHVAuaDhQRHxBaDmoM0AycELMU7hlVHFoamxNMCHgADv4tAF4DlwTbBrYHcQVD/0D2eO+J7BXvsPXl/IwBBgEX/ST4KvSH8VHwNPHO8073b/nr+K/10PBs7R7tUfDG9SD7T/8MAZ0AqP7K+4X68vuq/4IErQjQDBgQ5RAqEH4NLwz4DQYRPBb4GSIcXhrjEG0Gpv7p/fEBCwSoBmgHyQYSBBH8nPSN7jHt4PEa+JH/TQLD/zn7o/W18v7wmvC48lv1O/io+Yb3XvOM7sDrOe0Q8Xn3Df3e/1oBVf/j/Jr67Plt/V0BjwYJC9QNHRCYDy4OeAwtDGUP3ROhGOkcRh3vFsYLpwF8/uT/IAKdBGoGiQhuB3IBP/n58FDtmu7k8/f7ewEmAvr9Ifg49AjyXvEE8hz0KfeC+Xz5FPYt8dTsc+ui7h70R/qL/u//LQDH/TH73/lN+t/+hwNCCIkMgw6WEH8PDQ1mDPMMFhFxFaoZjB3FG9MUsQmGAJn+bwCiA9oFkwfSCEAGa//w9rzvY+2G8Nv2bf4iAtsAYfyD9gzz5PBd8I7ygvXp+CP6Qfg89L/ukesu7CLwRvbC+4X/+gD3/2P9Xfob+X77OACMBc0JkQzlDo0Ptg4cDTAMzQ7CElcXghueHNoZPRBoBa7/Ef8IAxwFogbjCOAHVgSs+9LylO7x7ULzyvk8/30Cof/p+un0TvD370zwsfMS9/H4Bfqw9pLyp+3d6jDtTPGt9578sf+qADj+Zvv8+Kj5JP2bAWYGGgpaDT8PQw8GDrUMXA0KECoUzhj8G3ocJBd4DX0E9/8fAS4DXwWRBzcI0AbiABv5Y/J37vbvvPQg+7cAbAFM/tL4avP68MHvDPFf9Bf30fk6+QP2rvHa7DHsTO7f8jX5FP3E/7H/c/2j+4r5yvox/gIC4wYzClcNRw/WDjsOMw3cDSURchTzGE0cYhuoFW0KogLBAOMBLQXeBagHrgh0BTsAf/d18aDvpPB/9kT8ywAtAWD8h/cP89/wIPEp8rz0dvcp+bz4xfQK8PPsiuyE71b02/nC/VH/r/6T/JH6v/lf+6v+4wJPBzYLhg6TEBcQZw52DUMOjBFLFVsa4x3RGjYT4wg6AoABtgF6BFoGHAhbCeADS/2g9SjwxO8l8bb3cf4nAW4A+Pp49Sfyj+878Pzx8fRS+QP6rvh69NruYuw17GPwoPZT+0X/mP++/YP77fh/+az7xf/6BPcIDA0+D64PeQ6CDPgMZA9mE/0XoBvIHDkYMQ8+BjkBkgGnA/QFEggbCbEHuAEj+lfzkO8r8DT0S/tvACkBCP5J+N7zYfCD74XxUvRH+Nn5S/mF9nrxpO2s60ju3fPm+Ff96v5u/jr8J/mV+NH5cf2IAtYGCAumDQUPow6oDKgMQQ5wEc8V8xmFHdEblhTUCuMCUQHVAlAFkgeaCQ4KQAWe/S72OfHf7+3x5Pdw/sIBhgBA+0n12PDv7rfvu/L19h36svqH+Hv0Te+x6+vr2e/79YL7l/+BAAT+Y/pd9zb3CPoL/5IETAmkDJYOog58DFoLTwtGDkET+hfsHDQdFBlZEL4FVwEYARoEqAdFCZQLGwluApL6lfIW8LLw9PRk/A0BmQKF/sb3M/Nb793u9PCj9KL5AvtI+i/3pPGR7Tvrae3v8kH4av1t/8P+Wfxh+CT3U/gR/IMBCQZqCpYM9gzIDDwL7Ao9DLUP5BSiGAAcuBu7FYkMwwN0AeQC4gULCYAKpApYBmv/GvjD8prxLPNz+H3+ogFdAFT7RPa/8X3vL/D/8v322fkH+y/5+fSD8C/tB+3f7/n0VPpx/db+mv3T+rP4z/ci+g/++gLaBxcKHgzLDPULKwuJCmwNNhFEFSQauRvvGQ8T2Qm9A2gBVgTdB9YJJwsBCSMEoPwq9pTzEvMJ9qX62/7UAGj+Avrq9Dfxa/BE8aD0Zvh4+j/6Bvcx807vzewv7gfyqfaE+l/8DP2E+zP5W/ha+D77ef+XA6UHnglAC3oLWQqDCjILJw5PEvgVSxnKGTQXuBARCDYDfwNuBhQJdgqXCp8HgQFq+wD3A/WU9Tj4ZfyT/+3/K/1X+AT0iPFG8Wnzyva2+Xf6/Pgp9mnyYe+F7l7w/POw98b65vuM+3v61Pgq+Pv4D/yeADAEUgcNCcMJbgrkCYIKHgwfD00T8BVZGKMY8xRFDqoGZgOmBIcHUQrXCqUJIwYPAIr6WvcC9z/4c/rJ/SgAUf8A/MH3F/SH8sDyGfUM+Ob5MPr49//0IvLz79fvy/FG9Vn4EvrL+kb6Jvku+Dj40PnY/JoA1QMpBscH5Ag1CU4JEAoLDIAPDhPSFQIXSxb/Eo4MmAZHBMwFpwjHCR0KpQjIBBIAJPsj+RD5t/nL+5v9mf/9/nn7OPj19MHzF/Rq9Vb4evlL+Vr3c/T28iHx1PBC8s70v/fK+IP5X/mM+Cz4F/jo+Xz8if87Av8DJQaNB5oIQwlYCo0Mqw5WEZATExVGFW8SXQ2fBzQF7wbRCLAJYAlBCIEF3wCn/QH8afvN+5H8j/45APb/i/2V+QP3G/a09bj2dfjk+Y75RfeW9crzU/Ip8tryPvVF97H4P/ma+Mv4evhM+I/5xfvK/sgAowLSBBwGWgcfCO4IfgrjDBYQMRIuE1sTIRF3DLkHzwULB6gIgAl3CfsHwwWJAlv/sv0p/aH9Af4o/zEBFQHR/or79vis9+D2xPdF+Q76vfkY+Cj2J/TS8njy6vKW9JD2wvcM+Ab4GvjE98P3pfg4+mT8iv6VAC8CpAMGBd0F2gZ4CJsKrgyXDg8QqRChD40MzwhPBtMGYQitCHoIwAfQBhsF5AK9AXYAu//3/9AAlAIhA/MBZ/9y/L765/mk+Sv6f/o2+pD5Lvjf9jn14vMy9Cv16/ZJ+O34SfmP+Oj3gffD9yT5Z/oC/J79Nv+oAOoAWAHpAdQCYAQkBrEIbgogC9YKZQm9B7oFdARfBA0FEgZ3BpgGRgYcBXIDnwHcAB4BMgJtAyAEfwS9A+IBXf9q/dL8wfwc/Vf9LP3M/Jf72fn+9wr3TPfn9yP5hPpv+277kfqw+d/4pvg8+Wb6B/zK/Tn/w/+J/1H/eP8LAEEBEAPaBH4G1gczCFMHlQXnA8YCnwLeA48FpQbWBnIG6QUSBQwEOAMUAwYEcgWMBrAG2QUnBOMBNAB9/4b/vP+o/53/M/9U/uz8HvvX+WT5//kc+/P7gvxN/Gj7K/oH+bP45PiA+Zb6tPuR/OL8wPxy/Ev8m/yX/Rf/5wC/AsgD8wNVA0QCbwHIAPsAHwJ7A6EEKQVjBT0FdwSyA1wD5gMwBVsGBQfrBi4G9gRzA2gCzwGQAZwBvgHUAWABXwAe/7L9q/xf/MD8Uv2I/Wj92fzY+736ufkJ+fP4cflO+hz7ifuR+yT7xfqu+gD76/sg/W3+kf9dALIAVAC3/0P/Hv+j/6wAzwF+Ar4C0gKzAqICoQL1Ao8DTQQJBT0FFwWMBNgDUwPmAtUCxgKmAn4CRAIDAnMBywAsANH/y//P/8H/Xf/b/lH+tP1K/ej8iPxv/H78rPzg/AH9C/3x/Pv8TP2o/SL+nP7q/j7/lf/w/w4A8P/5/xEAWADEACYBegGxAQ4CfALWAigDYwOZA8AD9gMgBA8E5AOoA2gDEAOSAkUC6QGMAVIBEwHmAJMALwCz/yz/2v6R/k3+KP4I/rD9Pv28/GH8S/xb/KT83fwU/VH9b/2l/Zj9XP1L/WL91f1w/uj+Ff8A//T+0f6Q/o7+6f5x/yMA5gCpAS8CQwIrAjACkQIpA6IDAQQ8BEgEEAShAxQDggIgAhYCIwISAvwBxAEbATQAiv88/xv/6f6c/kz+4f1v/fX8kPx1/GL8dPzV/EH9s/3T/dH9zf3L/RH+Z/7E/hv/ZP+u/8L/mv9Y/zD/Y//L/0YA2QBWAbYB5gEGAjECSQJdAnwCpgLXAuECogJMAu8BjwFTASQBCQHvAOYA4wCjAE8ACwDD/4T/Qv/4/qL+WP5C/jz+Mv4g/gz+D/4c/i7+Tv6W/v3+Tv+P/9X/GwBTAEgAOABYAIoAoQCMAI4ArADQAPkADgEgAVYBpgHZAQgCKAIlAgsC4AHLAcMBtQGUAT4B/wDSAH0AUgAvAAYA8P/H/6f/b/9I/yj/y/5m/vX9i/1R/VH9dP1z/WD9P/04/UD9P/1b/Zb97/1f/uf+Xv+K/4f/gP+K/6z/5P8pAHEAtwDpAO4A/wAOASEBWAGvAQECLAJcAoACYwIYAuMB6wESAiMCEALkAaUBcAE6AfUAqgBIAPr/2P/Y/+X/of8j/5P+Ef7q/Qz+Q/5d/jr+Iv4O/vT92/3W/QX+L/5p/sX+KP+I/6f/n/+H/6H/DwB6AMYA8ADwAN8A0ADSAOIA5wDxABQBMgFOAVIBMQH+AMsA4QAZATQBSAE4AfsApgBUAA8Awv97/2r/av9+/3z/PP/o/o/+W/5f/or+2f4Z/z//T/9M/1D/V/95/7r/6/8SAE8AhwCtAMoAzQC9ALkA+wBiAasBzQG+AYsBXAFMAUIBJwH8AKAAZgBRAEsALgDt/8P/nP+g/8X/zv/H/6P/dP9H/wv/yP6y/rn+t/6X/nb+bv5V/kH+Nf4s/mL+of7d/hz/Q/9s/3//n//P/+H/BAAVACEAQQB2ALUAxQC5ALwA6gA1AWQBhQGEAWcBbgF+AYEBXgEWAdYAfgA8ABYA///r/7j/jv+C/4D/gP95/2f/RP8u/xb/9/7s/u7+7f7S/rT+p/6i/r/+5P73/gD/J/97/8T//v9CAI8AzAD0AB4BOgFHAXABkwGfAboB3gEGAhoCJgI2AiQCFgIfAhEC7QG/AZUBZgEhAcYAZAD6/6H/VP8S/+r+x/6r/pj+e/5n/lT+VP5Q/kT+Tf5Q/lX+V/5V/lH+RP5O/mH+jf7S/gj/Mv9X/4n/y/8YAF4AewClAN0ACgEvAUYBVAFJAVIBfAGRAZ8BuwG/Aa8BoQGlAcgBrAFoATQB/QC5AGcAGAC8/1L/Gv/z/tD+3v7q/tP+qf6F/nz+d/6S/rD+uv7S/uH+2P7D/q/+wP7Q/vD+Mf+J/+j/FgAtADUAQwByAKIAygDyAB8BVgFwAWMBOwELAQABAgELAScBPQE2ASABDAH+ANwAugCaAFsAMQAUANj/gv8g/8j+ff5o/m3+ef6Y/rD+pv5//nH+e/6P/rf+1v4G/yb/Nv89/zX/Of9O/4j/2v8nAHkAxAACASMBMQFAAU0BagGMAbIB0AHGAa0BgAFIAScBHAEXAQwBCQEKAfcA6QDWAJ8AZgBJADkAHQDw/8f/hv84/wH/4f7X/tn+8P4Q/x7/If8O/+3+x/65/sr+3f7u/vr+Av8C/+7+7f7s/hD/b//E/woAVAClAMgAxwDkAPoACAEtAVYBXwFXATkB/wDBAJ0AngCVAIcAgACHAJAAYgAiAN7/lv91/3r/gf9h/zL/EP/k/rn+rP60/sH+7/4r/3v/vf/c/97/x//P/+f/+/8UACIAHAD+/9X/sv+N/4//vP/t/zEAeACwAMkA2gDfANMA7gANARIBGwEfAQgB0gCwAJ0AfQB4AHwAfwCYAIwAbAAyAPn/vP9+/2v/Uv8z/wz/0f6c/nj+fv6R/p/+vv7x/jT/ef+m/8D/1v/1/xMALQBJAGgAagBeAFIAOwBJAHQAkgC+AAcBUgFpAX4BmQGdAaQBpQGMAWcBWQFCAesAkwBVACQADgAAAPr/7v/V/8X/mf9X/xz//v7m/rj+mv6W/mn+Jf71/c/9y/0B/j7+a/60/hv/d/+5////NwBZAIoAoACtAMMAtwCHAFUASgBGAEIAbQB/AIoAvADaANUA0wDiAM4AuQDMAMsAvgCsAHUAJQDm/9T/1P/r/wgACQAXABkADgD0/9L/u/+o/6T/ov+o/6L/bf8s//P+2/7h/vr+O/+B/7j/7/8QADAATwBtAIUAlACxAM4A4wDhAKUAagBRAEcAVABoAHkAigCKAH4AYQBGADMADAAFABYAIgAuABIA2v+S/2L/SP8v/zf/Uv9t/33/bP9i/0z/OP9N/17/ef+q/+r/BwDv/+T/4P/Y/+T/CgA1AF4AgwCcAKQAqAC0AL4A1QD3ABEBKwEaAeIArABpACkABgD5//L/3//C/5L/Wf88/yT/Ff8r/1L/lv/c/w0AIQARAPr/5f/o//v/GQBDAEwAMgAWAP//7v/2/wUAEQAkAEkAZwBjAEwAJQDz/8r/tf+4/8H/2P/m/8r/v//I/9D/7/8dAGUAlgDAAM4ArACTAGQAEgDQ/5f/af9O/zT/Bf/R/sb+4/78/iD/Tv98/7n/2f/W/9L/xf+z/5//rP/N/9z/8v/7//v/EgA4AGYAggC2APoALwFQAVEBTAE2AfwAxQCLAFYAMwAYAAUA7f/n//P/8/8AABAAJwBEAFQAVQBJAC8AAQDH/4H/L//8/uX+xv6u/rz+2/73/jH/a/+R/9L/GwBgAJUAuwDSANcA2QDIAK0AnwCAAHYAfwB4AH0AjgCbAKUAsgDHAMkAygDQALwAkABPAAYAzP+a/3P/UP85/yz/Of9K/1j/a/98/47/qv/R/+r/+/8GAO//zv+2/6P/jf9//4D/gv+G/47/lf+2/+3/HwBDAGgAjACgAKgAnwBuADYAFAD1/9H/uf+t/6H/k/+K/47/sf/w/yoAXwCdAMsA8ADwAM4AqgCOAHgAWQA7ACQACwDt/7v/k/+K/43/mv+q/6//u//A/6r/av85/x//+P7R/s7+4/74/g//Jf8//3b/zf8qAH0AyQAKAUUBagFwAWMBUgE2ARQB9QDQAJQAVgAsAP7/0v/R//T/FQAnAEEAWgBPAEEANQASAPb/5P/U/7z/mP95/0//MP8y/zT/Qf9c/3v/m/+1/8v/2f/Y/9b/z//P/9z/5f/e/9f/1f/f/+3/AQAVADQAYgCGAKMAyQDvAPoA6wDaAMYAuwCyAKEAhQBkAEUAJgANAOr/yP/F/7//uv/T////BwD0//D/3/+//8v/5//c/8P/uf++/8D/l/9p/1b/N/8G//D+/f73/u3++v7n/uf+Iv9M/2H/jv/Y/wQAEgAtADgATwB1AH4AjQCyAN0AAwEGAQQBBwEaASABCQH0ANsAyQCyAHoAPAACAMP/jf9Y/yj/EP8I/wL/6v7t/gT/Ef8k/0D/af+Y/8X/8f/7//f////v/8v/tP+3/73/zP/m//n/HgBOAGUAcgCNALMA2wACAQkB/wACAfAA0gCyAJ8AmACXAJwAjQCOAJMAggB6AGEAYQBwAG8AYQA3ABMA4/+b/2j/KP8M/wn/8/7z/vn+Cv8g/zT/Tf9c/4D/p/+v/7D/t/+p/6P/oP+p/7r/1P8AABMAPABVAG8AkgClALUAuwDPALUApQCmAJUAjACcAIMAbQBsAEIAJAAUABAA+P8NACEAGwAnACEACADi/9b/t/+z/7j/lf92/0v/If8e/xz/FP8P/zL/Rv9B/0//Lv85/1r/XP9w/5r/2v8QAEUASwB2AIkAyAC/AMsA/wDUAAMB6gAvAa0BPgKbAmUC8QKnAh0Bvf85/Qn8Z//Q/2D97P1j/zn+wvzd/qf/r/7//3IAEf/l/r/+3/92/7/9ef9cAYMBpf/xAO0Acv3p/cH+GP86AKoCJAQFAyQDWgQKA4YBmQBS/7b/lgAFAg4CTgICAi0AbQD7/5L+zf6q/+D+bf+g/zf/UgAUAKb+Gf44/63+Yf5WAIf/Yf0s/8D/ev2l/UT/Xv9u/r7/xQC7ALcA1P9k/5T/BQDYADsBMQDmACICswJ7AlcC4AE5AT8CogGdAJoAUQPIAgwBAgJmAc0BdQBfAUIC0QDLAeUAvv6J/tb9I/7d/h3/5QApANgAW/+Y/Vb+uvkh+tD7L/qK++T9TP8Z/TT9af/1/i8Adf9R/hAACQBCAPYBYQEO/1gA1gHv/3v/IAG9AHr/+v8LAMkADwOJA3MDGgYwBIECWAZ8BNQAkQJnAykAh//GAs8AlPwu/vD8JPzm/wv++f6RAG39rP6J/v/+ovvA+kkAfvoq++f/q/0f/dX83P71/DT9vQDg+xD9lv/G/ocBxQBYAuwB2AE1AJb83gHbAaL/bQI5BV8DDgSZBkkBLAESA+4C+gHxAc4EGgJHBdEE7f//BRwDgf7xAHv/APzy/GABBP5C+koBvQAs+40AVv/V+sL7Ifsk+Qb5Evv0+Hj6LPyv+ur8+f5Z/Qn76/wk/J76aP5NABP/ZQFnA88A1QG0BHcCkgA8ApcEngWBCEQKqAfOCZoHmwMSBnYGyQRRBJoHAwbyAAEE8wWt/4b+sAGC/2j+zf+z/9H9wf3+++v48fps+qr3cvlv+YT34von/EP4P/Zp9+32O/VB+DD5Qflc/IT8Rfze/YX/if/Z/tP/FQBTAYcDWAO2AxAE6AQ6B/sHtwgOCNoIIQpRB04Jvwo7CWkJhQlkC9kJRwm/BwcC7gHaAO0AgAEY/lT/KQAi/5j8s/ov+jH3qfY79//1dfa6+Mb3x/Vq9U71NfVH9YH1YPVq9+f4gPn/+sP73vsZ/OD9QP47/+UC6APKBIMGwQdoCCAIYgimCIQJVwnQCJkKPQxQDZ0PpA8+DE4KigoyCYsFsQRABL4BvALaAvEA0gBoAJD+SPwh/Ef7YPgx94r2Q/XC9Mn0YvWf9bv1Qvbc9b31qfQv9E31g/S89OT1Vffz+CH6jvwZ/jUA5AECAp4DqwOVA24F6gXwBhUI+AkxDM4MSQ7IDu8QPRJmD0kOSQ3yCrkJmAmrB88GjweIBekDuAP9Aqj/J/4K/RD6bfoI+Cb29/Xg9C/1pfOL9MDzZPM49Yvy+PLM8zzzH/QJ9Hf0G/Wd9jH5+/mg+i39Yf5GABwBMwHcA38DBwOwBOMG9AgaCiwNbg4NDk4P/g8DEUURNRD7D0MPBg7uDGMLvghBBQkEigT8AuYBHgGM/9X/If8s/Kz5tvi09hPz+vIO9NvyXPLT8szzQfNm80D0qPJN8mTxivH/8sby0fTA9/j5/vug/t8A9QHoAv4CqALXAl8DTAN3BVsIdgrTDR8QPxGPEsgTIxQ/E2sRfA+hD7wPjgwrCg0JMAc0BkgFjgMGAVn/af7J/E37Ufm/93n2FvTE8nny1/F78STxDvGE8bLxf/G18OrwKfJX8pDyK/Pe9Lr2zfdc+vv8Vf8pATQCNgOSA5kEfQUiBUIF9wbZCcEMTw4AENYRCxP0E9wTEBQeE8YS2BGODtcLlQiVB3EG0wN1Al4Acf+l/lD+/f1K+8X4gvX38oXybPH/773uyu5T8ETx/fGa8cLwOfGS8SHyGvIZ8lnzN/UR9/b4F/xm/9oB3ALSA4QFGwazBd4EowT5BUMINwvIDTcPnBFGFGkVIRU6FMEU2hNeEc0ORAsrCb8HUAYHBQ0D5gDQ/wsApP+d/Vf7fvia9Y7z7PE38Qrwqu+s7wzwQPHn8b7yPvHB7xrwlPBo8fXxHfOr9GX2Kvk5/JP+uwDxAXoDvARkBAQFswRNBBQGjgi2CqYM4g/tEjYUMxXdFdYVKxVXFHQTexARDCYJMgh8BvUDDAJFADz/vf+DABj/aftO+BH2VPOc8RfwOO9T7trtrO8U8ZnyBfNj8oHx2fDG8fDxn/EC8rnzFfbv+Mn8a/9KAWYDowRqBcMEwANaA+MCjgQeBwYK2AxTDwsTRBU8FpQXAReXFXAUGxPOEFELOggmB44FmgSpAVMA2/8JAaACWwBa/Ef48/Xl86zxru8B7hPtEe4k8F3xCfMv8+/xDvE38S7ybvEv8fHxAPNx9Yb4JfyL/oMAegNVBfIFLwXNBFcEagPNBA8Hkgk4DKcPcBMrFQMXMhk1GYMXGhWhE1YQDwurB6oFlwRUAnAAQf+T//kBIALB/1T7aPg199bzOvDY7ejsHO3/7FrvsPGa8ij04fKZ8trysPJg8+fwu/BX81D21flj+93+1AGnA38GPgZSBfgD7gLAA6YEJgf7CZEMxQ8hExMXDBl8GHQYShdkFSsSpw3sCUkG1gRrA8ABhgDR/48BaQJAAUX+YvrB91T1YvKJ73DtV+0M7vfuDfFs8gX08fPV8s3z7/I4867yX/Hp8k/0Cfhi+iT8zP8xAssEIgVoBLkEQQOHAkoDHwV9CBsLGg60EL8TMxdJGDIYFxfxFUIUphAdDDcJ4gZeBEsCbgC2/wH/CAGyAfL/Tv4x+nv3j/T48WPwzO1l7Y7t7+4k8Zryk/SS9OXzyPRj9Zz0t/OB80r0x/Wt9zb6B/ya/okBygMVBekEjgRdBEAEygRcBm4IxwovDVIQsxPLFjIYYBfnFpUWmBSeD9AKDQi8BcUDSQHj/iT+F//7AGIBU/+m/NH5C/h09VTyf/D27SXt8O2D76zxAfNU9O30SvWL9jH2GPU09MDzUvW29lr4H/ps/HH/CwIDBGYEiARFBNsD8QMsBf4GFgn5C4gOGRGTE6oVwxbzFtIWxRXGEr0NsAnSBxkG6wKL/yH+b/62/+sAfgDZ/m38tPnS9vrzvvGs7zbu0O3P7pLwgPI19DX1pPXx9TP29/Ub9Xb0sfRB9aP23Pct+jX92P+SAo0DTwTABKgEqwQPBPMEMAeiCZoMcw7sEE0UNRa9Fx8X6RXGFJQRdA0uCNUF1gS4ASr/A/35/VIAQQGEAPH90/tU+c/2KPRz8VDva+5a7tbuIvFr84P1P/Y69tb2LfcD99f1afRq9IH1vPZ6+Pv5ovwJAGYCDQQ8BH0E4QTwBH4FCwZbB98JMwx1DjMR0RP8FYIWbxZaFrcU6xAeC1kHqAXGA+MACf0D/Nj98/+HADT/sv0H/MT5iveW9ALyNfCf7qruWO+M8Xn07fUU9i/2lfd2+JP3KPb29Jb0HvY/93b4mPoi/UcAAAJgAzIE2wR5BSQFhAW0BoIIugrCDJgOIxElFJsVCBaHFmkWsBNgDrcJsAbcBEkCP/5Q+2n7/f1UAP8AY/9N/V/7evlz95b09vGZ76buaO8i8bLzr/Xm9f71NfeY+Dn51ff19RX1VPWv9q/3YvgX+oL8/f+GAnoDxARUBQgGSgZhBqgHKgnsCjgM1w30EAQUexXZFZYVPxXSEv8NXQn+BScEJAES/Xr66/rQ/cL/Yv+N/TT8kPvP+rj4lvXC8szwUvBh8F/xE/Od9Fr1m/VK9y753/md+Nz2kvYO9yr4qvjl+Hf6DP0DAHcBLgJPA6AE+gVrBhoHxgjcCoMMag3pDlERrBPoFLEUkhSOEzsQsQvTByQGAAR3AIX8PfrL+0X+Y/9E/gf8Hfu5+iT6Tfh99b7zPPJZ8cbxrvLS80L0ivRO9bP23Phd+TD4bvdy92D43vgV+bP5L/t//QH/DQBmATADqwS1BYQGyQctCkUMjA0/Dt0POBLCE1EUFxRaE3oR9g35CVEHZAWQAnb+R/sA+638eP5u/gb98PuF+y/7FvoQ+Of1/vN08tTxGvJz85n0s/Tr9NT1EvjJ+Z/5p/jB9w744fhL+Xb51Pll+6P9Tf97AIoB9AKIBKMF4AZqCEIK+AsuDXYOhRDdEvAT0hOgEzMTXRD0CxEIzgVDBN4A1Pww+p/6Gf2f/j7+fPyC+937sPsW+vr3KfZC9PbyefIS8170+/QP9fz0bvbK+N35d/lf+Pb3p/h/+Zz54fmg+iL8lv1z/qL/IAEoA8AE8AWuB9EJ5QuADZkOxw+gERkTuhPnE1IToxEIDhEKuwe2BUkDFP8v+7X6GvwZ/kP+7fzj+6f7Ovyl+5/5bfer9Sj0W/Mi85XzJPRh9NX02vX694n5w/k1+Y34p/gs+ZD5aflE+TP6zvsy/Z/+CgDAAZMD9ASQBsMITAvuDGgNTQ4ZEB0SVhNKE6sSoBHDD9wMZwnmBrEEowHo/Uj7UfuW/JH9EP3W+3n76/ti/JL7vfm09xT2N/Vw9BP0avTV9NL0+fRp9in4SfmA+QH5sfgC+Zv5mvlG+Yz5m/oS/G39fP7r/xECMwQ0BjUICQqvC/kM9g1cD1kRohKXEoUSwRLWEasO7AobCP4F6ANkAI38f/r4+nb8/Pw3/BT7O/vb+y78YPuW+QX4bfZC9cb0nvSW9Jn0kPRE9eX2oPiT+U35+fgS+ZT5Avrm+cv5Bvrs+vP7Bv12/j8AIALqA/oFBQhUClwMhw1aDrgPoRFpEm4SVxLBEe8PgAwmCTIH1wVJA/v+XfuE+rD7OP3c/Ej7Zvr++mT8ZPxC+4T5O/hD9/L1S/U89XD1UPVf9cj1Dfez+Gz5nflM+aH5OvpD+mn6cfoF++L7aPwh/Zv+nwDDAqQEHwYoCKUK1AwFDqUOORDAEXsSixIXEjERkg72CkQIeQaEBB0B1vw6+lX67/vB/J/7e/rM+vn7C/13/MX6lPmb+J73f/bI9bj1evUJ9Qj18PV798j4B/nk+Az5pvlx+qj6ffqi+lD7G/zt/Mv9Yf9cASsDygTCBkUJggsxDd8NGA/LEPcRgxI6EqgRzA/7DFkKWQigBs0Dbv+r+6X6Hfuv+1z7NfrP+Y360fth/P77YPtW+gX5zfe19l/2TPan9bz0pvTf9Vz3Ofh4+K74M/kc+sP65Poe+6b7R/zP/Gv9iv5OAC4CywNvBYEH0wnOC/4MNw7yD34RNBJKEksSfRFGDzgMaQk7B90EOgFm/dr6UvrR+rT6fPrv+Tb6cPsk/EP8yPs9+yb67PgD+EX3BfeC9qf17/RN9Yf2affn9wH4Xfg4+SL6ovr6+p37NPz+/K/9lf7u/4sBQQOnBE8GZQgwCn0L1wxhDjAQmBElEnESaRIsEQcOsQqPCJoGpgN3/3X7iPnF+Uz6I/pu+WH5Zvps+0L8f/xc/Mr7x/qG+X74PPjH97L2aPXz9L711PaB94j3kveA+ML5wPpB+2b7+Puj/Gz9Lv47/5oAHQLPAzUFCAcFCaYKxwsgDb0ONBBnEeoRYRKyEY0PlAzoCUsIwQXAAVn9C/oM+Vz5Q/md+J74Rvlk+pL7evwy/Rb9jPw4+8b5Rfnq+Aj4e/Zh9Wb1V/Ye91r3cPfx9xr5Gfrd+j/72fui/DX9Hv5P/6gABgJUA3UELwZ4CPIJ0wqYC+wM5A45EE8RnxGiEc8Q0w1MC2cJogeVBMb/ifsM+d/4RPmr+LP32ffA+FT6s/to/Pv87/xw/EX7ufqV+sz5RfiU9tP1K/YM90X3q/aO9r/3Jfle+vf6QPsm/CH9Dv7a/h4ArgHkAgEELQXYBrgI4QmDCm4LFg1GD5sQWhGoEToRmA/WDM4K6QhPBhgCJv2q+WT4xfhS+Gb3Bveu94P5H/sZ/LT8bv3K/RT9JPx++9D6yvlA+NX2RfZ+9sT2afYh9rz2JPif+Xf6/vrO+8f8vP3O/vH/QgGQAoIDZgTXBewHbAkOCq0KEQwHDqQPpxA8EYwRnRA8DnsLeQnNB4EEmv/o+q34g/hz+Hr3QPaK9kT4RPqW+0L88vzM/eb9Kv12/Pn7S/vJ+d33hvbD9k/3OveO9vX1EffO+Er65vr2+oD7lvxD/r3/BAEyAlEDFARZBXEH1Qh8CaIJRAofDC0OrQ9/EBIRCBFOD/wM/gr/CHMGFQIp/a/5dPhe+E734vWV9ef2Nvnr+q37Tfxu/aL+t/7r/fH86vu/+gL5U/fA9tr24/Zy9s/1avYX+Nz5zvoB+1z7V/zk/T//RwBBAUQCbAPOBEsG8Qf6CGwJIQpoC4wNKQ/0D4MQEBGYEB8OkwuHCZcHaQR0/w37iPgn+NP3ZfZD9Yr1iPe3+dn6cPvQ/IT+Rf/i/rr9L/3D/GT7OPlT9+b2M/fq9iH2yvWd9qn4U/rj+i/73vtA/a3+EAA8ARoCJQM1BFgF/gaFCD0JngljCjYMIQ5yD6EQgREVEfwOZww8CsEIIgZZAWX8MvlT+BD4yPY29br0GPZp+Pr5EftS/Ob9Kv84/8P+Z/4h/rb8Wfpr+Hj3p/eQ91n2TvXY9YP3VvkT+ij65vpO/Fv+8f8XAR0CHANMBHQFxAYCCLYIoQggCfgKFA3lDg8QGxF/EVwQOA6hC8MJmAdvAzj+7/n393j3iPYD9Rb0ufTG9rv4RfrB+4L9aP8VAK7/RP/r/tv94/tv+cv3nPeN9/j2/fWi9bj2V/iE+en5H/pc+wX9vf5uAJ8B8AIWBDkFnAYNCCIJVQmDCZwKXQwwDqMPwhC5EeQQgw7hCxwKFwnnBYYAEvvu93L3Bvd29cPzbvME9WP3FvnZ+gD9Af89AAYAqv+8/3X/u/2t+kD4Yfd49wD3zfX+9HD1VPcG+bT5PPod+8z8eP4aAMAB6gLrA5QErwWKBwEJSQnxCKcJ2wtzDgMQmRBnEaARUhCkDfAKOgmGBhQCuPy9+Jz3GPfD9cXz7/KP9Mf2ovj/+Yf7Kf4FAFcAKADV/3H/L/7H+x75e/di9wv3+PVE9Wb1zvZY+EX5rfl/+if8w/1D/7AABAJGA4kEgwWnBiEI7whMCf8JqgvgDbcPFRFSEtYSxxHbDoELrgnnByoEY/4R+Yz2Gfbo9VX0tfIn80v1o/d5+S37Zf2E/0cADwCZ/3X/2v66/Mj5i/cp90X3mfaN9fr07/XN9zH59Plt+mP7/fy9/o4ABQIPA9MDhgTtBccHDAlvCdsJNguHDfcPvxHfEnYTqhK8D90M1gq3CD8FSf/u+RH3XfYX9jb0h/Kp8nX0/vap+Ab6d/zF/uP/IQDc/+3/YP+E/ef6cfjI94f3ZfYl9X30VPUB91P4I/nU+eL6ZvwP/vb/wAH/AswDXASHBXQHzggTCVAJkQoxDQkQ3hEzE08USBQ4EukOJQzXCXMGPgE5+0D39vU/9d7zBPKf8WXz7/UU+Mz5E/yl/isAgAAyAAIAuP/R/e76WfgO9+j2MfYm9Zv0IfW69iL4EPnV+dP6bfwH/mn//ABMAjUDAQQABZwGHgjvCFkJbQoRDQwQSRK5E6EU3xRrE2MQ/wxdCtsHSAMR/QP4rfXr9NXzMfIj8QfyjPTW9r34Nfvl/QEA1wBnABEA5f+k/j78U/lk98r2L/Y99Tr0RfSU9RL3dfhv+XT6/Pt2/TP/9gByArADEwStBDUGxgcNCbMJhgqfDKYPbBIHFFAV8xWBFHkRDQ5TC84IgwQC/gv4B/VD9JLzFfLE8DbxpfMi9k74D/v5/RsArQBCAOr/yP/r/n/8a/lD91r2yfX+9EX0SvQ19cb2OfhT+cT6EvxD/e3+7ACiAoEDpAPxA0wFLAeLCCEJ8gkNDGwPlBJeFM0VsRbCFegSEw/pC0UJUwVt/x35SvUg9GLzNfLv8CbxPPOy9SD4mfpk/af/YwAaAKL/jP/a/qv88vmT9yL2svUc9ZT0qPQz9ZT2+/dY+fL6WPyk/e7+awAIAvgCNwOrA6gESwYVCAwJ3gndC1UPuBILFbEWfhfjFh8U0g8ADFoJGQZ+AJ75o/QK8/jyX/IR8bzwkPI39Z73Vvon/Yj/twACAB3/GP+p/vz8Q/qV9xr2m/VM9bT0U/QK9Ub20/d++dX6F/xN/cT+WADZAQEDOwNOAzgE3AW7BwYJxgmHC8cOhxJLFS0XZxgJGLUVkxEuDekJuAZpAR76VPQl8t7xn/G58CLw4/Ht9JX3Hfrs/KX/FAG/AGP/Y/4O/t78Pfpn91b1mPSL9Gz0uPRI9U726/eL+Rf7dfyC/ab++v9gAa0CGAMxA/kDZAU1B6MIvQmQC9oOshKFFXgX3Rj7GPUWshLHDQwK8AZHAmz7KPVO8t/x1PEO8VTwgvE49CL34flZ/LH+UwAxABD/MP6X/Wz8KfqZ94X1iPSn9CD1YfWf9UP2jPdX+QL7J/zh/M/9Jv+6AAUChwLTAowDJgUlB78I/wngC+8OixKbFZsXLhnIGRsY5ROQDrUKCQiAA6v8I/Y38rTx9fEI8TLwxfAj8zv2+PiI+xL+1/8QAAr/P/7h/aD8c/rP95b1pvTT9C71KvVl9RH2Y/d0+Sb7E/zo/LL92P5/ANwBhAKKAiIDmwSEBnkI7gl7Cz8OJBLEFQYYdxk0GvAYehVCEDwL8gcvBCL+Pfc88prwJ/FC8UzwGfAY8kz1hvj/+hn9Mf8VAJ//df5U/Xv8C/ug+Cb2d/RA9P/0X/V59dH1xvae+Ov6f/wl/b39xP5bABcCAQMCAw4DFQT4BeAHPgmpCkMNKhHeFGYXXRmDGgMa+xaoEZQMOQndBRQAw/gP8+DwN/FK8SPwpO9c8Yr0ovcS+mH8i/6t/6X/hP5E/ab8mPsx+VT2cfT483/08PQR9Vr1b/aQ+Mn6K/wJ/QH+Kv/TACQCwQL2AjgDHwStBYUH8QhUCrEMbxBjFHEXYRlnGh4amxfSEmgNowkzBg8BMPq487Dw6vBQ8Xjwx+/B8LbzB/eK+b378v2V/6H/c/5B/b/8J/wu+hb3qPQ09OX0j/W39Z71SPYm+IP6Ffwn/Qv+m/6A/7cA3wGbAvUCmAPdBLAGvgh+CpcM7Q+pE+MWFxmjGjgbGhkkFJ4OnwqVBxADvftm9M/w4PCW8QXxJ/C98E7zVPao+ML6UP0Z//b+qf16/Er8CPxu+pX3OPWK9BL14fUL9uL1dvYH+An69fsW/cv9Xf4u/7oAEALEAvsCNwNZBEcGTAgoCj0MPg+/ErwVXhiZGpwb5RkkFXsPawvXCF0Ez/xT9QHxT/D88MvwJ/DU8EDz/fUo+Lv6lP1C/wX/nv1W/On7zPte+qH3XfWd9Nz0hPX19Sv23PYR+Mj5Z/t//DH9m/1T/qD/GAEiAlkC5AJfBEcGbAgBCqQLeQ4MEnUVrxdxGSQbiRq9FoYRugxjCQAG+P9D+MHyE/E+8Ubx1PDy8KnyGvVW91v5xfvH/S3+P/3l+xr7EfuM+nH4N/YD9eD01PWl9rn2yvaU9/r4nvr1+8T8If2n/f/+nQAMAgYDoQNfBAkGYwgLCnoLoA2KELUTbhaYGD0ahBoIGAQTxw1bCrsHJQPF+8f0PfG/8HjxbvEG8cnx4fNM9nL4zvo0/YD+J/7M/Hn7GPvS+oT5TPdW9eL0bfUN9gn27fWo9j74T/q2+yX8e/xl/Qf/AwGEAkkDwgNSBNMF/wfnCSwLgwzNDrIRgRT5FvIYvBm/GB4Vzg/xC5MJ3wVO//D38vJn8cXxePFq8Kfwz/KD9Zr3Wfl3+3v9M/5l/dL7/PrB+sP53vfT9dP0KPW89a71mPUf9qn36vmJ+z78pPxU/bD+ogCHAq4D7gN8BMcFmgeDCbIKvguBDU8QKhNbFWUXBxnGGBYWuhF5DccKpQcSAvD6YvXY8hfytvHU8KPwQfLX9PH2Wfgw+lj8gf1n/U78DPuQ+u/5aPiD9kL1DPVO9XL1NvVo9bf2l/g1+jn7BfwS/Xj+OAAyAqADkQRHBSMGeQf7CEAKDQs/DGgOERF8E8QV+RfJGDsXdBNQD5AMOgrNBd/+Qfg09IXyzPHh8FbwR/Ff80z1w/a5+E37P/20/a/8YvvI+jr6NPmi9wz2ZvV39UP11/Tc9Nf1mPd0+ev6tPuM/Pz92f/LAZ4D0wRvBRMGBgeECPkJAQvyC2MNbw+3ER0UXRZFGB4Y9BS4EIcNWQs3CHkCU/v59XLzhvKo8c3wKvHj8t/0iPaV+PH6/PyG/YT8cPu3+kX6O/ls95z1tvTQ9Aj17/QS9f/1SPcM+a36oPuK/Jv9+v7FALMCNQQEBbEFzwZKCPcJDwt5C10MMA6pEMsSxxTHFjgXgRVKEvUOXAypCTIFyv7j+IH16vPS8s3xS/FM8iD06PW+96v5qPvB/Eb8N/t4+hb6c/kT+FD2KPUD9SX1H/Ul9Z31zPYo+Fz5ofrk+0j9yf4BAIUBTAO3BJcFUAaIB/kITwoUC8ILNw1zD/kR+xPWFSwXmRbiEycQNA32CnsH1gGi+/D2gfSM86jyGfKd8hD0nPX09rP48vqc/Mv8xPth+sf5j/my+Cf30PU89fL08/QN9T/1PfbF9wD5/fkR+2r81P0b/6sAXALYAwwF4QWvBgsItwnDCoMLlQxCDnQQjhJvFAoWnxY1FfkRng42DG0JBgVR/+H5UfZz9E7zT/JQ8mfzx/Qp9nb3bvmf+5T8IfwL+yH6y/k/+ej3fPZ39Sb1F/X79B/1zPUX90n4L/kx+pP79/ww/q7/TgEXA6gE0gWxBsUHdgmyCjwL1AszDQsP1BDVEtIUThY4FrsTKBBLDRkLAggBAx39cviv9evzr/Iq8rjyC/Qx9SH22Pdo+m/89vwZ/N/6M/rR+fP4WvcT9nL1A/W79JL0GvVI9pP3nviA+aH6Bfxy/cX+OwAaAiIEawUyBhEHkwhcCncLJgzGDAkOxA+MEWsTOxVIFjYV4xE8DuUL1wlRBuQABPvY9rP0ZvNT8kXyX/OJ9Hr1bPY++Of6v/y9/Ff7UPo0+tv5qfgW9xf2p/Vi9Qj1wvR39dr2AvjX+L35HPuP/M79D//eADgDNwVoBu0G5Qe5CTEL2QtMDEMNpw5NEPsRoxODFfgV4hN4EHINWwuvCBkEn/7A+UL2RPTO8gfyefKq85T0LvW99jH5jvuG/NH7uPoc+gT6TPnv96P2rvVQ9e30dvSt9Mb12fas95z4vvmD+1D9zP5IADQCdwRPBj4H4AcBCU0KjQs+DKQMnA1HD/AQgRLBFFUWshUGE3EPcwxICjIHJQJo/J/3sPTn8tnx7/HN8rHzXvRq9VX3RfqX/NH8svuK+kX6Lfpr+QX4iPau9Tz1wfSS9O30tfWg9oj3nvgW+gP8rv08/xIBOAN7BfIGnwdnCLIJ+wr8C2EMywwfDtUP0hHIE40VURb2FBQSzg4fDE4JBgVh/yH6gvb881XyafGB8WDyhPPc9Ev2evjX+i/85/vj+pX6hPoQ+vL4aPc/9qf1IfWs9Jv0CPX49eD2v/cl+fP65fyi/jMAMAKoBM0GBgjLCJ0Jlwp2C/ULXAwdDbsObBATEiMU8RVjFmoUCBH/DaYLZwh5A9D9t/hJ9Ubz7vEz8YzxhfK18+z03fat+dL7kPzG+8L6jPqO+vD5jfjs9pn1APWr9Ir0rPQM9dH1rfbB91j5a/tF/dj+vQDiAi4FCAdCCCIJBQooC+MLNQy7DN0NbQ8PERoTDBU0FsgVKhPiD0UNvwraBl4Bzvt297L08vLD8Tvxl/G38urzM/U899n5pvvK+xv7WPol+lH6kvkU+Jj2jvUJ9bH0g/TR9Fv1FfYq90f47vkI/PP9mP95AbEDxQV6B5EImgm4CpQLHQxWDDkNoQ5CEAoSuBOKFZIWdhU+EigPgwxRCRgFdf8m+lT2DvSD8m7xNfH78THzaPQs9l34r/oh/Pb72/o9+kv67vkD+ZX3FPYv9e70v/SS9M30ZPVH9iz3dPhN+kP8Kv7+/9kB5wM3Bu4HCgkcCigLJQyyDCAN8w11D3YROxPVFFcWmBabFF0RQg5AC8oH+wIk/ST4xvTn8qfxzPDv8A7yd/Pu9Nn2EflZ+4f8BvwG+136MfrS+d34cPcJ9kT12fSB9FL02vTC9XD2aPex+H36hfyN/n4AVQKWBK4GVwifCfoKNwy9DCQNtg3kDqwQUhKhEzMVqBY4Fq4TMhApDWMKZQYUAWX77PZM9ILy4/BD8CPxdvKt8wr1Evej+Zr7N/xX+3X6S/o6+r75cvgE99j1K/Wn9GL0o/QI9aX1YfZp99744/oJ/Rf/EwFKA78F0geGCcwKAwwTDZENvg1JDp4POhHGEjQU5hXlFp4VhxLxDhkMdwlZBVT/d/m79YXz2fF/8CrwG/GE8vHzavWh93P6dfyA/C77TPoc+gr6YvnP9zj2P/Xb9H/0LPRv9CX1B/bi9vf3eflr+8b98P+zAcYDIwYvCMgJYwu0DJENFA5BDvkOcxBAErIT1RQKFpEW3RReER4OQwsICGoDXv3n97L07/Ix8d/vse/a8JPyBPS19fH3mfol/Nn7yvrr+cP5x/kV+VP34/Uf9Yb0afRe9Ln0fPVj9kj3h/g++mX8tv6hAM0CDgUhBy0J7Ao7DHcNZg5xDq4OvQ81EYoSvxMoFV0WQhbcEzcQzQwICr0GhAFF+0X2bvOo8U7wb++T7/nwsPJI9DT2zvhY+3b8//vK+tn5qPl4+WL4pfZH9Zr0V/RM9Ev00vS+9cT2+Pcs+dz6Kf2P/7ABxAPUBdsH9AmICwANQg7SDhQPbw95EM8RChNHFIgVGBZPFeoSKg8TDHAJZAW1/6r5U/Xj8lXxxO/L7ivvn/Cx8kb0Mfbg+Bz78/tw+036n/nC+UX5A/hn9hX1pvRc9Cv0ZfT19PL1Sfdm+Kb5zvtD/pYAwgLPBBoHHQkYC+EMJg5QD+QPKhClELcR5RIPFBgV4BUTFn8UZxF8DUoKMAerAgD9fPeq84rxhPCM7w7vqe8q8RDz2fQX91z51fo0+5n6vvl9+Xj5u/hX9831t/RC9DT0MPRK9O/0B/Yq92b4MvqS/Bn/hwH/A0MGmAjJCoQMAA5FD0AQixCXEB0RZhK2E7MUlxXVFUcVTBPDDyYMMAnTBcYA3/rB9a3ydPF/8InvB+/c77DxdvNT9V73Svlg+mb6uPko+Rb5BvmE+Az3jvXA9Dn0JPRN9JL0H/UF9l33/vgZ+5T9LwCnAi4FuAfxCQEMpg1JD5cQMRFTEXwRRRJgE4oUFhWwFRsWwhSREZkNWAqTB6QD0/3l96HzuPEL8dDv5e4W717w+vHo8wn2Nvjc+SH62/ku+RL5NPnA+K/3Ffb+9Fv0QPR39L301fRo9ar2APj4+R38Uv7vALcDhgYQCXALdA0RD5AQlhHJEaYR6BG+Ep0TdBQyFZgVFRXlEogP9wsjCf4FDQFF+1H2UPPJ8Z/wbe/t7ovvx/B/8mb0j/aF+GD5mPl0+Vv5dvlM+ZH4aPdK9kX1ofQ89BP0JvSD9Fr1q/aH+Jr6Kv2w/3YCcgUPCGMKYwxBDtQPQxHfEdoR6BFcEooTZxQVFYIVYxVFFFcRtw1UClUHfgNq/vT4w/Ty8pDxg/CZ71DvFfAh8cXytvTK9nD4F/n7+DX5p/nI+az51/it9zn2LPWJ9Pjz+/Mp9HX0VvX39gf5dfsR/ucAygNyBhgJcAtyDVUPBBHiEUASNhI4EvoS1ROOFOgUJxXUFA0T5w8tDN0ImwVwATf8Gvez8+bxnPDY7yLvVu9e8HbxLvNZ9Wj3uvhz+XX5pvnt+fb56Pnj+JX3LvYN9WX0GPQg9BD0YPRw9Sr3Svn6+/r+0AHdBLkHdgrNDMIOwBDjEWQSYhIsEocSWxNVFKwUuRTnFE0U4BEzDlsKDgfdA2n/O/qo9cvypPGm8IzvUe/T79DwS/ID9BL2EPg4+Xj5Zfly+az5nPk1+U348Paq9Zv0PvRF9Fz0mfRR9ar2lfgU+4j9PgBPA1gGEwmeC6UNoQ+DEZoSGhOpEn4S2RKNE0sUZxRHFEEULBPlDwMMPggrBfUBIv0M+An03fH58FDwju9w71fwSPGC8lb0dvY4+A75Zfk0+Tb5tvnn+X35Efh79g31M/Re9JX0qvQi9S72tfe7+Sr8Cv8UAvkEvQdWCq0M7w7kEDAS+xLmEo0STBLDEp8T0RMFFNAT+RJOEc4NvAmOBoED+f8/+3T2mPPt8e7wX/Dj78HvmfCV8YTyh/Sg9jf4Bvny+Ab5OPmy+fj5NfnC9xz24fRQ9Gz0dfTW9Jf1pvbD+AX7ov2/AMwDcAYtCbYL/A0gENIRJhNME/ESqRKKEt8SbROVEy0T/RJWEswP4QtGCPQErQEK/mz5Z/XT8nPxkfC772jvwu+T8FrxGvMt9fb2r/ha+Xf5ovkl+qb6mfrD+T74pPZt9er0wfSm9O30tvUL9x75iPuI/o0BQAQVB80JSAzJDv0QSxJIE14TGhMCE9oSZROeE4wTXxPmEmcRBw7nCRsG9AKP/8/7uvcN9G3yU/Gk8FjwO/Cs8NvwwPFO80T1Xvew+ET5fvkJ+ur6l/tT+yb6Pvhn9kj1o/R+9Hb0wPSx9TH3jPmW/Jb/lQKfBUoIDQvQDQQQuBG6EiwT/BJkEkwSYBLNEiwTGBMKE0sSXxDgDNIILQXxAZT+KPrV9eTyYfG+8Fnwxu/g71nwJvGm8lf0ffYa+AH5gvnb+Zf6i/sJ/Fr78PkR+JL2s/X99Kj0b/TC9Ov16Pd0+kb9ZwBQA0EGEAngC3kObxA6Ei8TfhM/E3sSRBIzEqMS5BKJEoESghELD1cLNgfrA+EAQf3u+Mv0BvLM8Fnw4u+s79bvOfAh8ZDy0PQF98T4svmv+SD6+fr0+1H8Xvun+c33Wvap9Rz1r/Tt9Fn1cfZY+AT7RP6NAbAETQf8CbkMLQ9LEYMSPRM6E70SHBKWEeURVBKLEnASHBKiENINLgoYBgQD2v/q+3D3ZvMc8TLwte9473rvqe+B8JHxNPOz9T/48vl++qL68vrv++j87Pyu+575r/cw9k31t/SG9Ln0QfWO9ov4Yfuu/i8CVwURCOsKcg3CD6AR2BKZE4kT7hIiErARAxJ+EsMSnhL2EUwQJg1HCWoFQgLW/pX6KfZJ8inwWe8P7/7uPO/L77nwIfLz85z2/PiB+jD7PfvB+4/8M/31/Gr7ZPmb9zD2EvWY9Gz0yfTE9fH28/iT+9/+dAKGBWUIEQulDe4PzhEpE+8T7hM2EzkSsREFElgSnRJSEmkRwQ91DJMI5gSGAQX+wfkL9Wjxtu/07tfu0O4B7/zv2vCD8ov02PZ/+eH6nvvl+2v8av3W/Uz9pfuS+aH3E/bD9A/01/Mv9DH1iva1+JT77v5fAogFjwiHCzEOcRBHEqUTZRRPFIQTlxJXEmESoBLIEmwSlRFyDyAMIAg7BNkA8vyU+FP08/A874Pue+7k7kHvJ/A58cjy+/RJ94f52/qR+xz8y/yo/QD+aP3G+7/5tvfV9aD00vN/8+7zz/RW9nH4Uvu5/jAChgWTCHILCg5XECkSlROEFJwU5hPsEooS2hJOE0oT4BLjEZwPOAwTCA4EmQDC/Fj40vOf8DjvzO677tvuTu/z7wPxdfKi9Eb3jPkp+9v7bfxT/Uz+k/7H/SX8+fn19xr2YfRI89zyMfMt9H71uPep+ub9eAHPBDkIcQtdDqsQjBJUFFsVyBXGFIkT8BLBEikTDxPpEgMS1A91DEwIawTNAPX8LvjV86nwr+5b7iHuX+45787vEPGM8sD0lfeA+Sn7BPyt/KL9PP59/p39A/z/+cr31/Up9PnygPLP8srzcvWm93b6qv0DAXUErAfWCrIN/g8mErQT+BR/FdsUChQxEwATTBNbE0oTcBKGEGoNJwkbBVgBk/1D+X70GvH07mnub+5m7iTv3e/q8ELyQPT99nb5R/s0/P/85f3D/gD/E/6F/Ff6APi/9avzavLa8fTxxfJH9Hb2dPnJ/DcArgMHB1cKRg23D8cReRPGFIsVQBVOFKgTTxNcE3YTVBPsEmIRog7LClsGoQKv/hz6bvUu8dPuwu2E7fXtb+5476fw//EA9JH2Kvkp+3f8X/1e/i3/V/+4/kf9TvsJ+Yz2X/Sv8qjxe/Ho8TbzKvXi9xj7tv5xAvUFdgl3DP8OLhEgE4oUgxWPFcQUEhRBE04TlBN+E10TBxKuDy4M9gc9BE4Aw/vt9nbyY+8p7rztuu0m7uDuR/Ce8W3zEPaS+NP6O/w2/Wj+Sv+u/zL/yP0V/Pb5ivc59S7z8PFd8Y/xpfI89Iv2b/mo/FUAOgT4B3ULWA6tEKcSShSDFeAVKhUVFC0TyxIMEx8TExN1EpYQlw14CXAF2gHj/Vr5e/Sv8K3uwO217c7tiu6z7+fww/Lj9MD3c/pr/Nz9p/50/9n/lf+n/gj92fp/+Af24fN/8pLxiPE+8nHzTvXO9wr7kv4uAtYFRglKDNsOIBEFE60UvxWOFb0UkRPdEsESsRIKE64SbhHzDhgLcwfPAxoA6fv29uby++9j7tHt0O2V7lXvPvCG8aXzifaA+dH7Tf10/iL/mP+P//X+1f3a+3P5Gff59IXzbPKu8QXywvIm9DL2xfgt/Kz/aAMUB2oKhQ3kDxgS+BNaFecVQBUkFAoTfxJyEngSeBIDEg8Q1gz3CBQF/QFn/ib6pPWv8WbvXO4V7pbuK+/H79vwNvLE9Pn33foP/Rv+6v58/8z/6/9j//r9wPsV+XX2avTL8tDxUvFe8T7yrfMT9ib5x/yIAEoEFwhrC1UOrxDUEtwUGBZOFnsVHBRNE9ISzRLaEkcSSxGqDk0LlQfrA70A3/yn+Eb0mvDF7gzuG+7o7k7vOfBz8TLzcPZC+fj7zf2u/pz///9yAGkAZ//F/Wz7rfhG9jj0efJ98fTwSvFO8rnzPvY2+aj8lQBYBDAIlAtMDvEQVhN4FQUXyhawFUgUGxP3ErESjRIvEosQFg5TClgGYwMbAHH8E/iA89PwW+/n7gLv9e6M73rwo/Hg85X2mPla/O/9Sf8QAKMA7gBbAEb/Qv3T+lH43fXN8y7yJPES8XzxmPJS9H72mfkK/e8A1QR3CNILrg45EawTyhXQFscWcBUOFFITyxK4Ek0SLxGLD8UMEAmDBScCG/9/+8/2EPOR8KPv2e+c7+jvn/CM8UnzA/Wz96v61fxT/sn+Xf8kAF8A4f9m/j/8B/q393j1pfNC8pPxaPHC8evyw/Q391z6mv0xAdYEKQiWC3wOMBGrE2AVahYvFhgVJhRaEy0TCxM+EioRPw98DM8I+gS1AWv+3fqC9sXy0vAB8C/wFfAy8APx4PFi82H11/fa+gv9A/7f/nb/TADNAPr/pv6N/Cz62/dz9ZjzWPJo8SfxefGB8of0Hvc5+oT9+wCqBEUIzgv5DsoRKBTnFa4WWRZhFVIUjBMEE3sSxhHJEN8O+ws9CHEEVAHz/UD6L/a78snw3u/D7+vvZvBB8UryxvPp9aj4gvt1/ZH+U//i/5AAlgD7/57+aPwV+on3GvVN8/TxHfH78ELxdfJ09N32EPp8/RcB4QSOCB4MZg8LEpYUbhbzFtwWhxVHFMET7xKtEsERThCTDhELXAfgA4oAuP3K+bH1wPLI8HDwXfA78PTwcfGV8h70APbx+HP7V/2a/iz/EgDqAN4ASQCt/nz8OPqQ90H1OfOk8cjwdvDL8BfyCPTG9g76lf2DAUoFKwnKDP0P0xLxFI8WRReuFogVJxRAE/ISJxJ/ESgQ4Q30CvwGdgNbAB79i/mN9Vfy2/Bv8HPwvfDv8N/x/fJK9Hr26/iC+479q/44/8L/RwB3AN7/Nf4h/JL5DPf99AbzuvH98LzwVfFa8l70C/cr+uD9ggFWBQ8JiwzND4US5RSaFkgXtBZdFR8UMRPEEhoSShEyEAgONwsvB5gDqwBq/T36CPa18hbxVvDW8AfxafFI8tnySfQ89oX4SPtC/Xb+AP9O//j/QwDr/4v+Tvze+Ub3BPX78pbx3PCc8AXx+/HR85P2APqY/W0BLgXNCHoMjQ9oEtAUihZrF9cWeBU5FCETqhIoEkARRxAnDj4LawepA9AAmf1N+oH2KvNH8W/wf/DR8D/x9fG+8rjzkfXy9736MP1+/kT/hP8UAI0AIgD9/sX8UfrI9zb1P/Ow8e/w6PA08SLyuPM79qf5X/1GARYFuwhBDF4PERKGFD0WNBfWFloVRxQZE9MSfRKLEfAQng6wCxAIHgQ9Afb9iPqx9v7y8PAn8CTwkPAO8Xrxi/LF8571Sfi8+mD92f5m//z/SgDDAIEABv/P/DT6pvdy9UXzpfGt8Dvwm/Bf8QzzpvXq+O38vwCABGkI+QtYD1kSmBRpFk4X6Rb/FXsUbxMKEyMSuRGGEIwOJQxTCAoFtwFd/gX7yvZt8xnxGfBH8FPwk/Aq8ffxq/No9a33d/q9/L7+h/8EAI4AyQCaACf/3/xG+sf3afVZ86TxqvCF8JXwbPH68mv13PiH/GwAJQTVB3sLyg7bEWQUXhYuFxUXURYSFVMUXBPOEigS1RBiD2MMKwmwBegB+P4l+2/3+PMZ8Tfw/+9Z8LLwD/Ew8pjzbPV79/j5kvy0/sj//v9HAHUAVwAZ/778Ffpt9wn16fIs8UnwKPBg8Frxw/L29Er4BvwVAOYDdAcNC28OphFLFCIWLBdXF6MWexU+FFYT2RJbEpQRyQ9xDRMKWAYzA4j/OPwy+CH0fPHO75zv5+/+78DwxfH18ur00PZq+U38Uv60/xoAmwACAcYAxv+1/Ub7ePi69RzzFfHw73jvru888KbxxvPx9u36IP9bAxwH1wpJDm0RNRQlFpAX4hcxFwsWZRSbEygTvBIcEiYQAg7LCm8HIARwACD9+Pgw9arxl+8G7x7vte/67zPxV/Ju9KP2H/lY/Br+8v+DAL4AUwHUAC4AMv6A++L49fVo84DxFfCC777vLfB78VvzDfYE+tP9FAIUBrgJhg2iEHET5RVpFyIYzhc7FigV7hMpE+8SrRGpEG4OdguaCMsEuAGJ/m76yPbX8l/wc+/17oDvxe9h8LbxJvNk9UH4+vqo/Wf/VgAVAUEBVgGTAOj+ffyT+cf2QvQc8p/wxu+U7+zvmfBt8gH1pviv/KEAzwSxCKcMCRDwEjsVFxfoF64XwxY4FZoUnxMEE4wS+xB4D6EMZAksBpQCkv+v+373wvPA8IDvJ+9/79vvePCO8RrzTvW994z67Py4/rj/DABpAJkANgDn/nb8tvkb98P01vIw8VPw3u8K8KrwFfKr9A/4/Pu//7wDlQdrC/8O8xGOFFkWRxdMF3MWchWRFIETKBNwEjkR+Q9nDb0KcQfPA6oAu/z3+Ob0VvF577/uI++c7xnw6PB48rb0Qff1+YL8b/6K/xwAKgCCAFwAXf9W/XD6wPdX9Vnz4fGy8ALwDfB48MHx9PMR99r6t/69AoIGTgobDogRWxQ2FjEXSRfVFsQVmhSFE7ASSBJbEU4QQg6nC2gIxwSRAdb9KPrL9eXxeu9g7rruO+/C787wUPJl9M32JPnd+w/+mv88ABgAJAAQAJv///2B+6D4+vWy8xfy4vBA8FHwffC48VXzB/bN+fD9MALiBX0J8gw+EBgTVBWfFhoX6RaBFT8UOhONEswS9REiEXgPcQzLCS4GJQMFALn7Qvfa8rLv3u7F7hjv0+8L8KTxWvOf9XD4vPqJ/Sf/zP8eABsAcACOAEj/9vzy+QL31fTp8mbxbPDT7wDwuvAF8qr0TPjJ/CUBxARcCNcLlg/FEuAUSBbQFqcW1hU0FB4TcxIgEhkS+hDQD8YNQAtnCMIETwFH/Qn5mfT38KHuC+5u7vvuHfDi8LryHvWA91X6hvyQ/sD/9f/x/6T/kf8C/1n9yfrU94T1s/Nf8kXxmvCS8A/xM/Lc89L20vou/2sDCAdrCtUNEBH1E+MVvhaVFq0VhxRJE5ES7RHcEY8RnRAZD0kMrwmwBlwDxv/9+o/2n/Kz743uGu627sXvc/Ai8gf0aPZz+dn7Cv44/3D/k/9s/4L/Gf+Q/UX7nfhZ9pj0HvPv8ULxAvEm8cPxQvPo9Wz5eP0xAeEEnQg2DMoPpRLKFF4WsBZzFlwV1xMmE00SLBLZEfkQ6g9lDbsKcQcPBAABMf0P+Xf0rvCe7hzuhO4G77/vzvDK8hL1oPdq+vz8GP/D/6z/Uv81/0r/R/5U/MD5Uvea9Qr02vIM8q7xyPH/8eTyw/Tl9737oP9EA6kGVwrWDVkRFBT7FRoX4RaAFiEVExRJE3ISnxJyEToQIQ4hC60IDwX6ASn+rPmw9ZPxN+8g7hHumO4k7yfw5PFi9BT3E/pt/Ez+a/+s/8f/uv97/6v+7PyL+kv4JPap9FjzJvKU8TnxvvGh8kv0C/cz+u39mgFRBSIJ5wyAEFkTvBUaF9IXjxc8FjQVnBMHE2kSUhGqEHAOTgwSCUkFcgLg/o/7PfeM8o/vke147dXtBO4074/w9/Kt9V34Z/v4/f3/uQCfACoA9f9z/xT+0Pvr+Ij2h/RG8z/yXvFT8Z7xn/Ik9Ef2gvkm/RIBnQTaB3ALDw+zEkUV7hbFF7gXTRfjFbQUnRPoEoQSERF4D7AMeQldBtQC7P8W/OL3yfP27yHuGO0m7aztde4y8AXyg/RB91L6WP1s/4gAnQBfABoAnP91/ob8+/lx9y71gfNB8lbxQ/GB8TnycfM79VX4AvwUAAYEdwctC7YOARLRFMYWExhYGH4X/xWRFJQTLxOQEpsREBB8DbEKHQfMA9sACP0c+Sf0BfCs7TrsmezR7JLtMe/i8KnzgPbH+fz8Ov+0APEAygCiAEEAaf91/dj6Jvjg9Q/0xvKk8SvxWvGq8bvyP/Qh9+v69P4RA5wGZgohDqcR2hQWF54Y9RgjGNEWNRU7FHsT0RLlETUQ+A3ICpcHNQQnAZb9Dfmc9OXvT+2y65bra+wR7QbvfvBp86f20fmH/Zn/PgFUAccAmQC6/wb/Jv2p+jn4y/VH9PzyXPI38kzyg/Ik84D05PZq+kv+SALSBVcJGg3QEGQUPhcAGbIZAhmgFwkWvBRBFFgTLRJUEJ0NjgryBqkDmwA1/Sn5TfTn7xDt4+sC7F7sOu2L7m/wLPMZ9rb5DP23/xUB/QDFAIQAaACh/6/9QPt7+Ej2l/Qx85byC/IE8hvygfLx81L2DfoN/hwCwwV9CXENYRETFbUXlRnoGWEZ2BfbFeQU7hNNEw0SBBDLDYcKNQerAxkA1Pyd+BL0X+8S7P/qLes67DPthO6e8HnzyPYd+mz9DgCWAcEB6AAfAJX/Fv/N/VT7dPjJ9RP0A/OM8lXyRfKN8vXyIPRb9hb6r/4AA8MG+AluDXoRMBVYGPkZRBqWGWIXyRVHFG4TORPDET4QMA3QCb8GKgOOAMT8Xfhn83bur+s96pnqYutn7ATuC/Ai86f2h/oS/tMAHgIpApgB9wCsANb/Ff5n+1348PU/9BzzivIw8g/yP/K68gr0ovab+jH/cgNIB+gKrA6sEiwW3hhZGlIaaBl7F7AVXxROE8cSfBHbD/QMiwkpBo4Clf9f+872ifHL7BjqtuhH6Urq7+sV7pLw7POV97b7ev8hAlsDDwNDAloBqACr/7n9Dfvj94L1sfPb8nbyNPKW8n3yCfM19O/2NPvQ/2wE+QdeC/gO/BL8FtYZcxshG7EZxhfBFc4UsxMhE7ER8Q4FDP4H5QSiATf+T/oK9QTwnOsJ6YfoDukp6prrYO1d8PPzR/jL/FMA5wLmA9QDOAOcAskBbAD1/ab6YPeh9PnyAvJn8TLxM/GC8a7yvPRs+PD8rAFPBvkJxQ0tEdsUVRhrGmcbeBrRGPUWMxVSFG8TqxJcEdUOxAu6BwMEfwDM/Jn4D/Pf7WbpTecE59vnrOlZ60HufPFw9eL5HP4XAkQE5wQTBNkC6AHKADT/evxa+Wb2+fOB8ovxavGK8c7xTvIS8271Q/la/pcD8weiCwQPxBLGFgsa8BsTHNsa4BicFv0UnRMREyASRBB3DWcJ+AVgAhf/Y/tH9hbxquvH5zfmHObY55zpkeu+7jjyGPfi+xQAqgMtBaEFrAQbA90BMwBP/nr7Gvge9cjyqPEz8VHxk/Hi8aHyGfT/9h77OwAbBYQJTg3BEJwUMRheGxUduhwlG4UYORbLFIgT4RJcEUEPAQypB88D3//a/Cr5WPTB7h7pxuXB5Pjl/OcI6mPsVe+R82z4vf1iAqMF+gYpBqYE1QKRATQAz/2b+qf2bPNx8bDwDvGQ8f3xSvL18vj0n/i3/RgD2weQC7UODxLcFfIZuByoHTocsRlmF48V3RTVE8sS/RCvDeoJagXKAd7+bvsP98XwtOp95s7kseUV573oeuop7UvxZ/bk+6wAWgQGBjYGMgXjA8ICZgFP/xn8SPjP9KfyifGD8WPxOfEx8avxy/Ma9wX8FAGtBbkJLw02EUQVZRk6HB4dixwnGjIYRBb5FEsUYBIpEUMOSQstCA8EfwEq/RH5dvM+7f7oieVJ5YHliuY86JTq0O6x81j5Wf6HAlwFjgahBpIFSASFAi0AS/2T+Sn2Z/PI8Svx0/D28Arx3fF68yn2L/q8/r8DJwgSDLUPMhMCF0kacxzhHF8bbhljFw4WNBXSE6MSQBCQDQ4KwgUpAv/9UvqB9bzvTuqt5UTkp+Q15ljoF+p57c7xLvfV/AsBmAQpBoMGrAXjA1oCSgAl/vn6Qff3857x1PCn8ODwMPGg8efyJvXO+Hb9ggJaB1QL3Q5AEvUVnBlLHEwdLRwGGucXfhaYFY8UHBNPEckOeQuZB2QD0f/8+2/3uvGB6wDnZ+R35GrltebS6F3rA/D+9F/6Tf+2AngFFwbDBXkEyAJhARj/XPym+CL1xfJQ8RHxtfC+8AnxJfJm9I33BvzCANEFHgoIDqoROhUfGcQbXR0KHT4bghmTF4EWZRWzEzsSog/JDDMJ0AQEAcH8ePg28y7tQOjj5PLjpeTV5a7nPeoN7hbzWvhY/VMBCwSCBZQFrQRjA6ABof/f/ID5TfZn89nxDPHW8AfxQ/FE8hH05/YG+5//owRHCSUN+xBDFBQYGBvnHHEdvxsZGvwXjhagFQcU5hKZEPkNkAo4BnwCSf41+hr1Ee+b6UzliONx433kMOZy6OPrgvDk9Uj79v9KAz4FuQU4BSAEjQKqABb+3fp893v0ZvJw8SDxHvFH8brxN/O89a/5qP6pA4IIYQz5D7gTdxcXGzkd0h2/HM0a6BhUF2gWFRW2E3URkw5JCyIHdwNG//r60PWp7yDqoOWh407j5eM45QvnUuri7k303PmR/kYCegRkBTIFXQQsAzYB0f5b+/338vS88tDxBvHh8LrwRPHp8nX1Yvnl/dgCvAcODPoPlhNOF5kaCx3xHSUdsBulGVYY/hZ/FR0U2xGODz0MOggqBNj/vvsN90jxcutF5iXjfuL44ojkTub+6FntefJE+G79dAEzBHwFlAXmBJkDuQFL/wf8gPgk9XDyBfFe8FjwxfBU8fjyWfUR+fn9JwOkCPAMoBDXE/wWfBoEHTwedh1RGzUZjheuFs0VXhSIEt4PsgzGCKEEmwB3/NH31vGp6x3mGuN94g/jbOSh5Urof+wM8hr4B/3uAF8DtQTmBBEEwgLyAOP+BfyG+Bf1YfJY8SPxW/F88aHxD/Og9bP5if6DA3gIygyuEDMUdBelGvcc/B1hHXobbhmJF4UWgRVDFHMSsw/DDAYJRAVIAfL8OPhv8lDs8+Zs433iEOMW5MXl4+fN6ynx2vY9/NT/SQJ1A5sDUQMVApMAVP5a+xD4ufSc8nLxXPGP8cHxkPL+89r2ofpi/2kEcQkODqwR6hSxF64aKB0QHnEdIhvHGDQXEhZ9FQYUHxKFD1AMCQkBBTwB+vxT+NfyeewW52XjfOIn403ksuWX53vru/DG9uH7bP+gAYQCDgN0AmIBuf9w/dn6fveI9GXyfvG+8TLyvPJo89f0p/ea+24AWQUQCksO7xE1FSYYERs8HTMeSh00GwkZWhd6FmIVCBT3ETAPMgyXCNEE8gC5/Cz4dvJi7BPnmuPQ4mPjh+S65c3nfeu/8Gf2QPvB/qIA2wEWArcBnADV/sP8F/og90T0TfJ78dTxWvIE8+rzp/Xc+PD87gHRBm0Leg/qEvwVuhheGzUd2x3dHJMaUBiSFsAVJhWKE6oRnw6cC2sIfwQfAXn8/fdh8m/skufl4xDjQuNp5K7l0edv61Lw7/Wb+lH+bwCsASMCpAGeALH+Vfyt+a32EvQa8mDxkfFK8knzcPSW9pX54v27AqsHUgwQEHwTXRYqGcwbYx3NHZIcaBqEGO0WExbNFAIT4BAJDiYLZweHA4b/f/tE9/Xxb+wr5wTkRePZ40HlbuZw6P/rovAd9oH6zP0QACIBwQECAaf/vf2J+2X5uvYx9BnyU/G78bjy8fM29V33nPoF/wUEzQhMDRwRZRRhF+0ZOxymHdAdnhw5GscX9hXjFA4UpBJpEIANFgqSBvkCYP+g+wL3o/HC6+TmBuRP403kZeUE5/HoW+wu8RP27Pr8/SkAIwEIAYEA8P5r/WH7+Ph89r3zN/Kb8RvyPvMi9Mv13/dn+9D/wQTbCToOFRL2FMEXJBqGHOsdgh3bGwYZzhZXFU4UYRNyERsPXQwXCd8FKAKo/gX7pvZ58aXr0+Yg5NPjv+T15RjnEOm27Jbx2/Ye+wT+6P/aABYBXQDI/gL9yvqf+Bf2vfNJ8rzxj/Kq8xP1p/bm+H38BQEcBvUK3w5BEgkVsxdxGk4cgR26HNcakBgdFvAUrBOsEisRuQ47DLQIdAX5AXD+AfsN9rzwzOoy5v3jZON35GHl+eaR6U3tSvIr9337u/7IALIBbwE/AKD+1PzM+nr42PVt8/zxwvF58sHzOPX89oT56vxyAWUGUAvMDxcT5BUiGFgakRx8HRgd3BoYGO4VcRS+E3sS6hB5DpULVgiUBD4BmP0b+or17e9p6tbl/uPq4+/kLuZp5/rpyu3B8rn3sfut/l0AVQEmAScAk/62/Nv6YfgC9obzO/Ic8rDyHfQ49Rv3tvl9/XYCWAc1DAMQERPUFVAY/hreHKYdkRxJGu8X/RU0FRYU1xKsELgN2wo5BwQElwAP/XL5fvQc79fp9eWV5KLka+VA5mPnF+ok7hTz1/eE+y3+AwAUAUMBmQAi/1b9LPup+Bv23POM8kPyhfJM84j0jvbw+Q7+EQP1B3QMrRABFFcXCBpVHM8djR0vHKwZYxeqFSkU2xLvEPYOwwwdCjkHvAN0AB/9c/nk9FbvNOp85g7l2+Qu5bjl/OYT6lDuNPN69/b64P3t/ysBBgH9/2n+sPzR+lr48PXb89Ly1PIx8yf0ZfWj9/76AP/OA24I7gzpECIUIBelGfQbdx1jHfUbYxkIF1kVIhT6EgYR0w5JDJ8JtQZTAywA2fw6+aL0DO8B6m7mEuUw5W7lFeY85+DpNu7D8kP3qfpD/Vj/OAB/AKP/UP7l/M36r/gt9kb0X/Mr88HzS/SN9aX3+vpp/w0EvwjtDPYQiRTHF4wahxzJHZodQxznGUMXVBXAE4IS5xCJDuMLvgjABbgCp/9+/ID40vOG7u7pueY95f7kGeXu5VznQeom7nXy+vaS+r79pv+DAI8Apf/G/j79Yvv/+F32lvR982nzwvNW9LH13fdE+3H/9AOhCBENOhHAFMAXThp2HOAdyh1YHMUZWBdeFfETwRL9EAEPKAz+CMIFZwK+/6H84/j08xPuTOnp5cfkyuTv5KXl4+a+6bLtRvLN9rr63/3S/7AAXACk/6T+bP3C+1r5y/ay9IXzW/PA81/0qfWk9+v6Hf/IA7oIJw1aEesUCxjeGiAdpR6ZHiEddhrFF9AVOBT/Eu0Qaw6CC+0HCQXCATD/Xfw/+LXzw+1C6UPmE+Vd5TLlmuWC5gDpEu268UL2//nz/BP/cgDaAIgAs/9o/rn8VPqj92f16/OH84Tz4/PL9Kz2H/qD/pYDYAjODPUQ1BSfGMYbDh4JH3EevxxHGtMX3BUWFKESsBA2DjwLrAeUBKMBMP9a/Ej4W/Or7S/pU+Yv5TflI+Wh5bDmLOks7ZfxS/Yj+kL9df90ANkAWwCl/23+efwF+iv37PSO8xPzKvOg89H0DfeX+iL/IwQWCbwNAhLXFVEZMxxOHkEf2R4gHYAa0xeKFeUTQRJTELwNqQp6B08EqwH0/gX8Pfh183bu+ekT56zlWuV35crlAede6R3tgfG69Yb5Yfy0/iQAqgB5AFb//v0X/Pb5z/ej9VL0ZfND88jz9PRV94366/60A7IIdA2rEYsV6hjsGxEeLx+2HvUchxrdF84VExRyEqYQBw4bC84HcQTCAfv+TPyo+L7znu7V6ffmw+V95bfl5OXe5izpp+wG8VP1GPlJ/IH+0/81AMT/C//R/SL8DfqC92L1wvMB8yfzw/NU9ab38/oz/84DyQiZDc4RphWoGFwbjB2hHqIe2xxcGrgXkhU9FLESxxASDvQKzgfTBD8Cnv/I/Av5ZvRd77XqpecT5q/lseXB5Zbmgujo60PwyvTd+CL8ev7q/5AAYQC1/2v+ovxy+u73sPX28/ry4fKK8/30U/dt+mL+6gKqB44M5hDFFCcY4Bo3HWUeOx7XHH4aYhiCFgEVfhNhEdsO0QuyCJAFqgLk/7785/jm86juC+oA57zlU+V45bHlkubR6CbsiPD99Pr4UvyD/ur/TQAjAJ7/ff7U/HT67feD9d/z2/KN8ifzafT29iz6Dv55AhcHNAzmEBAVdRj2GhUdTx5oHiId6RqRGKEWLhWOE5UR2w4BDBkJBwYmA+3/xvwj+Zf0v+/56rHnOObQ5f7lE+bI5s7oJ+yU8N/0n/jD+yH+0f+cAJQADQAS/5H9evv4+Iz2mfRm89Py+PLi88H1q/g6/IwANwVMClwPuxOIF3Ma/RzCHi0fPh7nG20ZOxdjFaYTORGKDpQLiwiXBXsCvv/+/Oz54/Xp8EHs4Oh05xnn7+aV5sXmbOiD657vq/Ns97r6b/1j/y8ANwDb/0X/Jv4a/Gn5pPar9I/zEfMW86bzdfVL+Pb7AAA5BB0JQQ5IE1wXNBpVHMIdhR4WHk4c4hlLFyQVFxPdEGIOlwveCA0GTgOQAMr9yvrx9ojyB+586oXoi+ca57Xm0+ZS6BHr7O7x8pX27vmK/KT+2v8/AEMAlP91/oL8B/qv96T1XPRq8xPzh/Px9H/3o/pw/rMChQeVDFQRdxWzGGsbVx1BHuodWRxXGiMYEhb9E7ERaQ8ADUkKNgftA/QAXf6Y+/D3IPMh7l/qZOi850XnoOaZ5uLn3Oq67qfyV/aU+Yn8vf4TAJoAhgAvAA//Hv14+s733PVw9H7zvPKs8t7zXPbh+aP9qAEPBgALHRCbFDsY1BrHHNcdwR2KHIAaeRh1Fn8USBK2D0gNpQoYCEIFQQJr/0f81/h99OXv/Os/6eLn/eZ45o3mzOeO6gju1vFl9ej4QPzt/rgAJwHeABwA8f5V/QP7jPhQ9or0TfNz8lnya/Om9dL4ZfxEAKEEbAl8DioTCxcbGnAc5x1LHoQdzxvbGcwXnRVTE8IQTg6WC3IIIQW6AQn/ffxd+U71YPBM7KbpZujI5/nmzea85ynqfe3d8E30wfdb+3f+WgAGAdMAcADG/2P+Kvx2+Rr3QfXX86zyFfLB8rn02/dQ+xT/VQMuCIINVhJ4Fq4ZMBztHVMedB2FG1YZQxcgFfYShRA1Ds8LHAkuBgcDYwDi/fb6Fvc98sjtfOqs6MnnFOfn5qbnv+nR7CTwlPMc99T6Ff4ZANAAkQAjAH//Uv5l/OP5iPd/9d3znvL/8azynPR999r6UP5mAjMHaAxvEYYV1xhaGw8dyR0SHXwbaxlfF3AVQhMHEa4ObQwBCigHNwRNAbn+0/sU+Ivz6+5t6zDp/ecy59rmhudm6WXswe8j86v2Qvq5/TQARAEyAXoAqf+B/sj8gvoK+Mz1/fO48hrykPIR9Jz2wvkw/SoBhAV7CmEPrBNHF+4ZGBw3HSUd/xvtGf4XCxY5FDoSzg9sDcsKGwgiBQICJP8o/Nz4rvQH8OTr9OiN5wbnFeek5w7pfuur7lbyBvbd+U/9AQCKAaYBPAFiAHX/Hv7/+6z5HPf99FnzN/I+8kjzjvVz+KL7QP9sA2cImA13EmQWdhm4G+4cDB3hGxYaMhhPFm8UIhKlD0IN7AqYCOAF+QIpAFn9QPo69p7xT+0z6qTo7Oeh58Pn2eg363/uO/Ll9XT52PyE/zQBjwECASkA/v6p/Zz7NvnX9p30FfP98ffxFvMg9fj35PpG/k4CCwdUDCYRPRVtGNsakhwOHXYc9Bo3GXgXgxVFE68QMA7DCz4JUgYyA0wAif2i+s72VvIh7gbrbemK6ATo7OfE6PPq7O1N8bD0MvjV++T+2ABVAfoAZgC3/6D+jvz2+U/3IPWQ81Py3PFi8ib02fbS+TP9FQHNBfwK4Q8kFIYXUhpCHP0cfxz3Gj0ZeRecFWkTzhBQDvgLnwkFB/8DJwGB/rD7TPgC9MjvYOxD6jXpfuhh6ArpzOqJ7Yvw2PM798P65/3u/8kAmwAjAG3/Rf6D/Dj68vfT9Q70mfLb8Ury6fN69mD5lPxgAOQE+wnzDk4T/hb0GQ8c9ByQHD8bjxnJF/AVxhNpER4P3wyTCssHvwTgAT7/o/wg+cP0RPCL7FHqBOlj6Dno0uiQ6gXtEPBR8+b2vPoO/l0ANwEoAcQAIAAi/0j95fpb+AL2D/Rq8nrxifHj8jn1DPgv+8r+OgM4CFkN5BG5FfMYQhumHLActBtIGpwY9xbLFE8Sxg9SDRELYghuBWoCvv9P/UL6XPbl8Rrus+uG6vPpaelW6TvqZexr76fy8vU1+Vf86f5UALMAYADV/w//o/2X+w35jfZ19NDy2PGw8a7ys/Rk94T6A/5FAjwHaQwkEeoU8xdGGsMbMRxLG8kZ/RdQFpUUXxINEL4NsguTCe4GDgQsAY7+q/vg94DzTO9a7Lvq0Ok86Qjp9+kl7CrvafKD9cX4A/y//kEAegD2/07/m/5C/Sb7h/gq9nf0NPNR8tLxXPIm9M/29PlD/ScBwwXACpYPqBMIF7QZmxtqHOobmBrXGCMXTRUjE88QdA5JDAQKXQeCBMcBcv/u/Iz5N/XI8JLt1esK61zqw+kL6pnrRO5B8R/0EPcY+vH8y/5g/zb/uf5V/oT91fuz+X/30PVw9Dbzi/LD8lH0xvZ3+WT8uP/+A+MIqg3KER0VCRhdGrUbpRt3GvQYlxdFFnsUJhKiD1cNLAuwCMcFugL3/079A/ri9Xvx8+3q6/DqUOrC6eXpRuu07Z7wfvNq9nb5Zvx9/lv/OP+y/kz+nf1l/Gr6Ifg29qH0hPO68svyAfQV9sD4ePun/qQCbgeNDBERwBSpFwwawBs4HI8bEBpkGMEW3RSvEjEQww1aC70I6QX5AkYAnv2M+pr2RfKK7hzsFOt26hrqN+pS66Xta/Bu82P2Z/ln/Hn+Yf8a/3D+Af6N/aX81/qQ+HH26fTO8xDz+/LC84v11vdp+nT9PwHrBeAKbQ9PE48WUxlVGxscrhtdGuoYcBeUFToTixAZDusLnQnfBtIDAAF6/sr7Vfgo9DbwW+3p6zvrteqb6jjr/ux87yvyC/Xk99r6O/2L/t7+c/4N/of9kvwK+/34JPeJ9Sn0EPOJ8kHzCfWN9zv6G/3DADkFQgoCDwcTTxYLGQsb0htmG/sZahjdFiIVEhOlEGMORQz8CVQHVwSnAVH/4vyk+W31P/ET7mjspesk6/3qletG7Yzv9PF89Fj3n/pV/bn+rf7h/VH98vxc/Pn69vj29kP1FPQe87ryVPMH9Zf3PvoI/VkAewRNCfEN+RFUFRkYUxpQG/0avxkSGKwWLBVBE/QQdw5rDFAK6QcwBWcCMwDl/eP63fZ48gbv8+wI7HnrJuti64Tsk+4N8djzz/bz+bf8aP7a/mP+z/1M/aD8bPuU+Zz31PVe9Erzw/Iz87D0APea+Ur8av9VAwAIsgzgEEYUHhdMGXUahxp5GSEYrBYlFW8TTBEsDwsN+gquCPgFSwOoACX+Ivtj92zz3++Q7UDsp+uJ6+TrKe0S73TxE/TV9s/5cvxW/gb/z/48/nj9k/wu+3/5lffN9Tr05fJR8nzy6PMq9tz47Psu/zADlwcRDDgQphOZFroY6hnvGfAYkhcXFsIURBOSEaQPtg3LC48JCQc9BKIBJv9B/K74c/SH8LztL+yX62/ry+ve7KruGvG984P2avkv/Gf+ev93/8D+1f30/ND7WvqO+Jn2yPQu80XyOPI+80r1xPev+tH9hgHzBYQK/A7MEg8WpRgiGm4aZxncF14WAhWmE80Rtw+0DcQLvwk3B4gEDALb/1r92fmq9aXxze4v7V3s6Ov36/rsxO4V8XjzDPYJ+RP8jP6V/1L/c/6B/az8dPvN+d73+/Vi9Ojy3/Gi8cPyGfXu98v6m/0TAWcFJQqSDioSQhXXF5EZ7hnbGE4X7hX3FMgT+RHyD/sNaAynClMIogUBA9sAfP4y++n2efJN74vtxuxY7BTs2eyh7gTxjfPS9YD4bvsI/on/T/9Q/hz9N/xc+9X5A/j99U306/LN8YPxR/Jt9D73Nvo3/X4AkgQRCZQNfxG0FEgX8hiBGcwYXhfpFbQUnhMsElAQeg68DO4Kqgj+BXEDIQGs/l/7Jffv8sXv6e0A7VXsOewb7fjuc/Go8+f1g/iP+23+3v/u/9/+rf2q/GT74fnY9+n1NPSv8prxCPHD8bjzifau+b38LQApBJ8I+gzFEAUUthbFGIcZ9Rh5F+0V1xSzE10ScxCnDhYNUAtFCXoG3wOUAV3/ofyV+E/0jPA17iztluyg7DTt4+4w8X/z1vVG+FP7N/4uAIQAcP/0/Xb8O/u8+eb3+fUZ9ITyNPFz8N/ws/Kx9Q35QPyT/0cDpwcyDEoQyROSFrsYqxlGGdMXEBbnFO8T0RIIEdcO+gwfCzUJtwb9A7kBcP/b/Af5hvS18EXulO1e7U/tnO2o7tPwWfPm9X34OfsQ/gQAhgCi/wv+rPxx+yT6RPgS9vzzOPL/8E7wzfCB8mT1pvi++w7/yAJtBygMahDWE1gWURgsGdsYeBe7FYYUhhN1EsEQwA4BDWMLxAl+B/UEhwIcAGb9h/kd9Svxpe7D7W7tU+2K7aTu4/B08wf2fvg8+yH+AwBlAEP/t/2F/G77Efrz96H1lvPt8cnwG/CY8ILyavXJ+Nf7+f7cAmgHQgxJEF0TwxVrF2wYAhisFiIV6BM7ExkSgxCXDvIMygtPCmAIvAUTA4wAkf3q+Y31wvE77wTuiO0t7XDtpu7v8LvzWvbw+JD7If7+/3UAu/9n/hn9xPsJ+sz3gPWQ8yHyJPGB8N/wZfIp9XT4nfvv/oEC8gaJC4MPjhKUFGYWeBeSF5QW2xSnE7oS7xGsEAEPqQ1mDAwL0gj6BS8DiADW/SX6wfWf8cjun+1G7V/t0O0t747xWfQd94X5Hfyg/owAGwEEAGv+yPx8++35vPd69V/zB/IK8WDwovAG8uD0MviD+8L+RQKkBj4LcA+DErIUVBZHF0wXIhaAFCgTVxKqEXYQ4Q5lDUEMEQsvCZoGtQP3ACT+gfox9gnyNO8L7sHtwe0C7kHvuPHC9LH3DvpT/KT+ZAD9APf/LP5u/On6cPlC9+v09vKe8f7wkfDs8GryHPWT+Mn76f5ZAogGNgszDxcSBxSVFccW5BbWFREUuxIUEocRhxADD7gNwwy0C7wJ6gb8A0gBnv7/+n72LPI37xLuxO2v7e/tMu/E8cn0iffI+Q78lv6HAAABwv/M/SP84Ppa+Qb3ffSI8nrx5vBn8I3w/PHv9IH4zPvR/iECZwYACwkP7xHTE3QVkRa/FqMVyxN6EtoRhxGXEAIPtA3cDBoMdQqnB6kE4AEf/5/7GvfE8qHvNe7L7Yrt1u0T76Lx0/SS99357/tF/iwAswCu/6/95fte+sD4nPZB9HvybfH+8LXw9fBZ8gn1mPgJ/C7/fAJiBsgKuA6MEYcTCBUuFmwWYhWwEzsSjhFDEZgQmw+gDuYN8gztChgI+QQkAkH/Wvu39h3y5u5j7eDsD+2w7WzvIvIh9ev3J/qB/Oz+wgA7Af3/Af4O/GD6lvhK9gz0XPJO8Yzw7+8a8Lbxv/RS+Kr7tv4yAm4G5grfDq0R2RN7FXkWjBZVFcYTdhLXEYARvBDSD9UOGQ4NDQ8LXQhFBXMCWv9m+8v2Y/Jm7+PtYO1H7cXtYO8A8h712fcm+l78nf5mANIAw/+7/a379Pko+CH2/PM78izxjvBK8KfwM/Ii9bz4VfyI/9cC2wYoCxYPzxGJE+AUuhXfFcQUBBOpEQkRFxHJEAkQMQ93Dq8N2AvtCIAFMAIU/zz7iPba8W3u++zO7DftDO7D76Py6vXQ+Pb63fzt/pUA5wB4/wr9qfrl+EP3R/VD87Xx/PCm8ITw9vCL8pj1NPmc/I7/mgKBBtkK4A7KEacTAxXrFRgWExVhE/QRThE2EcwQ6A/wDlIOqQ0JDEQJ2gWpAn//ofvn9hnyxe427dbs/uyP7VrvOvKS9Xz4lfqZ/I3+HQBnAP/+w/xt+pL4+PYF9STzyPEq8Rvx//Bw8ezyv/V0+cb8vf++Am8GuwqADlERIROTFK4V6BUKFUwT5hFDESsR5BAwEH0P5A4iDk8MZAkGBtICuP/U+wD3M/Ko7gLttezw7LDta+9J8ov1Sfha+i38Pv75/1cABv+V/DT6avjE9vT0E/Op8Rnx3PDM8CjxqfK39Ur5vvym/7ACjQa5CpsOYxFcEwAVIRZrFmoVsxNgEskRvxFvEYIQkA/PDvQNOgxKCdEFggJM/5H75fYv8sjuP+0q7WvtFu6573LyyfV/+Ib6OPwI/q3/4v+K/iT81vkx+Kz2/PQO86XxJPE68XDxxfEG85j1GPmI/Gn/QALHBQ8KFw4UEQoTixTXFVcWpxUYFJcS7xHYEYARtxDBDwkPLg50DJ4JHgbeAq7/8/tY94Py++4/7fTsQO3C7ULv4fE29S/4NvoI/OT9kv/9/6D+UPwA+lb4zfba9NbyXfHO8NTw4vBK8a7ybvUA+WD8Zv+HAmEGuQqfDn4RehMQFVcWvhbNFSAUqBL1EcYRQBFJEEMPnQ7nDTIMVwndBaUClv/k+1f3mvIi74/tRe1z7fDtae8V8l/1Lfga+sD7k/00/4z/Q/4E/N35Xfjr9iX1NvPQ8WfxdPGn8QDyH/Op9Qf5g/x0/0kC6AUPCh8ODhHnEm0UtRV9FvYVUBTWEgYS4xGjEb8QxQ/tDhcOcQx0Ce8FeQJO/7b7D/dD8n7u0Oy27Bztx+0Y77/xHvUr+F367/ug/ST/pv+I/kb8BPpX+AH3Z/Wc8zDygfGF8bLxF/JO86r17vhL/GX/gwIrBmUKaA6FEaATIBVGFrIW5RU+FJESjhE5EboQ8g8RD2IOpA3wC1EJ/wW7ApL/o/sQ90zyue4R7bfsNe0E7pXvM/JK9Un4fPo6/O39Hv9V/w7+4fvY+Uj4/vZs9bDza/Ln8ejxDfJr8pzz9/UV+W38cv+GAjIGSgpYDlcRYhPaFNIVOBZdFbUTFhInEfcQrBAdEHUP+Q5ZDsQMDAqXBh4Dwv/J+/b29fEW7knsFuyb7Fvtwe5h8ej0VfjP+of8Ef56/x8AO/8W/a362viB9/71M/SI8q7xgvGh8ffx2PIL9S74qPvv/hYC4gUgCkwOgxGSE/YU6xVMFo4VzBPbEZwQLhAHEJ8P+Q6PDh4ODg3ICmsHCAS8ACv9ofhd8wXvjewY7HrsDO1o7tbwdfTx92f6NvzM/Zv/iADO/6f98Pr3+HX33/X58yPyVfFS8ZTx2vGK8qn0BPjV+zD/BgJABSEJNw1wEHQSvhPTFKcVRxXFE+cRwxDQEAkR2xATEGkPAQ/hDYAL1gcSBMMANv21+Dzzee7u65LrQezx7B7uo/Bp9EP4+fqq/Ez+KQBmAdIAZ/55+zf5vPdC9jX0OfIv8S3xjfG+8UTyIvR792z7yf5/AXcEOwhoDMMP1xEfExwU7hS/FFsTjBFMEDkQjxCAEPUPVg/5DiEOHwzsCEwFCwKd/kv6E/Up8DPtS+yV7CTtFu498JbzSvcj+hr88P3t/28BOwFC/3P8JfqF+Nb2wfR68h3x2/AR8Ujxn/FA83j2dPoN/sQAjgMiB0IL3A7+EDYSQBMzFHgUYROWEUwQIxCaEKYQABBJD+QOTA6ZDIUJ8wWeAmD/YftL9lLx4+2j7NjsT+0S7rvv0PKb9r755fuM/XH/KQFzAez/Q/3q+lX5xvfJ9Xvz1/Fx8Y7xrPG28bbyaPUP+av8cP8QAn8FiwlaDeAPVRF2EpETLhSJEwQSihAEEEwQhBA7ELEPZQ/3DpoN3ApPB+ADowDn/Az4u/KK7nTsHeyI7DTtqe5+8T71vfg1+yH9Nv9CATcCPwHd/ln8X/q0+J/2LfRE8lDxLvEe8QvxxfH485j3YPuP/loBigSGCGcMWg8jEV0SnhNsFCUUthIUEV4QfhC+EGoQrQ8kD6IOcQ3uCn0HDgTgAGX97PjM82Dv3uxG7LXsde3A7kjx2fSF+Fn7ef2J/48B4QJyAnIA7v2p+8r5qPdH9TPz9PF88S3x7PA68e/yCfaR+cP8kP+vAoAGfQrEDfoPjRH1EgwUHRTvEk8RRxA1EIkQaRDsD5UPZQ+gDmsMHQmvBbMCiP8g+7n1ovBs7TfsCOxO7BztYe/y8sr28/lD/Kv+SQFYA6sDCQKe/2H9ePs3+VT2lPPU8RnxqPD/78XvG/E69Cb4sfus/uQB1gUQCokN0A9uEdUS/RMlFOwSLBHnD6UP4g/CD04P6w7cDm0Orgy8CVIGUANYAHT8g/dh8qzu7OyA7KrsSu0q72ryQPaN+e77Lf69ABoDDwQVA/8Auf6w/GT6kffb9Ony9vFE8XDw/++88C/zkPby+fr8AACZA3wH/wqgDYMPNxG0EokTMBPmEbQQRRB5EJ0QThDgD6QPNQ/BDe8KdwdJBGcB+f1d+UH0EvC67e3sz+wn7X/uT/EE9X34J/tk/e//hgIdBNQDEgLi/9f9rPvc+NL1a/MO8kXxYfCR77zvnPHY9Fz4g/tz/tcB2gW8CdQM8Q6bEDQSUBNWEy0SwhAAEBgQThASEKEPag9fD3wOPAz5CLYF7wK8/3n7Vvar8bXuT+3m7Prs6+1J8LLzUPdF+q38Qf/fAd0DPgTqAtUAp/6D/PL57fYy9FHyRPFz8J/vaO+f8GXz9fZA+i79UQALBA8IXwuzDX4PHBGJEvsSQxIEER4QGBBZEFoQ/g+rD5cPFw92DZ0KPgcxBCEBN/1b+Fvztu/O7RXt+Oxo7TDvUPIK9mX5+vty/gsBXwNfBJ8DwAGc/479D/sO+BX17/LF8fHwGvB67w/wNvJj9az4ofuq/jkCLgbaCaEMlg5lEA0SDxPkErIRgRDtDw0QLRDhD6MPmQ+GD3AO2AuLCFIFeQId/2r6LvW+8CHuCe2h7NnsH+778LH0O/gV+4v9TgD8ArAErAQwAzgBNf/l/Pb5ofbg8zjyH/EE8N/upu4o8DPzt/bS+b/8SgB2BIYIuQvgDd4PtRH+EjUTEBLIEAkQDBAvEN0PsA+tD8IPGQ/yDPEJ0AYdBA4Bx/yT96vySe+I7dnsqOxN7VXvlvJN9nf5M/z4/vgBcgRQBYsEsgKzAJj+0fuN+Gr1H/PG8aDwW++D7hnvnPEk9bD4tfvA/pkCzQaqClgNJg/qEGESQBO4EjUR9w9wD9MP8Q+SDy0P/w7tDqwN+wqbB24EvAFi/rP5iPQ78PztSu0y7XjtoO5u8TT16fjF+yz++ADLA6IFegXUA60Br/+D/Wv6GPdB9IjyaPEm8Bvv7O6d8KfzAvcV+u/8cwCIBJsI5gstDg4QshHhEukS3RGJELIPoQ+OD0MP1w6yDrcO1A2/C68IswURA+v/xvuw9jjyR++07SDt+uwJ7m3wrvMv9xD66fzd/68CyQQ1BWkE0QLcAL7+zPuf+OH1yPNs8tDwZ+/a7r/vXfJY9Vz4XPuy/tEC0gZaCgINNg89EYsS4xIFEtMQ/A+nD3oPFQ/EDqgOuA4kDmkMygnlBjkEPAFA/XX4wPNH8FDuR+3u7H7tXO9d8rX14vjV++H+9QFWBGkFHQXYA0MCKwBq/Ur6Nff69DvzifH579zuWO8n8dvz4fa/+SD90wDhBM8I8wujDr8QaxJLE+wS3xGrEA4Q1A9pD8wORw4zDgEO2gxtCngHvQQoAuL+QvpU9U/x9u7c7SPtQu2M7lHxyPT19/f63f0gAfwDhgWeBWUE7QIgAc3+zPtl+K71t/M88p3wFe++7gbwuPK/9Yf4gvsi/2IDlwcHC8QNDhDyEQgT7hLzEa0Q6g+HDzkPyg40DhcOAg6BDdYLFwlKBnUDgABU/D/3pfJY7+rtD+237G3tfe8B84f21/nu/A4AOQNMBR4GjQUqBFcC6P8I/cT5tvY+9F7y0PBp77fuNu9U8WH0jfen+tj9zgEDBtsJ2QwFDxwRjhIpE6cSUxFuEM0PiQ8QD1sOFQ7JDWMN7QtpCakGywMUAXr98fht9Lvwye657Vftxu1V71fyrvX1+OX71P4BAncE0QV/BUIEuQKiACb+2vq59z71VPPT8RvwIO9D7+PwlPNZ9lf5cPwZACgECwhvCwsOOhD9EQUTBxMoEhARbRAwEMMPBA8xDuINlQ1oDCIKKQeEBPwBuv5o+rf18fGZ70nuhe2Y7e3uevGf9Lv3w/rN/foAlwMwBYUFlwQeAxgBv/77+934Lfb480jyvPBk7yTvRPC08pT1Tfha+8r+7gIGB3wKbA26D8ER7RIuE4YSchHDEEAQ9w9CD34O/A2ODc0MuQoaCEgFsQLN/5n7Cfff8kvw9+4E7svtgO6p8LLzuvbG+az8t/9tAiwE3QRiBCQDYgFC/7v89fko98/0DvOK8Vzwm+8q8BnyvvSb9zv6c/1ZAYsFigmJDAkPHxG2EpETKRMyEjoRoRBREKMPvQ4CDnQN0QwZC3kIqwXfAkAAnfwx+OfznfAO7yXu6e1o7gDw7/IQ9jb5Avzi/r0B2QPcBGcEOgOLAZb/OP1k+qL3KPVi89bxfvCq79zviPEQ9N32nPmj/GsAnwSZCO8LqQ74EMoSrROTE8AS2BFIEb8QMRBfD4oO8w0CDYILCQkaBlsDXADp/I34KvTg8NruBe6T7fftiu8e8lz1VPhX+2T+TQGwA70EwgTZA2YCggD9/UH7afjY9aTzuvE+8ELvZu+08AvzxPWW+MD7cf/dAwwIrwuCDsQQ4BL9EyQUTRNUEtYRQRGkEJsPuw4tDj8NrwsOCUAGiAOiAC794vi09FTxU+9N7t3tNe587wPyFPU4+Cz7C/78AF8DuwS/BNYDfwKtAF3+ivuS+P/14/MR8oPwdO+K79LwHPPK9Yf4t/tj/6UDzgdJCzcOfBCBEsIT8RNfE18S4BF/EegQ+Q/gDk8OlQ0EDGMJMQZ3A9MAdf0r+bX0gfGl76LuCO4Y7o3vFfId9f73zfrU/cAAKwNqBJIEyAOCAs8Acv6y+6v4Dvby8zLyv/CV743vwfAD87L1bPic+1f/ngO4BygL8Q1JEGkSuhP6Ey8TLBKlEVURzBCnD6cOKg6hDSoMSgkYBlAD0ACg/TH5zPR+8aLvpu7y7TLuoO9E8lb1Ffjj+sH9tgAwA3AElAS+A24C3QCH/rv7vfgN9hr0RPK78IzvWe+n8MnyhPVl+J77f/+RA5wHDAv3DWUQRxJ5E5oT+xIDEmkRBBGVENwP/A6DDtkNeAzrCcMG6AMZAb39cPnu9GzxQO8D7l7ti+0I75Hxd/Ry9236rP3TAFgD2wQsBZUERwNPAdj+9vve+BD2k/OT8fjv4+7v7g/wZPJK9XL4+Pu7//YDBQibC48OyhCeEqkT0RMbEwQSRxHUEGIQqQ/qDmEOyQ1mDNcJuAanA90Afv02+bn09PC87nbt+Oxd7bXuUPFR9I/3x/r2/UYB6AOdBQEGOwXOA8YBW/9T/Pj4zfU780Dxo++c7nTuqe8A8gT1YPjl++j/JARbCAUM4g4gEbwSyBPtEyUT+RENEZYQThDHDwkPfg76DekMoQqFB1QETgHz/ZD55vTm8FjuEu1f7Jvs3+1q8LHzC/eX+vn9SQENBNAFewbHBSoE6gEs/xn8pfhT9afypfBO72buZe6z7znymvUd+cP8pADUBPMIbwwdDwIRehJRE3ETrhJWEVoQ3A/aD5oPFw+uDjYOVA0fC94HagQ5AfX9svnj9K3wAu7b7HjsyOwX7rbwGvSg9wj7Uv6PAUIE+AVQBmYFrQNbAar+avvf95L09vFT8C3vfe6g7u3vq/IH9p/5PP32ABEF9QhWDOYOuRAmEvsSFxNrEiQRMhDyDw8QDBB7DwUPpQ7DDbsLSgisBFgB6/2x+bT0aPCm7X7sHuxc7LjtVfDW82X32vou/nUBTwQJBn0GjwXKA3IBrP5/+/j3n/T18SjwA+9r7o/u5e+M8hD2yPli/RcB+ATgCEEMxg6KEM0RqBLKEvkRpRCpD3IPzw/rD6QPTg8HD1cOQgz4CEcF1AFZ/vr5DvWR8JntTuwB7I/s/u2Q8O7zh/ci+4L+ogFOBBwGpQa6Bb8DMwFu/lb76vd19Jvxuu+c7iPuRO6S70HyyvWo+U/9CQHeBLwISAzUDpYQsBFzEq4SBRLVELUPag/DDxAQ4Q95D0UPrA7mDJsJywVIAtH+pfqN9dLwke0V7K/r7Os57cjvRPMH96H6D/5aAU8EXgYaB2IGkgQcAln/Ivx2+M70wfHZ76fu5+3Q7cfuZvH59Oj4tfxiAGkETgjPC2kONhCeEYkS1xInEsUQlw8+D5wP8A+6D2APOw/xDnENRwp3BvECtv+v+5v2nfEb7ofsBuwp7Cftl+8d8+n2ivrW/RIB8gMWBgIHYwafBBACPf8U/H341PSm8aHvie727eHtw+448dP04Piw/E8AEQTkB3QLKA79DxwR2xE6EtARthBUD54O3A5hD5wPUg8vDyQPHQ6EC6MH7AOlAOD8Evi58qXuiOza6wHs5Owj74vyVPYE+nP9ywDgAwkGDQeRBuYEigKj/4H86/hD9Sby8e+57u7tue1z7p3wBvTY96/7ZP88A/8GdwpRDWAP5RDIESISshGbEGYPrw7KDjoPaw9EDzwPKQ9ADsYLLwiOBDMBhf3B+K3zl+9N7XDsT+wj7T3vcfIb9p759fxFAEYDhgWYBmUGGQXbAiUADv2s+Sj23PKH8BvvXe4Y7onuavCB80T3BPuX/lECCAZxCTUMTQ7bD+gQbxFDEXEQTA+IDokOBQ9qD10PYQ9oD9cOzwxCCXUF9QGC/hT63/Ry8Kjtlexf7Ofsse6y8Yr1RPmw/Ov/+AJ5BdgG8QavBZED4QDb/Xj6x/Zo89vwau+Q7hXuUO7E76jyRvbz+YL9HgHWBGIIaAu0DWsPpBBrEYUR4xC3D64Ocw7DDikPFQ/uDhgPvg4pDesJOgYEA87/0Puy9gHy6O6A7R3tQ+2N7izxtfRe+Ln76P4IAqAETgakBrkF5gNqAZ3+S/uz90D0kvH27/zubO5b7n7v9fFd9Qf5ifwiAMgDaQd+CuYMvQ4VEBcRThGrEFwPJw7NDQwOaA5gDl0Osw68DooNqQolB+4D5QAt/U74fPMC8EHuqe2l7aruA/GE9EL4ivuF/msBNQQkBqMGuQXPA48B5/63+xT4e/TR8RzwJ++A7lTuXe+p8fX0ivj1+2n/BAOYBtUJRQwqDo8PeRDgEEwQMQ/+DV8Niw3dDR0ONQ6ZDtwOEg7aC4gIPwUgAov+9/kS9S3xze7G7YjtUe5d8IPzIfeW+tj9+AC+A7sFnQYsBp4EQwJy/0n8yfhR9UryOvAJ73PuXO4W7wnxEvSm9z77wf43ArUF4giCC4QN8g7+D2kQJhA3D/8NMw0HDXYN9A1WDsQO/g6ZDtQM1wl0Bh4Dsf9z+6L2V/J37y3uzu1S7vrv4vKI9iT6iv2zAJ4D1wXqBrMGUAUvA2EAFf1r+cj1pPI98NHuB+7R7WvuFfD78nn2NPrN/WEB/wRfCEkLYw3+Dg8QjRBYEGQPNw44DeIMEw14DekNYA7ZDrUOXQ21CmwHPATzAPL8PvjT86Hw5u4V7h/uVu/r8Vn1yfgZ/EH/WwLZBC0GXwZbBZoDEQHt/X364fa38yXxae9j7uztWO6+70PygvUH+af8TADwA3AHbwrdDK4O8Q+3EL0QEhDSDpkN+gz5DEUNfg3LDTcOUg48DdEK4wcRBT8CjP7z+YD1PfJn8EPvw+5i74Dxm/TH99j62v3vAJ4DPQWzBQcFlgNkAYn+O/uu93H0wvHY76Lu+u0p7kTvivGe9BH4qPs+//UCgwa3CVUMRw6zD30QtxA1EBoP5w0FDeQMIA1rDaYN6Q00Dq0NxgvpCN4FCgPs/8P7LPdI8/fw4+9C72jv0PCh8wb3C/rX/J7/iwK7BH8F6ARjA5cBPv80/KX4M/WC8onwJ+8z7gzu/e4N8d/zBvd3+if+8wGdBdQIfgu6DWgPdRDCEFYQbw9GDmMNBg0ZDVkNjA3MDQIOmQ0QDHUJgga3A5UAofwU+BL0hvEk8HLvU+918A7zTPZ1+Tb8EP8dAoUEpwU9BfYDUgIlAEn9qfkX9ibz4PAy7+ztjO1D7hzwxfLY9Vz5Lv0vAf4EYwhMC6sNfQ+SEAwR0RDxD6wOfQ0KDQYNMQ1BDVcNrA2KDWAM/AksB5QExwEY/oD5RfVa8svw4u9n7wrwHvJO9Yj4aPs+/k4BIASvBagFfwT0AhoBcv7d+v/2vfNV8YPv/u0s7Y7tRu/c8dX0Mvj++yoAMwTEB9IKXA2AD9sQaBEyEV0QTg8hDlwNDQ0HDQ8NEw1FDUQNZQxqCsAHIQWGAiP/4vqI9kjzbPE28HPvle9V8Vn0hvdY+hD9OQBOA0MFjgWsBF8DvAFg/+/7F/io9P/x9O8z7ibtLO2J7t3wrfPs9pn6y/4LA+UGLgriDDMP5hDTEeYROBEXEMQOvw0uDfcM1AypDLYMugwnDG0K3AdMBekCHgAp/LX3LfQm8hzxIvCt777wh/P39sb5J/za/gYCmgRnBZ4EPwPhASUAOv1L+Yn1p/K88P3ubO3Z7LXtBfDY8uD1XPlx/foBDAZ6CUoMxA7CEMoR/BFjEWkQMA8ODmANGg0CDdUM1QzwDKwMRwu8CPEFdQPZAD/9yvi89DTy3fDc7y7vvu808pP1t/hR+x3+dAFnBNAFeQVLBAgDUwF7/pr6mPZp8/7w5O4g7T/s9uzn7pLxm/QZ+Fr81wAhBdgIBwzSDuAQJBKPEk0SghEgELQOog0sDfwMtgyADGsMRQwyC/kIRQbVA4UBTf4U+t71BPOR8ZHw1O/n78fx6fQh+O36hf2nAJwDXQVyBWMERQPMAX7/+Pvt93/04/HX79rthuyJ7P7td/A684b2hPoh/68DqwcoCycOsBBDEucSyRIFEsgQQQ/3DUEN7wylDGwMZwx+DNQL9AlYB88EnQKy/6v7Lvem88DxevBy7/7uS/Bb88X2y/ly/KL/FAN4BRkGPQUXBNUCsABD/Qf5NfVX8vfvzu0b7MDr9uxE7yLyUfU++dL9kALtBpMKsg1AECASBxMNE2USIRGpDz0OWw3fDJkMdgxpDIQM+AtdCuQHXgUwA3gAo/wa+Ff0JvLa8Lrv/O7f75nyFfYs+cj7zf46AgAFBwZtBVIEGQNNASj+/vn49cjyW/Aa7jrsfOtL7H/uP/Fq9DP4qPxyAc8FpAnoDLIPxxHYEhATphKbER4Qlw6GDR4N3AykDHAMdQxZDBYLxgj3BZQDRQHr/YP5FvU+8sfwyu/97iTvT/HR9F/4Mfvw/VIBlARzBksGHAXSA0wCrP+g+0X3tvMY8cjun+xM653rj+1K8FHzyvYg+/7/rQSwCBwMKQ+XEQETTRPYEv4RvBAqD8AN5QysDI0MXgxJDB8MYgtsCbEGEQS3Aer+3vpq9uzyA/EQ8EHvK++o8NPzgPeY+lr9bADGAykGsAbLBW0E/QK/ADP91vjR9MvxXu8u7XrrJOuV7B/vGvJe9Wj5Mv4eA4cHLAtgDg4R4RKZE24T1hK+ET0Qkw5jDegMrAxtDAoMvQsYC38J8QYrBM8BVf/v+7P35/OQ8W3wte9V7y3w1PJ59tf5svyj/+8CwgXtBnEGKAXWA/kB0v6g+lH28vJI8PPtC+wm6wPsA+7A8NrzpPdY/DsB4wXOCVMNWxCDEpITohM7E00S3xAaD7ANDw3KDIAMCwzGC2gLJQq/B9AEVgIGAOj8u/iV9Ojxk/Cx7wbvbu/Y8Xr1+vjO+6H+EQIrBbIGYAY1BR4EkAK4/4v7L/e78xPxs+6B7Fbr6uvY7WzwLfOW9gH7+P+vBJwIJQxaD+YRZhOwE2EToxJwEdIPNQ5ZDfQMsAxDDMoLVgsdCuUHBwVyAjoAYP2G+WL1dvL88B/wbe+P75Hx/fSL+Gz7IP5SAXsEXQZjBnIFcAQqA7wA4fyP+OD0DPJ87yDtoOvI62ftru838lj1kPl0/j0DYQcSC5sOeREyE6ETaRP5Ev0RYBCEDlwN9Qy3DDcMiAsTC1UKiQjPBQgD3wCT/hr73PZJ82rxgfCs7zrvXfCA8zf3S/rQ/MT/RgPiBZUGsQWoBMUD6gF0/uf58/UX86rwMe4Y7LPrE+1n77/xUvQX+Pr8EAJdBu8JZA2vEBIT8RPAEzsThRI/EV4Ptw3WDJsMQQx6C6kKwAlhCBUGYwMDAcH+7fs0+J/0QvIS8V3w3O+K8P3yefa2+TD8v/7aAbcEDgajBZ4ExQNzAqv/gPtS9zv00/F07xztBOzP7M/uHvFv87v2OPs0AL4EewgdDIcPPBKYE78TgxPqEtcRExBXDkkNvgxDDGwLngrSCY0IXgahAz4BOv/A/Dz5ifXl8pzx5fBG8JDwiPLn9S75nvvx/dkA9gO8BawFtwTFA74CYwCM/GX4F/W48kXwwu077H/sZe5/8JvyaPWQ+bj+bANjB+sKgQ63EX4T7hOVEyYTaBLYEN4OOQ2IDCkMgwudCqAJlwjTBmUE2QGo/3/9e/r69t7zFvJU8cvw1vAl8hz1fPgl+1X90f/YAg0FfwWrBLUDAANCAer9vPlJ9u7zqvEk7w7twOw37jXwFfJa9Ar46/zOAeoFfwksDZ4Q6hK3E5ITQxO7EmcReQ+xDboMUgysC78Kswm+CDoH5QRfAgoAAP5F++D3v/Sb8q7xC/H38ALydPS693P61/xI/x0CgARUBeoE8gMPA4YBsv74+mT3rvRW8vTv5+0j7Q3uyO+r8dXzG/ea+2AAqQRxCDwMyA83EkUTbRNnE/sSoRGkD9oN/wx1DJcLkwrPCVAJBQisBRADCQFl/9n8Vfne9aPzk/Ki8fPwW/GV88P2cvmS+9r98gDMAwMFuQTTAzkDIwKd//D7N/h/9T3zyvB17j/t7+2e733xbfM29mH6F/+CAzsH2wp8DmIR9RJJEz4T/BIpEnoQgQ4xDYwMDAwjCzAKZQlOCFwGvwNoAZH/dP1H+rf2//Og8ubxLPEl8bXyufXJ+Br7Qv37/woD3QTvBAYENgODAp0ARv1g+UD2/vOm8Sjva+2O7TfvJvH38l/1O/ka/pQCSQayCVUNkBBWEsISkBJ3EuURXRBxDhgNuwx7DM4L2wocCmcJtQcrBY8CkgCq/qj78Peo9Njy+fEp8bzwovFP9IX3Ffo1/MT++AFoBPwELwRSA9ECdwF5/nD69PaW9Gfy3++h7THtoO6/8JryjvTv96L8hAGABb4IJgyMDxIS7xKyEl4SBBIcEVUPiw2gDIwMagyqC4kKdwk0CCkGngMXAeT+UPz9+Jb1BvPJ8RzxyfBW8UnzTfYY+Xb7yf2pAEwDfgRABF4DywLGAYD//vte+LH1bfMV8cDusu1u7ivw+vG985P2zPqd/9oDYgflCm4ORxGYEswSthKPEsoRBRAnDgkNyQyJDKgLowq+Cb0I3AYoBKoBrf+f/YL6sfa58zXyxvFi8Ubxf/IZ9Tv4ofqr/EP/LQJHBGYEawOXAvsBlQBw/Zz5cPY49B7yje+97avtRe8o8bjy9fSu+LP9bgJABq0JNQ2sEMMSaRNTEywT0hJjEV8Ppg3xDLkMCAz8CskJvwg3B/MEkwJ/AJL+uvsn+PH0+/Ip8oXxPfEC8jn0C/dh+XH79/0GAVcD6AM8A4cCKQISAWH+vvqU91D1PfO08JTuEO5K7w3xYvIh9FL3CfzYAKoEDAifC2wPGRIPEzcTWBOAE4sSdxBtDmwNQQ2rDFQL2gmzCHEHXQXIApwA4v62/Gz54/V+84zySvL78TPywPNg9gn5E/st/dT/bAKjAy8DPAKqARUBLv/c+2D4yfXq87Hxae847tbuc/DV8Vbz//Vd+kf/bQPwBogKdA6NERgTkBPME/cTGBMpER0P+g2fDeUMjwseChAJ7AfnBWkDRwG1/7T9bPq79g308vJ78t3xsfHt8nj1Dvj7+fL7t/6xAU0DBAMeAtIBmwEeAOD8Svmj9rf0jfLy717um+4S8G7xh/K+9LL4uP0YAo8FBwnxDKQQxxJ4E6sT/BPIE0MSCxBjDtoNgQ1vDMsKSgkjCJMGTQTlAfT/Ef5P+8f3ofTr8lPy7/Gm8UvyX/Qj94j5nPv8/dIA7gJeA6EC7QG3Ab8AI/56+kT3JfUp887wve5L7oLv+vAl8sjzOPcx/PQAswT6B98L6A+lEqoTxhMaFCkU+BKqEJMOzA2UDckMKguFCXEIMgcxBaUCfgDM/of8Pvml9UzzbPIm8trx+PGC8xP2svjM+tz8kv8SAkYD2AIAAr0BUwGS/zj8wfg+9lr0J/Kz72zu7+5/8MrxCPOR9fD5Bf8/A5wGGwoyDsYRohP2E/sTNBS/Ew4Sqw8bDqMNOw0GDCsKwQibBxAGtgMvAUf/S/2W+gH3+fN28vHxt/F78WLypPRr99351vtR/vwAxwLqAhEClQFIAQsAKf2b+dr29/QP86jw9u4G73bw8/EF8+v0nPh4/d8BPwWdCJ4MjRD6EqsT4hNHFE4U6xJ/EJkO2Q2RDXYMkwrkCJ0HNwYIBKcB1/84/tr7VvgH9SzzpPJi8ujxNPLt85T2+/ja+ib95f8dAp4C1AFJAT4BrwBF/q/6nfeU9dnzc/Ff79zu9u9p8VHy0PMD98v7awD0A0oHTwuqD6ESvRP9E3EUxxS5E20RNA9GDvIN5wwFCzkJIQjuBuEEWgJkAO3+0fxv+cn1hfO78mHyyvG+8UXz5fVx+Ef6T/wQ/54BpwIDAkYBQwEKAST/m/s4+Bj2lfR98hTw8+7C72fxnPK281f2wvqV/10DaQYbCmIO3hFNE3ATuRM/FO8TCBK4D2YOFQ6JDeoLCwqeCHkHrwUqA+wAPv94/Yn67vYi9ODylvI68hLy+fIm9bz31fnL+yH+nQASAggCZgEaAecAff+M/Df5uPYI9Q/zzfBL73zvyvD68S/zZvVH+ef94QE7BdkIFQ3GEMESUxOqEzgUExSZEmsQ9Q5uDugNhwy+CmYJPAilBjkE4gEhAFL+l/vj98b0FPNy8vjxhPEf8vrzifa5+K36CP2U/3gB0wFoASUBEQEaAIn9Vvqu9+b1CPTC8fDvlu+d8Mzx3vKp9AP4bfyNAAIEfgedC5APFhIHE2oTEBRcFGQTVRF7D7EOSg5NDZIL2gmPCAkH3QRlAmsAuf5D/Nj4YfVF83ny//F+8ZXxM/PJ9UL4Sfpi/BT/UAEnArwBNQE/AakAfP4O+//3GvZt9Dry5e8H7+bvZfGW8ubzwfYS+6D/RAN1BlUKgw7BERITVhPVE2AU+hMIEtAPog5pDv4NewyiCjMJCAhFBscDcQGX/6f9lPrC9qPzIPLR8ZvxffE68k30DfeM+bb7+f1VAOUBJQKXASkBwwBr/7f8Y/nN9vP0JfMI8Y/vtO/98H7yyvP39a/5Mv5YApgFDwkADZYQqBIgE3MT9BMPFMcSmhAiD7cOiQ5ZDWELnQlFCLAGUATEAbn/1/0m+4L3QPSC8h7y7PGR8f/x3fOm9iP5DPsi/Yv/gAEFAoUBLQEWATEAlv0q+mb3mPXX823xcu8X70TwtPHI8pn0H/je/D4BqAQfCEgMZBDZEoMTtRNDFJEUVhMKET4Ptw6pDo8NnQvlCckIhgc3BaACmgDz/m/8iPjP9J/yEfK08fPw4/Bp8kv18vfz+Rj82f5xAX4CQgLQAdsBWQEJ/3b7Hfj49ST0w/Fs723uR+/H8CTy2/MD94L7DADJAz4HUQtvD1USaBOtEzIUkhTdE+IRBxA2D+4OFg5ODJ4KWAnsB7UFBQPvADn/z/wl+Uj19vIp8rrx/fCu8DLy7vST93j5ZvtB/u4AMgLKAUUBmwGIAaf/8/t9+Gv2+PS48tfvXe7v7qHw4/Ht8nj1wfnC/swCGAb2CWkOLxLaEyMUahT0FMoUDxO8EEQP4Q5pDvIM8wo8CdMH8AWcA4EB1f/M/WP6dfZ/807y9fEo8YLwEfFW80P2ePhp+s/8tP/IASgCugGeAbEBigB7/bv5+PYs9TfzdvBK7i/uhe888X7yevQq+N38fgEoBRQJdQ1hEckTeBTnFFgVVhX1E8MRHhA6D58ONw1tC7cJIQhIBvcDLQK5AOb+sfu598T0SPOm8nvxSPCQ8I/yMvVC9wz5iPui/v4AuwGOAeEBdAKWAbv+8foT+F32OfRF8XzupO3b7k7wavH88lH2M/vu//cDxQdTDOcQzxPnFBEVqhUCFvQUwRJ6EI0PHA8UDkMMFwp8CMwGygS2AvEAJP8m/G34+vQO8znyWPFQ8PvvofFV9Bf3M/k8+zv+4ABuApYCfgLXAicC9v8u/Jj4VvZI9Lvxx+5x7STu1O8+8Y3yQ/WO+cz+PwMLBwkLOw/kErcUbBXOFREWlhXJE7URPRCOD7AO9QzhCgQJGgfpBNoCIgGX/xX9evn19drzD/M28gnxRPBA8bXzO/Yv+Ov5o/yM/4EBKQIsArQCmALdAJb9NPoJ+Bb2UPPo75jtsO0074PwkfGY87b38PyuAa4FxAmQDsgSJRXFFfUVcBZBFpgUFBI4EFoPmQ77DKQKtQgqB5YFqgPLASAAyf2k+jP3rfQ+8wvyv/DJ73zwtvJI9WP3S/kG/Br/hAGIArYCFgMPA7IBmv4J+2L4Ovap83bwIe7D7eruOfAx8R3z0PbE+5sAqAS2CBMNJhHME+EUcBXdFcAVWRQnEm4Qlg/dDoYNfgunCTwIhgaCBG4CygDJ/pn72feg9P7yHfIF8f3v9u/L8Xn0/fYJ+V37Zv4UAZwC7AIJAw4DKAKv/zX8RfkG97/0vPHr7s/tg+7r7yLxvvLK9Wn6V/+iA5kHyQvwD/YSgBQcFY8VqhWvFMsS3hCyD94OjA3ACwwKmQgKBwAF3gINARr/XPzE+Ij1fvNJ8iXx8u/P70nxzvNS9mH40frY/csAjALzAh4DXwP6AtkAY/0H+o73evWw8sTvAu4y7ozv2vBb8u70Pfk3/r0Cxwa2CucORRI5FPIUJhVSFZwU9BLeEE0PfQ6iDWsMuQoPCWYHnQXFA9cB3/8U/ZH5DvZ88xnyCPEg8MLvzvAu8+z1c/jZ+rD9kgCfAnUDkANzA9kCFwEH/rT67Pea9fryM/BR7jDuhO//8IXyxvSI+GH9AQIuBgoKBQ52EX4TbhS2FOwUcBTuEvIQVA95DqcNVgzAClkJHAiOBnQEUQJMAPX90Po590z0bvJA8Svwie9I8GzyK/WV9935nfzH/0MCPwNbA1UDMwPbAfb+dft5+Er22fP98Jfu9e0a76rwK/Ip9LP3j/xkAZMFWwlwDT8RtBOVFJoUrRRoFCcTChEaDxEOcA10DOkKZQkiCLIG4ATHAsQAhf6E+wn40vSs8lLxPPCJ793vqfE/9NX2TPkR/Dj/7gFAA3YDlAOSA6gCBABv/Cn5o/ZF9E/xvu7A7ZTuQPC88a/z+PbQ+9wAOgUqCR0N9hCKE5MUlBSNFEUU9hLUELYOoA0GDTIM8gqWCYUIPAd0BWUDgAFz/4z84fhV9dbyVvE88E/vVO/P8GnzJfaj+GP7k/56ARoDlgOyA88DFwOvACv90flY9/T0DPJC7+fth+4c8LDxifOs9mn7ggDqBKoIhQx1EE0TdRRQFP0TphOXEo4QVg4PDZ0MLwwmC9IJwAjAB2UGeARXAv3/GP2p+RT2QPNU8QrwKu8G70fwlfJe9Tf4LPtc/iwBGQPfAy4EQgSSA4ABJv7O+vL3YPWA8rrvQu527uPvaPFR81b2w/rK/z0EPwgRDNkPtxINFDAU6ROgE30SdxAwDsMMVAz0CxsL3gn4CBsIzgbhBLwCuQAc/rT6vPZ684DxO/BL76buW++M8Yr0iPdP+m79lwD7AgQESARqBCwEmAJq/8P7tPhJ9pPzhfBa7hjufe8v8ebycvWk+ej+qQOCBx8LFw+YElYUUhSqEzsTgBKpEBEOLAykC6EL/wqwCa0IJwhvB+EFqgNzARb/Afwn+Hb06PFj8D/vaO6f7m/wcPOn9pH5p/zb/5UCHQSgBOAErwR0A5kA4fx/+aj2+/MI8c3uJu4R76/wc/IY9Qb5C/7oAhcH/AraDl0SNxSNFBIUdROFEn8QAw78C0ALGAt9Cm8JhwgoCHUHBgb9Aw8CBgD//Af57/Qe8oTwV+9R7g/ulO9x8tD15fgB/Gv/dgJ4BCgFWwVRBV8EwwHh/RL6AfdU9Gvxy+6V7UruCPAk8rH0Y/ha/WgC7QbDCoUO6xH5E3IUxBP7EgMSTBDaDa8LtQqVCnkKvgkBCZgIGAj6BhAFDgPOANT98PnT9a/ymPAv7xLuu+397qLx/vRH+JT7Ef9KAocEdAWxBZQFtgRSApL+oPo/92z0gvHl7oTt5+2x797xiPQo+Of8FwKyBrUKTg6VEcsTbBTdE8ISlRHXD5UNXQsaCtcJ3gmzCScJyAgqCCAHkgWvA6MBoP7P+pz2L/Pn8D/vDO5g7UPumvDv82T3yPpn/sIBXQSeBRUGFQZgBVgDuP+u+wD4AvUF8iHvUu087dvuIPHT80v3+PtVAVAGmQpPDqUR9xPgFG4UUBP8ERwQuQ1QC9kJawloCUEJ8Qi4CFYIfAcFBkAEIwI+/1v7HPeP8wfxOu/C7QLtwO0W8HfzAfeb+lX+8AGtBP8FbQZhBrgFnwPx/6f7zPfB9NDx++4B7dPsZO7P8LHzOvf6+2EBewbKClYOkBH1EwIVkRQmE5kRrw9nDQILTAnTCAYJZAlLCQoJoAjoB8kGAAXjAs3/1/t495Xz5/Dz7pftvOxB7WLvs/Kb9n/6gf4ZAt4EWwbgBu4GQgZuBL8AQfwT+Mz0AfIf7+zsTOy67UTwPfPC9k773AAzBtIKcA6VERkUQhUGFX4TrxGbDzYNuArCCA4INQieCKkImghtCPkHBAd1BXMDjwDB/Fr4UPRI8SnvoO2s7P/s3+4T8uv15Pnw/cUBzgSCBicHPge2BtsESAG+/Gv4+/Tw8QXvxOwd7GXt1+/J8mT2I/vJAEYGywpzDqwRShSNFRwVhxOdEYMPGA2CCoQI0AcHCHkIoQifCKUIVgiKBwoG9wMpAUz95/id9F3xFu9g7VXsZOxF7nHxZvWT+c397AEcBSIH3AcNCJgHugUsAk39t/jv9NDx3O5e7IXrl+wz70ny/fW3+ncASAYKC+oOABKJFMYVWBW3E44RXQ/ODDQKGwg9B34HEAiOCMQI7Ai1CPEHjAaPBNEB8v1U+dX0YfEN73ntZOxw7BvuPPE29VH5mP2nAQMF/AaoB7oHHQdXBccBDf1l+Kn0nvHB7obspevO7HTvuvKG9iX72QCSBkgLwQ6AEdQTDhW0FPcSuRCJDjwM5wkNCFcH1wfHCHQJpgmlCXUJ0whmBwQFygG0/Sn5tfQX8Z/u9Owa7EHs/e1J8W/16Pkt/ioCVAVHBwII0gf1BtMEOwFu/K33xvOi8PDttesI6yfs/O6H8nz2RfvGAJEGXAsRD9YR9RMZFaIUABOsEGgO+wubCcwHDwejB5sIigkMClMKLgpgCf4HyQXdArr+1vn69BLxne7r7PXr5+uC7czwAfV5+cH95gFMBWAH+we6B+8GFAWmAcv86ff48+zwOu7X69jqA+z07nbyIPaC+hIAFQYeC64OQxF1E9IUlxTkEqUQkA6ADDcKNAhxBycIiAl4CqwKogqECgIKfQjlBX8CVP6r+dz0y/AH7m7squvG62jtqvAn9dn5J/4lAnwF4Ae8CF0INwcWBdIBIf0Y+MTzXPCo7U/rVupC6wfuovF69fL5Ov9HBZ0KpA5lEVsTwhS9FHkTXRE3DwkNoQqjCKsHOghUCSkKgAqjCqgK9wl0CPEF7QIx/5f6o/Uz8WPu5uwt7Ozr9+z+72j0Ivk7/RgBsQR2B58IJggDBz8FggIo/vv4ePQq8bPuSOyg6vvqte2J8Uf1E/n6/REE5QklDsIQ1hKEFCUVHRTrEb0PwA2yC4cJIAg+CGQJaAqcCoQKbAoYCsYIRwYYA0T/5/ob9sHxre7W7PHrt+vW7L/vD/TG+P787ACSBHQHughLCAQHPAWWAmX+NvmE9AXxc+4U7HzqsuoQ7ajwUvRV+Eb9NAPsCDQNOhCJEm8UIhU0FEMSFxAlDuwLrwkiCBkIHwkMCnYKawqLCjIK5giIBnkDGADQ+/72QfLf7hXtJuzc62fs0u7W8qD3GfwGAL4DtQZxCHQIcQfDBUMDfP+E+sL13/EQ76Pszeqg6mrsxO9583T3MPzZAakHZwzWD1ASIxTrFHIU5hL0EN8OlgxZCsIIighKCSwKkQq3CtgKiwpJCeQG7wOGAHb8qvfg8lPvOO017MrrVuyf7nvyN/ez+8z/oAPQBrkIyQi0BwUGsgMlAET7SvY08k/v9OwN63bq2+v+7rzypvYN+3AATwZSCwcPkxGXE+kU4BSgE4sRfA9oDTILWglyCPAI8Qm4CuQKqgo/CjYJcwezBGcBb/3R+Dr0UPD17bfsPOyE7CXujvH+9Z76u/58ArQF2wd6CLcHRgYmBCgB1Pzv95zzXPDz7dXrqupC6+ftkfFO9VX5MP4MBIgJzw3CEN8SjxQIFVMUixJ2EGsOKgwlCtwI4wi2CXYKmgpnCh4KWwnABx4F6wE4/u/5Y/VN8X3u9uxT7Hbs3u3i8Bn1n/nY/a4BHAWbB5IIDgiiBq8E1gHD/fr4hfQl8X7uN+y+6vbqL+2Q8CT0+/eq/EUCyActDGMP3RHUE8wUXRTfEhERNg8pDQ8LhwlDCfEJrQrkCp8KSwqPCQ0IpgWRAhz/AvuY9nHyYe+k7b7sp+ys7WHwW/TE+Pz8zgBoBBIHYQgcCOkGRQW9AhH/V/rP9TDyUu/e7PTqr+pa7Hjv3/Jv9sj6JgDVBYcKJQ7uEEETqRSXFHIT0BEoECAO1Av/CWAJ+AmqCtkKmQpYCt4JhAgaBg0Dx/8X/NL3fPMd8DDuc+1C7fjtJPDY8074bPwYAGkDTQb5B/QHyQYBBcwCi/84+7D2+fIw8Mjt0uv86i/sBu9c8sL1ivl4/v8D8gimDFkPvhGOEy0UbhP3EXMQxg7QDO0K+Ak+CvwKYAsnC7gKJgr/CN0G1ANvAL78wviX9Pzwte6h7XDt8u2w7wXzUPev+27/xgKvBaMHDggDB0gFHQNJAE/8yvew84rwJ+4M7OvqWOuw7frwY/QZ+IT86AEXB1ULcA79ECMTOhQYFNUSVxGbD50NlgsiCgYKkAoYCwULqgo1CjUJZgedBIoBKP5m+j72S/Kj7zruzO3t7Q3vyfHO9TX6Gf6KAa8EKgc0CK8HMwZCBMoBPP7X+YL15PEH733sp+po6g7s+u4/8sL1Gfp5/xkF5gmbDZQQ/xJ6FKAUxxNjEqoQdw4NDFkK3AlWCtIK1gqkClsKwwkkCIUFcQIp/6r7hPdZ8xrwT+607aTtfu6w8Hr08fgQ/cYAEQT9BpYIjAhPB2MFOgMGAMn7Hvfx8tvvPO1A6zTq++py7bLwUvQv+Cn9owLdBxwMSg8YEggU8RR+FC8TlxGeD3kNWQsxCg8KgwrJCpwKOgp+CVUIQwarA74Acf2X+Vj10/Gg78Xuc+6f7gzwHPNr95n7UP+cArQFCwioCNsHLgZZBNcBAv5G+bT0UvGm7jDsReoM6vjrI+9p8tb1Pfrf/6AFRQrIDZoQJROtFLEUnRMPEmkQTg77CzQKuwlFCsAKuQo6CsAJ7QhRB+cE5gGx/vD61/YG81fwDO+F7prunu858iz2a/pZ/s8BIwW+B+QIeAgFB0oF5QJb/876NfZ98nfvyOyT6tLpDOus7b3w9vMG+Ef9AQMKCNUL6A6fEasTWRSkE1US0xARD9kM3wrxCT8K5wr7Cp8KCQpzCTcICgYkA9n/Xvx4+J30ZfF577Lule5I7xXxf/Sw+Nz8igDHA70GmAj7COwHSgZFBF0BXf2Z+Fr03PAQ7q/rJupZ6i7sAe8V8qv1TPrj/2YF1gk+DRkQnxIdFDMUQRPjEVcQaA5UDNIKbgrNCh0L1AogCj0JBAghBn4DeAA+/cX58fVj8vnv/u4X77XvIfHI86D37/vh/3ADfAa/CKEJ/wh6B2oF4gJH/7f6EvYQ8v7uX+xs6rnp0epW7VXwwfPR9wP9qgKqB7ULzQ6EEWwTIRTCE40SGxFCDzkNfQulCsEK9wrnClEKmAmRCNMGdQSIAY/+Mftt97PzwfBV7/buW+9m8J3yKvZh+nr+CgJZBR0IzAnMCYsI0gamBKcBVf14+Bj0tPD67ZHrA+oQ6vDrue7H8Uf1zfls/9kEWgnRDMwPdhL5EywUNRPqEXIQoQ61DDULsgrRCuoKdQqJCWsICgcgBa4C2/+6/Eb5nfWI8prw5u/875vwPvIp9Q75CP2uAO8DzQbaCGEJmQj+BiMFoALZ/jb6ofUM8iXvh+xs6s/pLeu07ZDwlfOe9/j8pAKUB0gLYg48EVQTIhSUE20SGhF7D4QNtgvOCs0K+gqUCqQJlwhpB8MFawOeALT9ivob97bzNvEU8APwofDW8Sr0o/ey+4f/2wLOBScIcAklCcgH3wWUA2kAHfx391fzNPCK7UTr+Olj6n7sMO8m8pb1P/rL/wcFQwmJDJEPHhKwE84T8RLXEYAQ7A4JDa8LPwtLCxoLLgr8CLUHNAYDBDEBI/4K+9D3ePS48Svw5e9s8IXxgfOn9qP6pv4QAvMEcgcWCVUJIggzBhAEbgHG/Tn55fR28drumOzm6qHqBeyE7kLxR/RA+Ev9tAJGB9YK2Q2XEMESmxNRE1ASFBGpD/kNiwy5C4wLZwvaCu8JnggJB9sETgKf/7L8fPng9dHy+vBx8MDwWvHP8oj1aPl+/e4A7QOXBsYIjwm5COUGzASYAk3/8PpG9n7y2O937VHrDOrM6jrtJPDk8gL2hvoYAHcFiQmdDF4P7hGqE+4TLxMAEsYQSA+ZDVEMwwu9C18LZQr8CHkHuAVMA38AcP1p+jX3/POb8W7wivA78WXyg/Th9xD85v/0AokF8QeXCZIJ6AedBXcD/gA7/V/43PO78LLusuzB6jjq0Ovd7uTxoPQs+FH9GgPZBxQLlg1jEOAS9hNhE/gRzBC0D04OrAyJC1ULcgsSC9YJPwiQBqQESQJp/038A/m49fDyN/HU8DLx8fFs8yj2H/o1/owBMQSeBqcIVAkxCPsF2APKAcT+U/px9cPxhu+47bDrQ+re6nHtpvBm81P2ofpMAMsFswlnDOMOnRGPE9MTsRIhEeEPmg40Dd0LMQsqCwsLZQoECWoHigVkA+MA6f2u+jz3LfQE8h7xKfGi8cHy5PRq+H78LgAqA7EF/wdMCf0IRwcgBfcCOwBH/I33ZPN98HLubOzB6o/qZuxT7yLyzPRu+Jv9KgOhB5UKEA3gD10SbhPNEnYRSRBQDxIOigyEC0cLdwsyCxsKmgjqBggFjQKS/2L8KfkR9k/zjPEH8XXxfPID9Kb2Vfpm/vMBrATzBqcITgllCGMGFwSvAZ/+W/rE9Q3ys+/+7SLszOoX61DtZvA18xn2EPpv/9ME7AizCy8O/xApE8kTuxIuEfwP5g6QDQIMAwvSCugKcQomCXEHoAWuA0MBVv4U++j3HPUD8+3xv/FY8nPzYfVX+Ar8z//uAnYFWAeBCHYIIQcpBdECOQDB/JT4mvR38Vvvau256wjr/+tt7ibx7/NM9+T7KAHvBY0JWgwYD4URAxMYEyIS1hBxD/ANewxqC/IKzQpoCpwJiQgqB0kF4gIrAGH9bPoy90H0YPLw8VXy2fLR8/H1i/mq/RkBsQPmBfYHFAl5CIoGOwQSAlL/W/vO9gDzkPCx7qzsD+sV6xjtz+9J8tv0r/j//VMDZwc1CukM8w9iEj4ThBJbEUoQGw98DcEL1QquCsAKOAosCQgItwbzBGoClf+v/M350/Yc9ITyJfKx8oPz2/Rb9/L61/4BAnYEhwYsCJ0IcwdOBfoCvwCw/Zn5VPUU8iPwfO7L7K7rZuy07lzxu/NV9mz6m/96BPkHhgo6DSMQORKqEt4R0BDwD8oOQg3KCyILIwsHC1IKBgmlBwAG7wNUAXX+nPum+Oj1xPPE8s3yZPNp9Cv2Gfmh/Pv/rQLpBPYGFwjCB/QFpwOTATP/4/u/9/nza/Gt7wHub+wW7IHt9u9d8ov0pvcr/EgBmAWeCDYL/g2dEAkSDBJEEVAQTA/hDXIMbwsXCxYLxAoRCgAJlAedBTEDmADp/QL7z/cB9VLz//Jg8+Dz5fQG92r6AP78AFEDdgVWB/wHCgf4BOIC1gAU/kL6KfYc8xnxZe9o7RXsgux/7u3w3fI09e74+f3jAnoGOwkFDDEPjRFkEvYRGBFREB0Pjg0IDD8LIAv+Cm4KeQlcCN8GwgQJAjL/bvyQ+aP2LfT48gLznPNs9Mj1TvjS+0j/AwImBAoGcweMByEG6QO6AYP/gfyc+MT0CvJa8NruTe177GHtlu8C8jX04Pbx+tf/QwSFByEKAQ3VD8ARIRJpEX4QiA9lDugMrAsQCwoLAwtsCnEJAghSBiUEiQG6/pv7mfjk9Q/0QfMq87bzyvTi9s75Ev0ZALUCGgXXBpMH2gYvBTsDCgFV/qv61fag82Pxp+/p7bns1uxz7q/w2PIz9Zn4Hv22AY4FlAh8C2oOsRDHEbwRMBFdECYPiA0PDEILBQviCloKrQnlCKgHqAX1AkgAsf3g+qj3tfQj8wrzovMq9BT1Pfe/+nX+XgFwA1wFJwfYB94GngRZAiwAbP2o+Zb1mfLC8FXvs+2v7Eftce//8RH0ifY9+ij/ywMdB6sJRwwrDywRnBHtEA0QWA8vDoUM/AprCrIKygpKClUJaAhOB18FoQKF/5/82/n69nn0CvP+8rbzr/QY9nz42/tF/wECEQTSBR8HKAfLBZIDTQH1/t37GPhl9MjxIfC/7oPtIu1T7qTwG/Nl9Vr4b/wkAS8FHgijCjcNqg//EBcRdBClD6oOJg2GC2cKPAp0CmYK7QkiCTQIvwauBBMCTv9g/Cv5NfYF9CvzKvOc8430ZPZZ+bX80f91AtkEwgaWB+EGDQXcAooAzf1D+nz2TfMW8XvvE+5F7cDtiu/U8Rr0kfYH+nb+4wKNBlMJzwsiDuoPvBCjEPwP6Q53DckLhgoICjEKfAp5CjwKpQl2CHsG5wMxAVX+Cfti9zX0ifJV8tLyb/Ob9Ar3m/pg/mwB7gMhBuEHYgghB90ETwLO/6P8p/i89K7xt+8d7r/sYOyu7VnwIvOg9X74pfyUAfQFAwkLCyQNKg9wEHMQgw9/DmoNJAy6Cs0JzgluCu4K2QpaCngJFwjXBfMC4f+m/En54PVB8yTyRfL68u/zpPWM+D/8v/9+ArkEoQbgB5MH0QVhA+UATf7O+sD2EPOW8A3vse3F7A3tD+/18b/0Z/fU+mv/RQQVCIoKeAxsDiMQtRAgEPcOvg2CDAQLwQlGCa8JXgq/CqcKGwoYCUYH2QQQAhT/svvc93v0Y/LY8SXyvvLz8132/vnc/R8BwgMdBgYIjwhNB8oECQJe/x/8HvgC9OLwBO/C7crsnuwK7tbw+fPJ9rD5jP0vAoAGmQmWC0UNww6hD4MPsQ62DYIMEgueCQAJdgluCh4LMQv5Cm0KTwkZBycE9ACl/Qv6KfYO823xWPEI8v3yvfSj94z7VP9zAvoEGwd8CDEIWQaoA/wADP5a+ib2W/Lu72nuXe2u7EPtf++W8qX1Z/jE+w0AngQ9CJgKRAzQDQgPUw/NDt8N7QzBC2EKYglKCSEKCAt4C1wL/gotCn0I8gXRApb/EPxG+Kr0MvJm8bHxcvKN8631B/np/HIARgOuBYoHQghUBxUFdwLC/4z8lPhl9BnxAe/F7fLs1uwi7rjw2vPM9uT5w/1VAqUGtQm2CykNdg5GD0MPpQ6XDWIM5wqICe0IWwluCkULpguSCxkL6QnRByAFGALm/hH75PZi83vxTPHY8azyBvSa9kD6Ff5WAfsDUwbqBwYIYwbhA1ABpP5K+yP3K/NS8KvumO3g7Cjt+u7v8Qr14PcL+yj/4QPvB7IKcgzWDSAPrA+AD64Okw00DH0KIQm5CHYJfAoOC/oKqAonCuwIuAbGA8EAnv0A+hT23fJn8Y/xV/It85v0SvcH+8r+2QFTBHkG1wepB9YFNgOiAOD9YPon9j7yo+8p7jLtpOxD7X3vrfK79Zf4FfywAI4FVgmzCz8Nyg4OEGoQuA97DisNwAs6Cv8IzQihCb0KVgssC4kKcwnUB58F8QLd/zP8MviY9FbyoPHK8UryVvN49bL4TPyh/5UCQwVIB+gHxwaeBE4C1v+o/IX4TfQL8fruou2j7JPsCe7H8Mvzlfa6+en93QJcB5YKmQwrDosPYRBfEJMPYA7XDCALoAn2CFMJMgrxCi0L9goyCqwIegb6A1kBSf5r+kD2DvOo8cLxS/IE82n0F/ez+in+EwGSA/wFlAd2B7YFMAPSAET+2fqg9rby+u9h7k3tkOwD7QrvMvJt9V74xvseAO8E8QiJCyENeQ6zD0IQ2g/BDmQN9QuCCloJDQmbCYgKIwseC5QKmQkVCPAFTAM4ALr81fgx9bXyuvHj8W/yVfMy9TT45PtD/wcCcARvBnMHygbKBEoC1f/x/C/5+PRp8Tnv/u0j7bHspO0j8Hrzr/a1+XH9BQKtBigKTwzYDVQPhRCyENcPZg70DHwLDAoYCfAIqAmICgUL1wojCgQJUwcuBXICNP9h+3f3VvSD8gTyJPKi8t7zQfaO+fb88/+MAuIEewbHBo0FewNBAcr+rvvT9/rz//AK77bt6+wW7aPuS/FA9EH3t/oH/8QDCQgnC0IN0g4FELgQnxDRD2wOsgzzCqgJNQmKCTYKxQr7CrcKzQkYCOAFegP/AN792/nN9fry/PEV8mTy7PKD9Hr3FPtU/uEAVAOsBTIH6QYABagCVwC9/Sv6CvZ+8i3wt+5Y7Xbs+uxd75vyo/WK+BD8tQCEBWMJ+wvYDZwP5xBBEY8QSQ/PDTsMuQqfCVgJwAlsCskKtAorCgIJUwcuBboCwv8N/B745fQe83TyNPJN8kfznfXQ+Bz89P6iAUUEOgbLBr8F/QMMAsT/fPxb+HX0lvG27xnu2Oyl7CLuyPCu84z25vk+/gIDQgdUCqcMtA5pEFsRMhE9ENMOLA13CygKlAmrCSYKjQrFCokKrgkdCBQG9ANtAQ/+6vkc9r7z1vKf8n/y4PKT9In38Pri/XMABQNaBY0G/gVUBHsCpwAe/nX6aPYS8+nwR+/E7dnsZe2B70by/vTZ94b7GACnBGEIIgtjDVUPjxDoEGkQgQ80DoUM7Ar9Ce8JbQoQC3ALggv1CoQJUAfQBEwCRv9v+z331vMC8nXxfPHW8Szzw/Uv+Y38a/8sAtgEzwY5BzUGdQSAAh0Aw/zb+DP1gPKA8KnuNO3h7CzumfBR8wD2Mvk4/awBugUACcYLKA79D9wQ0hAzEC8P2Q1EDO8KRQpuCvMKaguJCx8LGApSCPsFRgNFALT8qfjj9EnyOvEa8XHxYvJb9Hr3C/tr/lUB8wMlBkgH+wZ0BXcDRAGK/g37Ivez8zDxYu/v7Rztfe0v77TxWPQ899X6F/+GA0kHTwrPDPEOkhAvEe0QDBDODlUN4QvpCp0K7ApjC8ULrgvbCjoJ2gZHBIMBWP5u+in2xPL78LnwIPHb8U7z+vWg+U39bwAtA7oFiAfpB84G1QS6AlgAKv1C+Wf1aPJG8IfuFO2j7MvtKvDZ8mv1efht/AsBXwXUCKkLBg71D/oQFBGHEJYPVw7FDFELjwq1ClEL1QvgC24LYQqJCPoF/ALG/xX8+/cW9FvxRPBT8PzwMfJ29OP33vty/1YC7gQdB0wIzgcKBt8DowHe/gP7u/Yi89XwT+/Y7cfsA+3l7qnxaPQ698L6N/+1A1sHIgqNDOkOnxAlEZoQkg92DjAN1gvYCp4KFAurCwAM1gsVC4wJOwd9BGIBzf2M+UL1BPJk8C/wkPBy8U/zbvZm+jn+ZwElBKMGOghMCNgGvwSsAjEA0PyS+Kj07vEq8KvuSe3w7B3ufPAL85711Pj1/IYBfwWjCDgLsw2lD34QVhCXD8wOrg1KDCELywpnCzMMowyXDCkM8wqvCJAFLAK0/r76RPYM8njv+O7O7/fwc/IP9Qn5ef0gAdcDLQZJCEsJWwgLBowDWwGl/sT6W/bQ8sXwd+8e7grtYO1071byBvWe9xb7lv8fBJsHAQofDEUO4w9FEIYPdA52DYYMmwv/CiAL1gukDAUNzgzHC90JPAcNBI0Aa/zG91jzQPAk7zzv2O/+8F/zQ/e6+7z/zgKDBfcHfgkxCUQH/ATOAksAffzg9/HzhPEv8MLuYe0J7YHuHfG68zL2Nfld/fEB3wWwCAkLkA3CD+AQqRDDD90O8w3sDOELUAtYC8ALKwwvDG4LnAkCBw0EHQHY/bv5N/Vu8ZPvee8q8Bnxx/IH9mP6l/7AAVcE9gYzCcIJLwiGBRQDAAEY/vT5evU+8qDwiO9H7lTt6e0i8O3yafX993/75f8uBGkH2wklDHoOJhCDEN4P1w7jDfEM/gtoC2AL3AttDKcMPQzrCqkIswWAAh3/NPu59njyuO/37pLvrPAu8tz03Phb/ScB+wNmBo0ItAnvCI0GxQNUAcL+IvvN9hPz4fDR78buvu2p7UrvGPLn9HD3bfpb/pwCTAbtCB4LUg0xDxQQsw/EDs0NDA1YDLULcAusC00MwgyZDGsLPQlmBkAD8P8W/Kf3O/MV8N/uKO8k8IDx+PPC9038cACSAx0GVgjPCacJxQf6BEkCpv9h/E/4S/SH8f/v++4G7r7t4O4+8QD0lPZj+eT8+QDRBNMHHAoODMwN+w5eD/sONA5TDXwM/wv4C3AMAQ1ZDVINnAzSCssHGwR3AOn89fht9E7wCu4X7qLvfvGv8+b2UPsDALgDPQYsCNUJewosCTAGzwIHAFX94PnA9STyFPAd73ju3O0X7uLvx/LS9Yn4j/ta/5ADLge8CZgLNw27DnkPOg80DhUNYQwWDCUMUQymDA0NWA0DDXsLtQgaBV4BrP2t+Sr16PBG7t7tGO/b8PvyDfZV+hv/IQP7BQwIsAmACqUJKgftA/0ANP7U+t/2JvPI8JPvzO4l7h7uh+8Z8gL1sPeB+vX9+AGwBXgIdAobDLgN0Q4QD3cOiw3QDGsMVAxvDLUMEA1ZDSIN3wtRCekFSgK8/uT6cfYB8sruvO187h3wMPL/9AX5xf0mAngF4Ae9CekKkApuCCwF5QH4/sD77ff/8wjxa++f7iTuHe4s72HxQ/QT98/59/yqAHAEggfMCZALEQ1CDskOmw7kDRENeww+DFoMlQzXDBINHQ1xDIIKTgeOA/X/T/wv+Kjz0O/57TXuqe+D8fjzxPeM/DQBwwRTB2kJ9wosC3wJdAYnAzAA9/wU+QP10/EJ8ArvVe767bzuxfCO81T26/jv+4X/PwNkBsIIngo5DIANMw5EDtgNRg3FDIsMogz4DEsNgA2QDfQMPAseCEkEeQDF/MT4QfQ+8PTt5e1E7ynxfPP39q77ewBXBP8GFAnMCmALLgpZB/kDzgCe/ez52PVZ8inwBO9f7gruje5U8ATz5PWJ+FX7p/5FAowFFwgDCpALxgyDDbcNdg35DHEMDQwJDFQM0wxVDZsNcA0/DLMJDAYMAj7+XvoS9qrxcO5h7VruT/CS8pD1ufmo/gkDPAZpCC0KTwvwCsUIbgUfAgD/lfuj99fzKfGn78XuE+4U7lnvzvGr9Dr33PkF/cQAXAQ4B2kJKgu4DK0NCA7TDVINxww1DPEL+AtYDNYMQg1GDWYMUQoXB1cDf/+W+z33x/JG76jtCO5273fxUfRb+Cf9mwEHBawH8Ql6C4wLzAn1BuED3QBj/UL5LfUK8irw5O747bPtvO4A8bPzWvYX+Vn8DwCdA2UGgghNCvALCg14DVQN8QyYDCwM2guyCwsMugx8Dc8NPA2hC74ILAU6ATn97vhJ9DfwkO0F7R3uGfDO8mv2I/sNADkESQe1CZoLXAxhC6QIQgXXAWT+e/o29pzyKfDV7uXtZ+367e7v0fKo9UP4Hvup/lsCdgXYB8gJmwsADakNww2QDUsN0AwyDLcLugs9DM4MGg3HDLQLfAkoBkcCT/5l+hn2z/GB7jDt2O2S7/LxFvVm+VD+1QJOBv8IIQtoDBQM8QnbBoADKAA2/NH31/MD8WjvN+5x7ZHtMu/a8bn0XvcX+oT9KgF8BPAG9gjpCoEMbg2BDUMN+wycDA0MbwteC+4LvgxODTsNdgyqCsMHCAQFAPv7qvdJ86Lvue3N7T7vavFM9Er4F/24AWsFPQiUCiwMMAxiClgHLQT8AEr9+fjC9Lbx1u+b7qbtgO3N7kDxB/SV9i35TPzs/zgD0wXgB8MJdAtvDNAMugyeDGcM4QtXCyoLqAt2DDMNcQ37DI8L3gg/BToBR/0j+cj00PAv7pjtjO6L8DHzw/Y8+9//9AMmB6sJgQsuDDcLzQikBUUCkP5s+lL26/Kj8B7vGO7F7ZTum/A789j1Sfj8+gj+LAH2AzwGSgghCp0Lawy7DOcM7Qy4DC0M3gsZDNIMjA27DVoNGwzeCZMGgwJY/if68PXr8eXutu1R7jfwtPLm9f/5o/4QA30GDgnwCgIMqwunCb4GewMmAFL8H/ha9JXx+e/N7v/tGu6M7xLyqPQQ94/5mPzv//kChAWbB6IJQgtKDMMM+AwjDeYMXAzYC+sLnwxdDb0NiQ22DOEKwge8A53/rvuY90zzke+q7e/tru/78cb0hPgt/fIBvQVsCHEK4QsyDKkK1AeJBEMByv24+cP1pvKy8GzvXO4M7gDvMPG/8wf2T/gf+3r+0AGKBNYGDgkjC60MXQ2vDdENtw0kDUgM2wseDL8MHg0MDWcMBwuaCA8FGQEf/Sn5+/QT8Y/u+O0U7/PwYfO39gv71P/wAyYHugmtC4cMjAsvCTgGIwOm/2P7+PZF89jwUu8Z7njtAO7m73Hy6/Rc9zP6of0FAdwDKAZOCH8KNgwoDXENew1oDfwMSwypC64LRAzzDDkN0AyWC2sJNQZKAlb+U/o09gvyy+6B7SDuDfB08nn1iPlE/swCRgbvCAILVAwUDPsJ6QakA3IAv/yK+H/0gfGx72nunO2W7fPuSfHT80z28vg4/MX/AAOvBRcIVAogDC0Nog3YDdcNdg2jDOoL0AtUDAMNVg0dDScMMwoDBwsD6v7y+vX2yfJL74Ht2u2Z7/zx7vS3+EL9qAFcBUcInAorDDAMkwrYB+AE3AE9/gb63vWq8o7wBO/Y7WXtXO5o8Lzy+PRz98H6dv7pAbIEPAfNCQ4Mpw1BDnIObw4mDnENbQwADDQMyAwlDfUMJwxmCrIHJQRVAHL8YPgo9GLwSO4O7kPvHfGM8yn3rvtfAEAEVQf3CQQMtAxkC7cIsQXBAl3/KPuc9tnyf/AA79XtIe2v7aPvOvKw9B73Nfrt/ZMBlAQGB1sJewslDQEORw5ZDhEOgA2/DGwMrQwvDZINhw35DHkLsgjpBPAAMP1i+Q/15vBF7t/tJO/y8DLzcPbm+rH/nQOVBhcJXAttDH8L6wj/BV8DUwBX/Lz33vNg8cPvVu4s7Ujt1+448YPzyPXI+Ij8cACwA0cGwgg+C1oNdA6sDooOQg69DdQMIwwhDLgMVQ1aDbgMZAsuCfgFCwL1/e353/X18fzu3O2U7nfw0PLH9ab5I/5qArcFPQg4CmALBQv/CDwGngPuAHf9OPkY9TXybfAN783tVe1h7nrw2/Ir9fT3n/uX/xwD1QVLCLoK2QwiDmoOUA4XDsQNGA1+DGcM6AyyDQwO1Q2vDHQKIwcgAwz//PrJ9ozyLu+J7eDtje/s8fP0u/gf/U0B0gSlB/sJegtiC6AJ9AZNBKIBVv4n+vT1uPKI8Obuge347LjtpO/v8UH0Gfe/+vT+tgKyBUIIwwoXDYYOBA/xDqwOPA5mDYwMNQyvDG8Nyg2aDaUMywrOB/kD4//N+7X3c/PW79Pt8u287wPyt/QM+E38vgBmBD8HTAm0Ct4KZgnvBk0E/gEf/zz74fZQ8yXxm+9E7jbtce0V71vxxfNk9v75Mv4sAkUFxwdaCsQMeQ4DD9QOhw5DDq0N2wxwDMQMpQ1DDkkOiA3XC/gIAwXIAIj8XvgI9Abwlu0y7bzu/fC48z/3jfs1APYD1QYYCc4KXAvxCTwHTwTSAQ//R/vt9lLzC/GL7yLuEu1R7fruXvGS8wH2afmz/e8BJgW/B08K5wywDlEPQA8ED9QOAA7cDB0MdgyHDSAOBw4iDagLLwmBBUoBB/0Q+fT06vAZ7m/t9e5R8dvzz/a4+lX/XANbBnsIEwq/CqkJGgcfBJgBEv+L+zv3XPMB8cTvmO6X7XPt7O5P8Zvz6fXn+BH9SgGgBBcHiwlSDIgOeQ9wDy8P8w5+DmkNfAxyDDAN/Q36DWAN/guyCUQG8AGx/Yj5fPVn8UDuFu3q7Snww/Ly9fr5lf7PAvYFZwg0CjALaQr6B9wE+wFd/wb8+PcK9Efxm+9T7jLt4uz/7S3wqPIh9Tv4P/y2AJUEkQcrCqoMwQ7BD9UPbg8DD0kOLw0/DAMMuQyaDR4Oyw2aDHsKCQfIAkP+//np9crxgu7y7JTtuO9l8of1TfnL/R8CaAXPB50J0gqBCmsIYwVcArH/h/yb+Ib0d/HE75zuju3x7NTtBfCi8i319/fR+0AAXgRlB5kJ5QsTDpoP9A+fDzkPug76DfMMhAzuDAIOwA5pDgoNogpsB1YD+f6i+mP2UPLe7hDtOO0N77Lxt/RV+K78LAHiBKcHogkACwMLQglSBioDYQBC/Xf5SvXO8brvP+4i7VXs0+zd7mfxQ/Q69zL71/8kBJ4HEAp0DKAOGBBsEPkPVA+eDroNpgw3DIsMpg2eDrMOvA2GC14IVgTe/3L7CvfI8h3v1eyg7PftcvCG8z33uvsSAOoDzgYQCbsK6ApzCZIGcAOPAGv9vPm29ULy/+9r7jTtZ+zZ7NfuY/ES9NH2ifoq/6wDRwfQCTcMeA5MEAYRoRAMEEkPYQ40DXcM6QwBDiAPMg/0DagLVghmBAcArPtm9wPzKe/M7K3sZe7s8Nfza/fP+3kAZQQcBwsJdQq+ChgJ5AWiAuX/Kf2U+VX1uvF17y7uEu007ITsfu5R8RX03fZw+v3+fQMwB94JTQyrDnIQOBHoEFgQsQ/QDrcN6wz9DOAN2w4nDz0OAgyyCJIEZQBD/CL4wPOF7wXtouwm7m3wDfON9uv6tf+hA3cGjAg3CqIKGgn6BY4C7/8V/Wv5G/V48YDvcO6T7cDsAe3v7qjxZPQh96H6IP9rA8sGRQmZC04OdxBmER0RZBDaDyIPDQ4vDS4NKA4fDxsP2g2GC4UIygRzAPf7jfd48wfwru087Xfu3PC/8wj3Bfs//zMDJgb/Bw8JFwnMBzkFGwJV/5f8XPm79UbyAvCv7sTtH+077eHui/GO9Jz3D/s//3kDDwe+CQ4MSQ4REOwQshAcEHYP5A4mDqINzg2PDoYPtg/fDrUMUQn5BCoAkvtB9yjzeu8g7b/sMO6Z8FHzm/al+gn/xQJkBScHdAj4COUHRwUXAlP/1PzU+SL2tfJ08C3vJ+427Tftwu598Vr0Gvdl+qP+OwP/Br8JCwxuDoIQjRF4EeIQSBClD78O5g3WDaMOoA/JD64ORwzICJAEBgCR+zb3CvNw7z/t9uxw7tLwcPOZ9mX6l/46Au0E5gYtCIEINQePBJIBHf/2/Bz6hPYL88Lwc+9f7m3tPu2m7jbxGfT09mj62/6QA5EHZQq5DP8O4xDMEZoR2hAnEIwPxw4+DmAOSA9LEHUQUQ/ZDEkJxATT//n6YfYu8nvuFeyi6/jsj+9y8un1AfpZ/kcCDwUdB3gI8wjVBxsF4gEE/5L8l/n+9X7yJfDj7gTuXu1z7RnvyfGz9Hj3qvru/pwDsAeSCsAM9A7oEAYSGxKAEegQTBB7D8YOqA51D1kQVhDUDu4LMAjJAyr/lPo29inyp+5S7LrrBe2h77jyJ/bo+e39tAHCBAkHcAjNCIcH2gTPAQj/oPy/+TD2rPIZ8Mru6u1f7ajtMe/k8bb0jPfc+iH/9AMmCEMLmA3hD+gRDhMfE4cS1BEDESEQSw8CD3IP7g+tDwIOIAtOB9ACOf7L+bn13PFm7gfseOvH7FbvefII9iD6Sv4GAtQE+waZCAkJ8AciBe4BGP9r/Hb51fWC8i7wwu7U7TTtqu107wfyyfSF9xr7rP93BKUIlwsGDkYQERLvEsQSNxJ3EakQng/lDgAPug9sEN0PCQ7rCggHxAIP/qD5SvVE8eHtnett6+zsk++s8g72G/pU/ksCawWHB9wI3whLB38EWwGl/gj8zfgi9dTxou+G7r7tY+3r7Z3vRfLy9O73ofsYANsErgi6C0QOihBxEjMTKBORErURwRCgDwgPIg+dD+QPOg94DaoKuwY+Apr9QvlU9UHxte1d6xTruuwz70PyxfUZ+rL+ngKnBakHEAkmCZMHnwQ8AW7+ovt/+Nj0rvG075juB+6w7XTuZvAZ8+T1q/g6/IIA8ASaCFILrw0AEO4R2hLPEj4SmhHrEBkQjA+PDyYQhBDYD9MNewpuBuwBbP0Y+bn0qvAm7Rvr8upb7ATvK/Ic9o367f7gAt4FPgiXCXYJtgekBIEBlf6l+z/4efRZ8Tnv+e087SbtO+5j8C7z5fXZ+I38DwGlBUQJEgxfDp0QVhIFE/8SchLdEQ4RHxCdD58POxBoEIYPaw0fCi8GrgEx/df4kfRv8NDsuOqH6izs2u4I8t71SPrh/twC9AUyCJ0JuwkbCDgF4AHH/rj7Kvhp9DfxFe/p7UDtPO1i7pLwYfMq9j/5Hv2KAfkFYQkODGEObRAXEp0SghIIEmERsBDAD2kPqA9REIQQdg9QDQIKAwZ1AcL8OPjI847v6evR6cXpn+uN7vLx/fWQ+lL/bwOQBt4IMAoyCl0IPgXnAbv+svsR+Dz0EPHm7rjt8uwH7VTupvB28x/2Ofk6/eQBbgb5CcAMKA88EaoSFBPpEmoSohGGEFYP2w4fD7oPyg/XDtkMrAmzBSkBrvx0+BL0p++/67Hp7+nG64vuvvHX9ZX6NP8cAyYGsQhVCk8KWAg4BTECdf9f/If4lfSG8aHvX+5o7UztmO4N8b7zQPZN+Wv9RQK2Bg8KrwwXDywRfRLWEpcSLhJvEUYQBQ98DuUOlQ+7D6MOigx7CYwFKgGS/Ej4A/Sb79Drjum06bXrpO4P8gX2x/qU/8YDJQehCUILGQv6CJgFEAL6/pT7qfeY82fwjO547QHtSu327orxWvQK9wX6Jv6qAswG8glfDMQOwBAWEoYSUhLhEQAR3w/QDnEO3A5KDzUPJQ4UDBkJPQXuAJT8Sfjp85Hv+usu6n7qWuwM7zDyG/ay+l3/iQPVBloJwAp5CpsImwV8Amf/6fsD+A/0/fAH79HtS+2L7QzvfPE39Cb3Xvp4/u4C1wbtCSwMTA4YEDIRjBEmEZ0Q0g/fDiEO+A2qDmgPkw+WDnYMdgmEBSIBgfzt92zzBu+a6+HpVepk7EjvsfK59mv7KQBUBLQHJwp6CyMLAQnhBZYCY//d+7r3o/Nl8E7uHu157N/sjO5A8V70Sfet+sT+UQOKB4cK0AybDioQMxFaESQRjhDnDwsPKw4QDp8OmA/nD/YO2wyYCaUFFQFN/Jv38/Kq7izrXOml6a7rzO5S8nr2JPsAAGYE9QegCvkLzAvSCd0GmAM3AKv8dPha9PDwnu5R7ZXs/uyU7ifxQPRb9wT7Lv+4A9oH6go6De0OUxAFEfwQcRCOD50Okg3kDPYM0g3+DpEPCA8oDRgKGQZ1Ab78APhM89PuH+tJ6Y/poevE7mTywvat+9IAewUwCewLLw3BDHsKAQddA5z/qvsw97fyUO8X7Qnspus47DLuIvGd9Or3ePuw/y8EXQhfC4ENMg94ECsR/xBmEKEPyA7qDSUNMg0EDh8Pnw/iDvoM7wkSBnABoPzo90Xz8e4+60LpYulZ63Du5/EL9sP60v+CBEYIJguwDKwM3grDB00ErgDc/Gj42vMY8JjtZOzz64HsSO4P8W70wPdZ+4n/DAQ4CCgLIw2TDr4PexBQELIP0Q71DRwNUAxRDCwNjg5uD/MOQA10Cu0GwAIJ/mD5q/Qp8Ffs3Omk6Tbr+u1D8QL1uPnI/sED7QfxCtYM3gwtCwIIVwSzAJf8Dfg28z7vwOx46zPryuu+7c7wb/QN+MH7AQB+BJEIhQtsDdYO7A93EE0QlQ+oDrQNugzxC+kLsgz5DeQO1w6sDUYL5weTA+7+RfqM9e7wm+zL6RjpV+rr7ALwCPTe+DX+YAOeBx0LWA33DZgMhQnrBRkCCP5F+Tf0BfAk7aHr+upH6//s6u+F8/32mvrI/mMDsAe+Cs8MSg6TD2MQXxDOD+kOIw5GDXAMLgy9DCQOUQ+ID2IO4QuUCJMETwC+++/2G/Ki7YzqSekH6i/sKe/z8m33kPzRAacGmAobDdYNrwwLCq8G6wKr/sf5pfRC8BPtSuuK6tbqiexM7+vyovaN+vb+fwPaBwcLSw3iDucPjRBAEKAPoA5tDVEMJwsKC7cLIg2IDvQOig6nDMQJ3wVzAe38Afj88gTuVuql6Pjo2uqa7WvxHvaN+xcBJAaCCnoNug7iDWILIghPBBkABvuy9QbxeO1r6z3qSeqj6z7uzPFo9Vv5n/00AqAG/Al2DDAOfA9bEGcQ/g8XDwkO4wy6C1wL1AtDDcMOfw8jD18NtgoXB/QCiP6c+Yz0Ze9C6/rowuh+6hvtqfAD9TD66/8oBaMJnwz8DYINUgs1CH0EdgC/+432qfHw7bjrpeqW6p/rBu518UT1NPlI/cABIQbMCW8MLQ5sDyUQPBC+D+8O5A3IDK0LJwuVC84Mag5pD4oPYg7kC2YIFwSt//T66vXU8DnsZOmB6IPp5+s076HzufhV/tIDnQh7DHgOlg7NDLwJLgbrAST9t/eW8ovuw+tg6tzpwur/7GXwUPQ5+Hb8xQA+BfYIpAuTDdsO2w8TELEP+A7/DfEMtAvhCgALFwysDcQOCA9ADk8MRAlLBfEAefzR99/yMe6/6k3p3um564/uNfLe9kP8rAGfBnYK7QykDXcMDwrVBiMDwv6b+YD0JfAf7U/rZuq56nPsf+9M8yz3R/uQ/+IDzwejCsgMTA5nD/MPoQ8JDwsOAQ3WC9QKuQp8CwQNTg75DqoOGA1lCnwGPwLz/Zn59fQY8DXsBer96WzrzO0h8W71s/oSAAIFFgn8C3MNIQ1CC1gIzQSWALf7kfb08XLu+ut86gbqIOvQ7WTxX/U4+V/9wQHkBYEJCAwHDmIPFRAuEJEP5A7bDacMdwugCvoK/QtiDU4OVQ53DSsL9Qf1A9z/2/ti97fyN+4960Lq5OrF7E3vG/Po90H9ZgKwBkEKcAw7DT4M5AnVBtgCXf42+Vv0ZPBR7VTrDepQ6i/sOu8H88r2/vpy/wYEMghZC9oNiQ+0EAkRohDOD4wOLg2ZC3UKMwrfCiwMQA3RDXIN7gtRCcMFAAIi/vf5YvWv8BftPusV6zvsKu5d8cf1Dft0AB4FGQnZCzMN4AzXCv0HPwTi/9r6nPVU8fLtses+6t7pTesG7r/xhvVw+dr9WgK6BiIKvAzEDhkQyBCNEOQP9A65DVQM8Qp5CvMKGgw6DcINsA2bDIcKRweFA67/mPtD94nyl+4m7G3rJOyV7TDw8vPM+AP+tQLVBu0J9AtpDDwL+gjABesBNf0z+Kbz7+9D7SnrEeqA6o7s2O9Z8xz3Svv1/6wEdghpC58NaA+iEM0QVhBbDzUO3gxgC44KmwqTC7YMWA2ODf4MkQsJCYEF2gHo/af5H/W/8NXtXOxF7B3t6u5I8qL2nPtTAHsEKQicCr8LJgtpCeIGYgMk/yv6jvW68bHuW+yu6ozq/evE7gvyf/Vz+dD9YQJbBpYJNAxPDssPaRBTEL8P7g69DVYMRgvwCpALdQxLDccNnA2jDDQK5QYhA2X/m/sQ95Lyu+6j7DPssOxX7t/w5vSw+Wn+3AJyBoIJQAt7C1sK8Qf3BPwAevzV95zzXvCT7ZDrfOr66hft8O9f8+z2F/uZ/+QDugewCmMNYA+sEBsRthA2ECcP4w1rDG0LhQsQDOIMKg36DEQMiwoKCKsESAG3/bn5b/VG8ZbuTu1X7S/u1+/g8tv2fPvb/70DIQeKCcsKgwoFCakGaQNz/9z6c/bI8rjvR+1z69Lq3usU7hnxU/QW+Hz8+ABUBdsI/AulDooQtRG5EVMRaxAXD68NQQzKC+ALTgyeDGwMCwy8CpUImwVXAj//qfuh92DzEvBs7vztb+5q76bxN/WF+d39mgEVBfgH4QlFCioJTgelBGUBQP3K+Oj0oPEO78LsZuuR6xLto+9i8rX1rvks/rwCmwYtCjcNzQ9zEfYR8BFREWsQ2g4NDesLmAsXDGUMUQzfC/IKewn2BuYDpQBI/Zr5Z/Wf8RTvFe467vfur/Cd8633E/wLAJkDtQYbCTEKsAkTCMYF0wIp/8D6hfYV80fw9+3r6yjrB+xE7jbxL/TR9w78pwDxBHkIsQtvDqcQtxG7EUsRaRBID7YNZgzSC/gLdAyNDHIM5QvBCqYIoAVhAvP+Tvsy9xnz9u857sPtFe5q7+jxj/XF+dn9yQFQBU4IDwpJCmAJegfxBGwBNf3n+Pr00fHt7pnsM+s96/LsYu908sn1qfkW/l4CjQYjCmkNBBCKETMS9xGAEYIQAw9kDQcMoAurC/ILAwyvC/kKJgmRBmoDWQBV/an5qvXE8UjvQO5D7jbv3vDg87H34/vl/20DrgbvCAIKpwk1CB4GNgOq/3/7hPcZ9C3xpO6B7JnrNOwm7tHwyPNG90/7qv/rA9YHbgt0Do4QkBG0EXkRxBB5D9gNSwyiC6EL2gsPDPkLtwuNCm4IjAVmAnD/2/vU96HzUvCm7t7tI+4Z72XxB/UW+Wz9QQH/BAYI0AluCqQJMAinBSsC+/2r+RT2xvLg70Ltn+un6/TsXu8S8lf1Nvlo/dQBzAWLCcAMTg8BEZQRsREnES4QuA4IDQIMkAvEC+YLzQuTC6wKJwmqBtED9gC7/Qb6yvUz8tXvpe5g7qruTfAx8wn3H/vk/q0C/gWtCP8J4AnuCPcGOQSAAFr8f/j49OXx7O617Orrhexh7pzwaPPO9s36Mf9aA2kH+gr8DRwQPBHDEbcRKBHTDx4OuQz3C9cLxAuZC1MLwQqbCXkHxwT9ARj/x/vL99bz5PA3753uru6t7/bxRPUp+fr8uwBiBFUHSwnLCUwJBAi+BbMCyP7k+l/3FPQY8VHutOx37Intcu+z8cf0cvi7/BwBIAUCCVQMAQ+QEDkRcxEZEUEQog4KDQkMuAvGC5YLegsVC1QKuwgqBnQDjwCD/c35yfV18iTwDu+l7i7v3vCi80H3+/rg/pIC0wU4CF8JmwnNCCkHcQTpACP9XvkM9uDyH/AX7hXtd+207r3wS/N59l36ev6pAo8GHgosDWUPxhBhEWgR0hCSD/gNmQzYC5sLegs9C+cKNgr1CNMGOgSsAfv+0Pvo9x70SPHD7z/vU+9a8IbywfVi+f38kADwA98GlwggCbEIeweQBaICM/+Y+1P4WvVy8hbwe+407uHuPPAy8rP0IfgF/BkACgTEBzYL6A3LD74QIBHwEBcQvA4jDR8MlQtqC0YL8gqTCokJ0AdmBdQCbQCI/Qr6Bval8p7woe+O7+zvY/H18z336fpm/hECUwXGBxEJHQmXCB4HyAR+Abb9Ofrx9gv0P/Ec7y7uSu5y7wDxVfNt9ij6Tv4eAuwFVwl2DO8OShDtEM0QWxBCD7sNbQyLC1cLLwvzCoYKwAmVCJMGOQS3ASD/BvxH+Lj0+vGJ8N/v0u+t8JPyivXi+Fz85v9bA1UGNgj5CNYI9Ac6BncD9v9Y/AD56vUA84DwAO+g7lHvifBS8vP0Svg1/AIAuANUB5QKWg0WDysQqxCIENgPaw4TDRIMmQtnCwsLuwoUCvAICQegBF4CGgCB/Rz6avZi82rxgvAj8Hrw1/E59FL3hvrn/WQBrwQqB1UIqghCCCsHEQXhAVz+4vrb9+L0EPIK8AvvSu8O8GXxkvOj9nv6P/7sAYUFEwlKDHUOzA9kEIwQFRDKDksNBgyAC0AL7wqSCgkKSQnHB7EFfANkASP/Evx/+Cv1wfJY8YXwYPAy8RLzqvWM+Lv7Ov+5ApMFRQcFCDAIlgcCBmYDLQDv/MD5tPa681vx/e+X7wXwAfHB8lr1ovg9/NP/hgMaB2gKAQ20DsQPNRAhED4P9A3GDP4LtgthCw8LlArFCW8IXgY1BCYCCgBL/c75WvZ/87rxrPBF8MTwOPKR9EH3UfrC/VABdwSABqMH+wfFB7wGkAS9AXv+U/s0+Cr1tPLZ8BrwA/CW8PLx+/MC92P6H/7jAXoF+Qi9C/kNbw8/EIYQ6g/YDnUNeQzyC4kLQAucCuAJpQjcBtQEsgLZAIX+jPsO+Mz0svJj8dXwyfCq8ZDz8vXK+Mf7If9xAgsFugZnB54HIAe9BWgDaQBp/V36ZfeC9CPyuvAq8G/wSfHo8m/1p/g9/Nj/ggMtB5AKSQ0HDxEQnBCAELgPag4oDVsM1gtXC64K/AkQCY0HmQV3A5kBlv/V/In5Mva88xTyCvG78Dzx4PIT9Zj3aPqR/QMB2gPWBd4GPwcZBwAGFgRyAZH+pfuk+Mn1PfOV8czwvPBQ8YLypPR098H6Ov7RAZcFCgkEDCsOnA+UENAQWxAyDwgOKg1pDKgLsgrmCf0IlgesBY4D5QFHACH+MvsE+IL1s/OF8r7xvPHC8mD0XvaT+GT7r/64AfsDRAUKBlgG5wWFBEgCsP/1/CH6Rfeb9KHygPEo8XHxX/If9K72z/kh/YkAHAS8BwELgw0WDwoQkxB0EJoPTg4/DY4M5AsJCwkKMgkzCLYGyATZAioBLP+X/H35nPZ49ODy0vFe8ePxNfP59Cb3w/n2/BYArgJ6BJEFOgYbBiwFXwP+AFn+gfur+Ob1svMt8mjxY/EP8p/z5fXO+BH8if8xA7wGEwq4DKcO6A9hEEUQaA9ODj8NWwyxC+EKOgqICZUINAdTBaADAQJMAPL96PoM+JP11PN88qPxuvGK8gT0z/Uy+D37aP5GATkDpQSfBQcGngUSBAICjP/0/Cf6S/cC9UnzVvLb8R7yUfNA9fD33/oe/p8BPgXKCKgL7Q12D1sQkRD0D/cOyQ3bDA4MNwtsCpsJ0gioBwYGQQSkAh0BG/9m/G750/bf9E/zJ/Ks8fnxCPOS9JL2PvlM/FT/vgGPA/UEygXnBeMEGQPXAE7+pvu1+AD2xfNc8tPx3/HN8nr0+/YG+kb93AB5BB4IRQuhDT4PExBnEPcP6w6hDXwMxgsPC10Kqwn4CCwI5QZlBdEDUAKAAOj99foK+Nf1J/Sw8sjxkvFj8sDzk/X297X6t/00ADACogOaBBQFewQSA/8Awf5z/NT5QPfq9HHzsPKO8inzbPSm9nH5pPwaAKcDSAd2CgMNwg7ND1QQFxArD8sNfwyjC/sKSgp3Ca4I+QcIB6sFFQSWAhMB/P4r/DP5zvYw9dDzkPLs8T7yePME9er2X/lJ/Cz/SgHIAt8DogSkBHgDkAFS/yr9yvoU+JT1svPO8pjy9vIW9BD26vgM/GH/ygJcBtIJgwxmDnMPERAhEGUPHA64DMoLEwtWCn8JtAgmCF0HOQa6BEMD3gHr/1P9TPq898n1IfS88q7xs/GJ8uDzqfXk9+n63f1XABcCUgNzBMMEKQSAAnUAeP4R/Hr5ufam9HPzzfLY8oTzWPX/9wL7TP6lAW4F+QjcC/ANPA8zEEcQoQ9ZDgUNDAwcC1gKiAntCFcIdQdTBuYEogNBAmUA7f0J+4f4bfbH9GvzafIq8pXyu/Nk9Zf3UfoT/X3/RQG7AvkDlQQqBLgC1ADg/sD8QvqP90n1xfP98t7ycPP79HL3YPqW/f0AvgSBCK0LBA5zD2sQvBA/EAQPYA0ODPUKAQoJCRQIbwelBrEFgwRTAzkCngCE/uf7dvl396r1H/TN8kXyj/Jv8+T0zPZh+R/8n/6vAE4CuAN0BEkEJQNsAY7/Zf0C+1j4APZd9GzzOfOo8w/1Uvcr+mT9tABIBMgH4wpCDdEO6g9REPYPyg5LDR4MNwtqCmQJbwi3B/kGCwbFBI0DYwLrAN/+QvzM+b73BfZs9AXzYvKg8pPz/fTG9if51/to/n0AGQJnAzYEKwQQA1EBb/9//Uj7v/hb9rP07PO68yP0U/V/9076U/13AMcDSAdqCr0MPg4xD7YPeg93DvsMsQvSCvoJCgkDCDcHkAa5BaQEbANJAuUAAf+e/Cr6IPhW9sT0dvPD8vHyu/MM9cr2F/mz+yv+RgDpAUgDHAQgBEUDuwH//wz+3vto+RP3V/VK9PzzQPRP9Tz3xPm6/M7/KgOMBpsJFAy6DegOeA9mD54OWg00DCsLTwpdCVUIfAeSBpUFVgQQA+UBhwDJ/nL8Kfov+IL2EPWz8wnzGPPh8zD12vYd+Zj7HP46AP4BeANaBIoErwM0AnsAkv5m/Mz5Ufdf9Ur07PMT9Pn0ovYp+R/8Rf+gAgoGRgnJC4oNxw6SD7QP5A6GDSoMLgtZCkwJKgglB0kGXgUpBNQCmAFPALP+mvxq+on4+PaT9UT0f/OX82n0tvUu9x35dPvk/QgAlwHtAtUDEwRzAwkCcQCw/q/8Q/rG9+/13vR79IL0JfXE9iv5CvwA/zMCmgXRCH0LTw2kDoIPuA8dD+INogyOC6UKhglGCC8HIAYDBaEDQQILAcj/P/4z/CH6T/jG9mn1JPSF87Pzl/Tk9XD3e/nc+0/+WwAHAmwDVwSPBMcDWgK1AOr+yPwy+q33wPWu9DT0J/Te9Ij2C/n4+wP/SQK/BQMJjAs+DXAOSQ94D7IOVA0QDDMLZgpNCQUIBgc/BjUF0QNRAiUB9f9G/hj84vkv+L32SPX080TzmfOI9Mr1RfdV+eb7Yf55ABcCgwOKBLoE9AOFAvoAOP8C/VT6v/fr9df0V/RR9Bj16/Z1+VX8W/+oAhEGKAmHCyUNWQ4VDy4Pcg48DRgMMQtaCkIJHwgWBxkG6gRhA+YBjAAu/2n9NPsh+Vf34/WB9Fvz+PJo85f0Ffb091L68/yL/5wBUgOrBHQFbQVoBOACFgEc/7T87vlu94b1dvT28wr09fS19kr5Ovxu/8cCIAZDCbMLfw2jDksPWg+cDnYNMgwqCzQKEwnfB6YGkAVXBOsCZQH0/47+1fzY+s34DPel9W/0k/NM88nz8fSS9pL45fpr/dX/6gGcA9AEcwVLBUIEqwLNALz+Z/zd+Yj3xvW/9FP0g/SA9U730vmx/Mf/DQNGBjgJhgs8DXwOHw8rD34Oew1qDGQLVgoRCcYHfgYpBagD/QFzAAf/i/3I+9H5Efia9nL1f/Tf8+vzqPT69aP3mfni+0/+nAB3AvQDAwV5BSkFAgRnAowAh/4s/JL5O/eD9Zj0OPR19In1gPc0+jL9XgCtA/UG4wkiDMwN7Q6ID3wPtA6WDWMMSgsWCqoIJwemBTMEpgIFAXj/D/6i/P/6N/mX90/2VvWK9A70MvQL9WX2D/gB+kH8p/7TAI0C4QPiBE0F2wSqAwsCTwBO/u77aPk397r12vSG9Nr0EPYv+M/6tP28AP4DOQf/CS0Mqw25Dj0PGw9hDlUNSgwzCwEKlQgTB5QF/ANaAqQAD/+K/eL7G/pS+M72kvWe9AL03PNe9Gv18PbV+AP7bv3F/9UBeQOtBF0FVAWVBDgDjgGq/3n9D/ur+MP2dPXL9Lz0YfXn9hz51/vM/uwBIwUdCKYKiAzkDccOEQ/GDvEN9AzuC9gKkQkRCJIGBQVnA6wB9v9w/uP8Ovtx+dH3iPaG9cz0WvRv9BX1PfbN96L5yvsK/i8ADQKHA6IELwUYBUkE9QJUAXP/Wv0B+8D45vah9fz07PSk9TP3ffky/CH/PQJgBU0Irwp9DM0NnQ7XDnkOqw20DK8LgAoqCbkHSwbWBDgDgwHN/zr+qvz9+j75jvc89jD1cvQL9CH06/Qw9uT33fkW/Hz+sgCeAgsEDwWSBWAFfQT+Aj4BUP8h/b76a/il9oP1CvUp9f/1u/cl+v/8/P8GAwoGyQgPC6YMsA1CDk4O3w30DOALvQqVCVAI3QZpBfEDfAL3AG7/Bf6f/Cj7j/kE+MX20fUp9cf05fSj9eX2iPhh+nb8qP7BAIsC1AO0BBYF3wT8A4sC3QAD//z8xvqd+Pf28vWV9cj1rvZq+Nj6p/2CAHkDYwYJCR4LgwxqDeEN3Q09DTkMKQsgCgAJoQcxBtgEmgNJAsYAS//t/Zr8Gvt8+fn3zvb29Ub17/Qa9er1O/fI+Jv6q/zq/vIAjwK/A40E9ASoBLQDUgK7AP/+7/yz+qj4M/dc9v71NfYw9xH5g/sm/usAyQOqBigJ/go7DAUNdQ1FDY0Mgwt0CnUJSAjsBoIFQgQWA8QBXgD+/sr9mPwv+7X5V/hT94j25vWO9ar1b/af9xf50/rJ/Ov+1wBvApQDXwTCBHcEkAMwAq0AAv8K/fD6Afmy9+v2nPbb9tf3sPn9+4n+MAHzA7kGCgnDCt4LjgzVDIYMwQusCqQJlgh5B0IGAQXmA9UCwwGKAEv/Ff7c/I77Gvq8+Jn3v/Yt9uP1FfbG9uf3UfkA++388P7HAEcCcAM2BIgEPgRrAzYCtgD+/hf9N/uO+UX4dPcu95X3nfg6+k38rv5LAd4DPQZSCAkKVAsCDCEMyQsrC1gKSAkfCPEG0gW/BKEDgAJgAVwAWP88/gn9vPuK+mf5Wfhq9772ifa69lD3NPhz+Rj76fy4/lAAywEVA/cDQATwA0ADSQIGAWP/hf28+zP6Afkl+M33IPgi+a36kPzD/jEBrgP1BdQHVQl7CjALUAvjCiwKVAltCFUHJAYXBTYEYwNvAmkBfwCv/8P+nP1V/Bv7Bfr6+AT4T/cF9yX3lfdY+Gv55vqQ/Dv+wf8hAWgCWQPQA7cDNQN4AmkBAQBQ/qb8MfsB+hj5lfjB+Iv51fp1/Gv+swABAxwF2wZYCI4JTgp7CicKoAnwCBQICQftBe4EDQQoAysCMgFYAJD/rv6j/Y38jvuc+qP5tvgF+Ln3yfcX+LH4tfke+7r8Sf7A/y0BdgJmA8YDrwNHA54CnwE3AKH+G/3L+6/6wvlC+Vn5EvpA+7L8cv58AKkCrARaBrcHzAiCCaUJRwmrCPkHLwclBg0FIgRxA7UCzgHzAFUA4P8w/zn+OP1Y/JP7p/qt+fH4mviY+Lb4Ifn8+Tz7mvzp/Tn/lADkAcoCKAMfA+ICdgKkAWUA9P6d/XX8Z/t/+vf5Cvqt+qT77fyR/ngAZQIWBI0F4Ab8B6gIxgiDCBYIiQfSBu4FCQVABJYD6AIlAmYBtAAQAF3/f/6J/Yj8kfum+s75Hvmu+Jn44fh8+WL6ivvd/D3+iP+4AMcBmgIKAw0DugItAmwBYwAh/9T9r/zJ+yD7vvrC+lf7b/zQ/VD/5ACTAjMEiAVsBusGMgc5B+cGPwZ+BeEEWgTVA1ID+QLQAqECQAK9AT8BugD7/+r+rf2I/IL7fPp3+an4U/h/+AX5wvnA+g/8mv0k/3oAnwGVAkEDewM2A5wCzAG7AGf/8v2l/Kn7+PqK+n36B/sj/Iv9E/+yAGQCBwRdBUoG3QYmByMHxAYhBmYFugQmBJsDGgO7AoUCWAISAqsBJwGMAMP/uv6B/Uf8KPsi+jr5k/hb+J/4Rvk9+nn79/yQ/hUAcgGbAngD7wPuA30DuwK9AYIAHP+o/Vb8UPuf+k36Yvrv+vH7Tf3d/oEAKwK7AwwF/AWNBswGuQZdBs0FIQV/BPADgwMwA/4C4QK8An4CIAKuAREBKwAK/8T9g/xT+zr6Svmo+G/4nPgl+Qn6QPuz/Df+tP8dAWECVQPmAxQE4gNZA3YCTQHz/4X+MP0N/Cv7nvp7+tf6pvvQ/C7+rf9AAc4CKgQyBfQFagaSBmIG+AV5BfkEfQQFBKEDXgMmA98CeAL7AWYBrQDG/6v+bP0r/Pv66/kR+Yj4ZPiy+Gf5bfq7+0P94f5tAMsB8gLTA1IEZQQABEUDTwIhAcv/Uv7t/MP76vpt+kr6lvpU+3L8z/1L/9AASgKgA7MEdwXpBRUGBgbBBVEF0gRiBBUE1AOOA0cDDwPIAksCmQHLAOj/0/6E/ST87Prv+R75gPhA+IP4QvlI+n778PyU/jwAtAHoAtYDdgStBG0ExQPcAsQBfwAU/6P9bPyK+/P6n/qq+jD7Jfxb/aD+9f9dAc0CBwTnBH4F2wUIBvUFowVBBeQElgRCBOUDfwMUA5wC/wEzAUsATf8t/vb8vfuj+sT5JfnU+Nr4Tvkr+kv7m/wF/oD/8QAzAi8D4ANCBEME6gM9A1cCPgEBALj+gP11/Jr7CvvX+g77ovt2/Ib9y/4yAJMBxQLCA5EEKgV8BX0FUwUUBcsEcAQKBLcDdAMiA7ICGwJrAbEA3P/g/sX9ofyd+8z6I/qk+XL5rPlG+iD7JPxQ/aL+/f8uASMC6AJ9A8wDuQNJA6MC5QEBAfD/0v7Q/QX9Yvzr+7X74ftw/C79Dv4U/0IAeQF9AkYD8QOCBNYE3QSzBH0EUgQWBNADfwM3A/YClwIRAmMBnQDD/8j+uf2p/K373PpD+uT50fkX+r/6r/vB/PH9OP+CAKsBkgI7A6UDzAOhAyIDcAKbAaIAk/+C/pD90PxG/AH8BvxQ/OP8sf2s/r7/zwDZAdUCqAM5BIcEpAShBIAEKwS0A0AD5AKVAi4CrgE3AdQAcADr/0z/sP4X/nT9yfw2/NL7l/t7+4v74/t8/EL9Ev7e/rb/kQBOAd4BLAJMAk4CHwK/AT4BogD9/1X/qf4L/o/9S/02/Uj9jP0Q/s7+mv9iAC8B+wG1Aj0DiAOzA8EDuQOKA0gDEAPlAq8CYwILArEBSQHHACAAZv+t/vP9Nf1//OL7hfts+4T7zPtK/AH93v2+/o//WAAYAbEBDwI3AjwCKALkAWQBygAyAJn/+f5W/s/9fP1g/XD9pf0Q/rP+dP86APUAqAFNAswCEgMlAx8DBAPZAokCMQLwAcwBrQF7AT4BBgHOAHwABgB+//D+Y/7M/Tz9zvyJ/Gv8c/yl/A79o/1N/vb+nf9CAN8AYAG3AdwB2QG4AXUBFQGZABIAk/8a/6/+Wv4k/hL+Iv5U/qb+F/+b/yQAqgAuAaoBDwJbAo0CrAK5ArMCmAJ5Ak8CHQLkAaEBUQHuAHkA8/9m/9b+Ov6m/Sn90/ye/Ir8qfwC/YT9G/69/mv/IADAADgBjwHCAdoBxgGIATIB0ABmAPD/fv8g/9z+qf6I/oP+nv7Y/iX/e//W/zAAiwDaABQBPQFYAWwBdwGEAZMBsQHXAfIBAQIKAgEC2AGDAQgBeQDl/0X/n/7+/X/9Lf0D/QT9L/2F/f79if4U/6L/KACUAOEACAEiASgBEAHhAKMAaQA1APv/v/+I/2L/Sf83/yv/Kv85/1X/cv+I/6P/y//+/ycARQByALMA/AAwAV8BogH0ATECSgJKAkMCIgLKAT0BngAAAFr/rP4E/oH9Lv0D/QH9LP2I/Qb+kv4k/7T/NwCqAPsAKgE6ATUBIgH8AMAAgABIABsA7f+6/4r/bP9a/0//Qv87/0f/Xf92/4r/pf/P/wEAOABsAKIA3QAdAVIBdwGSAa8BwwHBAaIBawEmAdYAcgAEAJX/NP/j/pn+Z/5N/k/+b/6i/uv+Qf+W/+f/MgBsAJUApAClAJ8AiABhADkAGgD+/+H/xv+z/63/pf+U/4H/c/9w/2//af9o/3H/hf+a/7H/0f8AADQAYwCUAMsAAgEuAU4BbQGBAYMBcAFAAf4ArwBMAOb/h/85/wD/1v7E/tT+9/4e/03/hv+//+b/+/8LABYAFQAIAAEACAAYACcANwBKAFwAZABiAFQAPgAeAPL/vP+K/17/N/8b/w7/Ef8r/1b/gP+t/+H/IABqAKYA1AD5ABkBLwEtARcB+QDQAJkAUgAGAML/hf9P/x3/9/7v/v/+IP9P/4H/tf/t/yQATgBmAHkAgQB/AHAAUwA5ACgAGAAIAPz//v8AAPP/0P+3/6n/lf90/1X/Sv9R/1//b/+J/7v/+v8yAF8AhwCuAMwA3QDZAMUAswCZAHUASQAiAAYA+P/v/+n/4v/h/+H/1v/F/7j/tv+2/67/p/+k/6b/pv+r/7r/0v/t/wgAJgBGAGIAdAB+AIoAkwCYAJIAgABkAD4ADgDh/7H/if9t/1r/Sv84/zf/SP9v/6v/3v8LAD8AZwBrAFQATgBqAHwAXAAkABAAMgBkAIcAkQCAAFsAFwDF/4P/Xf9N/zX/IP8p/0z/df+c/8n/AgBCAHUAmQClAJsAhQBnAEEAFgDy/9b/wv+7/8D/1f/z/wsAJAA/AFkAcwCJAJcAmQCQAIAAaQBGACQABwDu/9b/wf+q/47/dP9k/2D/Z/90/4j/oP/E/+X///8UACcAPwBTAFgAVABFADAAEQDt/8v/sf+k/6P/qP+x/8b/6/8ZAD8AVQBlAG8AcgBlAFIARQA1ABwA+P/W/8X/wf/C/8L/xv/U/+f/9P/4//7/CgAXACQAMQA5ADsAOAAwACIACADq/8r/sv+e/5X/mv+q/8D/1//t/wQAGgAmAB8AEQADAPT/2//B/6//qP+o/6r/tf/I/+P/AgAfADcATABjAHIAdwB1AHEAagBhAFYASQA9ADEAIwAPAPT/3v/Q/8b/vf+z/7H/uf/I/+D/+v8WADIATABcAF4AUQBDADUAIwALAPb/5v/b/9T/zv/M/83/y//H/8b/y//T/9j/2f/d/+X/8v8AAA0AHAAsADoAQgBJAE0ASwBFAEEAPwA9AD4AQABBAEIAQwBFAEUAPwA6ADMAJwAYAAgA9//p/93/zv+7/6z/qP+m/57/kv+N/5j/p/+1/8P/2v/x//3//f/9/wIACgAFAPj/8P/x//L/8P/y/wEAGAArADIANgBAAEsATABAACwAHQAQAAQA+P/y//f//v8BAAMACQAOABAADAAGAAIAAgABAPz/9f/u/+f/3v/S/8P/uv+x/6X/lf+J/4b/jP+Z/6b/tv/J/93/8P///woAFAAdAB8AHAAYABcAFgAQAAsACwAOAA8ADQASABoAHwAcABkAGgAbABwAGwAeACgALQAqACMAIgAlACAAEwAGAP//9//u/+X/4v/h/+D/2//V/9L/0//X/9v/4P/l/+r/7//3/wEACgAPABEAEQATABMAEgAPABEAFgAXABEADgAJAAcABAABAAAABAAPABUAFgAeACgALAApACYALgA5ADkANAAzADUAMwAvACgAIwAlACoAJwAjABoADwACAPT/7v/m/9r/z//D/7P/oP+Q/4r/jv+P/5n/pv+5/83/4f/5/xYAMQBFAFUAZQBxAHkAfAB2AG0AZABWAEEALgAfABAAAADt/9n/yP+//7b/sP+0/8X/2f/u/wEAFQAuAEQAUgBfAGQAZgBiAFsATgA8ACoAGwAOAAAA7//l/9//2P/P/8P/uf+u/6b/pf+n/67/tv/A/87/2v/j/+7/+P8AAAoAEwAdACsANQA9AD8APwA9ADYAKwAfABYADgAFAPv/8f/q/+P/4f/i/+b/6//v/+//7//u/+3/7//1//v//v8CAAYAAQD6//X/9f/0/+3/5P/d/9j/1f/Q/87/0P/U/9b/1f/T/9P/1f/X/9n/4P/q//L//v8NABsAKwA9AE8AXwBpAG0AbABlAF0ATwA8ACwAHQAQAAMA+f/3//b/9P/w/+//7P/t/+//9v8AAAoADgAOAAwACAAEAAAA/v/5//D/6P/l/+L/3//g/+H/4//k/+D/2P/W/9f/2v/c/9v/3P/i/+z/8//4//7/BAAJAAkABwAFAAcACwAPABUAGwAhACQAIwAgABwAGAASAAwABgAEAAIA///1/+z/4//f/97/3v/d/+D/5v/n/+D/4f/n/+3/8//5/wAABQAGAAIA//8AAAIABgAHAAcACAAKAAgABAACAAEAAQAFAAwADwAQABQAGgAgACUALAA0AEAARgBKAEsASQBEADgAJwAWAA4ACgAJAAsACwALAAkAAAD3//D/6P/h/9z/3f/a/9j/2f/c/97/4f/n/+n/6v/o/+P/2//U/9D/zv/R/9n/5f/t//H/8//0//n/+v/0//D/6v/q/+z/7f/t//D/+/8IAA8ADgATAB4AJgAoACwAMwA5ADoANgA2ADcAMwAuACcAHQASAAAA6f/S/8X/wv/D/7//v//F/83/1P/e/+n/8f/4//3////7//b/9v/5//z//P///wQACQAPABYAHQAhAB4AFgALAAgACAAHAAgADgAVABkAFgAQAAoABwAHAAgABwAHAA4AFAAVABcAGwAfACAAIQAcABcAEwAMAAEA9f/t/+v/6P/n/+X/4P/d/9r/1//Y/9f/2v/h/+j/6//t//D/9//5//j/+f/8//v/+P/2//H/7v/s/+j/5v/p/+3/8//5/wEADAAUABcAGAAeACcAMgA8AEcAUABTAFMAVQBVAFAASQBAADMAIwASAP3/5//a/9L/z//K/8r/zP/N/87/1//f/+D/3v/f/+P/6f/z/wEAFQApADQAOAA2ADMALgAoACMAHQAVAAoA+//t/+j/6v/y//r///8AAAAA//8AAAUADwAaACIAJQAoACwALQAqACUAIwAlACUAHQALAPb/5v/a/9D/yf/H/8X/w/+//77/u/+7/8L/x//H/8r/zf/T/9z/6P/3/wkAFgAYABcAGgAeAB0AHAAeAB0AEwAEAP///f8AAAIABQAIAAYAAAAAAAcAEAAWABwAIwAmACIAHgAhACYAKgAsACgAJAAdABYACwAEAAIAAAD3/+z/4f/Z/9b/1v/V/9T/1f/V/9L/0f/U/9z/5//z//3/AQABAAMACAAOAA4ADAAJAAcAAAAAAP///f/7//n/8v/o/+H/3v/g/+b/6//x//z/BwAMAA0AEAAVABcAGAAdACUAKwArACgAJQAeABgAEAAIAAMABAACAP//+P/t/+H/2f/d/+D/3v/d/97/4f/j/+f/7f/4////BwALABEAFwAYABgAHAAfAB0AGgAdACMAKQAvAC4AKAAgABUACAAAAPn/7v/i/9r/2P/a/+X/8/8BAA4AGgAkACoALgAxADQANwA2ADIAKAAdABYAEgAKAP7/8P/k/9z/1f/R/9H/1f/c/+X/7v/3//3/AwANABgAIQAkACQAIgAcABIACQADAAAA/f/z/+v/6f/n/+b/5v/q/+3/6//n/+f/6v/u//L/+P8CAA4AFwAeACQAKgAvAC4AKAAiAB0AGAASAAwABAD9//X/7v/p/+f/4//e/9X/z//R/9T/1f/W/9j/2f/d/+f/9f/+//7/+f/1//X/9//6//3/AQAEAAAA+f/9/wYAEAAXABgAFgASAAwABgAFAAQABAAEAAUACAAMAA8ADgALAAUA/P/z//L/8//6//z/+v/5//r/+P/1//b/9v/x/+3/7v/1//r/BAATABsAGgAYABMAEAAQABAACwADAPv/9v/z//H/8P/t/+X/2v/Q/8v/zf/S/9f/3P/h/+n/8//+/wsAGQAjACgAKQAqAC4AMwAyAC8AJwAiABwAFgAVABsAHgAfABcABgD2/+3/8P/x//P/+/8AAAEABAAFAAUAAwACAAYACgAKAAcAAwD+//n/9P/w/+r/5//f/9n/2v/f/+X/6P/t//L/9f/0//T/9////wsAEwAcACEAJQApAC0AMAAvACMAEwAGAP3/+P/4//X/8P/0//r/+//6//r/+P/w/+b/3f/Y/97/8P8AAA4AGwAjAB4AGQAVAAoA/f/u/+T/4v/j/+7///8UACIAKAAmACIAGgAPAAcAAQD5/+3/5f/i/+X/7f/4/////P/0/+n/4P/Z/9j/3//v/wAADQAZACIAKQAuAC4ALQAoAB8ADwD///L/6//o/+f/7v/4/wYAFwAkACsAJgAcABEABwD8//H/6//w//3/CAAQABoAIAAaAAkA+//r/9z/zv/K/9L/4f/w//z/CAAVAB4AIwAkAB8AEQADAPj/8v/z//f/+v/9/wYACgADAAEAAgAAAPr/8f/q/+j/7v/8/wwAGwAkACIAGwAXABYAFQANAAMA/f/4//X/+/8DAA8AFQAUABIAEQAPAAsACQAJAAYA///1//X/+f/9/wAA/f/0//D/7v/s/+v/6//m/9v/0v/P/9r/7P/6/wAABQAIAAQA/v/9/wEABgAKAAgAAgD7//X/+P///wMABQADAP//9//z//L/9//8/wIADQAZAB8AHwAaABcAGAAYABcAEAANABEADQAHAAAA9//s/+P/4f/j/+L/4f/p//j/BQANAA0ABwD8//T/8//4/wAACAANAAsACwANAAgAAQD+//3/+//4//L/9P/5/wIADwAYABsAGAAMAP7/9//1//T//P8GABEAGAAXAAsAAwABAAIA/f/2//T/8f/t/+//+P8AAAwAEgAMAAcACgAPABQAIAApACsAHwAOAPX/3//W/9X/3f/w/wIAEgAZABMACAACAP3/8v/o/+b/6v/o/+T/7v8DABUAGQAUAAoA/v/y/+3/8v/2//j/+f/4////DgAeACEAFwACAO//2f/K/87/3v/q//D/8P/s/+f/5P/y/wcAEQAUAA8ACAACAAAABAANABMAFwAcAB8AHQAZAA8ACAAIAAoACwARABIACgAHAAkADgAWACAALgA7ADgAIAAPAAcAAgAAAP3/9//2//H/6P/p//T/CgAaABkAEwACAOb/z//D/8L/zv/e//X/DQAdABwAEgAHAAMA/v/0/+7/6f/j/+j/9f8DABgALgAzACkAFQD6/9//0v/d//D/AAAMABEAEgALAAIAAAABAPv/6v/b/9X/0P/K/8//4f/3/wcAFAAhACkAKgAgABMACQACAPn/7f/k/+3//v8MABYAGQARAAAA7P/e/+H/7P/z//H/7//0/wAAEAAjADAALgAfAAUA8v/s/+//9/8CABAAHgAoAC4ALgAkAA4A+f/n/+T/7P/9/xAAHwAgABYADAAJAAsACQD5/+f/5f/t//L///8aAC0AIgAJAPj/5//V/83/y//N/9X/5v8AAB4ALwAdAAMA9P/u/+7/7f/z//z/AwACAAEACAAXABcAAgDw/+n/4//k//P/CQAWABYACwACAAIAAwD///f/+P/8//r/+f/6//7////8//n/+v/+////AAAJABQAFwAQAAsABAD///z/+v8AAA0AFQAdACgANQA6AC4AGwAOAPz/3f++/7j/zv/z/x0ATQBwAHYAXAA0AAgA3//C/7f/wv/a//X/GAA2AEgASQA6AB8A/P/U/7D/mf+U/6D/w//3/ysAUQBTADQACQDf/7n/nv+c/7L/zf/k//f/CgAnAEMATQA9ABkA6v++/6v/u//g/wwAOABNAEkAPgAvABsAAQDq/9P/x//L/9X/7/8RAC0AMwAsACgAHwACAOH/0v/K/8n/0//p/wAADgAOAA4AFgAaABEABAD4/+X/zf+//83/5f/6/wgADQAUABcAFgARABAAGwAdAA0AAAAFABcAKAAvAC8AKQAbAAsA///6//D/4//c/+D/7/8CABUAJQAsACEACgD2//D/+/8IABYAGwAaABYAGgAaABMADAAGAPn/4v/X/93/7v/7//f/7v/i/9j/4P8AACUANgAqAAYA4P/G/8D/0v/z/w4AFwAQAAYACQAOAAwACAAGAAwABgDw/+L/6v/z//L/+P8GABYAGwApAD0AQgAtAAQA4P/M/7z/rP+o/8L/5P/+/xgALQA5ADoAMgAhAAkA8f/X/83/3P8AACMAQgBVAFIAQAAvACQAFgAGAPj/9v/7////AAAFABUAJwAxACkAFgAAAOf/1f/d//T/CAAQABcAHwAZAP7/3v/U/9T/0P/V/+z/BgAPAAsAEwAnACQABwDr/+D/3//i/+//EAA2AEIAIgD0/9r/0//E/6z/rv/K/+H/4P/c/+z/AQAFAPz/9v/7/wIABQAKABkAKQAnABQAAwAAAAAA9f/b/9D/3v/s//H/+/8NABAA+P/i//f/JwBCAC0AEgARAA0A9//q/wEAHwAgAAoA+P/0//j/8P/n//D/AAD4/+D/3////xoAIAAcACEAKgAmAA8A//8EABAADAD8//7/DQAOAAUADQAlAC8AIAAIAPv/+f/w/+X/7P/5//r/6f/m//P/8P/e/93/7P/w/+r/6//1//v/8v/g/9j/2//g/+D/4v/x/wQACgD6/9v/xf+2/6D/jf+X/7v/4//7/w0AIQAqABcA+f/n/+D/4P/m/wMAMgBYAG4AfQB4AFQACwDA/5v/ov/F/+v/HgBiAI4AhwBbACUA7v/H/8j/9v80AGEAcgBxAGcAXgBWAEIAGwD4/+f/7P/4/woAKABAAEMALgALAPH/5v/k/+3//v8LABAADQALABAAGwAdAAkA4f/C/8D/x//F/8v/5/8DAA8ACgD5/+X/yP+Z/23/cP+j/9j//f8sAGUAdABOABkA7//S/7j/oP+t/+D/EwAlABwAFwAJAPD/0f/A/8T/0//j//H/AgAbAC8AOQA3ACcAFwAUABkADgD3//P/BgAYACAAMABIAEcAGwDr/9T/yv++/8j//P88AFMAQAAeAAgA8v/Z/8z/zf/Y/+T/+P8aADcAMwAOAOP/0P/R/9D/yf/a/wUAIgAYAP3/7v/j/87/x//g/wYAHwAgACMALwAuABMA8v/f/+f//P8JAA0ABAD3//f/BAATAB8ALwA8ADMAGwAJAPj/7f/y/wQAIQA4AEgASwBCADYAGgDo/7z/q/+y/8j/6f8PAC4APgA/ADIAGwAFAOz/1v/Q/9r/6v8IAD4AZwBmAEYAFgDq/8L/ov+O/47/tv/9/zcAVwBbAEYAGgDx/9L/wf/C/8v/4/8QAEMAYgBfAE4ANQANAO3/2//Y/+L/+f8PABgAFgARAAoAAAAHABAA+v+//5X/pv/f/xQANgBJAEIAEQDZ/7v/vP/I/8n/yf/Y/+7/DwAxAEgATQAyAAAAzf+t/6f/sf/I/+r/DQAhACAAGAAXAA0A9P/Y/8z/yP++/7z/1f8IAD8AVgBMAD0ANQAgAP//5P/Y/9D/1v/z/xoARABoAHUAXwArAO7/tP+X/5n/qP/L//7/MABOAFsAYgBTACYA7v/K/7j/tv/J//P/IQA/AEgAQwA4ACIA+//E/4n/Xv9Z/3//zv8jAF4AbgBYADkAFQDm/6v/h/+O/7H/2f8IAEYAegCKAHAANwDo/5r/cf92/5r/vP/Y////NABsAIUAbQAuAOf/tP+h/6z/yf/t/w4ANwBhAHMAXwA3AAYA2P+4/7v/3v8AABsALQAvACEABADx/+//8//y/+n/2v/W//D/GQA9AEUAKwD+/9T/yf/c//j/AAAFABsAMAAxACMAIQAnAAoAz/+u/73/2f/0/xwATwBgAD4ACwDq/9z/1//Z/+z/DgA9AGIAcQBtAFEAIwDr/7v/pf+r/8f/6/8bAEkAVgA1AAYA5P/U/8//1f/l//f/DwAwAEkATwA4ABcABQATACgAIwAEAO3/8P8AAAsAEQALAPv/5//a/9v/5v/0/wkAMABbAGYASgAUAOP/x/+7/7j/x//r/xYAOwBMAEcAIgDe/53/gP+H/5//xP8AAEIAcgB8AFkAHADa/7L/pv+k/7L/2v8PADQAOgA4ADAAFADs/9L/zf/L/8H/wf/d/wYAFwAKAPz/+//9/+n/w/+q/7b/5P8LABoAFgAMAAUABwASABkADwAAAPv/BgAWABAA+f/1/wIACQAAAPv/AgAZADQASAA+AB8AAADw//3/GAAoACAAFAAeACsAIAAFAP3/AQAFAAkADAAFAAAABwAaAB8ABwDj/8P/s/+2/8v/7/8YADEAMAAaAO//wf+q/7L/xP/V/+r//v8FAAsAHQArABsA8f/J/7b/tv/H/+H/+v/9/+z/3v/e/+v//f8DAPn/5P/V/9P/1//i//L/+//6//P/9v8IACcANwAoAAYA6f/k/+//AAAQABQAFQAZACUAOgBFADUACgDZ/7b/sv/L//3/PwB0AIMAZgAvAPT/xv+q/6n/x////zcAWgBsAHsAegBSAA0A1P+8/9D//P8pAEQARwA6ACYAGgAfACQAFgD7/+n/6/8AABsAJgAeAAwA9v/e/9///v8hADoARQBAACIA///o/+L/6P/r/+L/1P/U/+H/9/8TACoALAATAPz/5//K/6r/mf+0//f/PwBsAG0ATwAfAOj/rv+D/4T/sf/u/xkAOwBUAFMALQD3/8z/qP+J/4T/rf/y/yEAMQA2ADkANwAnAA8A8//b/87/1v/v/xAANABMAEkANAAVAOz/wP+n/7L/0v/q//3/HgBIAFUARwA2ACEA/P/Z/9b/5f/x//r/CwAoAD4AQgAtAAUA4f/R/9n/6//8/w4AJwA9AD4AKAASAAgACAATACMAJQAPAPv/9P/1//j/AwAVACUAMwA/AEIANgAaAPH/z//F/9f/+P8oAF8AewBxAEgAFwDk/7v/qf+1/+T/JwBbAG8AawBOABMA0/+s/7H/zv/n//n/BAAVACIAGgAHAPj/9v/6/wMACgD//9r/rv+m/87/BAAjAB8AFQAHAOv/yv+3/7f/t/+4/8v/8f8MABAAAQDx/+//6f/U/8D/wv/U/9X/xv/C/9v/AAAcAC0AMgAbANr/i/9d/2L/jf/M/xUAUABpAF0AOgAFALr/b/9A/0P/dv/L/yoAcgCXAJEAZAAUALX/Zv84/0L/i//8/2wArgCtAIIARwACALz/l/+c/7j/2/8NAEYAZQBqAGoAawBeADkACADc/8b/yv/y/zQAcACLAIIAXwApAPj/4f/m//P/9v8CABcAKQAyADwAQAAsAAYA7P/x/wMABgADAAkAHQArACUAGgALAPf/6v/p//L/9f/0//T/8//s/9v/xP+1/7//2f/1/wQADgAYABcAAADb/77/rf+t/8X/8P8fADoANgAYAPD/vv+M/2r/aP+S/9n/JgBVAE8AIQDy/8n/qf+X/6X/yP/q/wYAGgAjABkAAQDt/9j/vP+p/7T/3f8IACQAKwAhAA0A/P/3//z//v/+/w0AIgAqABsAAADz//v/HABBAEAAEwDq/9//7v8HACwAVQBhAFkAUABBAA8AxP+c/8r/KQBsAGwAVwBOAEoARgBCAC4ACQDw//z/IAA+AEUAQQBJAGoAhgB/AFQAKwAbABsAFgADAPz/CgApAE8AbwB5AFoAKQAHAAcACwD0/9j/3P8GADsAXABnAFkANQAJAOj/2P/V/93/8P8BAAkACgAHAAcACgAJAAcAAgDy/9X/u/+t/7X/z//x/wQACwD+/9b/nf9w/2//jf+z/9L/6f/+/wMA/P/l/7//mv+K/5H/n/+q/7j/0//2/xkALQAdAN7/jP9q/4D/p//J//D/IgA/ADMAFgD+/9//sf+P/5X/t//O/93//P8lAD8APAApAAQA0/+u/5//p//F//7/QgBpAGMAPwAWAP3/9f/7/wMA///0//n/GAA8AFAATQA7ACYADgAFAAkADQAFAAoAIQA1ADoANwA+AEkAPAAgAAoAEwAoADEAMwA8AEIAOAAmABwAEQACAPr/BgAlADwAPQAkAAEA7v/4/w0AFwASAAwABwD7/+z/7v/5/wMADgAgACUACwDh/8X/y//h//v/DgAaABYAAADq/+T/3//U/8b/wf/H/9n/8v/8/+3/1v/X/+/////x/9n/1//r//j/6v/I/63/qv/H/wAAOABGAA8Axf+i/67/yP/V/+T/+P8IAAUA6P/G/8T/6/8TAB0ACQD0/+f/5P/7/yAAKAD2/7H/o//b/xYAGgAEAAMAFgAkAC4ALQAZAPr/5f/j/+r/9P8CABgAMwBHAEkAOwAnABoAGQAYAAUA7v/o//j/DgAqAFMAcgBtAD0AAgDg/9r/7P8OADQASwBIADgAKwAdAA0ABgAVACkAJAAEAOT/3v/9/y0AVgBjAEcAFQDr/9P/1v/1/yMATwBfAFMAKgD3/9D/yf/d//P/AwATACUAMwAwAB0AAwDy/+z/5f/S/8f/4v8VADgAMQAJANv/s/+i/7z/7/8AAOb/1f/v/xkAMAA5ADkAIQDy/8H/rP+n/6j/uf/m/xsAMwAlAAsA7v/M/7j/uv/A/9L/+/8kADIAGwAAAPL/8f/2/+//2//G/8D/3v8QADwASAA2ABAA6v/O/8X/1v/n/+n/6f/1/wUACAANABUAGAD8/83/vf/U////IAAyADkAJQD+/+T/6P/7/wcADwAJAPf/6//2/wAA8//j/+v/AgARAB0AMQAsAPz/zP/R//7/FQAUABMAGAANAPj/8v/r/9r/0f/s/xoAOwBBADAAEQDz/97/4//2/wYAFAAfABoA+//g/93/5v/n/+7/DgAzADoAGgD0/+H/0//O/9//BgAnAC8AKwAbAAUA4P+7/7T/3v8rAGsAfQBVAAkAzP+r/6L/s//u/zUAWgBVAEsARAAbAOf/1P/p//v/7f/j//T/FgAiABgAFgAgACYAKgAsABQA4//C/8f/5f8NADkAVABNAC8AGAASAAMA5v/P/9n/9f8PADUAWABdADUADwACAPf/4//T/+X/DQA1AFcAXgBCAAwA6v/0/wcABADt/+z/CgAkAC0AIwARAAAA/P8NAC8APAAVANv/vv/V/wsAMwBJAE4ASQAuAPn/wf+X/47/sf/6/0cAcwB0AE4ACgC8/4T/ev+Q/7P/7v9DAHkAWwAEAMH/sP+w/6z/r/+7/7D/nv+9/w8AWABiADsA/v+0/3v/cP+U/7T/vv/Q/wIAQwBoAGMANgDh/3//Uv9n/5f/xv/8/y0ARwBEAEAAOgAZAOP/ov93/3L/nP/2/00AfQBzAEwAGQDw/9z/1v/d/87/vv/Y/xsAVgBWADMACADw/+j/9P8cACgACQDb/8//9/8iADoAPQA0ACoAKgA5ACcA6v+6/8b/AgA6AFwAZQBKABMA+P8GAAkA4//E/97/FwA0ADcAMgAnABEABAASAAoA4v/E/+P/IABCADgAFgDz/9r/2f8DADEAOQAdAP3/6//b/9f/5//x/+b/3v/8/zcAYgBUABgA3P+4/7X/wP/P/+H/+P8TACYALgAaAOz/u/+2//P/OABGAAgAtv+e/8//EwA8AD8AHADu/93///8wADMAAwDV/8n/2P/u//z/+v/2/wYAJQArAAsA5P/d/+r/5//j//X/CwANABEANwBZAD4A+P/O/9z/+f8AAO7/2P/T//b/RwCGAH4AKgDc/8z/3f/u/+b/2f/e/wYAWgChAKIATADf/6P/nf+4/9n//v8kAD8AVgBlAFAADwDG/6L/sf/e/wYAJgA7AEMAUgBaAE0AGADX/7r/x//x/x4APgA9AB0ACwAMAAwA+//l/9v/3f/z/xsANgAiAP7/9v8AAPj/2//L/8v/xP+4/87/BwAsABUA3//A/8L/2P/4/wsA///Z/8T/1f/4/w0ABADr/9b/y//I/8L/tP+t/8f/+v8UABwADwDz/87/uf/J/9r/2v/G/77/5/8kAEwAOwAMAOr/1v/T/9X/3v/V/8r/4f8QADUAKgAJAPL/7v/z//b/7P/G/57/pv/l/zYAYABWADAAAgDs/+3/+//0/93/1v/m/wIAHAA8AFQAUgA8ACQACgDe/7v/yP8CADcAPgA1ADcAPwA8ACQACwDt/9T/z//h/wcAKgA7AD0ANQAnAAYA5//V/+D/DAA/AFcAQAAaAPr/4P/a/+P/+P8HABMAKQBKAF0AOQD2/8f/0f8CACcAKwAUAAAA8P/s/wMAKQBDAEEALwATAPf/6P/g/+T/8/8HAAoAAAAJADYAXABPAB8A7f/A/6j/sv/g/xYAPQBMAE4ATgA2APf/tv+T/5r/xP/8/yAALwA5AEEANgAWAPL/3//n//v/DAAOAP//6P/i/wQALgBHAEkAPAAhAPD/xv+l/5r/tP/1/00AhgCSAHAAMwDv/6v/jf+f/9z/GgA/AFsAZgBlAEcADwDc/7X/nP+S/8L/GgBnAIEAagBbAE0AGQC9/3j/hP/B/wkAWACbAKcAXgACAM7/uf+V/2P/Yf+x/y8AmACrAGkADQDW/8T/wv+8/6n/jf+F/7b/HgB5AHoAMQDo/9D/yf+s/5L/kP+l/8v/CABNAGcANQDa/6j/tv/U/9r/zv/T/+n/BwAVAAUA7P/P/8X/0//5/xsAGgAGAPb//f8IAPL/wP+W/6b/5P8rAFQARQANALv/jv+W/6//xf/Y//z/HQA3AEoARgAZAMz/nv+s/87/6P8BADgAYwBcAD8AJQAPAOb/yv/U//T/BgACAPv/BQAsAFIAVgAwAAEA5//R/8D/y/8FAEAAQgAkAB0AOQBCAB4A7//H/6n/of/S/zMAggCAAEEAFQAKAO//xv+p/7T/2v8NAEQAYwBkADsABQDb/7//sv+u/8L/9P9GAJsAtQB9ABgAxP+R/4D/nf/j/zQAXwBpAHQAfABVAAQAyP+1/7j/vv/S//z/MQBfAHkAgABrADUA6f+v/7L/4P8NACQAJwAsADkAPgAgAPX/4f/o////HwA0AC4AFQAEAA4AKQA8ADgAKQAnACMAFAD7/+r/6f/7/yIAQwBKADIACQDt/+T/7P/z//v/BQAcADQAOgApAAEA3f/c/wIARgBqAEwAAAC+/7X/0/8AACcAOQAxABgABAD6/+v/x/+t/8f//f8eAAwA6//t/wUADgDy/9D/s/+P/3T/gv+3/93/6v/9/x4ALAAGAMf/if9i/2j/of/z/yUAMQAfAPX/xv+s/7X/yf/L/8H/0f/1/wIA+v/2//T/3f+9/8D/3//p/9H/xP/m/x0ALgAMANb/of+A/4T/sf/m/woAIwAkABIA/P/i/7j/j/+J/6j/4f8mAGYAhgBmABkAxP+V/5L/pf/R/wMAOQBkAHMAdQBhADwACgDl/8z/tP+2/9n/IgBmAH8AfgBuAFwALADg/6P/kv+y/+//PgB9AIYAZwA0AAMAz/+i/5f/uP8BAE8AggB3ADoA+v/W/9b/4P/r//b///8OACcASwBhAFYALwD8/9P/u//B/+j/KABuAJkAngB7AC8A1/+G/2f/lP/+/2IAfQBkAEwAOAALAMz/nP+L/7T/FACDAK8AbQD5/5//lP/P/y0AfAB+AEQADgD+//L/3f/d//7/JgAwABMA6P/J/8n/9f89AHUAewBRACAACwALAP7/3//D/8r/+v8iACQAEQAdAFEAfQB/AEEA5f+Q/1j/WP+b/w0AZAB8AHYAagBGAPn/uP+e/5r/ov+t/9v/GQA6ACwAAADr/+b/3//O/7L/rf/E//T/HQAxAD4ARABLADkAAQC1/3v/ev+h/93/HABJAFAAMwATAP7/4P+y/5z/vP/o//7/DAAoAEEALQAAANf/vf+t/6v/zP/4/xEAGgAVAAQA4/+2/5P/l//X/ysAWABEACAAEgAIAO7/zP+9/73/yv/8/0QAbwBXAAoA0P/O/+//CwATAAkA//8HABwAGwAAAOn/+P8fAC0AEwDr/8r/uv/M/wMAJwAXAPX/4v/m/+n/6P/1/xcATgBnAD8A4f+S/4z/x/8fAGgAigB8AFEAKAABAOH/wf+s/7b/4v8mAFwAcQBcAC0ACQD3//j/7//Y/8H/t//T/wUAOABXAFgASwA7ADcAIADg/6z/qv/a/w0AGgAKAP7/EAAiACAAFAD+/9T/rv+5/+v/EQANAPP/6f/s//v/CgAEAN//tv/F//b/IgBFAFkASAD+/7r/sv/M/8//uf+///P/LgBeAHsAegBSAAwAx/+e/4z/hP+Z/+H/TwCfAJoATQD3/7z/o/+3//X/KQAUAMr/mP+1/w0AZgCYAJAAVgD4/53/ev+O/7j/5v8yAIYAkAA+ANr/tP/M/wIAPgBaAFAAJAD1/9//5P/q/9//5f8TAFsAkQCJAEgA5/+s/8D/+v8gABoAAADn/+f/EABLAGcAMQDU/6D/q//B/7P/qv/e/z0AjwCiAH0AJQC2/2L/Vv+I/8L/8P8jAHcAwgDIAG0A7/+U/3D/aP9Z/2H/of8QAIoAywC6AGIA3/9q/zX/Tf+F/7P/2f8FADgAQAAcAAsAFgAhAA4A5f+5/5n/qP/c/xwAUABVACsA5//F/93/BgATAAEA/P8EAAgABwALABgAFwD4/8T/qv/D//3/NABXAG8AdQBSAAIAoP9o/3n/qv/W/yAAmgDrALcAMwDg/7f/cv8s/1H/6v9wAJgAnQDAALwAPQCH/yP/Lv9i/6v/HACMAK0AewAnAOr/0P+//7b/vP/O/+D/7/8aAF8AlgCcAGYAFgDE/4X/Xf9M/33/+P+VAAYBEQG9ACUAjf81/y//Zv+k/+f/QQCsAOYAsgA/ANX/ov+j/7v/0//W/8L/xf8WAJcA1QCWABAAuf+w/7v/s/+t/9T/EwBTAI4AtACdAC4Ar/+A/63/9v8YABcAHwBHAHEAZwAhAN3/wP/F/9H/6P8bAFkAbwBPAC4AOQA7APn/hP87/03/kf/w/2IAvAC0AEcA3f/H/+L/3/+//8H/5f/8//r/+///////+f/9/ykAUwA/APr/1f/6/xgAAAC4/3b/b/+l/w4AfQDJAMcAcAADALf/if9C/wD/Gf+L/w8AegDLAPEAxQBtACYA7P95/+D+mv7p/pT/LACRAMUAsgBeAAUA4f/X/7b/if9x/3f/jP+q/+L/QQClAMAAYwDR/3j/dv+a/8b/HACbANQAgQDX/0b/Fv9F/9H/iwAKAfsAdgABAOL/2P+b/z7/Hf9M/7z/XgD7AC8BwQAVAJz/fP+g/9D/5P/W/9v/IgCNAL4AgwAQALf/mv+j/7b/tv+g/5v/3P97AB4BQAG7AOX/Q/8R/zz/j//a/xUAVACWAMIAxQCGAAkAf/9E/4n/8v8LANj/t//k/zIAdQCzAPIA7gBkAJL/Av/u/ij/gv8BAJcABAEGAZEA6P9g/yD/K/98//z/aQCLAHIAXgBmAE8A6f9//27/tf/v//z/HABnAKUAqACEAEAAvf8v/xT/g//3//z/zf/d/zkAiACVAHYAMADf/8D/7v8gAAMAlv8//2n/8v9mAHAAJQDu/wsAXACPAIcAUADp/3//Zf+0/wwAFwAZAFwAugDVAJgAQAD3/7b/fP9W/03/bv+o/9T/BQB5AA4BQwHaADoA8P/z/9P/cv8r/zj/cf/G/10ACAEeAXIAq/95/77/6v/V/83/8f/y/7f/kP+u/9T/2f/g/ysAugD4AJwAAACx/7b/u/+u/5r/dv9Q/2P/3f+VADABagE5Ab8AFwBV/5X+Pf6W/lL/9P9qAN8AMgEoAdkAkgAwAF//av4S/pH+aP8LAIkAAgE6AQgBpwBhAB4Apv8Z/8v+0f79/kH/w/+IADABRgG9ABYAx/+z/4v/Xv+M/xIAcABYAA4A9/8RAB8AJwBWAI0AeQANAKj/lv+e/33/Y/+m/wMAIwAlAFMAlACLAFEAJAATAP7/tv83/9P+8/6m/5oASQFbAfEAYADr/5z/YP8+/0z/ef+s/wwAmQDuAMYATgD2/9L/xf+7/6v/o/+4//D/LABsAKYAqwBTANL/k/+W/47/a/9n/7H/JwCVAPEANAEVAUgAIv9w/qD+Vv8IAIcA1ADuAM4AfQAbAMP/e/9P/0v/aP+T/8r/EwBkALcA6wDBAD4Auf9p/yv/9f4f/8X/fADdAOkAxwBvAPP/nf+r/+7/8f+k/3D/tP8sAGUAWwBGAEAAOwAxADMAKwDd/2L/N/+P/xEAQQANAOT/FgBxAI8AWgAbAOv/zP/R//n/EQD1/9r/7P8XAD0ATgBGADAAKgBAAEIAAQC2/6f/xP/b//b/QQCSAI0ALgDs/w0ANAD2/4b/Zf+Z/8v/7/8YACAA3P+P/6n/HABxAG4AUABNACoAzv93/17/af93/5//AQCTAOYAwQBnADMAFQDU/4z/Xf84/zH/fv8lAM8AJQESAbIANwDH/3H/L/8d/0f/e/+n//j/bQCxAKIAjgCjAIQA3v8Q/8j+F/+a/wgAbgDHAMoAdwAcAO//zv+s/6n/uv+y/5D/g/+q/wMAZwCOAFIA+v/l/w8AKQAiAD0AhQCOABgAiv9l/5//yv/i/ycAigCuAHAALQAsACkA2P9f/yD/FP8x/6z/iwBRAWAB1AAqAMf/qP+N/2b/XP+T/+n/QQB1AGUALAAKACkAXwBtADkA2v9y/zT/Zv/z/2EAZQA0ACoALgAWAAIAEAAWAOf/nf9z/4P/rP/d/xkAWwCNAJAAaAAeAKz/PP8u/6D/OAC3ABQBJwG6APP/Uv8d/yH/M/+R/1cA/wDrAFMA8v/1//D/of9d/3r/0P8FABAANQB5AH0ARgA1AFUAKACW/zf/ZP/K//7/IABdAIQAawA/ADAAAQCF/yH/S//b/zUAUAByAHsASgAiAFIAhwAuAGD/9P5n//H/0P9o/4L/JgCsANMA6wD3AIYAl////kH/0//5/9b/DQB6AH8AJQAAADYAUAATAMj/t//S/9z/p/9X/17/3f9iAIcAZABcAIQAgwAQAID/WP+Q/6L/hv+p/woASgBcAIAAoQBZALX/R/9w/8D/sP+C/6//GwBEADAAYQDlABkBlADx/8j/wf9O//D+af9CAI4AcACbAOMAfwCl/1L/yv8nAN7/h/+c/9r/zv+2/wkAhgCjAFwANwBAAAMAiP89/2r/uv/L/8z/+f9jANEA7QCSANH/HP/0/mP/3f8IADMAegCJAEoAIgAzAAwAn/+E//7/YAAWAKb/yP84ACwA7f8sAKUAbACG/wT/Tv/S/xEAPwCAAH4AJwDg/9f/tP9V/13/CgC7ANkAdgARAM7/mv+D/4r/rv/Z/wQAPACCAIsAIwC1/7v/EAAzABUAAAD8/+f/5P8aAGcAewBAAOP/of+j/9L/1f+F/0f/if83ANUAFAHwAGIAdf+v/tP+0v+aAG4A5P8BANUATgEEAawAmwBDAIf/Kf9h/2D/yP52/kL/mwBuAYQBawEsAWUAaf/v/t/+nP5h/tv+6v/oAHcBrAG0AXkB3gDz/wr/Vf7w/Ur+V/9EAJUAsgDyAPkAgwD2/8X/xP+J/yj/Nf/R/z8AJQAQAHoA1wCCAN//ov+4/5v/W/+U/yIAOgDC/4T/+f96AIoAbABpAC0AiP8e/1T/yf8LABkALwByALsA0gCfAFwAJwDq/5v/Yv9e/2P/ef/e/5AAIAEyAfgAnQAbAH7/4f6K/rj+Yf8mALAA/wAaAfgAigDY/w7/mf7S/n3/AAApAHkAJQGJARgBNACM/zb/AP/v/jT/v/9YAOsAbQG4AXwBlgCD/87+ev5k/sL+r/+eAPUAzgCuAK8AdwDV/yD/3f4E/0H/nP87ALcAtACKAHsAZAAaAK7/YP9Q/5v/FwCCALEAkAAwAMH/kv+i/8r/DAAiAOr/0P8XAEgA3f9L/zP/sv9lALUAggAnAPD/wv+h/7X/4P/c/6f/pf/v/zkAWgBaAEgAGQDt//j/JgAeALb/Z/+//4YA8QCPAOD/nf/L//X/3f/N/wsASgBJAFMAkwClADUAiv8a/yT/m/8iAFoALgD1/wcAYgCuAJ8AQADR/2v/G//i/uP+Uv8bAOkAcQGfAWEBnACO/7b+WP6B/kb/VAD3AAYBHQFbAR0BQgBU/9P+r/6l/uL+nP+KAP8A8QD/AEcBHAH//7L+Jv5F/rr+dP9wAFcB1gHmAXgBkQBm/4f+Rv5X/pf+Qf95AKMB3QEzAWoAAQDc/7L/ZP8I/+/+Sv/v/5EAEwFaAUwB3QA0AIT/8/6j/pb+8P67/6IANQE7AfAAhwAKAJP/M//z/t/+Mv/m/38AwwDHAKsAVwDP/5b/xP/T/4j/b//w/3EAewA7ABwAKQASANT/pf+6/97/AABmAAoBTwGgAEL/CP6b/SH+Zf/OAJUBgwEaAdgAqAAgAD7/Yf72/UL+TP/HAP8BPwKBAYcAFwAIAKv/+P6C/pb+Mf8/AFsBtAEbAVYAEgALAMD/Sf8O/wr/Af8q/wMAQAHRAWkB2QCSABsAFP/3/Yr9Bv4z/5sA1QGTAnUCkQFmAHX/1/6O/sH+Ov+7/yMAYACAAIMAbwBCAPr/w/+//7//h/9E/1v/vf8NACwAagDjAAgBbwCJ/xz/Q/+M/7v//v9gAHkAKgDT/8//8//6/xUAYQCSAGQADwDg/6z/aP9i/8T/OgBfACEAw/+V/6z/4f8wAIUAeADu/23/dv+3/6L/av+u/4YASAFMAbUAJwAJAAMAlv8e/0b/zP/5//D/EwBZAKAA2QDFADAAi/9f/4X/U//b/vH+xv+bAP8AGAEgAf8AXgBS/47+r/5L/73/MwDeAEIBKQHaAFcAi//z/hP/kf/B/7v/BgCAAK0AfQBpAJAAbgC9/xr/R/+z/5L/X//P/3IAjwBcAHQArQBqAL//av+Y/9n/2f/F/97/MQB9AGUAGAD9/wsA8v/P/83/7v8fAGcAnwCBAB8Avv9A//f+Tv/s/1UAggDcAP4AnAARALX/jv9q/2r/7P+wAO4AdADN/5b/2/8eACkAIQAmAAUA6v/t//T/CAAzAD4A6/+Y/8//OQA+AMj/Wf+L/zoAoAB8ABkABgAgAPD/lP+C/6f/v/++/9/////v/+H/CwBMAGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAF4AQAAMAAwATwBrACAAs/+A/5n/sf+m/6//3f8wAGUAbwBJADoAWgBNAPz/aP8N/0j/sf8QAEIAUgCBAJgAjQBoAC0A+P+w/3j/Xf9q/57/2/88AIIAggBdAFwAeAB+AGMAEQC7/33/VP98/8P/AwBOAGkAcwBrAEAAGADr/7//rP+N/4b/xP8bAHEAmQB3AB8A9P8FAA4AAwC7/4D/gP+H/8X/9v8AAC4ANAA2AB4A5v/1/+3/u/+Q/5j/7/8TAPT/9v8qAFYAWABPADkAMgAqAAkA3v+l/6D/2/8KABIA///+/wwADgDy/7P/k//A/wsARAAyAA0ACwD+/+z/y//G//H/IQAzACsAGgAbABQA6P/O/8b/4P/y//n/KgBDAEkALwD0/+X/2//t/zUAcwCaAJEAVwAPANb/t//a//v/4f/y//v/FwBCAC0AQgA1ABUAAADV/9T/8P8MADMAOAAgAC4AXgCaAJIAJwDA/4j/fv91/0j/V/+n//b/JAAoAFAAcgBXABwA5//a/8P/mv+C/7v/KwBSADwA7v/k/z8AQgAMALf/m//z//T/0P/e/wIAZgCbAJcAYQAMAA0AQAAzAKL/MP9V/63/5f/J/9v/GgAuAFAATAAjAOn/xv/8/x0A8v/A/+//aACdAGMA4/99/3b/tP8EAAsA2v/b/w8AVgBEAO//4v8GAFgAiAA6AOT/0f8dAGoAFACr/7b/FwA8AOn/i/99/6L/vf/p/wgAJgAyAEoAdQBZADkAFQAAAAcA+v8oAEsALQAdAAkABwAHAOP/qf9i/0X/Zv/N/wQABwAdABEAbACQAHAAcQAWAPj/yP+Y/8H/y//3/zcAUgBQABYAz//g/9b/lv+n/+v/OAApAP7/TACEAD8A5//E/9T/s/+9/0cAmQBqANv/mv+y/5v/o/+u/8H/9f8zAJMAmABLAFIAiQB8APb/dv9l/4n/oP+Y/7//EABxAOUA4wBsAA0A3v/7/8T/Tv9Y/5j/CQBuAIQAkgBtAD0AXgA+APH/s/99/3P/JP8W/6T/KwBFABUAWwCgAFoA3P+4/0oAZwD7/5D/ev8BAFAAjACbACwA0/+t/8X/sv9b/03/hP+o/63//f9HAD0AGwAPAFUAQgDq/+3/EgB8ANcA2gCaAAoACQBxACUAIP9L/s3+6f9HADsAAwDy/97/zv8mAP3/j/+b/97/DADB/9f/qwAkAc0AIQDQ/5//iv+d/7X/r/8a/xn/yv9EAIYAIQAUAF8AQAAfAMX/0/87AIMA0wCOAPP/nP/T/5UA4ACLAC0ADQBwAJAACQBS/5L+uv59/77/zP/J/wUATQD//8n/4//p/8v/7v9IAGQAAQCM/9L/CgDg/+z/KgCcAI8ALQAsAPn/m/9t/37/w//y/zMAcgB9AD8AFgAcANv/jv9Y/4D/FQCUAA4B8QBcADAASABfANr/a//l/0AAIwCz/6z/JwBSACsAxv+k/6L/q//p/7j/lf97/4n/RQC2ANIAlwAQAO3/mP8H/7H+s/43/w0A0wBoAYIBDAGoADwAbf+3/qH+V/8qAHwApwArAZ4BWgGxAOT/ef92/2X/iv91/3X/+/+mAFwBHgEnAID/g/8kALz/gv4h/sz+BwA0AOT/iAAbAR8BpAD9/73/Ff92/gv/EgDjABwBAgE5ASkBcwDg/3f/bf+1/53/pv+7/8n/PQBfAIMAjQD9/9b///82ACYAmf+w/xQAFwCW/6H/rgAoAdQA+v+p/+3/mf+O/2//Wf/F/x4AkQCDAEYAdAB1AML/u/5//u/+nP/t//L/TwB+AKAApgBNAA4At/+U/5z/bv+J/+//twBlAVEB9AB6AEIAHgBz//X+3P7M/g7/i/9tAFUBOwH0APIApwAJABL/mf6N/n/+yf6O/6YAOAF5AVQBgQC2/9P+z/4//xn/Ov8e/5D/pQAZATkBwABlAHEAJgCq/yT/1f7R/iP/zP9oAOIAZAEXAmICawHj/wn/vP6o/rT+Cv/W/0UAcADGAMQAOwCf/53/1f+G/9P+vf6S/08AtACpAF8AWgBhALwA9QAnAAf/U/6z/gAAjgA/AAIAWgABAekAEABj/xz/FP9R/6P/7v85AKsAbQGaAakAnf8q/1//m/+g/wYAXQCMAMAAzgChAAgAnP+U/73/2P/4/2gAhgBSACcACwBnAIQAIgDP/zH/6f4H/yj/ff+K/4j/7P9tAMMAoQD8/4j/h/9+/3r/ov8xABsBggEtAXUA8f/x/xUA9v9+/2v/DwDPACMBzwBWANn/df+p/y4AYADh/5r/DQAtAHH/e/5e/lb/2//u/0IAnwAyAdwA9v9X/77+2v5L/53/KQDBAC0BaQEHAZMAfQAWAMz/sf9H/1f/k/8YAAIB0gCjAJAAJwA5AIz/Av/0/sX+h/8wAFQANgAUAJkAsQAUAGr/b/8AAC8AGADg/+r/RQA8APb/ov+B//f/UwAyANX/k//v/5IAjQDZ/1D/fv8UAAcAXP9L//z/rwCyAFUAXACcAJYAZgAmAK//Lf84//3/lwB+ADoAMABuAFsADADz/3L/Kf9M/47/0/9a/1P/NgDgAO4ASwDv/ysAYQATAIf/Sf8o/6z/NQBvAMUAVAA4AGUAGgD4/zL/vf4o/1f/qf/A/+H/swArAR8BvgAwADAAZQBMAOj/fv+2/1QArgB/ABQA0v+2/5X/gf+x/7n/p/+a/7X/EAD5/7D/qP8QANUA8ABmAO7/kv+c/77/oP+x/4T/zP/nAJcBcwGMAN//DQDj/0T/+/4r/8//WQChAOMAugAWANH/aP/H/qv+Cf9TAPcAoACVAD0ACgCg/yH/ev+o/9L/PQCHANoAwgCIAJoAgQACAFr/U/8EAMIAxgARAHf/SP98/9r/EAD7/6//Wv9Q/2n/Wv+j/0QAzwDrAHYAIwAvAE4ATgDE/wn/rv4z/2oA5gC8AFQA+P8NAPP/CQALAHj/Zv/i/1sARACq//H/xwAVAZAAzv+u/8v/9f+T/9v+s/4i/2EAMgEzAUUBAgF8ANn/Nf9P/3D/Z//L/1MAywAIAQcBzQBPAC7/Wf4l/if+Pf8LAKcAJwG/ABkB5ADi/0b/hP7c/kT/VP8bALwAWAFWAacANQAAAAUA2P/O/w8ASABeAB8AOACsANIAsgAcAFP/6f4O/5H/nP8n/z//PAABAZwA0f+7/yoAPgDL/7D/IwCTAOoA2wBpAOf/uP8QAOX/P/8o/+f/vwBYAMH/kf9Z/xH/lP4w/y8AeADKANoA3wCOALX/s//y//r/4P+n/9//IgBRAFgAZAArAIL/Kf9L/x0AvABmAPn/1/8kAI4AaQAuAC8AFgAIAIn/G/+B/0AADgHyAGwASgBAAEkAq/8R/xf/S//E/woAVACoAE8Aj/8H/xX/mf/D/5//7f9yAJwAUQDY/+D/QgBgAKIAegAxAD0ASwCJAB4Anv+g/5j/pv/V/0gAogAuAKn/y//0/6r/OP9o/+7/AwDv/1EABgEKAX8ABADZ/6b/IP8f/+f/pgC+AB0Anf/g/1MAlQB8AEwAIwANANj/mf+b/3v/qP/6/1UAzgChACAAwf85/77+XP55/qv/kwDsADsBPwERATEAJv/X/q3+rf5K/2QATwFDAeAAlgBEAP3/1f/n/5X/O//V/74A9wCDAHUAMQERAev/MP91//H/h/86/6b/PQBwAD0APgD0/yX/zv4B/1n/kv+v/44A4QBmABcAAgBtAAAAJv8m/4T/AABDAF0A+wAQAV0A6P+L/+H/9P9//4//rf8OACgAEQBuAJsAgwDv/2T/TP+V/wkAEADu/+P/GwBrAFEADwATADQAFACn/4P/OADzAJUAff8V/7n/egA2AKn/DQC4AO4ASwCD/1v/av+8/yEAIADm/wAAeQB7ALT/KP98/8v/Y/8z/ykAQgFdAdkAkQBuAKT/6v73/lf/kv/p/8sAfwFnAeYAiAD5/8v+AP6H/oP/PwBcAH0AGgEKAeAAawCg/zn/4/4t/4b/WP+1/0sAqgCsAEsAhgC2AFIAxf8+/0T/W/+O/y0AkgC0AI8AmACsANP/vv5f/hf/z//E/8L/NACuAJEAIQDI/67/lv+8/xkAfAC6AH0AMwARAI4AAgGjALP/EP9D/67/9v8qAEAA+f+R/5T/LQCiAMkA7QCkAOv/RP80/3b/Rv8n/+n/aAB4AH8A8ABvAXUAVf/T/rT+y/4i/10AbQFDAa0AxgD+AHkAWf8H/4H/if8h/xL/8v+tAFQArv+6/1cAjwD3/4H/oP/Z/83/V/93/wAA8wCNAQoBVAC5//z/FABA/9P+V/9XAP4A8gADARABEgBb/zP/Tv83/wr/CgAEAfcAcQBDADoAiv8B/1z/w/92//v+rP+cAKkAXgA6AFwA+//I/yQAPACn/0P/4v9KAKr/Nv/3/ycBHgEwAFEAvgBwAB7/MP7w/pz/+/9DAMYAoQHQAREBFQDs/or+3v4c/7j/MgD3AEYB0wBiAC0AHgCX/yn/DP+h/woAPQCOAKEAzQBhAOb/eP8s/1L/gf+//yUAZQBLAPb/n/8NAGEAJQBg/+b+S/8pAAwBKgEpAXEArf+N/1j/y/+K/2H/4/8+AL0AxwCzAMYANwBB/0P/wf9ZABQAjP8TAFcA6v9//+b/nwCZAOb//f85AMn/RP8X/7v/1P+F/9n/ewANAegAQQDE/wn/7f6U/zQAnQBEAGAAmABMALb/V//W/4cAogB1AEEANwD//0T/AP/g/nD/GwBxAOEAxwCkABAAHf+n/gz/pf8gAOn/2v94AKYA6gBrALn/hf9G/53/r//O/18AxwDXABEAmP/6/8AA2wD8/5j/kP+e/4v/wv/EAAUBFgAt/zH/HwB/AOj/Rf8R/53/UgDIAJwAQAAnAAwA2P8j/wj/qP9tAO4AmwAWANj/BwCIAJQA3P8S/6H+Lv9aAEsBpQG5AOv/nv/N//b/W/8//5L/BwBeAJEA9gD6AB8AJP+v/sf++f6Q/4oAVAFPAScA1//6/y0ABgDw/qD+1/7e/2cB2QF6AZkAz//m//n/0/+B//H+KP+t/xYAbQB2AOkA4wAlAGf/4P4q/zT/Qv+s/10A+gDNAJcAfAB7APj/FP9V/k3+Rv99AEQBKAHDAFMA7/+s//7/bwArAGj/VP9iACEB+QCYAJAAAQCf/u39Df/DACABaQASAFgAbgD0/4f/cP8O/wz/NP/s//IAkQEJAhMBwf/W/p3+GP89/4j/xv/b/yYA+gCOAeUAe//L/lD/ef8I/xP/IAANAX8Az/9AABcB9ADK/3D/BAAoAMn/dP8wALgAcQD+/63/+//y/xwATAAdAN3/iv/F/x0AJgAKAOv/7f94AMYAawCI/yH/2/8BAGT/7P68/7IAXAD5/00A5gAkANn+8f7n/4sASwA+AKcArgCCAGsAagC//+D+tf43//f/agC4AJ4ARAByACkBHwH8/8j+0v59/3n/kv84ACcBYgH2AJoA6f+8/jn+xv5u/zv/EP92ABYCRALPAK//vf/A/y7/nf4X/wMAzQAgAVABKwFyALr/YP+u/53/8/5u/vb+iQDkARUCBwHD/wT/If/R/x0A2v9i//7+m//BAMQB7wFGALj+M/7X/sn/1/8nAJEAfQD2/wUA1QA2AQMAnf6T/kD/4/8TAMkAbQHzAOz/oP8+ACIA7P47/iT/iwAlAd0AAQE5AdwAx/+L/jr+o/6P/3cAtgDUABYBJwGfAE//+f5I/z//sf6V/kMAsQGeAbQATgBqANP/nf5k/gn/pP/7/zIA4wD/ALIAfwA3AJX/z/71/s7/SAAoAFsA4QAAAVoA3v/g/+f/lf8T/0X/4/+iAOUAPQCa/5v/jQD3APT/0P7I/tD/ZABjAHcAxQCPALP/u/9gAMoAFADo/tD+Tv/z/4YAzAD5AH4A1//T//r/IQCT/+r+Dv/m/w8BKQF9AO3/BAB/AKEAfAAYAKX/Of9O/8D/KgBfAF8APgAHAPz/EgA4ALn/Pv/9/vL+Y//S/6cALwHBAB0Ar//N/zAA8P+j/0j/Lv/O/2gACQHoAHsAYABPAAUAUv+s/rz+kv+kAD0B1wBkAGgAuQCDAHr/ov5e/rj+rf/YALABbQFnANv/xv+w/yn/5v5A/6L/0f8qAB0BvwFdAUUAiP9t/3n/bf9b/3P/tv9eAPoADQF1AOP/wP+b/1b/eP8xAIUACAB0/8f/YwAvAOL/DAB8ADAANv83/+T/aABrAPr/NQBVAHUAuwB0ABAAFP+q/u7+Uf///0EAfQBtADIAOgDl/13/HP9G/8j/0v/h/7kAqAHMAbgAp/+J/9f/3P+S/2L/Y/9s/33/GwDaACsB8wB0AFAA/v9q//j+uP7q/hP/pv/wABYChQJ6Acn/c/6w/c/9Jv7Y/s3/ugBvAacBtQFtAXcAMP+N/t7+fv+N/7H/mwDUAR8CRwFyAPn/rf/d/oL+9v7H/4wAdwA9ACcALwCPAC4AYv8q/4b/swDHAAoAlv8Q/43/BQCYAAsBfwBDAOr/tv+4/2j/uP/Z/xcAQwAkAGcANQASAMH/l/8dAG8AhwASAM3/BQA5AEgAMABtAKEAQACE/wf/O/9G/+r+CP/a/xgBRQGTAA0AmP9e/x//Xf8DACwAawAOAXsBDAHq/3H/xP90/6v+of6q/+0AmgDQ/6X/0P8fANL/8P81AA8A9v+s/8D/4f8LAJsAfQAmAPr/QwDjADoARf/P/jv/JwBjAIoApgCOAEMAs/99/83/IgBJACwAMgBiAE0A6/+G/1H/Vf+C/8X/BgDK/5n/nf/I/+n///+KAOUAhwCP/xD/df8iADQA3f8XANYAhQFPAYcAl//J/pD+Mv8EAEcALACBACwB7QDc/xH/Z//r/7n/ff/G/2gAkQB0AIQATQC//17/iP8PACUANAB2AGMA/f9m/5f/EQD//8v/7P+rAPIAZQDI/4D/mf+j/+P/jADcALcAJwDd/9b/Xv8M/x3/AQAEAU4BKgG9AD4Ahv/d/r3+bf86AK0A1gDtABwBwgDs/zX/BP9L/4X/3v+LAB4BRwGzAAEAsf/B/w4ArP/y/s7+tv/FAJcAv/+4/1MAeADN/2z/9v8TAOH/7P+dAOUA7/9d/6z/WQA1AKf/r/8AAN7/p//a/zQAQADG/27/J/8F/5T/oAA9AQ4BfQCxAAcBZwBk/3n+8P7V/1wAqwCyACkBCQEpAGT//P43/+7+bf6Q/nn/4AAqAZwA4f+d//7/3P9h/+r+Hf/h/0sARAARAEcAnAAyAEH/n/4i/x4AUQDv/6H/EQCKAHgAYwBcADkAtf9i/8X/UACSAJwAsADEAI8AKgDB/x//p/7b/pr/SQAqABEATwBzAEMAzv/H/9H/pP+E/5f/8f8yAIUA9gDwAJ8ADwDb/+z/j/9g/0f/uf9uANAA8QBrANf/v//u/xkAAQDt/yQAQQA9AAAAsv9w/1L/rP9CAKMAlwBCAO//mf8b/+v+Wv9QAO8A1QCMAIQAoQBNAKH/B//4/mL/KgD9AHABLQEoAE7/Rf8nANIAgACx/3D/FQCiAKkAWAADAJD/+/7//tP/vgD2AI0AIgD0/8z/n/9x/1H/bP/p/0UAXwBTAG0ArwBgABAA+v8aAAsAiP9e/5b/1v/+/0AAhgBsAAAA7f9CACkAZP+b/qj+d/8UAF8AwgArAS4BZgCr/6n/6//Z/y3/9/6Z/5sACwF5ANH/jv/0/xEAlP9G/5f/VACqAFcAOQB9AIAAUwAZAEsAVgAKAPz/AQDY/13/P/+6/x8ACQDQ/8v/8v/w/wQA8P+2/6//7v9kAHgAcACKAJEANwCw/4z/l/9q/zL/kP9iALcAUwAEADAAWQC3//P+BP+0/2AAYwA5AGAAlwCNABwAn/+c/8j/yP+D/1P/sv8xAIMAfwB4AHMALQDI/4L/m/+t/7z/5P8wAJAAsQDRALYAQACb/w3/If+b/w0ANwAQABsAYgCgAIgA7P8+/9n+G//K/2kAywCTAC4A2v/L/xUABwDT/4r/lf/a/9D/7f9wABEB9gBfAC0AiQBZAEn/i/7O/pT/BABcAO0ASgEMAbYAZQDv/x3/i/7n/qv/dQD+AEcBHQFzAMz/g/9a/1b/Wv96/7D/0f8uAHwAhwBEACMAUwByABkAff8v/3b/8P8uAFMAfQC4AHMA1f9m/4P/6v/i/27/HP9U/9T/RQBIADYACADk/ysAYwA7AIb/Ef90//j/LgAqAHAA8wDQACoArf+m/5D/DP/q/oH/UACAAEcAewDuABEBmADf/3H/Yv+q/9r/q/+U////uADgAGEA6/+9/3P/xP5+/h7/CgB0AEQAPACPAMsAjgDT/y3/OP/L/zgABwC7/w4AfgB+AAAAyv8ZABAAv/+L/9z/TgAoAN//AABZAHwAHQDe/+//6v+p/2f/yv9XAJwAVwD7//f/1f+T/0j/c/8/AOgA+wCOAPn/9v85AFcAEQCM/4H/+P+MANYAqABWAP//of9w/0D/V/+e/9r/7v/s/0IA1gAoAcwAFACI/5H/zv/V/6//tf8PADQAFwAgAIMAmQDs/zz/h/+FAAEBhwDH/43/uf/3/zgAfgCdAIoAbAA3ALr/Dv+m/sv+bf9DAP8AMAHaAEIAuv+L/4r/2f8uAGwAjwBqAEwANQAcAPf/wv+v/83/4P/r/wAADAAVAAkA/v/V/1n/Df9S//D/ZABUAF8AjgBbANX/av/P/zkA1v8q/xv/FADkALMA5P9Z/1r/Rf/7/vn+lv+TAB0B/ADEAI0AXwDx/4v/h/+c/+n/SACoALsAZwAmAAYAz/9T/+T+4v4s/4v/0v86AMIAAQHTAGsAEgCl/yf///5a//T/SABnALcALAFsAfEA4P8E/93+Tv+K/4z//f/QAIYBcwGuAOv/V//6/tH+8/6f/1wAuwCnAFkAPwA6ACwABQC3/2T/Nf9W/8r/MABTAGYAiwCyAJYAOwDx/5n/Tf9Q/8z/mQABAeAAgAAuAAIAu/9i/zL/Tv+a/+f/YwDmABoBmACF//b+Nv/5/2cAPAAxAFQAggBjAO3/nP+J/3v/n/8RALgAJwHIAB8As/9r/yn/8f4g/6X/EgCBAO8AJwHoABoAdv89/y//Gv8W/7H/lAAVAQQBmwBSAN//EP+a/vX+BQCyAIQAJQAZAHcAjgA7APP/1f/Z/6//a/+Z//H/JgAYAPb/JwArAM7/UP8O/2L/8v9UAJgArwC1AH8A8/+R/23/gP+U/6L//f9sAMcApQAXAKb/hv/i/ysAMAAKAP3/VgCAAEUAz/+j//X/BAC6/5f/BwCHAFIAvv+M/+f/FAC+/1b/UP+9/ycAgADfAOsAuQBSABYAJADv/4j/Mf95/z8ArACwAKoA4wDuADUAMv+5/vj+g/++/+H/OgChANMAlgBQACYA/P+0/3T/b/97/5r/zP85ALsA5gC2AEAApf89//T+8P47/7v/cwDUAAIBBQHOAHQA2f9R/+X+dP54/lj/5wAQAskBrQCr/yL/6P6t/t/+hv8FAFcAowAOAUABtgD1/57/zP/r/5H/GP8Y/2f/qv8JAIQA/gDiAHIAWABsAC4AU/+Y/qf+W/8hAKEAAQFAASUBrgAKAIL/Jv/S/rr+9v64/4kAtQCDAHgA0QDCAOv/Kv8j/7X/IgA8AJAA6wDwAJgAHwDd/1T/yv7V/rH/vQDCAAQAZP+I/yQAZABJACEACAAHAAAABwAoAD4AawCXAKIAggBRAD4AQADu/zj/q/7j/vf/AwFBAc0AdgCdAKIAAwD+/nf+vP5S/9v/NwCUAOAAxABhAMf/QP8Y/yX/f//T/x0AXwBqAGgAUQBTAIMApQBuALb/5/7i/qf/aQCIAEIALQBLADMA2P+q/6H/u//B/+L/UACIAIsAWAAUAPz/9P89AH4ANQCv/y7/Uv/x/10ARQCv/4P/2v8qAA0Alv+K/9H/AwD3/8P/BwBdAEsA0P8//1n/zf9QALoA0wCrADYAy/+Y/5D/iv+J/+D/eADwANQAHABk/wf/Kv+o/w0ASgBDACMAAgDZ/+b/9f/U/57/lf8XAKsAvABaANH/kv+O/5v/0P/0//b/9P/8/ygAKAACAPz/+//p/5j/iP8EAJ4A0AA2AIv/oP89AOQAtgD0/3n/cv/X//T/yv/X/xoAZABGAN//tP8HAIcAnwBGAOP/yf/Q/6P/e/+y/1sAAwEVAX4Apv81/1f/wv8XAEgAcQCKAHwAMgDR/5j/nf+t/6v/uP8kANYAPAHjAAQAW/9S/7r/3P+K/0D/df80AOkAFQGpAPb/Xv8V/zf/tP87AIoAkQBZAP7/pP+v/zAAoACPABYAuf/N/wcAEQD2/9T/3f/l/wcAXQCXAGQAuv8d/xj/mP8CAAYA2P/s/04AigBeAOT/jf+U/7v/3/8FADgATwANAM//0v/w/9b/gf+V/xUAhgB5ABsABAD9/9n/mf+h/z4AzgDqAJoABQCk/2H/O/9a/4H/vP/S/xQApQABAbYAxv8n/1L/1f8QAOX/5P8jAF8AfwBeAEQAGwDF/4X/av+Z/+z/TgCQAGIA3f9u/5//NQCTAIAAOAA1AGkAXwD5/2b/Vf/C/xwAPAAdACIARQA2AAwA0//C/9//5//u/+P/4/8AACgAUQA4AO7/u//w/2kAqwBrAOn/o/+p/7f/ov+q//b/MQBEAE4AawB3ADQA2v+y/73/0v/c//3/IAAiABMANgChALIARQC4/3T/tP/K/5v/b/+b/zMAlwCqAIEAMwDf/6//rP/e/wUA9v/o/9H/8v8uAEIAQQA/AC8A2v9e/3X/LwC4AHAAmP9S/8D/JAAjAAAARQB/ADUAu/+R/+j/KQADAPf/NgCNAGYA3/+H/3z/rP/H//X/RwB0AFQA8f+l/6n/n/+B/3z/zv9lAJcAYQAaAPj/9f/o/9X/6f/9/w0ADwDo/9v/7P8MACEAJgAjAA0AGABCAF8APwDd/43/e/+o/8//6f88AJ0AvQBxABAABQAPAOv/r/+x/+n/+v/i/9z/KwCEAJEATQDq/7r/nv+J/43/nP/k/zcAcAB7ADkA6P+p/7X/EwBKADgABgAOAEcAMQDV/4//mv/u/ycAMwAsAB0AFwAJAAYAAgDm/7H/jv+r/9v/EQAsADcAUwBGABYA5/+6/9/////h/6P/Yv+T/+X/MgByAHcAcABRABcA8P/I/6T/mf/C/x4AQwAAAIj/Xv/b/4cA1ACmADMA6P+//4H/Y/+b/xkAcABqAFQAVQBRAC0A6f+n/4X/mP8DAGUAYwATAOj/GQBGACMAxv+L/7D/AAA7AEgARQBAACcA4f+N/3//xv8NABcABgAPACMABgDd/9v/9f8sAFQAUwA2AOD/h/9i/4P/6v8qAEsAQwAHANL/q/+5/9f/sv9m/1T/zf+VAAMB0QBMAOb/2//z/+r/2P/t/x4AKQAKAAgANABQAA8AvP/N/x8AYAA8APz/3/+5/6L/j//M/ygASwBYAFgAYgBXABIAuv90/2T/k//m/1oAowCcAFMADAAIAP3/zv+f/7L/7P/2/+j/8f8YADUAGAD2/wQAGwD8/9D/4v8fAEoAMwAPAAwAAADp/8v/3P8tAHEAaQAAAJT/nv/S/+v/2//L//z/LAA4ABkA8v/x/wIAEgAbAA4A+P/m/+D/8P/5//r/CAArAEwAQQATAA8AMwAzAOT/hP97/63/6/8tAFcAUAARANr/7P8yAFAAJgD9//H/BwAMAOf/2P++/6f/0P8lAMIAHAG+APH/Lv8T/2b/uv8AACIAQgBRADsAKgAuABgA4f+Q/3D/uv8aAF4AZgBOABQA2v/s/xMADQDg/8r/EABaAD8A4f+O/5f/rf/I/xkAeQCtAHQA+/+2/7D/z//a/9D/6v///xsAFADo/8z/s//P/wkANwBRAD4ADgDb/67/xf/+/ycAQgA1AB8AGwAqACsABwDm/97/2v/f//v/JABMAFcAKgDZ/7f/y//l//3/BQAZACgAHQDx/77/zf/8/w8A///y/xQALwAeAP7//f8MAAsABAD4/wcADADv/+L/4f8DAB0AIwAvABEA7//k/+7/CwAOAPz/4f/z/z4AVgAcAM//vP/t/xoAKQAWAO7/0f/X//P/EQAaABAAAADq/9f/2P/r//r/+v/N/5v/ov/c/zEASQAgAAEA9/8ZAEwARgAqAAAA6v/e/9n/+/8EAAUABgAGABYAGQD+/8b/q//J////LQBAADMAJwAZAAEA9v/u//3/LAA8ACcAAwDd/9D/4/8IABoAEwATACQAHADy/9b/2//+/wAA5//l/wYALgA4ACYADAD2/9n/z//r/wgADwATAB0AIgAsABwA1P+H/4v/1f8QABwAGQApADIAIQANAAUAGgAkAP7/2v/P/9j/3v/W/8//zv/o/xAATAB0AFEA/P+d/37/tP/t/woAEwAgAEMAWABeAE0AIQD1/8n/s//T/woALgAqAAUA7f/3/wcAFwApADIAIQACAPz/AwAOAPT/wP+h/6//+f9IAHEAWQAQAND/q/+x/9j/+v8IAAEA+//9/wEAFgAnACYAFwAGABIAHQDz/73/uP/l/xQAGAAUACAAKQAeAAsABQAXABgABAD7//D/6v/n//D/AQADABAAIQAUAB0AIgAAAOD/4/8CAAoA8P/Z/+z/IQA+ACQA7v/P/9b/3//h/9P/xv/N/9r//P8dADQATABSAC4A+P/Z/9f/5//j/9b/5/8DACcAMAAhABAA8f/U/9f/9P8NAAQA8v/U/8L/4P8LAB8AEAD7/wgAHAAOAPf/7P/v//D/6v/7/yMAKgAHAOn/6v8CAAoA/v/0//L/9f8IABYAFQAMAOv/zP/J/9n/CwA6AD8AIQD0/+//BwAbACQAAQDO/7b/y/8AADIAPgAiAAUA+v///wQA+v/m/+T/8v/5/wQAFQAbABYADAATACsANwAeAOD/rv+1/9P/8P8IAAAA9f/1/wwARgBjAEUABwDM/7r/0f/0/wMA9f/j//P/JABXAF8ANgD6/73/m/+y/+H/9//t/+r/AgAjAC8AJwAVABEAFQAIAAUADAAHAP//7f/k//D/DgAwADEABgDN/7f/yf/p/wQADQATAB4AJwA6ADoAIgD3/8r/2P8NADsASQAgAOH/xv/J/93/7v/t/+3/+v8PACcANwA3ABoA+P/o/+n//f8SAB4AGwAEAPv/AAAFAA4AEAANAAEA9f/4/wUA///g/9L/7f8dAEgAUQA8AB0A+//x////DQAEAOX/2P/p/wIAFAAQAAAA/f8GAAsAAADh/8T/vv/a/wUAJgA7ADgAGwAPAAgA//8DAPv/6P/X/9n/9f8LABgAHAAXABkAEwABAOv/3f/b/9//8P8BABMAEgAFAAQABQACAP3/9P/r/+7/+/8NABkABwDr/9j/0v/j//f/AgAJAAQAAQAIABIADAAAAAIA/v/s/93/4/8AAA0A/f/v//P/BgAVAA4AAADy/+z/8////wcAAwD+/wkAHgAsACgAEwD6/+j/7P/3/wIABAD3//X/+v8EABgAIAAeAAIA3v/T/9r/+/8QAAcA+v8DAB0ALgAtABgA/v/w//H/AQATABAA/v/u/+r/9v8JABMADQAAAPn/+v8BAA4AEgAGAPr/+f8DABoAHQAKAPj/8f/7/wUABwAAAPX/5f/W/+P/CgAwADMACgDh/9r/7v8BAAwAEQALAAcAFAAuADMAFADp/9L/4P/8/wUA+f/r/+P/5f/9/xcAIQAPAPv/8//1/woAIQAgAAQA5P/p/wMAFAAIAPD/7P/0//z/+f/0//T/6P/r/wcAHwAkAA8A7f/b/9z/6f8CABMACADw/9r/2f/p//z/BAD+//b/8//z//f//f8AAPT/7f8CABsAHAAGAPf/AwAUABEAAwD8//v//P8DAA4AGAAaAAsA/f/3//f/+P/3//z/BAAKABUAIAAkABwABADl/9z/9f8SABkADwABAPv/9//m/9n/5f/z//3/EwAcAB0AFgADAPb/5//i/+f/5v/4/wYADAAXACEAJgAWAAYA/v////z/9f/9/wgADwANAPr/6f/q//v/CQAKAPr/7f/x//j///8CAAsAGgAdABUACwAHAAAAAQAMABMAEgAMAAEA9f/o/+f/+/8RABsAFQABAPn/+f/1//T/8f/n/+H/5f/6/w4AGQAeABgAEwAMAAkAEgASAAgA8v/n/+//9//9//r/+v/6//j/AAAEAAgA/v/h/9z/8/8TAC0AMwAlAA4A+f/n/+P/5v/t//v/BwAPABkAHQAOAPv/5//l//H/+/8CAAEA+v8CABAADQACAPr/+P8BAAYADAAUAA4ABgD7//H/9v8KABkAFAAIAPj/8P/9/wsAEQAXABoAGwAdABoADgD2/+L/4v/z/w4AIAAbAAUA8P/k/+n/AwAUAAkA+f/x//X/8P/t//H/8P/3/wEAEAAbABQAAQDv//D/+/////7//v8CAP3/+P/3//H/9//7//n/AAAAAP//BwAQABAABwD2/+r/6f/p/+z/9v8EAA0ADgAQAAoA/P/r/+z/+f/9////AgAKABcAHAAiAC8AKgAUAPn/5v/q//X/AQANABwAKAAkAB4AHQAcAA8A+//u/+z/9v/6//X/+f8AAAoAFwAlACkAGwAFAPX/6v/j/97/4P/x/wIACwAYACEAEwD1/9r/3//t//b/9P/r//X//f/6////EgAkABoAAgD4//j/7f/b/9n/5v/6/w8AFQAVAA4AAgD+//f/7//q/+r/8P/4//j/+P8BAAgAEQAaABsAFQAQABIAEAACAPj/9v/y/+//8f/+/w8AHwAgABAA/v/x/+n/8f8BAAsAEQAQABQAGAAYABoAEgAHAPr/8P/6/////f/3//v/AgACAAEA/v/7/+//5//u//P/+P/0//P/AwAPAA8ABwD+//b/8P/t//D/9//w/+b/5v/v//j/AAD9//f/+P/4////AAD+//n/6//q//r/BQAGAAcABgAHAAYAAgD///P/6v/o/+f/8v8BAA4ADwAMAAsABQAIAA8ADAAAAPT/9P8FAB0AIgAWAAQA/f/5//z/AgAEAAYADAATABMAEAAEAOz/3v/i//L/AgASACkANAAwACQAGQARAAMA9P/q/+7//P8GABEAGgAkAB4ACQABAPz/+P/s/9v/3//1/wkAHQAqACMAEgAAAPf/AQAIAP7/8P/q/+7/9f/9/wAA/f/6/wAACwAOAAgAAQD1/+X/3v/o/wAADAAMAAwADgAQAA8ACAAAAPf/7P/o//f/CQAOAAEA9/8BAAoABgD7/+7/6P/r//L//v8JABAAEAAMAAcA/P/y/+7/8P/3//v/AAAIAA0ABgABAAAA+P/n/9X/2f/i/+7/9f/4/wYAFAAUAAUA9//x//L/9v/0/wQAEAAQABIAEQARABQAFAANAAAA7//f/9//6v/1//3///8GAAgABAAFAAEA9f/n/9//4P/w/w0AJwAwACkAIQAeABkABgDu/9v/2//o//j/BgAPABcAHAAVAAMA6//W/9L/2P/i/+v/9/8GABIAFAAWABYAEgAHAP//8v/w//X//f8OABgAHgAgACIAJwAaAAAA8f/w//f//P///wQACQAKAAcABgADAAIA/v/3//r/BQAPABEACwAGAAgADwAXABkAEAAIAAcACwASABoAFwANAAMAAgAKAAoAAQD8//f/+f8BAAkABwABAP3//f8HABMAEgALAAQAAQAEAAkACgAEAAIADAAXABQACgD+//D/6v/t//D/9//+/wAA/f/4//z/+P/w/+X/2P/Y/97/5v/1/wYAHAApACUADQD2/+//+v8FAAMA//8BAAkACAAAAP3//P/5//H/8f/6//3/+P/x//H/+P8BAAoAFAAcABoAGgAVAA0ACAD7/+z/5f/n//D/+f8AAAYABgAAAP7//f/4/+z/4v/i/+z/9v/+/wQACQANAAcAAAADAAUAAgD+//r/+P/4//n//v8DAAYADgARAAgA/f/y/+7/6v/q//H/+v8AAAEA/////wQABgABAPn/+f/8//3///8AAP3//v///wIACgAJAP//8//2////BAACAAAA+f/0//r/AgADAP//+f/4/wQAGQAjAB8ADQD6//b/AgASABYAAwDy//X/CgAfACUAEAD8//X/9v///wMAAgD//wAADQAXABkAFQAKAAcABwAFAAMAAAACAAsADQAKAAwACQAAAPr//P8BAAIA/P/0//b//P/8//v//P8DAAUAAAD7//j/+/8AAAAAAAABAAMABAADAAAA/f8AAAQABAAAAPz/+//+/wAABgAMAA0ACgABAP3/+f/5////AgAFAAsADwAJAPz/7//w//n/AgAHAAAA9//w/+z/7P/0//3/AQABAP7/9v/1//X/8v/x//T/+P/6//z//f8EAAUA/P/3//X/+v/7//j/9f/2//n/+v8CAAgAAwD7//T/+v8AAAUABwABAP///v/9//3///8AAP7/+////wMABQAHAAgABAAAAAIAAwACAPz/9v/2////CwAWAB0AGgATAAoAAAD8//7/AQAGAAwADQAOABAAFAAPAAQA/v8CAAgACwAFAPn/7//s/+z/5P/k/+//+v8BAAQABAABAPr/8P/r/+z/8P/5/wIACAANAAsAAADx/+3/8P/0//T/7P/l/+H/6v/4////+//1//b/+f/4/+7/7P/u//b//v8AAAEAAAD9//r//P/+//z//P/8//v/AQAGAAMA//8AAAAAAQAAAP//AQAIABQAHAAkACwALgAhABIADAAHAAgACwASAB8AKgA1ADcANAAoACAAIAAgABsADQAEAAkAFgAiACUAIwAbAA0AAwD9//f/8v/s/+n/7P/6/xAAIQAhABMABwAAAP7//P/y/+//8/8AAAsAFgAbABQAAQDs/+f/7f/9/wwAFgAcABgAEgAKAP//9f/v//H/9v///wcADgAYAB4AFgAMAAYA+f/n/9f/2f/p//n/AgAKABMAFgAQAAQA+P/u/+n/6//s//D/9f/6//z///8EAAIA//////z/9v/1//z/BAAKAAYA/f/4////CgARAA8ACwAHAAQAAAD+/wAA///+/wQACQAQABEADgAJAAAA9v/w//T/9//2//X/9P/8/wAA///6/+//5f/l/+z/8//6//7/+f/y/+v/5v/o/+z/8P/3/wMADAAJAPv/7f/q/+v/7f/z//v/AAD9//f/7//q/+v/8v/2/+//5v/g/+f/7//4/wEABQAAAPr/9P/2//3/AQAEAAQAEgAeABYABAD5//X/9//6/wAABgAKAAoADgAPAAwABwAAAPr/+f8BAAsAFQAfACYAKwAtACwAHAADAPb//P8MABwAJQAkACEAGgASAAwABwAEAP//8v/p/+v/9v8AAAUACQAOABEADgADAPX/5f/a/9z/5//5/woAFgAYAA8AAADy/+f/5//3/wYACwAMAA0ADQAQABMAEQD+/+r/6f/1/wMADgATABoAHQAXAAkAAQD//wAAAAAGABMAFQAMAAYABgAEAP3/+f/9/wAA/f/4//3/BQAKAAcAAAD7//P/6P/d/97/7f8BAA0ADQAIAAEA9v/q/97/1f/T/9//9/8MABEAEQASABQADwD9/+f/3f/k//f/CwAfAC4ALgAcAA4ACwABAPT/6//w/wEAEwAdABoAFQAUABYAFgAUAA0ACwAGAAAAAwAKAAwAEwAaABkAHgAgABkACgD2//L/9v///wUAAQD//wYAEgAUAAYA9v/q/+L/1P/S/9v/6f/0//L/8P/y//P/6f/T/77/sv+0/8L/0v/h/+v/8f/r/9n/xP+8/8f/1P/d/+H/7f8MACYALAAdAAYA+//+/wsAHgAxAD8ARgA/ADQALQArACcAIwAjAC4ANAA5ADgAMAAqACAAFAAKAAAAAAAIABcAGAAKAPz/8//x/+v/5f/m/+v/6v/W/8j/zv/e/+b/3f/X/9n/2f/T/8z/zv/S/9v/3v/g/+n/+f8DAAEA+//7/wUAEQAdACUAIgAoADYAOQAvACwANAA5ACsAEgAOAB0AKQAgAAEA8f/4////8//e/9f/2P/g/+b/4f/g/+j/8//3//X/7v/r/+n/6v/u//f/AwAOAA8ACgACAAAAAgAAAPD/5v/1/woAGAAVABUAFgASAA0ABwAPABsAJQAnACMAJgArACsAJwAkAB8AGQAdAB4AJQAqAB0ADQADAAIADQATAAsA///2//P/AAAEAPj/6//o//D/+P8AAAsAFgAdABgAGAAhACsALgAlABkAGQAnAD0ARwBAADIAJgAkACgAJgAiABwADgD8//T/9P/3/+//3P/N/8j/x//F/7z/s/+o/53/mP+Y/5r/nP+Z/5X/k/+Y/6H/of+c/5b/k/+a/63/u//H/9X/3f/d/9//6//6/woAFAAWACEANABFAEkASwBWAGgAcABnAGgAagBvAG8AaABuAH0AggB3AGMAVQBOAEsAPgAoABcADQAKAPr/4P/T/87/xv+r/4b/bP9t/3j/cf9e/1b/W/9f/1b/R/9I/1X/Zf9z/4D/lv+q/7b/vP/D/9n/8v8HABkAKAA7AFIAbACAAI0AmQCpALwAxgDKANUA3wDuAPYA+AD9AAIB/ADiAMUAugC1ALMAqwCaAIMAaQBQADgAHQD+/9j/uP+g/43/f/9z/2b/Xf9O/zr/Kf8h/xv/Cv/5/vP+9/4D/xH/HP8i/yb/JP8l/yn/M/9F/1n/bv+G/57/wP/p/wcAEwAaACwASwB4AKoA1AD6ABoBMQFKAWYBgQGLAYEBdQFtAW8BcwFsAVUBQQE0ASQBFAH2AMgAmQBpAEYAGQDz/9L/sv+Q/2L/Nv8a/wT/5P6y/nv+Uf49/jv+Pf4x/hP++f33/Q/+KP4x/jj+SP5k/oL+p/7c/hL/Pv9n/57/7/9EAJAAvQDlABsBawG9AfIBFwIzAlUCgQKeAqoCrAKvArECnwKAAmgCUwI0AvIBnQFSAR4B7gCnAE0A8P+i/1//Ev/L/oH+Pv72/bH9ff1P/Sj9/PzU/Lj8r/y//NL86vwG/TH9Xv2H/bb97P0w/n/+0/4p/4//AgBoALcA/wBhAc4BKQJoApgC2wIoA2kDlAO1A9gD9wMFBPAD1QOlA2cDGQO/AncCQAIGAqkBLwGrADAA0f9q//H+cf4C/rj9e/03/fL8sPx3/Dz8APzW+9T75Pvv+/v7FPxE/IX8yPwN/VH9pf0J/n3+CP+Z/xoAiADuAF8B3QFZArsCFANXA5MD0wMXBGQEmwS1BL4EuASrBIgETwT1A40DKAPMAnUCGgKxATwBtwAyAKX/Ff+U/iP+tf0+/dD8cPwm/On7pftZ+xj7AvsQ+zL7SftQ+2P7jfvO+xb8ZfzO/FT95v11/gr/qv9VAOsAVwG3ASsCuQJGA8ADFARmBMgEJwVmBXUFiAWTBZYFYgX1BIYEMAT/A7QDSQPYAmsCAAJfAasA7/9D/6v+Dv6Q/Sn91/yB/BL8nPsn+8n6ffpN+jL6K/o3+kz6aPqc+uj6OfuL++D7V/z8/Kv9Sf7R/mr/HQDOAHYBIQLkAp4DIwSCBNgEUwXHBQcGIAY5BoYG1wbhBpUGAAZ4BQ4FwQRlBN4DUgPIAkMCmQHDAPn/SP+2/h3+eP3y/If8F/xb+3v61PmI+YP5cvlT+Sv5Gvkm+TX5V/mJ+d35R/rF+mP7IPwH/dn9jf4z/+P/xQC7AbECcwMQBKkENAW/BTwG3QZlB7gH4AfzBxQIAgieB/cGVAblBYwFKQWrBCwElgPeAv8BAQEPAD//eP6p/ev8SPzb+3z7APtr+r35PfnQ+HL4GPjN97D3pffQ9wD4Wfjk+Gn5BvqM+j37APzO/L/9qP6t/50AmgG8AuUD8wSaBRAGhAYVB70HQwi6CBEJWgl0CR0JfAjKB2cHPQfpBiwGOAWQBB0EcQNKAuIA5v9S/8n+2v28/Ab8kvsI+wz6CPmY+J/4sPgp+FT3zPbT9iT3FPfe9vf2pvek+Ef5vflU+k/7gfxg/Sn+Gf9sAM4BsgJtA0EEdgWbBkkHxQc+CO8IdQmyCdgJ7AnSCSMJQgjDB+UHBQhSBwcG8QSDBDgESQOuAVsAxf9S/0f+yvz6+/X76vvz+lD5Vvhn+Lj4GPjB9uf1LPYC9xv3pPZe9tz2y/dB+ID4Kfmk+jH8Dv15/TD+y/9yAWwC6QKfA/oENAbJBhwH0wfsCJ8JugnRCXcKBwtxCvsIxQe8B0QIGAjnBpIFIQX9BDgErQIsAXYA5P/F/iv9JPw6/Gf8ufvw+Wf4J/iQ+IT4ZfdE9gT2U/Zn9vb13fV19l/33Pfg91r4qPlH+0T8r/xf/cL+gQDLAZcCVgNZBG0FIgakBlUHZQhiCeoJPwqRCukKtgrbCe0IdQiBCF0IwQfoBjIGqAXJBI4DRwJdAZsAj/9K/jj9q/xW/Kb7cvpM+bD4ffgU+Dn3Xfbq9fj17/Wy9Zn1/vW09hf3QfeX96H4EPo6+xr87vw9/q//3QDFAaYCzwPcBJgFDAauBrgHxwiuCVIK8QpWCysLYwpzCQIJAAnwCFYIZgeWBhEGogXZBMkDiwJTARIAwP6//R/9z/w8/CD77fn5+Jv4Vvi798321fVs9Vv1YfVa9WD1wPUd9n72Cvf+92v5pfqR+zb8D/1u/vL/cAGJAn8DWwQDBbQFWAYqBxQIAgn6CcoKbgtzC+8KLwqdCXoJPgnFCAsIcAcPB28GiAVOBDEDMQL4AJP/OP6a/V790vyi+zL6XvkC+b34/ffg9gL2kvVu9Rn1vfTA9ET17vVC9oz2PPeK+Pj58/qt+5n89/11/5oAbgFqArUD9ASlBe4FcwZ4B94I/QnZCooL7AuzC5UKfwkHCUcJjgn7COoH2AZiBisGfgVWBN8CkwFSAPv+5f09/QH9gPw9+6P5bPgZ+CT4y/fV9qT17vSo9In0ePSo9Ej18fVa9qL2efcI+bX68/uW/FL9jP4fAH0BYAI/AzAEDAV1Bb0FsgZbCEIKhQv/C+8LfwvjCiUKrAmUCYoJLAlKCHsHCgfIBjwGAgVxA+cBmgBp/z7+cP3w/HH8a/v6+eD4b/hn+Ov3zPac9cv0dfQZ9NXzA/S99Kz1JPZm9hb3d/g/+rb7n/xh/VH+jf+nAKUB0QIHBBQFigXYBaYGGgjcCRILvwvtC8gLYgubCgQKwAnCCX8JrwjCByAH/ga5BswFMwRkAtoAef9C/lr94PyJ/Lb7UvoG+V74Lfi596T2XvVr9P/zzPOq8+3zi/RM9c71G/br9nP4YPrs+7/8XP0w/lH/ZwBjAYwC1AP3BJMF5AWWBt8Hkwn4CsgLFQz8C6oL9Qo8CuQJ3gm0Cd4I1Qc2B1AHhQfCBvUEvALVAE//B/4M/W/8Ivx6+2D6NvmE+Fb49/cW96f1VfSU8yzzCfMr89vz4/TM9Yn2WPfC+IL6BfzI/A79nP2R/tf/FgFgArYDzwSMBQgGzwYjCN0JYgslDDkM3AthC/EKkgpJCgUKcgl9CKwHdAezB7AH3AZABV0DjAHG/zX+Av1V/L/72vrJ+cf4cPiA+Ej4XPfB9VX0c/Mk8x/zTfPm87z0mvVs9nz3Avmr+u77ofwK/a/9t/4FAGYBqALLA68EXAURBhQHgggWCl0LJgyKDIEMBgxMC58KQQrsCU8JVgh6Bz8HWwcoBx0GjQT2AnEB8/9f/hz9Mvxj+3H6RvlV+Mn3kvdE93v2dfVz9MbzaPM481Xz0POq9IT1VfZr9/f4xfox/Bj9y/2v/uj/EwETAgAD5gO7BFEF/AUKB5sIYwrGC6IM/wzVDFoMqwvlCiYKYQmfCNEHPQccBxIHxQbKBU0EqAL0AET/nv1a/Gj7jPqd+Zj46feZ93v3F/dN9l71jPQL9L3zpfPB8yb02PSq9ar29/eY+VT7wPzB/Zn+lf+zAMMBtAKSA28EOwXxBbAGvwcYCXEKjQtFDKUMpww5DHgLjAq8CfEICQgeB2wGPQY7BtsF4gRvA/ABaQDk/nD9GfwL+xD6KflS+Jr3OPf69rT2KvaG9Q71y/S19Ir0ZPR99Pf00PXZ9jD42fmx+139mf6b/5wAyAHtAtwDlwQZBa8FVgYcBwkIHQlrCrALtgweDccM7wv3Cj8KfQl0CBoHzQUOBdkE2ASNBMIDnwJXAff/ef4N/d374Prk+cn4wPcq9/r27fbG9mP2+fWW9VT1J/UK9Rr1SfW99Vf2RfeX+Dn6J/zM/TT/WQBkAZwCqgOpBE8FxgU9BqwGYgdTCJUJ7wr+C5cMmAwzDHsLnQqvCa4IhAciBvYERAQIBN4DVAN1AloBPAAS/+T9v/x7+zr6DfkT+G33+Pa+9oj2TvYK9q71e/VR9TX1IPUP9Vb16fXa9hL4hPlF+w391/5wAMAB5wLhA90ErAUoBlwGfgb6BscH4AgSChsLAAx7DI0MGAxUC5EKqgmJCO4GOAXtAy4DzgJIApgB0AAIADP/L/44/VL8Yfs7+gL5B/hs9xT3vfZl9hD24PXR9dr18/X99f71BfY+9sj2r/fi+Gz6Mfz7/bn/RQGmAtgD2wS4BWIG2AYDBywHnAdaCG4JfQp6C1EMwAyXDLkLiwqCCZwIfgfSBQUEsAIcAgMCuAEnAWYAjP+Z/oP9f/x7+336YPkp+Dr3vPa79uP25vae9jz2HvYi9kP2KPbn9dj1D/bK9t/3bPlQ+0P9Lv/kAIIC7QMVBf0FhwbLBuAG/wZZB98HpgihCcoK5wvBDBwNwQzAC04K5QicByoGVQRkAh4BowCZAGEA1P9Y/+P+S/5d/TH8F/v8+cz4jfeK9hP2IfZz9pn2b/ZM9mL2rfbV9pL2P/Yp9oD2Kvcr+Mj5zfsL/isAFALaA00FcwYpB48HoQdvB1QHZAfuB8cI0An5ChUMAw1UDdwMwgtVCtAIEAf+BMYC9wD4/5r/dP82/+P+qv5s/uv9Hf0a/PT6pvlL+Cb3bfY39k72dPaR9pj2uPb/9kf3XPcN96f2g/bi9tD3Dfmv+qD8zP4JAQUDwAQcBiQH1AcoCEkILggTCC8IkwhbCTYKHgsUDOQMGg1UDOQKRwnQBykG4ANlAYj/wP6Y/mv+Av6n/Zr9mP1n/d78F/wR+8v5gPhM95T2Y/Zx9o32iPae9ur2WPeq95X3N/ff9gT3qveu+AL6mvuT/cv/FQItBOYFMwcYCKcIygieCEgIAwgaCJsIbgmUCs4L1QxYDfkMyAsjCoMI0gbBBEUCyP8x/p/9qf2R/Sv99fzm/Pb8u/wS/Db7DvrR+JD3tvaX9ub2VPeL94j3nPfd9yL4G/ig9/D2d/aV9n337Pih+qb89f50AdMD1QVgB20IBQkICboIYggRCPgHMQjWCN8JDwtFDBoNSA2QDOcK8QjvBuEEmgIiADL+J/3v/AX9A/36/N382vzK/G/8xfvA+pz5ffiG9932q/YQ94731Pfl99r3Afgh+P33d/fy9vX2f/eY+AX6xfvf/RUAQgIzBOcFSAdBCNsIAgnXCJYIXgh+CDEJLgouCxcM3wxRDfEMlAuWCaIH8QX7A3wB+P5H/b78wPyO/C/8EvxR/Jv8fvzy+zL7V/pV+Ur4fvcY9zr3mffM99732/f29z74WfgA+F337/Yi9//3SvnC+pH8y/46AboD0AV6B8AIcgmeCW8JDQl2CDMIagjvCNUJ/wo5DFwN8A0jDVkLWwlNBykFbgJL/7D8V/sh+xX7E/tC+5/7Nvyb/KH8SPyh+7b6W/n09/b2o/br9j73a/eI9+T3jPgP+Qb5c/jS95v3/ffR+Pz5a/tO/ar/DAJfBGQG+gdMCQkKHAqsCREJtAiJCKMIEQn4CUsLqQybDYUNVQyWCpMIdAYcBCUBHf7z+/r6pfpt+lr6fPoi+w78mvyi/E/8tvuj+ln5Ffgd9772v/bq9hj3dff393342/jR+ID4T/iT+Cb59fkC+078IP5oALoC5gTPBkgIWwkMCkAK5QlNCccIdgi0CH8JkArcCwUNlQ0kDaMLwAnSB7gFFgPU/7z8xvok+vr5sPmD+c/52fob/Mb8rvws/Hf7bPpD+Tj4e/dJ90r3PPda97z3Vfjg+An5svhN+GL41PiV+YP6bvvJ/N/+PgGSA7UFawfICLwJIwobCtEJXAnOCK0IKwkMCkgLeQw8DUsNdQzoCgIJBAexBLABTP6N+w76efk5+ej4zfho+b36I/zT/Mb8OPx7+6z6ovmI+Lr3a/dg91L3WvfI93j4+/gO+cr4svgc+eD5n/pe+2b82f3K/wcCJwT6BYgHxgiyCUgKPQqkCQgJxAj9CIoJUQpPC34MUA0CDaQL4gkyCHIG8QOJACH9rfqC+Qr5k/gV+C34RPnN+hv8wvyz/GH84Pv7+sr5s/gC+Mf3svd593f3/vfN+Ez5YvlS+Xb5Dfq7+j37xvu5/Dv+GQAjAisEFwa5B+8IvgkgChwKuQktCeoICAl2CTIKNwtfDAINawzWCisJtAf+BVcDxP9p/Dn6Kvl9+ND3gvcH+HD5BfsW/Jn8uPyp/DH8FvvP+dr4cPhc+PL3bfeX9234RvmZ+XL5T/m9+ZD6H/t/+yT8Qv3V/poAbgJHBEMG8AfwCIkJygm/CY0JQwnnCAcJpAl6CpELZwybDPgLowoPCVoHNwVRAt/+vPuk+XH4nff49uT2m/cN+dL6A/yh/BP9Kf26/OP7s/q6+Un5zPgv+ND38feM+Cj5SPlJ+aT5Pvrw+m772fuP/Kr9CP9+AEoCTgQJBmwHkAhnCdcJ0glkCQoJJwliCcQJiApkC2QMoAxcC+UJ5QiEB00FFwIw/ij7jfly+D33NvYm9lL3U/n7+gX8A/2z/cr9Yv1d/CD7i/oU+gL5K/j79zz4zPg3+TL5PPnP+Yr6Kftz+477Ofxc/b7+bAAnAvADBganB4oINQmpCdcJjwn8CLgI9wiICVwKPQv/C2EMqAsaCtQIpgeKBSkCP/4I+wH52veD9k/1ifX49tj4rvoi/Bn9FP6X/gH+HP09/E37fvqL+YX4GfhK+M34OPlb+ZT5F/rZ+lL7jfv0+4/8WP17/uz/gAGpA8cFNAcjCOUIgQm0CYMJ/QjiCCoJRgnNCcIKgAvJCywLiAlPCMAH7wWNAs/+svvK+Uv43/a59Vj1jPZ0+PT5VvvA/KX9Ef4n/pn92vxJ/HP7XPpb+bb4sfgP+Un5UPmI+e75p/pV+5/78Ptr/Fb9pf7o/1gBNwMhBbcGrgc7CAQJlAliCQMJwQivCC0JvQkiCu0KhgsDC7EJkQisB9gF5gIu/8H7yvlx+Mr2ivVY9Qv2yPen+QX7ovzt/Wb+ff4z/oj95fwX/OH6y/lS+WT5kvmu+b351Pkh+rr6P/te+6P7Tvwp/Uf+r/8lAcACjgQRBi0HCQirCOwIzwhyCEYIZQiCCBYJCgoYC7ELQwsnCgcJOgiUBlMDhP9m/C76VfiD9g31zfTa9Ur35vjB+nz8CP68/qb+lf5Z/p79mvyN+5L6Evre+d35/PkE+hr6YPri+jf7c/u3+wv8t/yz/cz+CgDUAb4DLQVkBmQHOgjOCNwIhAgkCDAIiQgACZcJgwp0C20Lnwq5CccIZwfwBBcBTv2q+qH4mPbB9Aj0p/Qk9vL36vn1+9P9Jv+e/6//n//5/qr9R/xB+436D/rb+bH5zPlG+rH6GfuE+8P70/so/Ln8U/0i/hH/dgBaAjYEpgWwBqMHiQgGCewIlgiXCNkI3ggpCSAKQAvCCyYLugmcCOgH/gWDAn/+H/uz+JL2jPRS87HzBPWn9rX48/o9/R3/GwBIABkAtv/c/rT9cfxW+4f6Cfoj+kP6Ufqi+vb6RPtj+2H7fPvy+6L8Uf06/pD/dgGiA1AFgQaUB3gIHAlDCf4IuwjUCPcIHgnLCdwKwAuYC2sKNgktCKgGwgOS/8X7EPmz9oP0GPPq8vnz2fW398r5Xvy7/ksA7QDaAF0AnP+6/n/9M/xC+436TPp0+pj6lvrE+v36B/sP+wb7VfsH/Mj8kP22/osA2wLoBGYGhQdkCCUJigllCQMJ7AgBCf4IcAl+CowL7wtIC/AJoQgQB2YErwCq/D/5hvYj9Fby5fHb8oz0xPYg+bz7YP5aAGMBiAFKAaEAlv9T/iD9HfxU++z6y/rV+u76GPs0+yT7yfqU+qv6Jfv7+8L8yf2F/+8BRgQeBlwHdgiICf0JsgkcCdUIwQjsCDEJmAmSCpELfQtbCgwJqgebBWsCQv5X+lH36fQT88/x0PE983316veA+jX9jv9tAUwCQgLAAf4Azf9W/uX8rfsH+6b6ivpw+lH6b/q7+tn6v/rP+g/7xvvF/Kn90f6mAPoCLQWyBsEHmghpCfQJtwk4CeAIBwlPCagJaAoVC14LoQpDCccHFAZuA6j/tPtV+Mz1sfMp8rPxpPJQ9JD2Ovnu+6f+wgAHAnQCYQK+AdYApP8Y/rr8g/vV+oz6aPow+hD6aPqZ+n36Z/qf+lj7Rfw7/Vr+3P8HAlQERwZ/B4EIcwndCfsJhgkSCfgIEglVCaYJagpUC0kLBAo8CJUGlgRsAXj9dPmS9nr0svKe8cvxhfPR9VP41PqE/R4A2QG2AsMCTwKdAWYA3P6G/Ub8Vvvd+qD6Nvrj+QH6MvpU+kD6I/p/+lT7avxj/Zz+ggDBAioF4wYGCBcJ4wk2Cu8JawkICTYJcQmoCT0KCgt6C9MKKwmBB9sFLwNk/yf7mvcm9Unzt/Er8WTyp/QY9675Yfwn/4wB4AIzA/0CYAJ6ATcAwP5k/TX8Uvu4+oP6NfoP+jL6LPoG+rr5xPlF+i77V/yE/Rf/WAHaAwMGgQecCJsJIgobCpcJLgn1CP0IPglsCUcKVAt/C1UKoQgRBwoF1gGC/W75c/ZS9HLyMfFc8RPz0PV7+NH6hf1gAHICbAOjA10DuQKkAQsAi/5I/V78t/sE+3T6JPoD+uD5y/mC+Tn5XPkf+iv7Vfy0/Xj/4AFDBGUG6gf6CAEKiAo5CngJDwkICRsJMwl+CWIKYAtUC+0J8wdNBiIEnwBH/Cj4b/Wb8x3yGfF38aLzVvYJ+a77jv5UAToD9gPgA44DzgKYAfL/Yv5A/VL8ffvo+nr6Gfrq+cv5vPmM+Vj5ZvkX+ir7Yvzj/b//OAK9BNcGNwhYCXoK1QpXCm0J0gjPCN4IxggYCe0JygqQChIJNAeaBbQDKQDI++b3bvXX80fyYvHu8TL0H/eu+Tn8/f6jAYIDBgTNA1cDzgKrAfj/bv4x/WL8oPvi+kX62/mf+VP5Gfns+PL4aPlF+nL7/fyY/oUA+QJvBVQHaQg7Ce0JKQreCTIJwgjVCBkJPQl1CUMKzQo+Co4IeQbPBGkCwP6E+hb3C/We80Dyg/GN8gT1zfdH+q78jf9nAhQEdwRCBO0DTwP/ASEAZf5F/Vn8SPtL+p/5Wvkz+fj4xPjF+PT4dvlt+pD7G/3D/rgADQNJBRYHLwhLCQsKUwr2CTYJAQkgCVkJWAmzCXkKzgrmCfgHDgZlBNABx/2Y+Xn2w/RC8+jxaPHE8pX1JvjE+of9vABlA7QECQX6BN0E1gMRAgIATf4U/d/7vPql+RD55fi2+I74Z/ib+Ab5oPmT+rX7Gf2t/s0AFgM/BQgHagibCWgKrApACsEJnAmjCW0JPgmYCUwKVAoNCRMHYwX5A2QBR/0f+Wj27fRu89PxdPH28rX1W/jR+sr9IAHpAzUFmwW2BaMFrASjAoQAyP56/ff7Svr4+GT4PfgF+Mr33PdL+AX55vnR+vf7OP3e/uEAKAMWBaIGGQhCCVYKpgp2CkQKUQpQCgcKBQoyCpUK/wlDCFoGyAQPAwYAMvy5+Fr2nfTF8m3xiPE+88z1Pvjr+j3+rAE6BIEFLAaJBn8GWwVEAx0BSv/F/fv7Efqn+PD3rPd090j3X/fD93X4M/lL+qv7Of0C//kAPwNZBSEHgQihCVkKjApoCjcKOgo9ChMKHwqZCrIK9wlGCGsGwASXAlT/Hvuk91/1tPMF8sjwTPFb8yz2x/iH+/7+ZgIDBTgGvQYdB/YGxwWcA3gBtv8s/kr8S/rP+An4qvcn97j2wvZJ9wn47fgD+lf7Bf0D/zABXANlBREHawizCZwK6grMCp4KsQq1CoIKfQqzCr0KvAnMB8IF8QPCAUn+WvoW9/n0UvN78XvwIPF680b25fjt+6L/fAMwBmcH4Qc7CAMIegYTBK8BpP/X/aP7YPnd9zj34vZv9iv2Ovbn9sv3xfgO+nr7Nf0x/5AB2QPEBYAH+AgxCskKxQqdCrcKBwvxCqUKsAowCx8LnQl2B2cFhwPcAPb84/jK9efzNvKS8PnvQ/EO9Bv3APo1/f8ApgQeBx0IjwjYCEoIkgYUBLYBuP/C/XX7OfnP9wr3gfb19aP13PV69nr3gfi9+Vz7UP26/x0CWAQ2BuEHfgmoCikL+QrfCi0LXwscC7UK5wpJC7YK1QiEBt0EHgPt/5z7u/di9dPz4fEJ8N/v+vH09L/3YvrJ/R0CtAWcB0wI/Qh+CasIcQaaA1MBXv8D/Un69vfd9nD27/VM9Sn16/UC9y74Mvl3+lT8Y/6kALoCnQRaBuEHWQlrCtEKwgrZCkQLbAsxCwcLSAt4C3YKUwgDBioE9gFg/g36aPYb9Fvyf/BU7/Xvh/LO9a74w/vU/yYEcAcSCbYJDgrvCYEIvQXKAlwAQP7I+yH5Lvcy9tr1UfXu9C71+vUh9yn4VPnf+uX8Bf8MASwDLQUqB7kI0wmRCt8KMQuBC7ALnQuEC7IL3wtqC8wJnQenBYgDVgAS/Af4G/X68vbwL+/67rXwnPOy9s/5vP1BAjUGmgjICaoKCgtQCvQH4QQ/AvX/bP1c+tX3gvbl9WH1uPSt9FX1VPZV91X41/mt+7X9uf/WAS8EKga8B/cIBQrNCiULUwtuC6ILkQt6C8YL3AsSCxsJxAagBEoC9f6B+mL2nfO+8QTww+5C76nxCPVW+Mj78v9SBOYHzgmXChILBQukCdEGowMBAdX+U/yD+TH3CfaX9R31jfRz9Dv1SPZx97n4PvpL/JD+5gAJAyIFIAezCPQJigrUCgULWQvJC5gLewusCw8M3Qs6ChkI8QXjA78AQ/z397b0o/KF8JPuIe7u7zvzXvaB+Vv9GQJ3BhUJawpMC9ILHwuxCJcF6AKVANT9kfrb90f2svUF9SH04/OW9Nf10/bi92/5e/vB/dH/4QEMBBgGxAf0COcJoQoRC08LhAvYC/ML+QsrDB4MLgsICY4GSgTHASz+hPmc9TDzefGt71Lu+O6t8S31b/i9+w0AsARECAUKzwqOC5kLEAr8Bt0DZwEC/yD8/fjH9tP1dvXT9C70XPRQ9YD2f/er+Fz6gPzh/hIBJAM3BUEH2QjVCVQKtApEC7UL6wulC3UL8QtfDJ8LWgn6BjwFZQMEACT7+fZ+9O3yzPCo7qruLPGN9ET3D/os/iwDIgcICfgJUQs/DE4LXQgQBdsC1QDX/RH6X/dI9q31wPSb85/zsvQB9uP25fe8+QT8d/51AG4CmgSrBlQISwkGCpMKCQt8C8ILtAt6C7sLUAz4CysKpQeRBakDoQAx/LT34vQs80DxJe+M7p3w9PPn9on5MP0KAloGvAi4CboKwQtGC+sI3gVxA2IBnf41+074yfYG9hP19/Ow84L0wvXQ9uf3l/nN+yv+MAA1AlYERQa7B8AInQkJClkK1wpsC7YLmQvSC0QMMgylChMIxgXJAxgBvvwN+Nn0F/N28WDvmu5E8IrzmvYy+cH8eAHxBXEIfgm2CvoLzwt9CW0G5wPNARP/ePtA+IL2yfXY9L/zf/Nh9Mn15/YJ+Kn5w/sU/gQA7wH0A8QFOQdGCDwJzgk4CrcKPgujC4gLoAv2C/YLrgolCNUF8QNpAU/9pfha9Ybz6PHd78/uUvBO81L26fg7/PAAXAUXCE0JmwrnC9kLyQmpBkAEMwJ4/8r7f/i09rz10fTU84HzSPSG9dL2FPjY+Rj8Pf42ADACXgQSBkMHTAhLCRAKSQp/CgQLnQu4C3YLgwupC70KQwhoBV8DVwHL/S/5YvV783PyxPBC79jv4PJj9gX50fv1/70EGwiJCVoKcAsEDHwKSgcmBMUBff8p/Jf4LfY39d30HvSh8x70vfWl9w75cvpd/NL+HAHoAmgEywU4B2gIHgl+CaUJQQouC6QLfQtGC7kL7QubCogHmgQOAycBcf2N+B312vMD803xm+9h8IPzyPb8+GL7lf9JBGwHwQixCSsLxws4Cv0GEwQZAqP/GfxZ+BL2Y/X69D/0wfOO9FX2Hvh++eH64/wv/0QBCgOLBOsFEwcfCN0IIAlMCd8JxQpNCzoLDAtiC6ELbQqYB+YEKwP7AFD9qPhC9cXzv/IL8ZLvrfDp8xb3Tfni+y8AtQShB6EIhgn6Cl8LvAl9Bo0DbwEI/537Cvj+9UD12/Qm9N7z1vSn9qD4D/qP+7b9FQAhAqoD+QQHBgwHBAhzCIYIvwiLCYIK+Qr5CgALiQuKC+YJ9QZWBMcCeABg/Mb32/Tc87vy4fDZ73rx1vSH9335O/y9ACcFnQebCKMJRQt0CzAJ1gUZAz4Bs/4P+5j3y/Vy9e/0PPQT9Dr1LffO+Cz6xfsr/ncALgKVA/IEigavB1UIlAjUCGAJ5wmMCtsKxwrFCvkKygr9CEEGzAP1AcH/F/wG+Bj19vMo87zx6fAW8jL1LPhc+uT8vADoBHwHaAgOCSYKgAqqCGAFhgKJAE7+Gvuw96j1RvVy9RH1p/R59X73v/lK+4f8TP6sAAcDcwRMBTYGdAebCM4IpgjoCPIJ7QrbCmAKcAoiC8EKOQj8BOACpQEZ/5T6U/Zl9CH0J/NL8d/wNfPj9pX5aftG/o0CWwYRCIUIJgkWCq4JEwfSA2cBqv8r/dT5Cve+9ab1hfUI9fn0KfZY+FD60/tY/Vn/rwGuA/oEvQW6Bt4HkwicCGAI3ggJCuoKtQocCmAK8AoMChQH1wMPArUAtP0f+Xj1WfRB9Djzn/Ha8cP0Vvic+k/8VP+BA78G4QfvB38IVAl3CHMFJAIEAIv+IPzS+EL2nvU09k32wvXg9YL37fmu+6j8yf3j/zgCugNyBAUFIAZfBxkIBQgBCPAISQoRC8UKbArDCu4KhwlbBlkDmgHY/3v8Ffj59Ab06PMR8w7y6vIR9p75//vt/e0ArQRjBxQI0wffB+EHjQaUA3wAaP7h/MD6EvhX9jX29/Ys99H2RPca+Yr7Gv3m/QX/EQFfA4wExwQZBR0GPAd7B0MHkgfiCCMKUwomCmEK8QpeCvIH6gS6AjQBe/5p+rT28PSu9BL0/fLQ8s70Afiy+ub8av+vArIFVAfYB/0HLQhXBwkFKALN/xf+A/yN+WP3gva09gH3B/cE9/z36vnz+0r9M/6S/4YBegONBOQEZQVgBiAHMgcoB7YH8Qj6CUQKTArtCoELbAqpB7oEzwLkAHz9Fvm69ZX0P/RZ823yE/PW9RX5ifuR/V0A1QNdBlIHVAdtB2gHHwZzA4sAjv4T/Qn7mPj59gX31vce+Oz3Ufj7+SH8iv0l/uz+hQBQAoUD6AMlBAMFFgakBnsGwQb3B0EJ0gmyCe8JuArYChQJCga+A18CUwCU/Gr4AvZ/9VX1UfSM88X0v/fJ+tX8nf4kARcEHQaOBkMGEwavBSQEewHw/mL9L/xd+j74Nvei93v4qfhZ+M/4kfqd/LP9IP4S//QA+QIbBD8EhQStBc0GAAehBuwGKAhoCc8Jtgk5CvoKfQorCD4FEQMnAUn+IvqJ9gL1uPQ29Grz1PMc9lH5//vh/RkA8gKCBZMGdgYzBvUFGgX7AkgAMP7i/Gj7Wfm492T3CviT+Jz4yPj0+dn7Wf0B/oX+vv95AcQCTwOfA1wEdwUqBlAGiwaIB+MIqwnzCVQKGAs3C6MJBwfABBsDsQD5/Dv57/YZ9k71K/TT8zf1v/cd+hT8O/4GAcEDOgWVBZ0FqQUTBVsDBQHo/pT9UPya+v74T/jA+E75dvlb+e/5Z/vB/Hn9/P0E/5EAFwItA78DagRmBUIGbQYpBn4GfAdtCJEIjghSCV4KPAotCLAFNwQRA3EATPy4+BP3kvau9VX0CfTX9b34Cfu5/Nz+xAFmBLsFvgV1BW0FvASsAuf/1P25/JD7x/kR+N/3+fje+eL5uPmp+nb83v05/lL+ZP8mAZsCNANyAywEUQUUBvIFvwWHBu8HygjICA4JVApEC0sK1AeuBWYEcgLC/oL61/fL9tj1g/TZ8/T0ZPf++Qf8+f2OADADywQVBc0EfAT1A70CowCA/i/9ZfxU++/5N/mE+T/6h/pP+n36cfue/Er9oP1b/rD/MwFNAvgCjwNaBDsFsQWDBWYFPganB2kIewgACUQK9ArHCUYHKAXWA64Byf3i+bT3sva19bL0nvQn9qP47vrH/Pf+kwG+A8gEuARGBPYDaQPKAWX/j/27/ED8J/ur+Rv56vnq+tj6Xvqx+tr7Bv2D/dr9tP46ALcBkAIiA74DeQQLBS0F8gQfBSoGVAfoB0oITgnACikLpgkrB00F1AMjAfr8E/n49ir2TfVk9LL0t/Z0+ej7C/4zAIACVwQTBcYEFAR0A5MCAwHo/kX9t/x2/J/7ZPrw+Zn6YPtG+7f61/rI+9X8ef3Y/an+GACZAZMCEAOTAzAEuAS3BHkE0QQBBjQHsgc2CIMJHAtQC2MJ4gZHBbgDlABA/NP4PPeP9qH12PRo9YH3I/pQ/C7+NQBlAg8EjQQpBKwDVgOAAsQAxf6L/Sb9pPxo+1P6cvpG+6/7QvvI+kf7cPxN/WD9nP22/kwAogFHAqkCMAP6A34EPAQDBJYE4gXrBl4HPAjTCS4LlgqCCMsGpwWnA9X/vfsu+Qn48fZm9bv03/Un+FH6CvwG/mwAtgIDBDAE+APPA14DCQIsALD+8v10/Wj8KvvK+mf7+vu3+zT7SvsG/K38u/y+/Fn9kP7T/7gAcgFPAlwDIwRfBFsElQRzBYIGEAdiB04I3wmcCpQJnAccBhwF+gI9/4f7Zflz+Fj3FvbA9fX2IfkL+5z8Rv5XADsCIwMTA8EC0QKRAmABtP+i/oT+Y/6B/Ub87fuP/Af9gvyT+137+vub/Lv8rPxI/aX+FAD1AIQBPQJBAwIEFgT/AzkEBwXdBUkGsQa+BzEJqgmzCBoH8AXlBIcC+f7F+wv6Lvnr95P2dvb39/H5efvh/Kb+wwBvAgED6AKwAoQC+QHCAFX/Z/43/uj9G/1r/GH82/wE/Zb87/vY+1L8o/yw/MX8e/3E/gsAAAG0AZcCeAP8AwME0AP8A58EewXsBTAGQAfUCGQJGwhTBmMFiwREAr3+9vup+tL5sfjT9xH4MvmU+s/7F/2d/ikAWQG4AXkBYgGcAWEBPgAD/8X+LP8A/w/+Qv1A/aL9jP3e/DX8FfxM/IH8wvwe/eL95v7y/+gA0wGzAiwDVANUA1sDgAPQA14E5gSXBa4G9geSCNwHmwaIBXAEXwJp/9H8O/tD+j/5gvix+MP5KPtR/HH9x/4uADsBfgEqAdoA0AB2AH3/f/4z/on+rf5N/ub9C/6I/ov+6/0m/dH8yfyi/HL8l/xh/Yr+tf/DAMABsgJLA40DhgNAAwMD/gJZA8UDbgSLBd4GvAd4B5kGzgXCBNcCOADp/Vn8Svtx+sb5vvlV+mP7ifxs/Sb+8P72/4QAZgAeABQADwCD/9L+hv61/t3+jv45/i7+Yf53/iv+yf2F/aD91/3v/Qr+cf5U/z0AFgHCAUsCwQIGAwsDuQJ7ApcC2QL3AjkDNwSGBT0GEAaPBTEFmQQxAzEBU//o/c/83vsg+7z68frI+6r8O/3O/bb+lv/e/8b/0P/9/8P/Av9x/lP+Yv4y/tn9qP21/ff9L/5V/mX+h/7c/jD/Tv9P/3f/2/8/AJkA9wBmAdMBDgJCAnICmALEAv8CRQOEAwUEvARJBUwFwQQ/BM4D+wKAAfH/4/4Y/jv9VPzO+877GvyI/PX8ef0e/sr+UP+B/4X/ef9a///+dP4Z/vX92f2y/ar92f0a/lj+kv7d/hX/L/9R/4T/r//F/+P/LQCfAB4BjAHXAR4CagKNAmoCTAJlArEC4wIMA48DewQXBdEEOATWA4ADdAK5AB7/G/6O/dn8IPzh+0H88vxo/bX9F/7A/kf/Q//9/sD+uv6B/gL+jf2D/d39Dv4K/gv+a/7//lL/T/8f/yf/cv+o/6P/nv/t/3QA7AAxAWgBzAEsAkwCLwIYAigCPwJcAoUC1gJgAw0ElgSUBCkEswNPA3kC7gBb/0X+nf3u/Df8Evx+/BX9ef3g/YD+Fv93/3f/Pv/5/qn+Zv4N/rv9sf0P/pP+v/7P/gT/Yf95/xn/uP6a/sv+8/4S/23/CAC4AC0BigHiAS0COgLlAYkBWQFMATgBMgGgAYYCogOQBEMFtAWUBcoEegMuAs8AKP+D/X38TvyD/ML8Cf2C/Q3+j/7d/sj+e/5N/lr+Qv4E/uz9QP6m/rL+0f5T/+3/6v9c/wj/Gv8v//P+o/6L/sP+bf8NAHQAogDVAD8BXwFMARcBEAEQAdMA5ABUAQkCigL+AsEDmgQ/BUYFzwTpA7MCdQFSAEH/9v3J/Gv81vxR/Ur9Q/3D/W7+uP5t/kj+fP6e/o3+af6C/qj+7/42/1j/cv+k/+3/0f9K//r+Kf88/7r+P/6K/jz/nv+W/9X/kQBCAZcBlAGBAXYBjAGMARoBsgDkAKkBSgKTAkgDeQRNBegEwwPmAjQCEwFD/4D9mvyE/L38wvzk/Iv9of5w/3P/Hv8O/zb/4/4a/q39//2H/q3+x/5u/3wAMAEDAWgAAADT/1X/Z/6S/XT9Av62/lH/5v+6AKUBRAJtAiUCtQE1AdcAjwA+ADAAkABvAVsCFQPWA4kEzQQkBMYCbwF2AJz/Vf4h/dz8kP2A/sn+rf7p/oX/xv8S/xj+mP29/f796P36/Y/+eP/9/yoAfgDzACUBqAAAAJv/YP/9/mf++v3d/Sv+0/6J/wAAWQATAfoBXAIJAnkBKgHrAIEAFwACAGMADAHaAYsCLQPlA20EGASyAhgBIQCB/2n+A/1y/Bv9Hv6o/uD+V/8AAFsACAA2/3D+Af7O/Zj9iP38/fj+8v+FAO0AmQE4AuwBwwCK/+7+oP4M/k39Av2v/fH+EACqAAMBmwE/AmYCsQGpABQA/f/5/6X/pP9nAI0BaQLKAj0D0APsAwoDjQFUAJj/Bv8s/l39Uf0Z/gf/af9g/4T/7f/6/zz/Rv7g/RD+P/5I/pr+dP92AAcBGgEHARgBBwFmAHD/vf6v/tn+rv5r/oz+Pv/l/xUACQAbAG0AqAC4AKkAnwDCAOkA/AD3APQAJgFuAaMB0gE2Ap8CgALiATMB0wBsAID/av7g/Sb+g/6X/p3+7v55/7H/fv8g//b++/7q/tL+2v4x/57/5v8pAJMAFwFEAQYBrQB3ADoArv///mj+Jv45/oj+7f5P/+D/gwAGAUABQgEQAY0ACQDY//z/HgAlAHgAYwGAAi0DUAMYA5sC4wEdAVwAgP+b/vL9y/0J/m3+xP7y/hL/SP+p//D/uP9J/y3/nv8WACMA+f8CAGoAygDqANkAqABSANz/dv8v/9/+ZP7l/dX9Rf7r/mH/k//b/3UAKQFxASEBmQBOAFcARwD6/9T/IgDJAGUB/wGZAvUCsALFAd4AYwAIAD7/NP7D/Tv+Ff91/0T/KP+N/y8AXQD1/2//b//s/x4A1v+n/xAAogCrAGUAjwAhASIBNgBI/yb/VP/S/sD9Tv0J/jT/w/+w/8j/dgAyATEBmQAhACoAWgAzANT/2/9jANoA/AAEATUBYgFBAcIAKgDo/9z/pf8Q/5D+uf5c/8j/mf9r/7b/LgBhACYA4P+1/8f/9v8ZACIAKQCTAB0BSgHyAHMAHwCa/9v+X/57/q3+df5a/gL/LQDOAHAA6/8JAIgAqQBPAAUAMACtAPYAzgBjACMASgCVAKwAjACFAJ8AcAAHAM3/1f+f//P+hv7o/rn/DQDd/9j/KABZACEA4v/x/xkAGQALAEYAywBYAa0BkQEyAd8ApAAdABb/Gv64/f39Pf4t/kr+7f7I/0IAaQCWANkA5gCbAGsAiQCpAGoADAARAHoA3QDpAMwAxQDZAL4AGwAf/2L+Xv6w/sL+qP4B/+f/qADQAJ0AkQCSADwAv/+a/+P/KQBUAJEA7wA2ASYB9ADRALwAgwAeALP/RP/X/of+j/6w/pD+aP6l/mf/JQB7AKMA3AAxASwB1gB6AC0A6v+b/3T/ef+y/xoAfgCwAK8AyADhAJcA4/9U/03/VP/y/o7+4/62/x8A8v/q/2YA0wCMAP3/6/9NAIEATgAwAF0AkwB8AEIAJgAhAA8A+v/3//n/3P+N/yr/7/7s/v/+Bv8X/2r/AACZAAYBRwFTAQIBgwA/ACwA3P80/8X+9P5o/5D/fv/E/3UADQEnAdQAcAAjAM3/YP/0/sv++/50/wAAgwAJAXABXwHSAE8AGgDn/2L/4v7r/ln/q//F////hADqANYAiAB1AIMAPgC1/2v/of/s/+z/wv/K/yMAfQCgAKAAoABhANH/Yf9Y/4L/X//y/sT+Jv/t/5EA0QDXAPMAIgERAasAEACJ/0X/Hv/y/tz+F/+L//r/UQCWANMA8QC9AFsAFAD8/87/c/8g/yT/k/8fAIoAsgC/ANQA3QCUAPb/jv+4/w0A8P+U/83/lwAFAaYALwBLAIoAOQCX/1D/cP97/1H/Uv/I/3QA2wDdALwAsAC3AJoAHgBy/wr/BP8c/xv/Mv+T/wwAUABLADMAOwBLACYAwf91/4n/2v8GAO//1P8TAIMAowB1AFoAcwB6AE4AHQD8//T/8//r/9n/xv/T//b/AgDj/9T/CAAxAPj/k/+l/ycAXAD1/5D/3/+UAPAAvABaABoA9f/x/wYAAgDD/2T/RP+D/+D/EAD4/7H/eP+D/7T/rf9u/2z/1v85ACoA2f+y/8b/8/8pAFwAbQBbAGoAwQAZAREBowApAOL/xv+8/7f/tP+7/+f/GwAuABwAAADt/97/tP96/03/NP82/3v/CQByAHwAcQCeANsAxQBxAEMAXABOAOD/dP9Z/2r/dP+O/7v/zv+l/3L/i//N/9D/d/8q/0//tP8NAEsAjgDHALYAYwArAFUAgQA9ALr/nv/5/y8ABADP/+r/OQB+AJgAfQBLACIADwABAN7/mf9G/wr/D/+A/zwArgCDADwAZACwAJYANAD9//n/+P/x/+z/0/+Y/4H/2P9BAEoABQD+/0gAbQApAL3/c/9K/1L/uf9KAH0AJgC7/7H/7P8GANz/rv+u/8X/AQBgAJUAcQAsABIANABuAJcAngBrAPH/gf9v/6T/xP/S/wQASABvAIYAvwDvAL0ALQC0/6D/u/+//7v/0v/n/8P/ff9s/6f/1//P/+r/YgC5AGMAw/+t/0EApQBIAL//0v9IAFAA1P95/4r/sP+i/4b/lP+//8r/vv/j/1IApACPAD8AFgBBAIcAgAArAO3/6P8GAEEAjgCvAIgAZQBmAFQADwDi/w4ANQDk/0n/Ev9o/8f/1f+n/5H/h/9d/zz/XP+s/+3/BgD7/9v/0v8WAJ0A5wCpACIAyP/K/wkAUgCAAF0A1v8//yz/qP8eADUAIQAWAP7/yf+y/83/4f/a/+r/GAAtABUAIQBsAKYAmwCCAIwAfgApAN3/3P/1/+v/4f8CACoALAAYAA4A9P+a/z7/Mf9R/1H/Rf+L//3/KwAMAPT/FwBSAIUAuADdAKMA/P9v/3D/2f87AIEAswCwAEQAnf9H/2r/pf+p/5P/n/+v/7b/0v8LAD4ALQDs/9D/IgCWALEAVQD2//j/LQAzAAAA4//6/ywAXgB5AHMASgAUAPv/8f+3/13/Sv+r/zIAYgAfAL//sP8NAHsAnwBxABkAu/+N/7j//v/0/5D/Y//8//EARQGaALH/Yv+t/wIADADq/7n/jf+l/wcAUQAvAOn/7P8QAPX/nP98/8f/GgA8AEoANwDr/7P/9P+KAMgASgCU/2D/rf/0/+//z//L//L/KAA9ADkAMgBFAHcAmwCCACAAwv/D/xcAWgBGAAgA0P+R/23/u/9QAHQA9P+A/5j/yf+W/3f/DQDZAM0A9f9k/7n/WACDAF0AXwBzADMApP86/1//8P9gAFcAGwD6//D/wv+F/5j/DABZAA4Anv+F/8z/JABdAGcAQAAnACoAHwDt/8z/IwC7AOYASQBo/xf/jf9aANIAywBUAIj/yf6w/m3/WQCyAFQAuP9N/zT/if8pAKwAsgA8ALH/bv+S/wwAkwDAAGgA3/+D/4D/+v/IAEYB3gDG/93+wP5Q/y0A3gDVAPX/+f7g/rb/nwAEAfYAowAOAGj/Lf+F/ycAvgAGAcEA8v80/zf/9f+5AMQAGwBE/9D+Af+h/zwAXgARALr/mP+2//f/QQBwAHoAZAAZAJr/L/9b/z8ANgFQAW4ATf+0/gn/AADkAAQBNwAm/87+mP+2ABEBnQAHALn/pP/P/y4AfQBpABAA2f+9/5T/iv/Z/0MAYwAuAN//yP8LAIEAwgBsAJ3/AP8U/7j/eADgAJYAq//Z/u/+0f+sAPEArAAcAIv/SP97/+H/EAABABMAUwBbAPP/hP+T/x8ArQDAAEwAqP9V/7f/lQAiAdEA6f8w/y//yP9hAH0ALQC8/3H/c/+l/+j/JAAuAAYA6f/u//X/7P/g//b/GQAAAJ//Z/+3/2UA/AD/AGAAof9p/+r/lQDYAMEAfAD2/0//Fv+f/0EATADq/7D/pP+B/3v/4/9qAGkA6/+5/ygApwCVABUAw//l/y0AUQBTAFIANwDo/4n/fP/g/z8AOQAHAPb/6/+y/2z/g/8HAG8ASQDT/3L/W/+x/1wA1ACiAAkAo/+y/wIAQwBuAI4AgwAgAI7/P/9//xkAhQCMAEoAyf8l/9L+Lv8KANYACwGMAKj//f4s/yEACwE+AbkA8f9h/0H/qf9lAOMAyABDAL3/YP9U/8z/eACkABIAV/8Q/zn/lP8GAGMATgC8/z7/a/8hAMEA3wCJAAMAgv9L/5n/SgD1ACIBsgD1/3r/iP/v/2AAjwBjAPn/f/8+/1r/w/8jACoA7v+1/7j/9v8xADIAEAD1/93/z//l/zsAoQC7AHQAFgDZ/5//e/+h/wgARwD//2D/+P48//n/lQCqAEwA2f+f/9f/VwDIAOAAiAD2/4P/h/8HAJUAqQBNAPf/3f/O/6j/n//e/xoA//+q/4f/rP/c////DQDj/47/jv8YALAA0QB6AAcAw//E/w4AhwDSAJYABwCo/7X/6//5/+X/2P/k//n/AwDm/6z/m//W/ygAPwAUAN7/tv+g/6T/1v8jAD8AEADY/9n/+v8FAPj/AwBCAG4AUQAOANT/wf/Q/+D/5f/5/yUANwACAJ//af+k/zAArADPAJwAVQAqACQAPgBgAG0ARADo/5P/ff+Z/5z/iv+e/93/9P+4/4z/1v9bAIMAHgCz/9H/WwC+AJwAMgD3//b/8v/t/x4AZwBNAL7/N/9I/9j/OQAuABUANQBEAAsAuP+h/+n/SABsADIA3v+x/6j/s//W/yoAggCFACMAt/+s//D/NgBCADgANAALALL/cP+Y/woAbwBqAPn/eP9O/6H/HwBmAF8AKAD2/+7/CAA4AF4ATgAWAP7/FQATAN3/rP+5/+T/7f/P/7H/tf/T/wIALgApAO7/tf+v/9b/HAB8AL0AlAAFAJH/pf8VAHEAhABmACUAy/+G/4j/0v8gAEkAQQAOAMX/i/+a//X/PwA+AP3/sf+R/7H//P9FAGUAMwDH/3D/hP/8/3QAjQAzAK//XP96/+r/VABwADcA1v+L/43/1P8ZACoAFQARACsAQgAyABsAMgBuAIwAZgAqABMAGwAgABwAAgDM/6f/1P8+AIQAWgDg/37/fv/K/yAAUgBFAAYAuv+h/8b/AQAzAEEAFgDS/63/uv/R/+T/AAAnADQADQDP/8D/BABiAJQAdgAVAKv/fv+y/wwARABGADAADwDQ/4v/nv8aAIQAaADs/5v/uf8AACgAMQBBAEMAGADq/+z/GQBDADYA7f+e/4f/rv/+/0QAYABNABAAxf+h/9X/QACOAHYABgCN/1j/iP/1/1QAYgAOAJT/Vv+U/xsAcABUAA0A8//7//X/5v8GAE8AewBWAP3/qv+K/7T/DABHACIAvv+D/53/0v/w/wUAEwAAANb/1/8TAEIALgD+/+n/8P/3//7/DQApAEIAQgAnAPX/y//V/xoAZQB1AEkAAwDN/8P/+P8/AGAAUAArABIACwALAPv/2//L/9n/8//3/9//s/+S/5r/wv/p//H/4f/R/8//4f/x/+v/2f/P/9z//v8oAEQAOAAKANT/zf8FAEMAVABFADAAHAALAAkAJwBWAHMAcwBMAPz/qv+l//P/PgBFAA8Azv+o/7D/8P89AF8ATAAcAO//yv/A//L/QQBhACYAw/+D/5H/1/8dACwA+/+n/23/gf/Y/y4AOwD8/7z/zP8kAGsAYwAwAAYA8f/s//n/EwAkACEADAD2//L//f8QADcAVgBHABoA9//p/+j/AAA0AFEAMgDu/8T/4f8WACMACwAAAAAA5//I/9j/GwA+ACcA/f/h/9T/2f/y/wYAEAAZAAoAxv9u/2z/6f9xAIUAEwCe/5r/8P9VAIEAWQD3/67/wP8HAD8AUgA+AAAAv/+3/+//KAA+AD4ALgAEAMb/qf/X/y8AZQBMAP//u/+n/83/EQBAAD0ACQDF/63/3f8hAD0AMQASAPT/0/+//9v/EgA4ACsA+//X/8H/u//Y/xUASwBXADMA9v++/6b/wP///z4ASgAHAJL/O/9c/9z/UwBoABcAq/9v/6P/MADDAPIAlgD5/5P/zv+AAA8B/ABgAL3/cv+h/zEArACVAPn/Zv9N/6H/FQBcAEYA4v90/07/gP/T/xUARABGAPn/h/9s/8j/OQB7AHUAIwCw/37/w/8/AIoAfwBEAAMA1P+0/8f/HwBwAG0AHwDF/5b/oP/c/zYAgABvAPz/g/9v/7T/AAAvAEgAQAD9/5z/fP/X/3EA1AC2ADoAtv9+/6n/FACMAMIAcQCm//b+7v6W/0wAcQAAAF3/+P4O/3//9v9EAGcAXQASAJ7/hv8SANwAMAHSACQApP+c/xIAtAD6ALoAOQDR/6T/rv/8/18AfwAxALv/cP9l/5P/3v8PAOr/g/9D/2T/tf/V/8L/wP/k//b/7f/j//n/NABnAF0AJwARAEYAeABkADAAHwA5AFQAWwBNACQA/P8BADcAVwA7AAQA7P/h/8r/xf/t/xYAGQD5/8f/mf+b/83/9//3/+7/AAAEANv/sv/F/xMAWABeACEA2v+9/9z/KABhAFwAJQDm/9f/+P8sAFAAVwBAAAAAw//L/w0AOwAkAPH/1v/O/8f/4f8FAPf/uP+U/6T/tf/G//L/EwABAN//8P8kAD4APAA9ADcAHAAHABwAOwA0AB4AHgAfAA8A/P/1/+//7P/2////7//V/9v/9P/y/9n/1f/o/+r/1//S/9H/vP+l/53/ov+u/9v/IQBRAE8AOAAsADAANgBGAHcAlwBpAAoAz//e/wIAEgASAAAA2P+b/3j/mf/i/w4A///S/7j/0P8JAEsAXAAwAPb/4f/s////IgBGADAA4v+y/+f/RgB4AGcAOQAbABoANQBYAFkAOgAPAOb/rP+D/67/BwAqAPv/sv+G/33/pv8CAEgANgD7/+v/EAAwAEcAagB2AE4AHgAnAFsAdABVABUA4//Y/wIAOQBNAD4AEQDD/3X/W/+U/93/4v+b/zr/D/8t/4D/0v/N/3n/Qf92/+z/QgBUADUAAADk/wMAVQCzAOwA7QC4AGUASgCTAAMBKgHmAIEAQQA7AGMAkQCOAD4Auv9L/zn/d//B/7//Z//v/pf+j/7S/ib/QP8X/+T+0/7//m7/AABYAC8AvP+C/+7/xQB4AZcBGwF0AEMA2wC8ARICtgEhAakAegDGAGMBtgFPAYkAIAAtAGEAfgCSAGwAtf+8/lP+xP5P/y//Zf6G/SL9Xv0G/rf+GP/M/u79O/2M/fz+owBUAacAYf/g/uH/wQHgAmcCRgHeAFgB5gEkAkICKQLUAbQBKQKcAiMCNgEKAb8BEQIFAUj/IP41/gf/bP+S/uf8vPvv+/X8pv2P/Q/9tvzV/Iz9i/5R/5L/kf/b/5oAMQHcAEEA3gD0Ap8EsgP3AGz/6QD+A6gF1gTOAlcBMgH7AdoCCQMIAkAAr/7K/aL99f0x/tT9uPxV+zz6I/qu+xb+Tf8S/rv71fqz/D8AKQPCA+EBcv+w/l4ANQMtBXwFHwTkASEAhwAnA5wF+wVBBOoBPQD6/0YB/wKRA+kBmf6U++j6I/2n/13/Lfz6+I/4RPrs+8r8Lf0Y/Tr8s/sG/f3/yQKKAyMCWACHAK0CiASZBNsD7QMiBGUDVwJbAoYDfgSYBL4DQwLQANz/g/+h/3MAKgG///T70vh/+cX8l/5J/bv6J/n9+Ar6/vvm/aj+RP4L/tP+RwCeAYkCIQOdAw8EAwQ4A40CbAPdBZQHfgb6Avb/jQCJBOMH3AYnAoL94vsy/df/mAGjAAL92/g898r4fPv3/IT88/oS+R74L/kI/CL/rwBlAOn+FP5e/1QCLQUSBhMFJQPjAbACQgXXB0kIbgZ9A4cBSQJdBQMIzwYFAi392PtI/jMBiAGB/g/6PPe092z6cPwb/O75sPcb98L41vtS/gD/K/53/R7++P8dAo4DWASpBEQEJgNEAnwDzwalCScJgAXqAVcBQgQTCHwJzwY1Aeb7JPqE/e0CRQTm/v32lfNs9lb7wf2P/JL5n/Zr9QT3C/tF//gAjf+h/Gb77P3PAkQGDAYEBL8C6QLMAwEFFgc+CXQJ3gaiA9cC/ATcB2sI0AWjARD+bfzr/BP/iQC3/tP5aPXl9Pb3kfuv/If6C/dX9ej2V/po/Tb/zP8j/8H9WP2C/7sDCAdWB14FVAPiAm8EYQf9CZQKzwiyBRwDIQNHBkcJVAg4A6n9gPuk/O7+3/9m/uH6q/ZE9Pj0Cvjz+pP7lfky9j/08/XT+vr/LAK6AGX9mPvH/QgDAAh0CTwHswM+ApsEEAk0DIQLNQhqBeMEcQZMCKQI7wVCATD+d/4gAIf/Zfxg+Uj4g/g/+E33gfZh9rP22/ZI90b4hPlU+vj6V/xa/ub/UgDLAJMC8QT5BVUFJAXABjwJmgrsCUkIgwdMCFEJWQkHCWgIrQVwAJH8I/4VA4kE4f5/9kfyUPTS+Gj7ovoZ9+3y2/Dk8vr30PxM/sX7e/h1+Gf8SgEsBAIFmgSkA5cCIwPDBrsLPw7xC2UHKAXnBogKpQxKDG4JyQT//8H94v+HA8ADjf6b9+TzqPSu94f5x/gh9lnzRfKB85n2yvkz+7r62Pl4+rT8Zv+CARMDXgTdBJAEhgRVBrUJPgxHDBgKJQgXCJ8JSAvgC+0KbAcRAj7+Kf+RA0EFdAAh+PDyQvSr+Mn6wvgz9aLyvPHw8v71zfmj+1X65ffM94T7TAAmA2wDfQJFAmcDoAXfB90JYwtZC84JVQjoCOwK/wt5C0IK/AjPBa4Apv3c/wIEYwOk/Nn08PGV9I742fmn9z704vGC8SnzffY8+jL8ZPtj+fT4ivvK/0wDBwU2BTsEQAMABGAH5wudDrwNZQrjBy8IOwoBDMYMJgyBCDMCBv0b/b8B3QSvAZX5yfKH8bX0hvh/+Qf35PIY8LLwLvSA+E77Pvub+cj4Zvq8/SYBwAP8BEUF2wSMBN4FegmTDdgO9gwBCpoIYgkFC8wMpg3VCxAG6v4D/EL/dASjBL39wvQ48Efy9vZP+bf35fM+8KDu1/Ae9gj7NPwU+vj3Y/iq+w4AuAOjBagFlwTyAwIF8gccDIAPaA/hC5MIqgg9C3kNeQ7GDfkJ7wKW/BD8ogGkBs4D0Pl88JzuYfPP+MP5t/V78N7tKO/y8mT3kPoJ+7r5o/hf+WH8owBDBLEFkQXCBCYEJgW0CMUNpxA4D0gLxwjLCTgMuQ37DS4NWQnyAcb7Q/x6AmsGvgFM91fv4u6y8174A/k39cDvAO0570304vgj+7v6Afkb+Az6Uv5NAlAE5wRwBaMFYQUVBpMJlw72EDsPWAveCI8JWAzzDkoPvgwPB7//YfuZ/RIElAY+ACb1tu2Y7mX0yfhk+Fv0fu+27G7uwvOd+Yr8i/uC+Nj2Zvn2/gwEYwYPBtQEKwQFBUIH+go5DyQRHw/sCqwIIApfDaoPYw+yDBIHN/8b+sT8RwT3BpP/T/P4677tdPQ2+Wn4rfOI7h3s++2O8+b5Sv0l/Ir4n/Zh+Yj/rwR7Bu8F9QSaBDsFjQfGC10QMRKqDzYLIQm3CpoNpw8ZECMN1wUH/T75Bv6xBZIGdv2m8QHsXe579Kz47/fk8g/t8OpJ7ur0yPqJ/IH6zves9yL7bQCSBP4F6AXGBbMFAQZKCAkNbBEoEsEO2QouCmUM1A4CEKIPDgxpBGX8K/rG/1QG3wSn+ofvrutW7wr14/c49kDx5Otq6rPuH/bH+7z8C/pG97H35vtyAUIFbgYUBqcFsQVhBgsJ+A1MEmISfg7cCrUKVg35D6MQ5Q5jChMD6vub+qgAowaLAyX47O3s65DwQ/Uh9hT0d/CI7NjqYO7M9Zn7h/zl+Xv3e/gD/RsC7QTVBTIGrAbkBjUHkglwDuES2hKlDgELNwvzDdMPNxDjDlwKXwII+/P62gGfBm4BcvWg7DnsIPFH9ab1zvKO7nPr3+s58Ir2DPvO+wr6sfjg+Zb9/AEgBdYGqweeB+UGMweDCroPQxMtEhgOXQv1CxUOTw+8D7kOfAlpAPj5Nvx9A+IFev4T87vslO128Wz0EfX48nzu7+rp637xCPit+zj77vh7+Cj7b//5AhsFpAbxBxwIpAZ9BqQKAhFkFOYR4QyRClwMTw8/EKsPrQ09CKr/0vku/HgDuQVv/TrxIutd7WTyzPQ59Pnxcu706jvrA/G2+ND8w/vX+Ez4UvvK/0wDoAWBB7cIDAhQBuIGFAwKE7UV1BE5DKQKGg3ND9cQKxAcDT0Gpv3O+Tr+BwU3BDP6N+9U63rur/Lh86Pyn/Dg7Tfrousb8Yj43fwt/D35fviC+zYAHQThBr4IEAmcB10GdwhQDgEUiRRIED4MCQyJDkIQNxAkDxMMTgUw/Wj6TP/EBP8Be/cb7vTr3+7E8bTyPPIX8GHsJerO7Ijzyvn2+6D6BfnF+Qz9HQGKBDUH/gg0CZwHgwYsCW4P3xS7FAIQDwxIDCsPtRApEJsOJAsSBE78k/pFABsFbQDf9G/sQOwX8MzxP/HK8Nbvuuz66Urs0/P7+t/8Zfqg+HH6a/7nAbcEvAcuCvcJeAfMBgQL3hHNFegTUQ/CDIoNPA8yEGoQ/Q7WCQkBKvos++MBjwQL/UrxROv+7J3wVvFo8A/w2e7B6w3qA+7j9XX7t/uH+Vj5EfzG/+gCJAZZCbQKdAnQBxIJxQ08Ez0V4BIwD3INKA43D78P+w/xDe8GV/0j+cf92wOlAaf32O6S7A/uhu+d8GfxF/BV7NPpBuzu8aj3e/rB+nv6RPuA/bQARwTPB0kKnQoUCRwIegq1D1gU4hSOEeUNHA3eDhEQ8w/jDlMLZgPe+mP55P+mBMz+APPz69ns+O9o8OvvQPBn7w3sCupV7XP0B/pb+2r6svoE/cf/AwLGBK0IsQtEC5cITgjNDNASTRUvE9APCQ7jDRUOOw+oENsOGwfi/FP4kfzwAtYBpPhe70bsye1x7wbwX/AW8NPtPuvT68nwpPYO+jr74Pvk/Pj9yf9RAzwItwuhC5gJ7whnC+wPyBMkFHoRog6BDcgNkw7OD7YPhQo1AFT4bfpZAhMEdfsA8R3tbO5674DvO/C98J/ufeuH65nv/PQY+TP71/s3/Hz9zv/qAp8GCwqvC+EKQwnPCZcNJRLtE+QRhw4LDf0NRQ9bD5AOqQuuBJb7yvci/VUE9gHR9bPre+tT8LPxvO8o78fvZe7X6/HslfJ3+Cv70fuJ/AL++P9UAlIFcAi/CmoLkwrfCbML/g9HE7QSiA9eDT0Nlw3NDcQO2Q1NB/z8mvcZ/FkDGwLa9/7u9O1q8GrwAe9z76Twle8F7VHtwvFF95D6D/xW/bn+LgClAZ0DowYNCu0LawsCCpEKOA7rERQSEw/PDPIM4Q0JDvIN0QyMBwT+P/eE+kUDjQQW+mzuFeyT8Ffyiu917tPwjPFa7rPs0PCW97P7U/yV/E/+pgBqAvcDegbACTMMBgwUCsAJQQ1ZEX8ROg7GC5QM/w2QDc4MLAzZB43+3/cN+/UCAQPi+Anvru0p8dPxme8R78XwEfFB7wjvlvKx9+T6FPxs/en/LAJHAxwERAakCewLPQteCQQKqA3JEFMQmA0RDJgMRw3vDGEMmwouBU79afnr/PQBsP9W9g7vYu/D8mPyLu9H7jvwWvGS8D7x3vTE+Jv6F/zD/pgBDwNQA1kE9AbyCV4L3QpRCp4Law7jD6kOtQxZDKkMMQzSC/oLewn9ASL6d/lw/xcCqvvL8sTvkvHu8cXvIO8U8QTyevDF76vyQfce+jD77/wrACkDMgQRBCgFZwiEC9QLGQq5CUsMQg84D9gMQQuFC+YLXgtfC0YLVwcw/9n4GPrL/9QA1/lp8druCPEk8tPwIPAt8YbxZvAF8Uz1BvrO+xr8If6kAT8E/gQ0BZkGVQlyC4sLUAr4CakLzg3vDT4MOwuWC6oLrwq1CU4IGwRF/Qv5ufs0AAP+YfUA75nvpvJz8gzwf+8l8VvysvJn9Ln3jvo5/CX+LQEOBEUF4wQOBUUHiApZDIELsQmxCbYLVg0xDSsMSguUChoKNAp5CWIFWv7A+Y37x/+u/m330/Co74/xO/Ki8ZrxvPFS8dLxmfQZ+F/64/t5/vwBhATABLwD2gNDBukJbQwLDG4JrgcGCQMMsQ0SDS0LNgmrCNwJpwp4BxcAGfoo+vj97v6G+jr0VfD+70HxZfKS8vvxOPHt8fL0DfiK+bn6/P3IAgQGtgV6A+sCXAWDCY8MfQyyCYQHQgjKCq4Mlww8C8QJ/ghPCSgKbwjkAW/6y/hH/e7//vqK8sHu/fC+8wPz0PBj8L3xo/Py9Xj4RPqu+0n+PgJoBboFRgS+A6wFDgmPCywLvwhlB6QIyQpmC3IKYAk9CeMJsgqeCoQH0wB1+q75sv2h/8f6CvOC7y7xHPND8rjw1PBK8hP0IfZw+HP6Y/xd/3MDZAZ3BqsE5wPlBYUJ+QubC1wJmQfSB14JhQqCCtEJIAn2CIkJgAlTBsX/GvrY+Ur9qv1A+EHy7vCY8tPyG/FS8K3xvvNA9d32HPlc+8n9bQFaBT0HUwaUBL0EeQeuCuELhQrcBysG+wYiCUYK4Qk7Cd8I+AgXCckHjgOp/UT6r/t6/qj8H/ao8CzwVPIU8/nxO/FX8iz0vPWb90L6E/3X//oC8QUpB0cGNwUOBp4IxQrNCvQIuQbJBeEG4wi3CQcJhQgLCUsJ8AdIBSACrv7q+6z7Nv03/FP2su9h7v7x1/TI83rxePH089L2D/k+++j9QwH/BKUHswfKBbAEOwZKCRoLYAoSCDcGEAY3B04IlQi0CC4JCQnQB6UGVQXTAX38dflC+8f9+/qW85TurO8U89jzd/JI8h30HPZP9zX5z/wMAb4EKgeDB+gFrwSpBRkI2glyCZoHGQb4BbMGcwcBCKIIYgk9CQAIDAerBpwE5/+t+1v7Fv15+2/1VPCX8JnzQPQs8tLwEPKE9MX2+fii+8X+LQI4BR0HTQd7BkQGlQeMCSsK0AjQBuQFOQa+BicH/gchCQsJlQe0BvkGmgWwAG77svqr/db9/PcU8WjvXPLj9Gj0tvJD8lXzWPVP+O37IP+tAS4EhAZQB14GbQUYBhYIbgnJCMYGAgVgBOEEXwZ5CFUKZwplCGsGXAaABqEDgv6Q+4r8Wf22+XfzIfCD8erz7PNV8ojxcPK69JT3cvpS/W8AzgPcBlQIwAdpBlMG6geiCYkJbwcxBX4EHAUqBpgHaAl9CjoJsgb4Bb4G0ASt/j75g/kK/eH8Hfcd8UDwLfPq9L3zOvKo8sn0X/cf+gL9sf9DAjIF3wfeCOsHrAbGBikIIQmCCMcGDAULBFQEPgYBCWYK0QgHBh4FCAZNBY8A3fpu+fj7tvxt+KPya/AM8v3zCfQm8/Xy2fPd9Tj5/fxGAEgDNwYzCK8IAwheB8UHzgg9CXQIuQbFBJgDtwMpBdUH/gklCfQFFgThBPAEPAFj/NL6J/zX+5333fKd8Vfz/fTh9LDzBfPW8w/2YfkC/TQA5QJYBU4HUAgZCGgHcQdQCOkIQAiOBrYEeQOQAz4FowfzCAUIDwZeBf0FQwUVAZj7dvlx+7D8TPm+8yXxpvLM9M/0z/PI87f0K/YE+ST9wwDnAqcErQYcCCYIrAfaB0YI5wf5BjAGdAWABNwDpgQUBz0J0Ah8BlYF3gXMBDEAA/uK+Wr7A/y++Gv0s/Jj8xb05/Of89/zEfVK9y/64fxJ/z8C8gWbCKsIPwfoBlUIkAmtCFYGzATmBE0FPAW9BXUH1wgYCD8G9QVsBtEDzP2j+Qf7+v3/+5n1cPHR8tf1zfWF85by8fNY9p74X/oB/ML+IgOlB2EJlAdzBR0GYggFCZoHJgatBW0FhgQJBE0FsQf9CCYIlQbUBTYFrwJH/nv7Bfxa/Pv4HvRo8jf0r/V09KHy4PK19KD2k/gC+8/9DAGoBH8HDQjgBlsG6gf0CfUJ/wcFBiUFUQXiBXUGJwfdB/AHIAc1BroFMQQQACL7Pvna+uD76/go9F7yXvRs9oj1V/MJ8+f0XPd/+dv7B/+QAjYFYAY9BrYF2AUVB5AI+QgzCCoHUwaeBT8FBwYbCNcJPgn8BmkF1wQFA77+qvoR+qv78fpw9iXy6fH885T0p/N184T0hvV49v/4Tv16AYsEvgbIBxYHwwUIBhoI3QnvCd0IlgdNBjYFcgWBB2EJDwkFBywFcgTBA0AB9fyY+Tn5Mvpc+RL2+vJ68r/zMPQ486fy2vNc9lP5T/x6/84CugWhBxYIEwcWBrkGcghcCeQIOQgoCCMIwwdjB5EH6genB7gGvgWwBIQCu/7M+vv4G/mT+Of1v/Kr8bPysvNi8w/zKfQ/9mP4j/oj/UkA2wMqB9MI9Qf/BZIFFAe6CGQJlwkOClsKNAnwBpEFIwZqBwEIgAfQBQsDbP+H+/T4dfgt+IL2VvT/8sLyUvMX9JH0+/SL9av2Cvk6/Cr/OALWBYUIngi1BgAFRQU5Bw8J0AkpCjIKKAlvB3wG5wbXBwgIfgcQB+UFXAI9/T355vf29/f2aPRf8p/ycfSV9bX0yPJW8qn0iPjp+37+YAH0BMcHEAhRBvUEoQXtB/8JpgpICmwJIggWB/AGYgehB4kHZgcjBwwGOQMO/y37p/h59/72CPYa9NHydfOx9HL00vIA8rjz+vYc+iX9sQBmBIoHJwm1CAcH5wV1BjEIpAnMCRYJewjFB8YGdQZbBykIuAfFBk8GBAXTALL6yPYn9634tPfi9B7zTPPH83vz+PL+8uHzJfa6+YX92wBXBOEHwAnSCJMGkAVsBowHDwi9CJkJdgkjCNkGugZoB8EHYAf/BuIGvQVrAp/9M/n+9vP2u/bB9IbyAvK98g7zfvJB8sjzd/YV+RL8GAAHBNMGZwiBCPsG/ASTBIwGGwkiChQKdAp6Cr0IdwYOBlIHQAj2B2kH3QYgBOH9OvfJ9Mj2mfjA9sbyovBw8c/y//LV8qPz9fWK+TL90P/nAbYEHgjzCa4I3gXBBCsGPAihCZcKwwpyCVcHIAaHBgQIYgm4CdoIxgaFAxv//fnw9Rr1ufYS9zj0pPDr7wHyqPPI8oPx0fKV9rP6QP6ZAQIF4wfrCKIHkAXaBJ8GyQnCC4ALmApQCtYJgAidB40Icgr5Ci8JbgZgAwf/6/lx9sj1YfbK9UPzXvBD717wBvKb8oDym/Oq9on6Ef6UAWwFVwjlCH0HGgZLBqwHSAmVChQL5AqHCsIJqggxCOYIGwqyCgcKBAg2BEr+RviB9S/2o/Z29DDxoO9j8Bvxd/A78Obxa/QU9z36B/7uAVQFrge6CD4IpgYCBrQHLApkC5QLYgtSCucIiQjtCYoLMws/CeEHCQcuBKH+4Pit9Zf1Rfb+9NrxI+827rTu0u/18A/y8vMZ9/T6uf4/AnwF5AefCK0HfwbJBoMIXQq1C4sMcQw+C9sJhQkoCswKrwrbCbgI/QYGBI7/6Pnb9PryJ/TV9H3y4u5c7Xzu/+9d8ELxTvRz+Nr7kP6oAQsFtwfmCNgITAjbB2gIAgpVC50LhQtYC5gKmAlTCRYK/gp1Cj0IxwUxA9X+Ovkn9eDz2fP28gjxcO/Q7tbuzO/J8Xzz6vSN9zL7YP53AVgF3wg/CvQIuQZKBhoIRwp1C9cLyQtWC5QK4wnCCVMKlgoSCjYJOQfYAnL8iPYc9Dr1YfaJ9MPwBO6p7e3uUvD68LfxMPRt+HH8bv9oAhwGTAlVCnYJEAiAB0sICAr1CxwN3AyoC4oK5AmtCcUJ2gkbCSgHcQTmAOf7R/bF8v3ybvSK83nwW+657hTwvvAU8XnyefU3+SH9ZQFuBQcI7QjqCB4IuwaKBpkIiws3DfQM3AsRC4sKNApuChYL+ApQCWsGqAJ9/l36xPY+9BXzk/Kp8X3wN+8e7g7uN+888RP0ePfw+qb+qwItBtIIegowCtsHlAXWBbUI6QsiDWYMyQvYC6kL/Ap8CngKLAqhCM8F6AGW/Bf3N/Qz9PbzB/IP8EbvE++r7nDuMO9/8S71rvkO/nAB9gNZBq8I9AknCTAHawb0B30KkwzWDR8OHw1PC+QJpQkWCkIKfQmOB4MEHQBV+vX0x/Km80f0vPLe78Ltbu027hrvVvCP8rf1mPnC/WgBtgRDCOUKGwugCQsIsgcYCfoKVwy3DegOdQ4tDL0JewiwCEwJtQigBgUDov3G97bzAPK98VjyZ/Ks8Gnuqe2h7gLwO/GK88z3Ofwc/8gB/QWmCtwMUAsaCK4GAAhxCisMowxxDGoMcwzwC7wKewnNCH4IqQdoBWwBiPtG9V/y9fMH9ov0RPAc7T/to+7m7uzu3PCC9Hv4Q/zx/9UD8gceCy0MKgvzCOQG6wYFCaMLIw7sD5gPtgw4CdcH5QjoCd4IsQZXBH0AgvrM9EbyB/P38yryx+6B7bfueu8L77zvqPKB9uL5Mv0iAUkFigiGCngL+wpMCQ8IjgiyClYNNA9ND6kNbwsdCiMKPQp0CTgIJwbpAb77H/aa8+PzufNJ8cbuLO4m7vXtwO6U8GXyIfSV9lj66f5xA+oHIgygDSALzgfUBvEH8AkfDAAOGg+wDpgMBgsNC3AKOgi6Bq8GEwV1/4b3W/KW8pb0+fPp8BbuW+017zLxKPHE8LbyIfZa+RL9uAF0BhAKPgvrCd0H8gaLB0YJdQt8DRsPmw8mDqwLNQpdCsEK2QlIB8sDkv9++vb1OPTr9Kj0j/HF7cDs3+7w8OLwYvBa8i72e/n2+xH/VwPgB0UL8QvYCRkHKwbUB8cKKw0kDmEOGw7cDAsLywnkCMoHCgeQBhYEwf0K9sXxZfLx867yKPBa7zrw0fCk8GXwH/G98xr43vyEAMYC0QTqB9IKwwpCCJcGkAf3CQIMKA21DXkNngzkC58LywrKCO8GqgUMBLUADvug9MPwe/Hq8yv0e/GZ7mDu3O+g8PjwGvMa9yj7l/6eAccEBwhlCqcKEAkjB2sGuAc4CkEMVA0ZDpMOxg1zC+0IrwdiB+gGjQVUApX8vfVI8R7xPPND9MPyX/BI78fvCfCL75Lw+/Qq+7j/5gGJA/EFigj1Ce4J8gj+B/UHZwncC+kNmA7sDYQM+AqrCesIJwidBqcEmAIC/7j4gvKq8Pfy3PTu8pDvz+428Hfwbe8f8KrzYfhf/If//gLNBmwJOApcCsgJTAiCB3oIjwqrDFIO9A4gDhYMyQmPCAYIEQffBVgEsABi+j/0V/HO8WXzjvP98W7wze9j78juQe9a8lT3bvvM/b4ARQVeCdsKAwqRCO0HcAjBCWMLegy7DMkM8wy7DHULhAm0B7UGggYdBmwDuvxd9Ozvv/FY9Vr1JPL875Hw9vBz7zHu0+/u87b4KP0PAaMEpwfWCUULUQtjCe0G4gY5Cb0Ldg11DqcOgA2JC/EJpgjZBgsF4QTyBKUBCPr58dXuL/EC9EzzHPEs8V3yTPFk7mftUvCN9c762f+xBE4IwQn0CQoKngmECLgHWghvCgkNQA/dD1cOzwvrCeYI0QfABnUGpgUAAhX7EvTE8BvxCvLE8Ybx7vHi8frwmO+x7kPvRfLA9+b9wgIzBqUJhQydDGAKGQgpB2QHxQhHCw4Oww9ED6YNbAzJCvcHXgXTBAgFUQM2/iD3ofHM7y3xG/O+8pfwDPDv8WjyNPDO7uvwPvUG+nP/ogXxCr8MQAt3CaAI7wdDBzAIJgtSDuEPiQ80DioMownoB50H5QdUB9EEYv8F+MbxbO/i8EfzhPPr8XPxafLt8SrvNe0b70T0Svpd/9kDVAikC4IMAguACFIGmwU8B68KLw7fD4MPTA7EDOgKFAlBB5oF2ATFBJsCWPxL9B/vQe8B8grzE/IV8mbzO/P28N7uYO8+8nv2KvzSAoEIYQsADD8LQgkcBxIG3AYwCUcMFA+aEB8QaQ00CksIUQdmBsAFngXLAzf+Zvak8K3vePGS8l7ybvJC8wnz6PDE7iLv5fGX9fj5d//WBSELEg25C/wIwgb8BeMGAwmMC/sNvQ8QEMIOYAzZCbMHAwZXBdcFDgVCAH74C/I+8N7xhvIb8a7wWfKh86ny2vAV8LzwOvOW93r9VQPiB2gLfQ23DB8JpQXaBMYGFwpIDfIPOhEREAoNSQqcCOMGUgXUBJgEXwLm/Pv1BfG9743wNfFQ8ePx2vIc8w/ykvBu8KDyhfY3+1cA1gW3CjYNjQzbCXEHfwbYBkAIpgrTDX0QHREWD5ILcQh9BtQFFAb9BecDAP+l+AnzLPA+8DLxXPHz8KDxf/Mz9Fby0O8e8PjzdPmN/j8DcAijDHQNDQsCCCUGlQWIBhoJsgzLD+sQzw8LDZgJ1Aa4BbAFvwVcBcsC1vxi9THwCe9E8DTxU/F/8rj0JfXh8mrwaPAh80P35vsnAYcGqQpzDPELoQm2Bi8F6gVqCNEL6w6WEAIQXA0GCu0HGgdQBmsFxwQrA4j+pfcI8gfwxvCG8bfxgfIB9Af1LPQR8r7wsPEQ9dj5I/+ABKUJ8wytDGAJ4wXBBLQFSQdNCVgMiA9aEDMO+wq5CIoHuwZaBnQGWwXOAJf5mvMa8dnwwPCD8AnxdPI/9DH1ZfSO8qbxwPOI+OP9sgJQB2UL6wwxCwUIowX7BMIF+QdeC4MOZg/GDZwLPAoBCXIH7QX8BAEEhgHR/C/32PKb8HLwUPGn8ZvxefIL9Gf0WvOy8iv0pPfj+5gAHgZaC8kNhgxICXwGPwUhBTMG+whlDCQOjQ3uC3kKVglNCDAHHwbPBF8CFf6t+Mfz3fBa8BHxv/Eq8iHzs/Rv9Yn0c/Oe9Ab4/fuq/6sDOwh7C7kLcgnOBmMFHQXyBecHlAqyDPcM3wuDClsJRwjlBoMFcAQJA67/Tfol9TPytPHn8YvxePHz8gr1zfVf9c/0SfVG90v6Tv5BAyUISAsfDAoLrwh+Bh4FfAQNBVEHMArfCxIMWgsvCr0I8QZQBWsEmwNBAev81PeZ82jx7PAI8W/xe/Iq9Iv16/W89V32P/hm+q38RQCoBcsK3gzMC60J8gdTBrAENgSIBbAHmQneCo4LPAtbCfQGeAUHBYYEKwKy/Zr4n/RW8kDxr/BN8BTxffP/9e32sfb59hb45Pk4/HX/4QNjCNwLoA1HDboKLAeeBAMEIQW/Bh0ITwlNCowKpwkxCIAGwwRtAygCr/9J+xz2FvIO8FbvH++076vxKPQJ9k33XPhb+b361vzK/2IDRAf6CpQNFA4tDCkJkQbFBAwEpgShBhMJlwqLCoMJiAhgB7IF6QPfARX/L/vY9vvyEPCE7hHuuu6d8JLz2/a3+M/4jPiM+d371f5uAqEGJQu3DuUPkw69C4EIoQUvBJgEMgb+BzsJrgknCXkHTQXPA0UDogLXAEb9RPhY87vvXO3c69brDO4d8mv2UPmV+jX7+vsf/RP/OAJbBmIKSg3IDhUPIg6ZC3cIPAZ+BcgFKQa0Bo4HUgj7B1UGawRrAvP/fvxl+Hb0PvHV78PvNPAk8eTyf/Xg9635yvou+0H7lPuz/T4CgAfZCkIM6g3qD9oPsgzhCNkG8QWNBJYCoQHJASgC8wK8BMwGBAe9BJUBjv5N+5T2T/HD7X7sr+yR7dLvS/Mg9xT77v13/8oAKANWBtkIjwoQDCUNQgzlCLkFEAUQBkoGJQVjBCMFgAa+Bq8FqgSXAxYCKAAY/gz8ifmj9tTz7vGw8LLvPfDk8tL2X/pN/ND8Pf1T/h7/lP/QAckGkAwkEDsQIw6XC10JiAdSBYACSgB/ALYCcATDBFAErANuAh0AdP1F+yf6hPk1+Sv5E/ho9XbyCPIl9Dv2cPcO+Vf8CgChArYDMAQeBWcG0gfyCGgJFglLCEUHZQUcA84BvwEzAgUD/ARjB/wH9QV5Ah3/CfzZ+BX2APXC9bn2NffW9wf5LPqs+nr7Fv0K/4AAWgFuAk0DggNdA1wDugMqBP8EzgX2BbcFYwU4Bb0E0AMxA9sCQgIvAawAKQEaAWr/p/yf+ub5GflL93D1fvVh95/5OfsJ/Mj8y/0p/5AAywH7AkQE/QV+B/0HSge/Bc8DxgFMALP/IQB8AToDiQStBAgEfQNyAxgDzgG7/7z96/yi/GH70/jQ9gf3lvjV+VD6Evu3/Lb+AQBxAO4AmgEZAkICrgKdAycELgQmBF0EZAS2A6UClAEAARUBWwFwAS8BAAHxAHoAd/8l/k397vyl/In84fxw/Vv91/yT/NP8nf1t/gf/mf9oACIBWQFqAXkBiwFqAfMAlQDmAO0BlQKsAuQCkQMZBFoDzgG5AFMA5P8k/+X+AP/U/q7+Cf+4/7T/r/6R/Q79Of14/XX9WP1t/SP+Of8AAD8AQgCEAAABGAGZAPv/d/8J/7b+Gf9rAO4BBAOqAzgEXQS3A58CegFbAEP/p/7b/p7/PgBHAAIA5/8aACwA7v9L/6X+Uv7//bH9Zv2N/UH+L/8EAEYATgAgAIj/uP5U/vv+JgA3AcQBBQJjAqcCdgJoARYAff+3/08ApQCRACgAyP8DALwAIQGlAMf/X/9t/zr/m/4e/lT+JP8CAJYAnAAnAIj///5Z/nb9Bv1v/Vn+Rv8dAEgBWALFAq0CbQJDApsBVwAd/1/+Tf5m/uD+7P/vAJsBEwKmAuMCXgI+Aev/E//g/j7/zf8RAEEAmADsAG4ALv8T/mj9Q/1c/Zr9Bv6J/uL+IP/O/5kA7gCuAGQAeQCQAEMAXP+w/rT+Pf8gAPUAwAFkAtQC4wKRAjsC1AGNAYYBqQGSAQABPACJ//z+SP5u/cP8gPzQ/Ff9o/2r/ff94/7O/yUADADm/8z/gP8C/8X+9f5f/wwAUQEDAyUEYQQxBAcE5gNzA7YCDQK3AXcB9ABaAI//kf6p/RT9wvx0/Er8TvxF/Dv8hfx1/X/+7P4f/5T/QACwAKQAQgDS/7r//v+kAJABRgLDAlcD/wNJBPcDHQNjAlUChgJdAoIBUABZ/8j+Hf7m/Mz7bPtz+5X75fuV/Hv9Qv79/tD/rwBPAXUBOAGzACIAq/96/8z/jgB4AUwCHQP2A5UEgQSIA0YCbwEHAYwAzP8g//T+5P51/gL+1/2y/U/9wfxq/IT84vwT/UT9Ef5n/8kA6wGVAuwCFgOsAs4B5AAzANP/x/8pAKgAVwElAo4CegINApIBCQFhAKP/8f54/vb9hf05/fz8/fxQ/en9lf4P/yr/GP90/z0A3wAfAV0B7gFiAjQCiAHlAKMAggBrAIAAuwD+ACcBMQH7AHMA8f+U/0r/D//c/rT+k/55/pj+0P7u/uP+7P5K/5b/gv81/x3/l/8zAL0AUwEFApcCuAKBAgwCegHVAAUAXv8J/xj/nP9HAL0AvwCKADgAxv9m/+r+Wv7e/ZX9g/3W/Wz+0f4k/6v/YADlABMBAgHiANMAkwBlAIAArwDnACYBfgGyAcUBnQEoAbwAZgAZANL/vP/B/77/nP9C//r+1f6z/or+S/4K/uX9Gf5v/sj+V//7/4wA+gBEAXYBhQFgASgBJgFYAXwBhgGjAbcBqAFTAboAIQCx/2r/If/s/tv+vv65/sv+4v7f/s3+sP5n/hj+4v3m/Tb+nv4t//b/yQB8AeYBBALwAdABowFUAQsBzQCrAMkAFAFgAYwBgwFGAQABjwDj/zH/lf4w/vX96v0C/kv+qv7I/sv+0v7E/pv+kf7u/ob/IACsADgBxgEpAk0CSAIhAukBmwExAbgAZABZAHMAfABTACUAIQAcAOH/Zv/y/r3+p/59/mb+lP7M/tr+xv6s/qf+ov6O/oz+5P57/y4A+wDLAYYCCAMhA+QClgIbAnABxgAuAK7/Xv9V/03/Tf9f/2P/R/8A/7z+lv6O/nf+ev7k/l3/r//X/wAAMgA3APr/pf+Y/7f/0f8GAIAAOAHRASYCRAJOAjkC4AFdAcwAMwCl/z//Cv/h/rn+qP6h/o7+f/6A/pT+t/7w/lX/xv8VAFMAnQDwAAgB1ACGAFYATQBBAEIAewDVAB4BQQFZAVoBNAHtAKAAaAAYAKz/V/8Y/+b+t/6d/oz+gv6T/pn+jv6L/pr+1P5L/9n/RwCzADABlgG8AagBfAFEAfgAnwB9AJ4AqwCeAJkAogCXAHEAPQD4/7L/Wf/x/qf+hf52/nL+kv64/uj+G/86/17/gv+3/wUAVQCRAMoAEQE7AVQBaQGCAZABfgFTATwBQwEUAa4AZABMACsA3/+D/yn/0/5y/g7+yP25/dL9+v1U/sf+L/+D/8b/CgA6AGUAhQCWALAA1gARAUcBXAFgAWIBVAEgAeIAtwCqAKQAjwCHAJ4ApAB5ADQA6f+E/wL/aP7j/ZH9UP0k/T/9nv0g/qz+U//v/4IAFgF1AZYBmQGpAc4B5gHlAckBwQGzAXABBgGlAGoAKwDp/8v/5v8eADgALgAYAPL/ov8m/5z+G/6j/UH9Ef0l/Xj98v1y/v3+rf9UAOQAXgG4AQECJAIdAvsBAQIbAgQCwAFhAfcArgBuAAAAoP+G/4z/j/+l/8L/yf/E/6D/W//7/nz+6f1w/Tj9IP1C/Zb9Af6a/lf/IwDdAGsBvgEHAlYCZwJIAisCHQL8AaEBMgHNAI8AWAAVAPr/BgAUAAoAHABWAGkANgDf/4H/Fv+R/vL9bP0b/fj8Cf06/Y/9Fv7I/pX/TQDfAFIBxAEuAmoCgAKHAnsCSwLuAYkBIwHJAHQANQAuACwALgBSAIkAsAChAHEAHQDB/1j/q/7U/Rr9mfxP/D/8Yfyw/FD9Lv4Z/+r/nQBPAeYBSwJ+Aq8C8gLuAqYCTQL8AYoBAwGrAIMAhgCGAHEAgwDJAPkA1wCkAFwA+P9s/6v+7v05/YP85vua+5T71vtn/Bn9Av4g/zcAKQH6AZsCKwO1A+gDtQNiAwADewLZATYBowBEABIACAAwAIMA5AAqAW8BlgFpAeUAFgAr/x/+F/0U/B37Xvr5+SL6tvqB+4H8v/0l/40A2gHoAsgDigT0BP8ExgRGBI8DxQL+AToBmQAbANr/AgBZALsAMAGfAfoBFwLZARkBDwDv/o397/tb+kP5tviO+Mn4ePms+jX83f2T/0QB3wJDBFcFEQZuBm4G8AUFBd4DqgKTAaYA6f+M/7j/IACLABcB3wG0AhkD4QI1AiMBov/N/eP7+fkT+Iz25PVQ9lz3m/hN+pv8Jv9mATcDwgQtBlcHyweHB/QGEwa+BDED2QHaAA8Acv84/6T/jQBuAUYCGQPIAycE2wPSAhkB8f5j/ML5V/cs9a7zL/PP81j1cfcA+tj82//aApsFzgdFCRMKbQo6ChQJGwfUBKYCmgDn/vL9uv0W/v/+mwCzAl0ESgXcBU0GGAZ6BKUBVP4M+8H3dPTF8TzwDfDl8JfyTPW9+Gr82P8cA3IGXAlwC4sM3QyNDE4LHQlYBqwDPwHZ/tn86PtY/J79Vv90AdcDLgbVB4oIIQh3BlYD9v5O+gn2XPI17/zslewF7nnwP/PF9nX7dwDaBEwIMAu9DW0Psg+QDroMPQr/Bm4DIgCc/b77lPqg+if8rv6IAVwECQeBCUoLlQvUCUgGmgF8/C/34/Fk7Y/qnOkk6ibsgO+i8xL42/xEAo8HzAv5Dm8ROxN+E8QRkg51CgAGaAFn/W36ZviA9y/40PqO/lACwgUMCd0LOw2GDJ0J7ART/5P5APTO7qTqWehP6BDq5uyc8EP1gfoBAHYFiAq0Dp4RohOnFDgUhRHmDPYHVwPZ/nv6Rvc99gn3KfmC/PwAqwV9CXkMpQ4vD/sMBwjPAXj7cvWi707quuaP5cDmm+ll7fLxY/eP/cYDkAmaDqQSeRXKFqMW7xQoEZYLVQWK/3D6EvYw85/ywfS5+Jr9OwPzCLkNwxDiEQMRvw3pBzEAT/ih8frrKeft41vjiuUj6T3tBPIb+PX+fgV3C8MQKBUSGDkZrBhnFhAStQutBEn+5vh59HDx7fBU89z3j/2xA7oJ/g6JEpwT1xFvDfMGN/8j96nvm+lX5fniz+IH5RXpD+5v8575rgCoB5INWhJ9FpoZgRq8GAAVGBATChoDL/yK9u7yWPG+8Y70o/kMAI4GZwwwESUUmxTaESgMuASf/KT0E+2s5o7iHeHO4SPkQejf7VD0Kvs1AlYJ2Q/kFGsYyBqZG9EZSRXuDv8HOgGr+uL0APHB7x7x7vTO+rcBlwiHDv4SmxXXFdESdwwtBK/72fON7OTlL+Gv3+ngmuNV52zs9PJm+tkBCQnvD7wV1hkoHLAcIxv6FoMQ6AiKAQ37cvUz8S7vN/AO9Ir5BAD4BqQN1hKNFaYVJBMkDocGi/0N9drtzOf/4lXgUuCf4lfmB+vV8K73Av9dBnQNvROeGJ8beRwwG9MXTxIUC1MDf/xV97Lzh/F+8XL06flKAHEGGwwlEVAUeRShEY4MxgWF/dL0Wu3P5w7k4eGa4ZLjW+dA7J/xnfdW/l4FKAwRErsWxhnbGr4ZchZ3ESMLBgQj/Zn3NPTN8jXzhfUJ+nwAYAddDesR4xTIFcUTpQ4WB3v+9PUm7rLnHOPY4Ovg2uIw5s7qg/DY9mT9HgTcCg0RBRY2GYkaBBpxF8ISjAzHBUv/oPks9abylPLR9Nz4Jv44BH4KCxDME0EVZxQLEf8K3QIM+gTyHOtQ5U7hBuBm4Uvk++e77ArzU/qCASoIYQ4QFHoYlBpFGvwX5RMnDk8HiQDS+p322vPY8k/0GPg1/bsCcQj2DVwSqhR3FBMSYA1NBrn9ZfVr7rPoOeSb4ZPh7+OX56nrafCC9uP9QAWMC7kQERVLGFcZ0BdSFJ0P2wlvA2n92/gH9oz0p/QA93H71wAuBkUL9A97E7IUJBM9D3oJLQIF+jLyuev/5srjD+JZ4uLkB+nD7cfyxfgZALIHGQ7IEhcWMhhzGEEW/BGVDMEG4gCr++73/vWt9e/29fmI/t0D7AhKDdgQKBOTEzwROwxxBR/+9fYf8CPqyOWy457j5eQ75+vqE/AE9iL8jQJRCZwPVBTxFs4XGBd2FNQP+gkeBOX+k/qC9wL2RPYr+Hf76//4BOkJGQ4fEdASqxL0D2sKKwPj+2D1Wu+W6R7lNuPV47zlHOh/66jwPPc2/uEETQsqEYkVyBfcFzsW9xIdDi4IPwJO/Xf5n/Yr9af1Gfj9+6IAiAVpCrQOrBHLEuMRzg56CYsCSfvZ9GXvbuo/5v3jSuRf5i/py+y38RL4GP/EBaYLvxCpFMgWBheCFXgS+g1eCK0C1v0U+lf3zvUB9i746PtXANcETwl2DZkQCxJJEV0OnwmEA9D8dPbe8Ofr4+d85SXlWeZY6HDrQ/CW9nr9HQSLCoMQ+hQeFxgX3xVoEysPXwleA3n+wPrD97L1cfVw9xP7O/92A+sHUgzLD1IRlBDPDYsJFQT1/dT3OPJJ7UvpzuYG5sfmY+j+6ljvg/V9/AoDFAnBDnsTQxaUFh4VmRIADyoKlwSE/5v71Pj+9nD25Pc0+3j/vwPGB9ILYA9ZEb0Qrw1CCToEwv7O+M3ysu0f6vjn/eZN5xTpSuyz8PT1E/ywAuoIBA69ETMUKhVFFHYRcg0qCbkEBwCW+1T4+vZG93v4rPp6/pQDdAgRDLEOnhA6ETsPmgrWBFD/8/ki9HXuHerh5zznWeeI6IHrFfBw9RH7GwGEB2sNjhGiE0kU5xMXEjgO8AidAyj/jPuL+K/2p/Zi+Bv7cP6rAoAH4gvuDpMQGBEDEIMM8Qa+APr6hPUd8HXrp+jX5yDo/+jy6rfu5fOS+UT/LwVAC1kQghOGFBEUfBJsD9oKmgXHAMH8fvlF94H2iPfr+Q397gCQBVYKIg4wELkQCxC8DUMJTQMo/Z/3mfLQ7eDptueg58/ooepU7Znxc/e7/WkDkwiHDbQRCRRDFOISWBCnDLEHNQJX/Z/5C/e39RP2NPig+5X/1gN1CNcM9g9IEfQQLg/eC9AGlABe+gb1cPBp7FPp4ud46Enqpuy/70f0HfpEANUFjwqTDm8RqBJREpYQpg2lCRgFyAAY/S76BfhC92L4Evux/owCagY8CrMNAxB4EBYPRAxGCCgDZ/2499jy1u5s6yLpmujY6R7s9+6+8s/3oP0YA80HFwy4D9UR/xGfEFMOBwuaBosB+vy1+bj3zfYY9/r4j/wYAZwFmwk6DVQQFxLHEWQPeguLBq0AQ/o49HXv8Os66cjnTuje6kXur/F89W76LQBsBagJDg3SD6QR2xEsEOsM2wiIBCkA//uI+ID2TvbH93r6Cf4pAqwGIAu8Dh4RRRLxEa8PSQt1BSj/HPmG84vu0erF6FnoRule63fuhPIZ9/H7GAFDBuUKaw6kEKURZxGgD0UM+QdrAwf/+/qR92n1+PRP9h75Af2qAa4GjwulD2ESoBMlE8cQXgxbBuf/o/me8/ztielL5zjnZeg76jnt9fGO98/8kwF3BkAL9g7vEH0RRhHSD6kMIAhMA+3+DPvd96L1LPXW9u/5mP2SAVIGZgtvD5QRPhL6ERcQngtUBSD/9PkE9brvFOu66AbpkOpg7MHufPJY9238KQGfBc8JIA0rDwQQ6w+4DvQLswf3Atz+nvva+Jr2zvVI9576g/5DAlwG2wq8DvEQchHcEN8OxAoPBTf/BPre9MnvxevC6b7p1uq07HDvPfOg9zD8vQAjBT4JYwxCDhUPGg/0DSwLPAcMA2v/Zvy8+av3C/dw+DT7Wv6TAWAF2gnUDSkQ/hDUECoPJQtWBXv/oPr59dbwZuxy6vLqGOwy7SrvD/MS+I78WwBcBLcIVQw3Do0O7g2NDBUKggatAkv/mfxa+pb4CPhW+Tv8e/+kAi4GMArADcEPbBA6ELoO4wr7BAj/Nfqm9Xzw0OuA6cvpSevp7EDvQfNL+AT93wCTBEIIQQvtDFcNVw2rDKIKQQepA9UAj/56/G/6OPnG+eX7lv5MAV8E9weEC9ENmg6lDuINTQuGBrQAg/tL9yzz0u6z6znrmexB7tHvbPKv9mb7K/9TAtoFfAn2C/MMOw0nDfUL9AjgBB8BVv4m/CL68/hM+TX7DP4gAaIEhQghDIsOuA8sEHkPiQxbB3wBb/zJ97Hy9e0M62rq9eoG7AvuUfFC9QP5r/zVACYFuggSC1gM5wzqDAIM5AnhBpMDpwBN/l38/PrT+k38f/7DAGcD7AbLClUNKw5mDpkOTw36CKoC//ze+N/0IfA97CDrJuxl7XTuyvC39M34yftc/twBygWmCAsKyQpiC2oLGwq5B1EFHQPVAGb+XvyV+1f8Cv7a/xUCZwVjCb4Mqg5jD4QPSQ57CuoEdv/E+hz2OfEz7VHrgOtX7E3tXu/H8oL2cfkl/Jr/vgMtBxIJYQqLCw8MKQsPCYkGAwSJAen+0/z3+138kf01/5EBuwQ9CFYLbw29DpMPbA8cDTEIJALe/Kj4KfQ+77/r1+p66xTsD+2p77PzifeX+gL+TQKaBpsJRQtqDAsNgwx8CrIH+gRRApr/Ef2V+5f7kfw0/nQAngNyByML3g0WD4wPuw/0DrcLuQU7/yH6DPZ88bjsz+mO6Zzqputy7RrxlfWA+bX8WgCUBDUIUgoqC7MLnguFCl0IywV0A0sBL/8m/Vb8AP1o/gQA7wHOBHoI+gsUDu0OWg9AD4INUQmCAw3+ivkE9U3wlOza6n3q8upK7MvuGvJb9bD4qvzqALEEqwcYCvwL/gz4DNkLzwkOBxMELgFx/mf8Wvu8+y79FP+ZAdIE2gimDD8PkhATEdUQhg6WCT4Dev2l+Knzi+7e6m7pPOlx6c7q/+378WL1h/in/IMBxgWzCMcKhwx5DSUNlAtACYAGagNpAPP9a/zR+yT8t/1LAH4D7QZqCqkN1A/rEEERZRAoDW8HLAH/+2H3RvJG7UHqnunb6RPqC+vo7dbxqPUQ+a/8FgFPBZ4I6wqKDEwN8wy0C6UJFQcIBOQAYP7m/FL8oPwl/r0AAgSvB1oLVQ5YEHMR2BHSEPIMqQZ9ANz7pPd28jbt5ukR6Xjp4On06nXtHfHm9L34/fx4AcgFMgnuC8YNLg4dDScL+Ag2BugCpf9J/Sn8AvzI/H7+AwE5BAEI0gusDiUQ/hCTEaMQtQyEBnwA5fuh94vymu1K6s/oW+i76GLqMO118PjzTvhg/TMCNQZyCVEMKw5VDhQNBQvDCOQFjQKI/279Zfwh/An9Iv/rAfIEQwgdDGIP+RBdEYwR/BB+DRYHbwBY+yf3FfLY7G/pQOgc6FnoRuqk7V3x0vS/+LX9eAJGBvkIWwsPDTMNGAwlCvAHVgWVAgIA//34/OL86/0AAL4CowXNCEcMPw/DEOsQ9RBqEPMMZwbi/6X7IvgZ853tX+rq6QDqwOnC6sDtQvFL9Kn3KvzaAIgEgAdGCl4M1wzbC14KkQg6BlUDzQAw/zL+wv0E/pD/JwL9BO4HFAsQDu0PkxDfELUQMg5ZCIMB3Pyd+Rf18+4y6uPoKukW6Ubpbusp77zyavYM+xUAFwQhBwUKkQyhDZIMmwqSCFcG0gM8AQ3/bf0E/RD+/v9MAtoEEwjyC1oPGhFeEXsRgRE6D1UJ3QFr/E75kfUX8DnrLukL6U3pH+pX7EDv4vEd9RH6Z/8hA5wFNQgNC8AMXgyjCkoIxgV6A5oB9/89/hP9nf0RABQDcwWMB6kKYA6bEMIQDRDrD8IOcwrcAwv+ePrw9k/y0e0C69bpZekB6rbrFO5R8EPz4PcY/aMBvAQ8B9IJ2AtZDAUL1QhbBhsERwK/AE7/Gv4e/t//qQIpBRQHdAneDMAPnxBBECsQ2w+eDCAGzf9P/LT5+PQa72frd+pe6hPq4epO7fLvmvJk9lf73P/cAnEFKwhTCvoKSgrxCAcHBgVlAw4CsgBt/zn/TABHAogEjAa/CGILAg6TD80P0w+uD6ANSwiYAQ39w/po96Pxp+z46izrG+vR6v3ree518av0nvjM/BMAUgO/BnsJawoHCn8JdQjUBrYExwI7AQgAcv/H/zUB/AIHBXIHUgo7DesOMg9KDyQQ1Q+8C6wEyf5e/J76vfVR75jrQeu461brVOvx7JPv1PLf9mv7NP/oAc0E8Qf8CdoJiAiMB2gG/gQ9A5kBbgCr/w0AXAFiA3sFegfECWoM4g7tD4cP6w7xDmsNEQiAAJL7lvrW+Crzwux56sfreeyv68/r9u3u8FH0p/gn/SIALgIEBUkI3AkMCZQHdwZqBSkEzwKjAWcAwP+cAN0CfQVJB6oI6Ar5DUIQJxDdDpUOcQ53C84EVP6e+536D/eW8N3rFesr7GbsxutG7HTuDfJA9lv6qP0tADYDZwaSCO4IHwgJB9sFpwSfA8sCygGaAGgANgLuBAMHEQhcCTkMRg9EEDgPeA7hDqYNowjSAQ79XvuA+eH0+u5v60Hrh+wa7bnsR+2/79fzOPh/+979UwCoA4cG4AfGBxEHJAYABSIEnQO1Ah8BMAA5AekDQgZ4B5QIuQrjDR8QMhDzDmgOUQ7vCx0Gcv+c+0v66/f08hruB+yB7Hntnu3Y7cvuWvEo9Qj5IPw0/rgAugMSBvcGmQb2Bf8EIASsA2cDvwJ3AfEAhwKOBSEIAgmmCeAL/A6AED0PrQ3MDY8NognhAsj9sPtM+qT2ufEm7tbsQ+287fLt++0t73PyYPaW+dv7mf65AUUEtAVvBpYG6gW3BOID3gN+A18CTAGZAasDPAYECAAJdgoBDXYP6g+UDrQNWQ5WDfcHqgBA/Ij7Ffoo9X/v9+yR7XPuQu7m7YfuovAR9Kj3W/ou/Pj+ngJCBfgFkwWNBUkFRgRlAz0DAgMQAp4B/wKBBWoHeAjcCT4MdA6KD/0OyA2CDW8NGgsDBUj+CvvI+in5EfTc7hLtIe7T7mnuHe737lzxzPRa+PP61Pxn/6YCHgW0BVwFRQUSBXUE5gOrA0cDgQKUAgcE6AV6B8sIrQrrDI4OKA+iDgAOAg5cDa0JEANK/SD7gfp/9wvy3O0R7frtY+5D7oPu8O988vL1FvlA+3j9aQBkA+QEHAVWBZYFPAVnBMMDeAMEA3gCygIPBOcF0AesCYMLNQ3CDj4Pcg7FDfoNAA0FCL8ApPuB+tX58/WQ8DvtEO0Y7p/uxu4777nwX/Pc9uD5Evx//pgBQARGBTYFdgXeBWIFYgSIA0AD+QKfAiYDigRkBkUIRgpEDOcN8Q7ZDt4NYQ1+DfgLfgYD/6r6JvpS+fX0gO8W7UXt2e0K7sTuCPBM8Y7zMvfA+ib9Uf9XAuYEsgWvBf8FNgYuBcYDegOmA1MD1wJ1AysF4AaICG8KVgzODZEOWg5sDfAM5AznCg8F2f2f+Tr5Svjv8/7u3+xp7RLukO6j7+TwP/Jv9Bf4pfsO/uj/YgLABJAFmQW1BcQF6QTTA5IDwwOJAzoD8QOHBUkHEgkRC6YMxw1QDgEOKQ2bDE0MtQmJA6f8ZPl3+Qv4O/Nn7rHsOO3I7Wjutu/V8DPyy/Ts+MP8/f7cAPcC8gQBBnUGqAbjBWsEeAN/A6EDUgMbA+EDeAWtB/oJtQsWDUIOzg7wDa0MzQykDLgIPgEl+435hPl59jbxee2X7Abtvu0O71zwF/FO8mH15/l6/V3/3QDwAiYFdwbZBn0GaQUzBJoDvwPJA0wDOQNiBG8GiQhaCgIMcw2HDrMO1g3JDIIMrQt1ByEAMPoB+Vz5GvZH8LjsK+0/7h/uqO5Z8LDxsPKU9Yf6UP7I/wkBVQO4BZgGlAZmBmUFEgR7A+wDAgQuAxUDZgSrBvsIpgoFDDQNbw6lDm4NbAxjDGILJQZ7/sj5Qfmi+HH0Pu+37O7swu167pfv1/Cw8X7zAfc4+2X+//+qAbEDrgXCBpkGBQYlBXEE7QOdA6gDxgMIBNgEnwYbCe4K7AsjDXEOnw4EDd0LYQwfC1IFO/3Y+AP5UPjH81/ubexi7Rnuj+7E72/xlvIm9NL3G/zt/mMA8QFiBDAGxwahBvMFDgUdBLUDpANUAzwD2QMyBRcHMgnuCi8MYA2hDi4OJAwnC7kLmAo+BBT8Svj++Jz4w/Ok7vHs2+3J7mDvPfAo8VPyivQ7+ND7VP4vAFACngRXBhIHtQb6BSYFfwTRA30DaAN1A7cD3wSSBx8KJQuZC18NPw9aDroL6grmC9QJggLk+jv44viO93Ly2+3y7LvuZfBy8ErwLvFq80T29/h/++f95/+oAdwDCwY8B2kGHAWgBMcE6ARFBLQDewPqA+EFqQjHCk0LkQtoDRUPPQ75C/cKRgs9CMwANvqe+IL5y/b38Fft3e317zfwwe9b8MbxgPOv9c346fu5/QH/6wDlA1UGvQbYBfUEAQVLBSkFoATNA6ADaQTYBpUJ0QoUCwkMYw4vD1oNZQtjC4ALAQcG/2v59fib+QP24u8r7ejuQvHH8Inv2/BG8/r02fUH+KL7Bv44/6kAigMEBnEGuwVLBU4FbgUpBZ0EzAN2A/EEmwf3CaoKNQsQDf0O3A56DCgLuwu+CvIEW/14+Yj5evjS82/v0O727wLwy+/B8FzyBfMe9Of2s/lg+538bv+XAmMEGgWnBUQGAQY5BdsEqARFBPsDFwQtBUEH2wn3C20MQQ2fDtUO+wwLC0oLAArNAxT8h/hm+Tb4+/IN7yjv2/Dz8D7wOfGU8ojzu/Tp9qH5DftO/L3+2wGPBFwFYwXCBT8GMQYqBVYEfgSKBCUEswQtB2oKkgu8C04NIQ8MD2QMJguoC6gJJgO6+7P49Phj9+7yXe8k7+zwj/FP8ZTxkfI39Hz1cfeU+W/74fyS/pEBgQSrBWAFYQUrBkYGDwUvBCoERgTbA44ETwcfCj4LewsvDSUP4Q5YDBsLbwtJCccC4ftM+TT5xvbw8RHvuO858Qrxw/DK8RTzMfRc9fT3h/oB/BT9Gf9ZAr4EeAVJBZEFIwYQBvIEAATCA7YD/wPdBFwHognBCowLAw30DqYOhgwSC5wKuQgIA6b8bvm4+B731PIY793urvD/8TXxnfAH8mH0X/aJ95b5dfxv/uz/GQIjBbsGHAZ9BRIGaQYQBWID/gKHA4UDYwTEBiQJbQolC00NtA7GDdsLegt+C9QHOAFV/DP7Jfpj9tvxPfDr8C/xtPCY8MnxvPK/81T1+vfI+rj8Tv6IAMcDOwbFBgQG6gV+BhwGFQROAkYC9gIoA6IDzgWHCDEKywqEDFIOBg7nC64KAwvRCM8CqfyJ+p76YfhX89LvDfBm8cTxsvDT8Bvy4fP19Rz48voa/fT+OAH9A3MG3gb1BZEFxwWCBfsDawImAj4CqQKdA7QF4AcWCboK0Az1DawM1wqICyMMzAj0Aen8Zfz9+xT49PKZ8D3xYPGB8F/wK/EH8pryy/SB+JT7Ff0b/kcBQAW+B1sHwwWOBd4FhgVoA80BQQEkAeUBZQPWBS8HAQj+CSYNrA7IDGMKogrICwEJ7QIr/t/8v/v39xT0ZvIc8kTxmvA48TTyTvLW8jP1z/iC+6f8av6JASMFFQcCB1YG/QXsBRcFYAPjASoBugDuADkCDgUcB6QHrAgiCw8Osg0aC5oJzQkxCQUF8//K/D37Gfnl9T7zFvJu8evw4vAd8Q7yA/Pc9Iz3g/pv/V//ngE+BPAGEwgvB8UFEQX8BMQD6AFGANb/1QBuAqUEVAaIBxYJXQuhDWkNkQuECicKiwhtBEYA8f3e+7/4M/Uk86ry6PEw8f/wp/Gq8q3zlfUY+NH6Gv1T/80BSgRWBiMH3Qb0BY8F5AShAyYCxgBkAHoA0wEMBLIFrwbSB1QKqAyTDOUK2gm5CbkIAQWDAIf9uftW+nT3R/RV8vbxEPO88hjyXfIN9Nj2y/jq+vX8Tv/gASAE/QV4BlgGvwXzBO8D8QL+AfYANQAsAHwBeQMtBf0FNgcvCQgLUAtHCroJ8Ah9B5wEkQEw/7X8XfrU99L1lvTE8zDz6fLz8qfzsfRb9qn4u/rt/N/+pwFYBJQF0AWABdEFhwU0BOoC5wFbAZIAWQCJARMDgwRCBYAGkgj3CTgKEAm6COkIeQdKBAcBsv9J/jL78vdQ9u/1JvUj9LvzNfRo9Or0n/b3+BX71vva/dwAwwNsBSYFjAWzBfQFcwX+A5MCXgF4AWMBFwGXAQIDEAR3BMMFvAevCAAIXgfGB3cHIAVxAp4Agv9R/T364/fR9sv2xPWa9Eb0DfW79rf3yfhk+rH8Av9jAFACZQQbBWcEeQN2BPwEcANiAV4AZwHaAZgBzgGoAggE/QRIBnsHrAcqBz0HgQetBkEE6wFzAIr+zPz++rf5SPgl95T31/ec93/2efYi+J35XfqS+h78lf62AD4C0AJsA6cDPQTfBDsEIQP2AQwCYQKmAicD6ALTApYDjgXRBv8F0QTrBOoFuAWfA8UBIQFSAIj+pPxa/Pb7JPp7+MP4+vn/+Hz3n/c7+ar6JPuw+7/86/7vAPQBMQJoAlcDCQTnAzUDigJzAo0CPAJiAp4CwQLLAi0DywQ4BSYFYgUWBQUFzAPpAs8BAgAU/x/9f/x8/En7w/nX+KX6A/sb+Xn4z/lV+yr7bfsZ/R/+xP5lAIMB4wGZAfQBBgMtA8sCjQGiAZwC9gKEAhcC0gI5A6kDkwTBBDMEQgSnBAUEiAJ1AcAA4f6O/V/9C/1O/AP7Vvvv++n7Xfsl+pH6fvsK/Gr8v/xf/hr/i/+4AW0C7gEhATMCywNuAnwBKgGtAWYCiALWArwBEAJMA9EDAQRZA8ED4APaA2wDRgJoAdwA9f8a/uX9nf07/Lb6Y/t0/Iz6jvk++rH7SPsS++T8uf3D/av+FwG6AbcA2wBIAq8CYgL5AbABdgGxAXsC7gFYAmoCxQKGAycEsARsA6cDUgQhA1YBdQB9AHr+kfyH/fv8mft5+778xvyq+678JP02/Zn8w/2C/uP9yf4K/yEADQDv/4UADQGeAWMAOgBnATICSwHKAGcCHAPyAh0DPwNNBLEERQSCA7cCWQMTAdn/tv+2/j/+IPxj/f/8lftN/VT9nfwJ/KP9CP9d/RX98/4F/4f+pf6v/0EAkv6v/3wAZwGmAE7+BwIVAncA5AAqATEDEQFjAnUEYwInA/ICDQQYAqwAeQKn/xf/kv6k/p7+6/vD/Yr+jf0K/h7+Y/6r/Qf/Tv97/Zz+GP9C/17/jP/k/yz/SQBgAe7/GwBIAYAALQDOAEQDRgFN/yUDewSCAvgAbwMCA6UA9AHeAeL+b/71AEL/NvxQ/SoA4P3G+6P/+gD+/VL9JADVAXP+7P26ANv+t/8D//H+XwBN/m//HP/TAHD/4v2wAb7/wQDUAB8BcQOkAJECEgWFAtAA5QIWBFwB6P6aAOwA6v7H/Tv9v/4y/i/90P1L/1b+B/5NAPD9Rf9PAHD+BACr/4EAjP/VADECFv7SAVUBDwDCACj+SgKcAMD+TgBiATwC5f7pAREDhv+mAs8C9//6/2IAlgHP/j7+Gf+k/aj+dP7Y/S3/YP78/SYAFP5qAMv+Av4iAuP+cf/q/ygCrwA5/qoBiQFgAET/RQCjAPcAGv/Q/p4BnQBeAOr+pgLLA/H+ogCMAjMCbf4LANECtf0Q/ab/GQAo/Hj8rQEn/Yj9HQBh/TICT/7r/d0CCP+2AJX/xQCUAsn/sABeALwAyQFTAFL/FQD5/xAApf9E/x0AOgBHAFQB7gHRAKwATAKFAjv/FQE5Av79yv4CADf/SP/D/Ar+JwF1/Qf+wv4H/9L/jv7gAJf/RP9SARABuACwANUA0wEvAZ0AuQEoANYAt//V/50BB/8qAPX+MABxAdr+yQG5/wIALwHy/nABJ/8a/jsAU/6l/kn/rf2j/wT/jP4AAbz9XADuAE3/YAGF/pcCaADD/isDFQBkAEIB8QCaAM7/6QCZ/r3/I/95/ywAuf2QAFb/bAGFAJn+YQKp/yEAUQHT/k8A7v/t/Ob/uv4d/67+/PxfAr3+yP8BAfL+lAKEAJsAPwJNAPQA6AGoAfUA7gDBAZMAhgHp/3sAbP+X/ucBWfwpALP/lfx5AW/+Tv9Q/+z/YwB1/i4ADABF/z/+mv/c/5f/UgCW/gMBbQEMAJsB9ABsABgBfwHhAXQAP/8tAs4B9/+nACoABQPv/gr/VgKJ/tMAAv1lADb/H/z7Aan9lv5l//z+SQAU/hT/2QCp/qn9YwAY/pL/Sf+x/qABOP7nAcYAsf4CBH3/OgGJAv39LgTyAJ3+8gGhALECOP4NAcoCFP2YAP0AH/8b/+T/6P7D/6r+4v0BAu76lQAwAF38UQKa+rUBJv88/dYAAv19A0f9b/77BAD+NQCJAggA+gL+/3gCCwK7/gcFjP88ASgCFv2vA1gAhf2TAcH+sf7aAKn+nf/w/qT+CwI5/ZQAxgA4/a0B3/2MAHL/jP3wAZf+WABf/5//GwLw/DUADgGIAGoAEf5fAuQAAf+T/4UCeP+I/+YCs/9+AT3/EQHTAIf+xAF8/x4AU/9/AK4BJ/0BAsYAQv/zAPD9WgFi/lv/fgC2++L/A/9c/n7/sP3HALj/OP/LABP+3gLxADX9BQQrAN3/7wJZAN4BUABpAzYBuf4EAhoAgv/C/oL/Tv8RANn+ff7/AHT+VgK1/e4BeAEd/aUE6vyAAmD+4PwDA3H89v8R/o7/CgDl/agAGf7jACkAtP2gATj/9gBv/y7+pATk/pcAMQF5ALwD/vtGBRcAcvwXBon7rgIn/mr+8ATZ+x4DrP5KAegB+Pt9AVr/gv8h/S3/uv///Jr+fv7e/4z+i/+e/sEBywGT/JEDbf8qAXUCn/zFBYP+hwFOAhX+UAW6/F4CMwIi/N4Cqf+RAHr/bP0yAxYAYf7lAT3/fwBDAUEAqv+tADkA7f7OACr9jv8b/5X+rf7t/LECPfsXANUB3vynAgP9TwKQAUv+5gGl/nEDsf0EAsACRv4+BaP8AQMgA3r7AgOkAXD+EwD2A078WAAPA5z7cQJb/17/DQBp/E0DAPtE/dwCk/lHAff9ZP7WAOX85wE9/X0CeQB1/WYEiP54AcUB8/6SAdwAIwCCAKkCg/yUAqIBc/yKBPj8DwGNAs/8ngLS/uv/WQLG/qYAHAA5AIoAwv4iALz+mQBU/zr9x/+t/1P+bv36AOH9DwAaANH+ZgJq/B4Ebv/s/pwDq/yxBJr9igFgAWL/PwPC/BEFHP80/+kBbgBXAgP95gJi/5MAfwA2/q8CPPyLA+j9RP4JAxf6pQJZ/a39wQDk/RABPP1uAaT/4P5IAqz+PwGmAUP/EwOP/l8BxwOB+3kBCAEK/9f/Ff9zAST//v8IAE8Bjv9s/2QCcQBj/3ICEf9vAKMAyf02AzH9vf9ZAGb9MAEN/Jr/wP+z/Uj+2/8JASH8igJn/tj+wAI0/cQDofyFArECx/rmBHn+zAHvAM/+KQTQ/j8BIwBLAWkAEQEKAQoB/v+t/jQEnPuGAEQB+fsMAur9x//7/KkAZQCs/D4Biv5vAcz/4v79ALIAdgAP/3IBvf8qAHgDRv0kAZUCSP6kAlL8GwIAAKv+6QKl+w8Fm/zY/0YEf/t7BOD8nAPC/t/8yQVJ+5YAP/9EAbb9O/53AnH9EgJn+fED/QBW+nUE+vtoAyD++//NAZP8MAVs/PkAagJl/YMDxAAi/vIBQAA2AGD/ZQCUADX/8gGB/5MABf9kAEIBX/0aAEwAXv6C/nr/NACv/or9XgKh/z78pwTP/Y//PgMn/vsD6vtkBLYAFf5ZA6X95AUk+hsEngIH+kgGBvukAmj/FvzTBP/6qwJh/uz9OQWO+k0EDvx/Am8C+/h/BQX8DgF9/sL+hgFN/MsDvv3h/zP/HwF7ABz/lwEM/RIFq/w/ACYCzP3RA//8jAUd/LcBQgPv+70DNPwNBOz9hv4wAl7+wAG5+gQGzfw2/ygD3vrpAzr86v5pAun8IgChAaD/X/+k/zwDT/5u/8ADSPwNBrj8LgL4AlX7pwWf+BMHv/uB/2IEsPm8B374GwZN/on9UAXz+cYFMP2PAC8Apv/mAMX8QwTA/JoBNv2N/2kDw/iuBT76qwFvA3D2DAkw+AAE2/+/+RkK1fcKB2D+tf6lBCX4Dggb/FX/GQOG/iUDgfzaAmUA9/8fACoAIwNB/ZsArf8eASj9cv5LAqX++P/9/O8Cwv2I/0oAbfz2BPL6dAFCAaL8DgO1/YQBH/8yAUEBhv5hBN79s/9EAjsBffziAV4A4P6nAzj75QW4/bUAzgP/+5AGefmyAwMBbvtEASL+6wPV+HgC0gEC/EUCxvzYA637v/5dAu/91wGB/CoEAv9YAFoCJv53A7H7TgRn/9P9UgTq+xIDxP25/wMCgvz1AgT+xAJdAOT9igI0AKL/kP7SAQL/oADq/nn/1QK++tICmP+O/iABjPsvBzD69P0DBc77XgNS/ZoAUAPw/QoAPwA6AOMB1f71/7sCbf2iA1P9n/67Atj86wH8/gkB1QBrAIj+x/+VAQ/8cQLs/vz/ngBo/qsFrvrAAf4C3vu9A1n82P+7AGP/LwC8/hMCkP1cA/j9DP2WBC3+7f6//5wAMf/l/jsEB/zC/5wCNP0mApT+/QHb//P/fAHk/h4BAv5qARwACwE1/ZIDCgEu/aEB//yWBHT70f8yAYX9sgLn+kQEr/xGAXEDbvqQBsP90/9BARb9HAIp/mICqf5DATABxP1UAkP94gJ4/hz+GAZk+vkCDgIK+8gEUf1pABgB8P8mAfX8UwUj/jb+ngFx/lACOfy1ACYBJP3QASn+fwENAPH9OwTn/f7+/P/bAXL+gv0GBQT9EgBqArb9BQIUAFP+yABB/twAp/+0/ucAyv/1AGoA9P9B/kAC8v6B/3kAcv/dAjn+YQCZ/woATQCO/ZIBwf9p/63/iP9MASv+8QArARsA3v8Q/6YAoQBb/3T+oQH2/UIB9f7D/mEDuvudA079uwBnAZP8gwXC+l4ER/87/0IElPrLBB3+TwH8/6/+6QLW+uYDsPwJ/zcC2f6pA2f78gQEAR7+gwFD/gwCA/9b/xAAUAJi/FQBO/+H/j4ByvuxA4z8HwAlAKn+wQLZ+5gDEP/4AG4Arf2DBBX86AT8/V//bQVx+lcEtvzr/uQD5PpzA0L/n/7PAgD/eQBA/5EBZgAl/+/+YAHG/1n9AQNZ/IEC//9i/cYEQvqVA9/+cv9YASb8vwRo/oEB7/8s/j0EC//u/lcAWgLw/jv/QAD1/xQBwvsABJX91f26BPz7xAOD/csAxQEz/AMDY/+V/zX+WQFN/rj/zwDP/tYA8v37AbL+P/5lAin+dwAHAlP95wJq/97/zAAzAC0EPPo5BWIAl/xbATD9ZwSx+9YCQv4sAVQCCfkmByj5aQP1AHv6cge4+QcELP3T/7UCNfuSBNz8DQKf/lb/zQHY/OoCsPx5Ar0AK/43A4L/3wCxABECpfx2AoIBdvuyAm3/IP6xAYUAtf7xAP0Aef9Y/30B8P02AZ7+vf5EAn77LwXM/FIANgNz+0cEiPyJAC3/Nf2YA7r82AEA//4AXAJ++3wFZ/3ZAEMCj/2kBOT7SQMT/eT/GQOM+0UEF/ykAkIBs/u3BDT+4ADN/3T+rQBs/s0BWv6//l0A3wCw/sYAbAGc/EQE2vzy/xgBsvxSBCb7lwOD/07/FAQ6/DkEiv40AcsBcvyMAXn+DgCZ/rH++QCGAeUAJf3sBFb/sf4uAUX+dwEM/48A9v6TAaH99/+nAMX+5gDz/DEC1P4HAHQAC/1aAp3+FwC4/tUAKwH8/DkEbv5wANQDi/zjA9n+/f0SBHX6UgNa/5L95ATU+4kClwAeAFcBaf4aAwr9kQDa/47/CgAg/cgCmf0fAcH/Rf6pA8T8+P96ADkAv/9I/XMDnP9GAPP+HgHgApD9IwL6/ooCvP9H/zMCHv2RAhj/nP/+/xb+QQIYACYANv9XAO0Anv0wAqL+ev0iAu/8jAEb/r//dAOt+msCtf9//nsA0f72Atj9yQDT/nEAxgJY/M0BiQAuAZH+v/9sAxT8bgOy/qoAygES/YICyP4UAFr/ygEH/pcBOQFK/CcELf3XADD+9P/gAsj72AIT/3oAVQEZ/QIBXAH7/dL/yABdATr/XP9QAj7+eQFg/37/mwAjAMT+IABnArr7uQJ+ALn9hwNF/NUC0P/B/cYDWvxnArT+DgDoAQv8awTu/T8Ab/+u/m0DdfybAVP/qQAKAbb8gwLh/ez/l/9XAREArfyfBLf9yf9hALb9KAPa+wwC+v9I/jACQ/3PAxv+fQHYAXD+EwOs/UQCEP+9AAcCGP7TAN//VQGn/k8B8P7E/zD/9v9EAXn94gGD/oEBoP4r/w4CqP+AAJP+BwLd/n4AAv73ANP/8P23ASP9GgPM/L7/0gKp/DoDzv0KA4sArf1UBKH8PATs/LUAnwLv+5IEPfxbAkX/+P6iAmn7twMI/mv+JgBy/uQCift9AuoALv31AoX6SgWM/aH73Qi8+hcAsP/xAH8AA/tGBbH9GAEqACX/rAR9/GkCsQBYAMgARP9TAlf+zgAi/0UBtf5nAMsAt/xoAxr9zgEmALn7OgXz+1kBUADA/UsFJfpHA8cBM/s7A7j+mQAJACn+YgLx/uP82AIH/6f+fQId/pgBWP9zAQsC1P34AY/+yQA1Aab+XwCsAUP+BP8kAwf9wwBJ/z//oQEE+1wChADi/BgB0gD2/dAA/gIU/L0CyP/m/UICLP6fAeP/wwCL/1MBGwAp/k0CnP9JAHf/UwFBASf+BAF7AKf/4QDI/ocA8QCc/n8AnQBy/5H/GQCu/4//x/+K/1X/dABUAA//vAEhAF7+ygFd/zD/hP/a/kgBfP5gAI4AIv4SAU4Asv5gAS8AgQDJAF/91AJ/AAv/WAHP/voBn/7Z/2MAq/89AFX+WwLY/cv/PwE6/+H/S/4fArf+Jv+FAVX/ygCyAKn/NQCiADj/7/4gAZQAw/wBAS0Dg/6u/xn/2AJi/9z8zwOe/XUAg//W/zMDgfsDAz4ABP5mAcf9AgOk/v/9fgIH/4AAAv9vAZICSfxTAQ8Czf25ATv///4oAfH+nAFL/xL+EgOq/Uz98QHl/rf/dv/lAN4AN/+zAPIALACH/gcCFQCV/rcAgf+mAZX+LAD6ALP+uAEt/lIAHQGQ/xj/Rf8CA1b+TQAXACAApQFB/U4BcgHx/mD/kwDIAP3+7v8NAMn/GgBr/ywAwABx/hkA4//M/iAAJwDXAEX9WQODAFL8JwQe/hkAjgEb/UkC5QBt/RwBJQIx/hD/NQI3/xr/+gKl/v//CQLY/jwAWP4BAhn+rP4UA3b9zADn/pP/nAFh/qP/FAA+/07/6QAjANEA3f48Auz/DP5ZA1L9KgMZ/6n8XgXi/HYAKgC8/xcBxfxxA4f+F/+XAmv9agHo/8H+QQLO/gYBhgCd/gwBpf4zAOH/a/8FAcsAQ//k/pkBJ/5w/yMBIP7BAM3/MgDeAGf/MwGk/1z/SAHd/p//PwEdADEA8v/aAKL/+v6VAG0A2/9F/Y0BKQIA/VQAdQJt/4L9SgJnAKj8qwK2//b/CADU/vUCtfzyAPMB0P5yAVn+lQHH/rP/1wD7/tQBUv9rAFH/7/+DANn+yf+Z/xMBFP6BADkBFf/O/+P/bwHg/TkAkwA0AOv/1/7iAdD+u/9iAIcB+P9K/lACpf+x/zcAyP8WAMP/Nv8RAFcA3f04ARcAJP+wANUAqQDr/usABgD6/0EAGwAkAB0A1//7/sUA4P/x/gj/mwDS/0j/lAGP/4YADACi/8MBqf5iACAAswCnAOD+swHr/7v/l/91Afj//P5aAKv+zADj/pgAEwB9/vkBOv5A/8QAoP9tAU3/pADh/83+MgEq/2D/tf8hAMUAf//U/9sAJgGh/k0A3gDk/7j/Ff/NAlb+F/92ATb/OgBQ/kcBIgDz/gsAV/9RACD/EgDkALr/7P6JATkAZf69Adz/w/+YABgAAQAGANYAMf8rAPP/NQAjANr+kAGE/9gALwGz/2MB8P24AGYBOv5z/oMAhAEb/VgAzQHF/qn+wv8PAXz9ef+mAMv/t//G/iwCVf9VALkBmv/aAP7+cwED/+X+MwEU/+gAJAD7/2gAeQBUAIz/zQDXADH/IgBiAUYAnf9Y/8sA4P/g/o4Apv+x/hQA4P+yAE//pP7uAmL+9/5pAfb+hAC+/joA0AATAMf/fQDSADj/HwD3/6v/RQDzAPb+iQDCAEj/rwB1/4EAvv9sAPP/8f4aAWf/VP9lAIz/Tv8zAU//HP8zAeX/oABb/2j/ZACb/9P/EQAeAXX/k/8SAm3/bP9nAaL/Wv+1/37/g//d/2wAb/+L/ywB1//U/20Axf9V/2YAZwEF/8YAhP9fAP8Ay/70AD0AOwDs/xEAcv95APz+Mf9nAJX+zAF6/uj/wgA1/4EBhP4QAdwAov9VABAAYADI/xAAff87AXn/OgCWADX/JwEM/xwA2QCd/m8AoADC/sAAlQBY/wsA/v/a/37/agAJAIP/Vv8RAGAA4v6F//3//f+8/r4AmQDM/+YA0f8VAoH+M/85Ak/+rv5mAbIAG/9XAFoBpAEc/6L/FwKY/8j+JADo/8n+Lv/VAPT/2/9MAF8AtQBp/uv/xv82/xYASv+YAN7/9v/I/5AAtQDj/nn/4f9kABb/SQDGAEAAyAAs/zwBqABC/+H/PABT/3T/7ABg/44Aef8QAAcBy/7U////FABK/6AABAGoAMQAh//jAEAAMwC8/2j/kAC7/+n////q/8r/wP+bAB//MAAnAYb+iwDc/5r/VgBK/5MALQCq/ygALABm/7n/Sf9SAED/Pf8XAZ/+TgCc/0cA+gDI/tcACACYAHAAUQByAaX/5ACSAKP/sABX/yoAQQD8/uAA2P9i/4MArP8OADv/5/8vAOr+mP/y/z3/Mf9bAJ3/jwCX/5X/MgER/0cA7v+f/+n/ZP8jAWoAqP8qALcAaQEdAJr/JwGTAGD/IwA8AAAATv8K/7sABP9X/wgBLADD/0n/yADEAEwAyv+NAFgAi/4iAHEAxAC+/2MAdQETALsA1/88ALYAev+O/zQAav8K/1cAqP+n/6b/nQBfAA//5QBi/9n/9/8r/zgAgf7q/+T/s/5V/8n/Qf/k/kIAw/9eAKf/RADCAHv/yQBP/wQBzwBw/18BeABZAH4AcgDJAIYAHgB7ABAAmABTAJj/nQAK/6r/WwB+/wAAgP8OAKIApP9i/wQAKv/z/mr/AP+E/4z+o//A/zf/oADj/4MBEACV/0kBif+2AK7/JACGAcT/QQEhAF8ANAHn//QA4v8jAAMADQBJAIn/1QBe/+T/4f8q/2IAE/9JAHT/0P+aAMv+NQCj/7D/lf6w/gwAvv47/0X/9v8AAHkAuwDFAI4Ajf8PAWUADgDV/4gA1QCa/hIAIgEmATwAXQBfAXgA6/9y/5oAOwDb/lH/6f+c//D+xf/o/+7/MQCgAN4AEQBoACsAqgAeACT/kP+J/xAAhv/Z/xEACwAVANL/SgBaAL3/yv7C/87/V/9a//D/aQBg/88AoABEAFsAHwDzAEUAzABLAGkAaQC6/4MADABbACcA9/+8//z/bgBG/0oASgB2/5L/oP/r/+L+Iv9w/xj/UP/6/oP/dgA+ALj/NwBKAB4Adf9D/wcAdP9AAFcAmQAgAb0AlQGZAK8BnQHwACYBAgCWANsAVwGTAEAA1f+fAFYA9f6w/0f+DP8N/nf+j/9c/jz/XP5L/6b+cv5n/8H+dP9M/0oAXAAOAUQBNwBQAa4AnACrAG0AqgCVAC0BwQCbAYgC/QFJApgC9gFBAQQBewBWAFn/9v9MAF7/I/8W/gb/I/5A/WX9/fzp/Ij8fv3I/T3+Jv6w/oT/Rv8CAP7/XwB5ALoATwELAVUBpwDnAG4BQwGJAc0BFgI+ArMCagIoAwADPwL0Ao4CqALeAeIAeQFyAGz/mf4U/j3+i/z4+737d/tL+/v6zvsT/ED9av3s/SD+pf3I/uv+vv+2/6QAWwE5AfUBBgLcAvMBFgLNAegBfQLaAYsCVgIgBL4DfgS1BQYFwwToAvwCcQHwAKH/0/46/jf8kvwn+1X7Gvpl+Y36GPlY+tH7U/tU/Er8XP35/i3/dQDgAEQB7gAjAdEBcQFxAZkB4gEaA9IDXgTvBD8FgQUMBdIF8wV9BcEElQTnBG8EMQNvAX8AZ/8D/lL8xvvi+Rz4Nfgv+H74cPck+Hv4/Phm+nX7mv1p/Yj+iQDMAUwC0wH6ARMBkwCMAPAAwwEQAlYCMAOuBeUGKgeuB0kHFwd4BnYG7QVOBQAFIQQZBCYD3QFZAOT9fvtu+az3+/U39DD0EvX79TT3Ifgq+tP7Jf0P/hT/tQA9AdgBSQIeA6UDUwOyAioC6AGdAQ0CPQIjA+UDrgTnBe4GuAfRB/8HkAekB7wGFgYoBWEDIAKK/7b+c/y6+p/5wfZ59o70pPM288jyjvRr9Q34HPry+x7/EgHbAiYEowT5BK4E5wMLA3sBwQBYAKkAnwEkAu4DvwQ4Bb4FqQbwBvIFawZFBo0GawaQBuwGHQUiAysBi/8p/VD6Hvgu9qD0EvQM9CX02vP/8/j1Hvd1+QT8Q/5EAfQCRgV+BoQG3gWpBBQE7ALaAdcArwBdAcEBvwK2Aw8F/AX1BTMGNQbCBowGLAakBr8GeQY6BZkDagH3/nz8K/pB+Fn2bfUr9Az0JfQy9Ef1n/Us91z4+Pr5/TEAjwNtBSEHRQf8BmQGagRlAiYAdv/n/hb/JwDHAW8DGwTRBaIGMwZFBuQFkQW5BX0GWwesBr8FkwSEAyYBu/0M/FH5mPYe9e30SfV39GP0DvUI9gL37/dF+mv8N/4XAbIDNgY7B24HQAftBWYERQJhAJ3+q/0O/tz/7gFVA9IEhQYOCAgIqgepBs8FiQUxBZ0FMQWABIQDsgJKAXn+0fvg+If2a/Qh86fyo/K28470dfbI+LT6/vw2/1oBkwIABFAF5AW4BbwEHwTWAkUBKgDk/+P/Lf+Z/4YBWgOXBJgF4gbAB7sH/wcgCIUIYggCB1sFRgP0AX8AMv6p+6P5ffj79tL1mfTy8//zwfNm9I71bffr+cj8TgBIA2AFqQb5BvIG6AUwBHQCzv8P/jv9o/1P/0QAtQEbA+4EjAbiBmEHhAbyBYIGUQceCKcHDAfvBa8ENwPrANL9J/p69+P1IPVQ9Ar0KvQf9Nj0svYl+CH5GftB/RMAngL5BM4GBQfFBiEGCgXYAokAyv5i/fz81/2W/1IBhgJ3BBkH5wcfCPEHcgddB+YGuQenB08GagRtAqsBd/8K/W768vf79pb1EfVw9Cf0vfTV9Ef2zffL+fT7A/4/AfAD9AUwB6IH5AZBBXsDCgGH/rb8jvwA/br9i/+jATkE9AVjB3cINQjEB2EGygblB7IHDwdnBTsFFAS+AbX/Cv2A+gj3QPU39X/0h/OK8zr1SfYk90H5+fqT/OP+vgF7BIAF1QXvBW4F/QMUAsoAdP61/Ln83/28/+sAqgJ+BD0GmQfTBz0IKAf5BSMGIAe7CHgHvgWeBHoDOwIr/3j8X/mO9hX10fQj9WD0XPSn9S73S/h3+fb6tPyL/vQAcAOnBDkFmQXNBU4E/AFVALb+Fv0R/AP9lv5tALMCswT2BmsIYQmFCfMIVwgqBzYHOAeCBqcEJwKLAZMAuf4f/Nj5s/jy9vf1gfW79EH0ovQ/9pD3nfhz+rj8vf+lArYEvQUoBbYEHATuAuwANP7w/On83f2n/tP/JwJUBFoG0QfJCAgJIQhuB30HhQdEB6oGQAYGBSoDDQJQACr+UfsS+Xz4ffdq9kT1D/VV9Vf12fb79/r4ZfrM/P3/3gG+A8IE9wSIBJUDngJiAPf9d/xo/Bn9qv7sAJICEQQ7BpIIowlOCQoI4gZtBv0GrAfMBgEFiAPEAhYCzAB0/mn72PjZ98b3TPf89RH1f/US9l/3o/ig+U77Ff0nAJQC8QNvBKsDcQN/ApIB+f/r/Rz93/ws/nv/RAFlA78EPQYYB44IIAnoB6gGMwa3B5IIHghTBskDdgL3AN3/ff17+pX4QffT9433Tvcs97r2UfeF9974NPnz+U387f7vAdMCEATKBEYEdgPtAb0AEv79+/D7Cf1r/sH/gAK/BJIGsQgeCkgKvwhtB0sHTQc6B3YGvQRhAv8ASwF8ABH+Zvvf+aD5Dvkz+BL3IvbV9QH2H/cg+K/44PlE/N3/hAIDBJcEPwTlA+0C5QHk/zX9g/uy+4/9Ov/0AOoCAAUmBw0JJwqvCb0HTQZYBh0HXQeLBlUFRgMMAu8BEwFY/qX6A/nY+AP5Q/io9gD2DfYw92H4GPmX+Uz6tfyN/xkCPAMuAz4D2wJnAlkB6//0/RT8Efxy/UP/lAALAiIEggbACMAJNQkJCE0HfQfoB+UHPwdDBRYDqwErAWEAHf5r+yn5y/hU+Q755/dz9j/2DPe990X43PhC+oj8OP8IAqkDNQTbA+UCGgK0ADr/Wv2X+yD7OPzZ/soAjwKlBKoG9ggQCi8KZAhsBlUGNgdcCGsHugXLAxICYwFhAI/+cvtG+Yr4UfhM+Lv3//am9hz37/e1+HP5o/pn/PD+twF/AyEErAPqAvwBkQAP/1v9/vud+3H8rP6uAJkC4gTWBuMI1QnqCfYIPAevBrcGbwcPB4oF5gP0AU8BfgDv/kf8sPla+Sb5v/h693v23vYl99b3Hfjj+Hf6rvyR/8QBsQMsBK8DygKJAVoASP5p/P76Avu8/K3+ogBIAukEjQfsCJgJYgm3CDUHoAanByIIgQehBTIECgONAW4ARv6G+0T55/ht+cX47vcY9+X2ePcz+Nv4/fib+bP7m/4pAX8CJQM9A8gCegJ+Adv/uf3S+2f7Tfw//tL/MQExA5AFCwhBCX0J7gikB3UHDwhBCdQIagZRBI0C5QHHALn+z/s4+fz4mfm9+b34pPdU91n3oPcD+En43vib+rb9wgCuAr8DzwMTA+IB2AB8/0f9Y/t9+lb7bf3b/xsCwwPtBTMIuAkICvUI0AeuBu4GbwjvCJsH8ASHA/wCKgJaAEL9dfrO+Nb4APk8+Ff31/Yg99b3nvhe+a75bfqC/Fj/5AH7AvECeAL9AU0Bxv8X/nT8VPtN+478/f5QAfcCxQQUB2IJWAq7CYkIYweMB4YI/QgRCMwFDQQJA+wBGACo/Vn7R/l++Mb4ovji9wn3MveZ9+/3vPiU+bT6EvyA/jkBtgLmAkMCgwFuAIH/Sv6D/ED7W/v9/Az/5gCqAnsEbQZGCGsJhgnOCNsHRge4B+0ITgmHB30EbgLUARQBp/6A+wT5GfjG+Cv5xvi992P3XPgf+Zn5j/nE+cv6wPzK/xECAgOqAvgBfAGgAKf/7P3r+7P6MfuR/fD/tAFHA5gFhQiDCh4LEQqlCH8HJwe2B+cHQwf5BK8CmAEqAcEAdf41+5X4DvgW+bP4Xvc19tP2YfhK+T/6x/qa++n8Iv+NAU4C3AH3AEkAbP/l/n7+DP2s+5X7av3w/+kBhAP5BJcGXQi0CbUJHAjoBjYHTwhDCd8IkQd4BUADxwF/AKn+hPt4+Fv32feZ+D/4WfdK9w/4gflc+pL6sPpd+279kP8NAX0BSgHdAEAADACc/0L+UvxS+1X8W/4QAGEB6AIZBY0H1wnYCv8JnQgbCM4IRgnMCDwHGgVvAx0CNwHU/3j9Dftn+R/5FPmi+Mj32/bj9nv3nfh3+bn5XfoI/On+PQEyAu8BNQGjAPn/Mf/W/Ur8U/uR+zn9ef+XAToDqQScBsIIGgrFCUkIRweHB+wIrQnTCE4HyQW7BF4DYgH4/gP8n/lu+Fn4evjc90r3Sffe99H4Uvl2+X75P/pu/Ov+lgDaAMsACgH/AJAARf+u/Vj80/uD/Jb9H//XAPACGgX3BskInwl2CX0I3Ac+CAwJtwnNCN0GEgX/AyUDMwFM/jH7Kfmv+Oj4u/jo9173wPeX+BX5JPkg+Yj5x/rZ/Df/xwBBAU0BMgE3AaYALP9d/cz7dPs//OH9rP8rAQkDVAXMB1MJlAkZCV4IQQiaCFAJiwl9CPQGawVaBA4D3wAb/hL7GPme+Az54fjB9yz3cvdL+Kn4f/h++Pv4m/rJ/Ov+YwALAU8BVAFFAY8AAv86/Qj8E/zx/Gn+JAC1AZoD4AUhCB0JjAjmB4QHcge/B6AIaQm5CFcH9wWSBAIDtgAP/hf7zvhT+Kb4oPim91P3PPgw+cH5aflE+df5HPv8/IH+m//4/00A9QANAXkA6v6H/cr8c/wP/RH+xP9vAUwD0wXWBxUJJwkSCbsIJQhOCNIISwlhCO4GvgU/BLoCyAB3/qr7efk4+XP55fiQ9+D2ffcd+GX4OPgs+AL5p/rl/KH+4//LAC4BQgGwAOT/pf41/VX8Ffwc/c3+twB9AvcD+AXZB+sIvgjLB0IHMAfqB9kINQnTCJMHegZnBeIDpwEX/sz65fiL+ND4IfhH9wH31Pfw+A35lfj295L4S/oA/FD9QP6d/8gAXAGGAQkBHgDc/rj9Ef0K/RL+nP8iAcgC/gSFB+8IMAkCCc0I1AgtCb8JoQmfCGUH0wVHBNwCQwHx/uL7QPpd+n36UfmA98L2k/bJ9vr2rvaN9k33w/lv/P79//4mAI4BJwL2AToB3f+w/qn9Xv3D/Yz+LQDcAa0DYwX/BmYIywiWCPAH5weLCOII+QiDCMYHGQY3BEkDzQE+/x78efqE+jH6FfmA95r2c/bl9oD3Hvfl9hD4lfrX/Pr9IP8wANcA6gCOAPH/4f75/WL9av06/n3/IAGGAhIEwgX8BqYHoAd5B4MHAQjlCGIJjAleCZ8IFgfxBBoDQwGy/uX7G/ri+fH5cfl/+G73Ivda9373Cfdl9iH3CPlS+zL9oP79//YAygHsASQBAADa/hf+d/3F/QT/NwB8ARUDMAXmBsYHPgghCNYH7AebCAcJ1wigCAMIowbpBHcD2wGB//D8Cvs1+hz63/md+AX3ffYC92f3yPZ99nn3Pflq+0X9yv6p/3oAagFgAdQA4f8Y/0j+mf1B/kT/bgCUAQcDvwSuBfQGJAhGCIgHMgeXCJ4JjQnTCMwH7gZiBdYDyQEz/xz9qvs7+4D63fks+dL3yfYy9nr2WvYY9tf2d/gV+0f9+f4uAPAAcQH8AHMAs/+p/qP9K/04/nj/zwBeAhEEmgVcBmYH3wdHB44GowaNB0EI6Qj9CCMILAdaBksF/gIIAMz9LPzc+rz5/Pgx+Cf3x/be9g73F/cH97b3F/kk+xj9Xf5k/woAfACOAD4AsP+X/uP9Cv4Q/0EA6ADNAdgCIgRtBTIGmgalBh4H0Qd4CF4JBQoWCgAJawcfBpAEgwLO/1T9wvsT+yP7Tvqd+EL39/Yr93z2r/Ve9Vf2d/iq+rz8Qv7Q/wkBcwFZAa4A0/+U/s/9EP6Y/nb/hQAMAocDpwTeBaEGGAcPB9IG5gZ0B6kIVQlQCdwILAhtB+AFxAMKAe/9sftx+tr5AvnF98j2Uvay9t/2ffYh9qn2gPhz+gL8iP39/jMAvwD2AAcBjADq/yD/1f5G////KAEAAt8CKwSdBb4G2gYBB20H2AdsCNUIbgl1Cd0IygciBq8E8wLxAGT+z/tv+qb56fi794X2y/XI9WT2ePY39sP2kvjV+kj8Pf1x/sf/qgCqADMA4v/x//f/uv+Z/1oA0gHlAmQD5wM9BZoGUwd9B3kHDgiPCDUJoAlGCYgIQwdCBpAEBwKR/3j9Uvz7+rz5yPjs92b3ffbr9a714/Wf9kj3lfhC+kj8Jf5T/zUAvABjAWsBoQCX//T+O/+z/1kAAQH0AUwDmwT4BawG1QbIBhEH0gcOCFoI1AhTCQsJuQe3BosF2QMrAXL+h/ye+ij5FfhJ9yL2cfXS9Rj2JfZI9hz3d/ju+b37Zf1p/iz/DQDlAO0AlAByAGgAkgDCAGcBQwLkAmYDIAREBRQGZAabBv0GZAeqBxMIwwgCCXIIUQdkBoMFyQN2Acn+mPws+1P6RPmX91f2A/ZS9kb29vUv9iX3e/jG+fj6Tfym/en+2v9FAJ4A1QD5AMUAhgCOAPkAswEjAuACDgQ+BT8GBge8Bw4ILAh9CPMIPAnMCBYINAcEBrkEQwOUATb/9vw9+/X50PhW9wv2FPUY9Z/19/VV9in3vPha+qj71fwc/jL/0v8XAFUAwAAEAcAAiQAPAakB9QElAuUCGgQSBbwFQQYGB+IHnwj2CPcI7wgfCcEIVwdsBdIDxwIZAcj+uPyT+5/6JPmx90/2jPVu9Wb1LvUI9Ub2Vfg++qH77fzO/hkA0QDzANAAmwBxAJsAQABnAEwBigKdAzcE1gSlBbwGSgccB7UG4QaUB0wIjQh1CB4IRgc+BqoEkgIwABH+YvzR+oT5uvjr97v2E/b89RL27fUE9t/2Mfjd+UT7qPxE/ggAZwGvAYoBRwH1AFoA0P8KAJAAjwFBA6kERAXBBVYHygiRCMYHogeVB84GzgZoB+0GwwUnBcoENgMPAUz/Nv1n+mT4xPe/9iv1pPRd9Rb2cPZM95r4APrI+079Df5M/ir/ZgCsAHEArwBNAUQBQAG7AXwBzgBKAY0CUAPRAyUFCQf+B2kIQAkkCvwJqgnTCWsIAgZ/BF8D/QBT/uL8A/wo+wL64Pi09zb2QPW49D/0ovS89az33fny+9L9VP/mAGEB/gCOAEkAiP+a/iP/gQB2Ab4B7gL5BNAFvAU8BloHcQeoBmIGjAbYBpkHuwgDCdgHfAZtBZMDwQBQ/sX88Pok+XP4M/iW9772iPa39sX2R/fE9/P3lfhY+i78m/z8/OL+3QAfAcoAHwE1Af8AhAFEAksCJAIYA4sEAwWhBTUHVgg1CMIH5QewBy8H6AaCBnYFFgSbA7wCvQAU/0/+NP1E+xX6tvnH+If31PYb9jv1W/XN9jH4I/na+jr9W/46/gL/WgB8AMX/zv8rAPr/HQBJAaYCvQMBBSQG7QZtB/sHIQiGB/sGEAd+B18HRQf3BgQGBAWvA84BAwDS/lv9c/sh+tf58/mg+Rv59vho+Jf3ivee97f3evgT+rb7xvwL/kD/1P/9/0AATACp/3f/SAAsARoBpgG0A1AFPwY8B+kHmQf5BtsGrQZyBqAGIgcEBxYGnAVIBbYDjAEtAC7/M/3X+q75Ofme+I34Fvkg+cH41/gB+bP4cvga+W76K/t7+7D8iP7B/1UA3QBKAY0BeAEZAfoAfwFkAkkDaATjBU4HDAgZCPYH5wddB4wG/wUoBT8ErAM9A3gCvgFkAcMAkP/n/YD8VPvq+Zz4qfcx9/X2Q/fv91H4TvnL+qj7zvsX/Lz8RP27/R/+mP4z/xMAMwH4AYYCUAP1AzgEkgRNBb8FjwUFBiYHWQfIBtEG+QY5Bk0F3AREBDwDKgJNAX0ALP8L/un9aP07/An73Pmd+IP3ePf590j4//hN+m/7yfv/+5f8OP2q/fX9b/7K/v7+iP8VAPQAIQJqA6MEQgXaBTUG9AWLBWMFqgUfBncGjga/BqUG0wUxBbIE5AO1AucA/f79/W/93Puu+rj6kPrL+Qv51fha+Ov3Zvhf+Q36mfqq+5H87vws/RT+J/85/3D/gACsACEAtgA3AloD/QPRBKMFCAbwBa8FowVrBVUFpgWrBZsFfQUeBbsEOgRxA4MCvQGHAPL+iv1V/HP7N/pW+TL5DfkI+Sf5n/n1+WL6Cfs8+3z7OvzU/Or8dv26/sH/PQCfAFYBHwKfAtQCKgP0Az0EvAPdA7UELAU1BecF6QbKBkIGFAZqBUEEawOvAlMBBABy/yL/jP7J/Sb9afx/+536i/nh+Mb4oPgn+Q36WfqD+nD75/ys/eL9Mf6R/uH+Lf+u/y4A0gCQAVYChgMBBOsDTQTVBPsECwWUBYoF9ATdBCYFIgWuBE8E5gNGA5oCfwEJABj/m/4H/lL9lvz/+5r7JftH+jL5Ivnr+Tr6avoy+yf8cfyS/DD9r/3n/Y7+ff/z/0kAvABGAcgBUwIyA8MD5QNuBIAFYwU2BKYEigUhBdkE8gTQBGgEtwPzAlcC6QEwARMAC/9d/qP9+Pwm/dD81fuT+zL7mPpC+kj6cfrQ+nb79fuQ/DT9jf3+/c7+5f7T/vz/kQBGAPIAHAJxAmwCwwL5AiADjAPGA/MDOQRsBJoEzwQrBQMFRATCAwQDCwJuAbAA8f+C/x//4P5u/r/9Lf2G/Mj7Lfu0+lz6ZPrV+nb7J/zA/Lj9s/6H/jv+hP7I/u3+DP88/4P/FwASAewBggJsA70DjAOfAy8DywIFA68DHATsA+EDNAQ5BF4D1QLwAmsCJgHV//D+Tv7u/aP9fP2r/UH9pvyN/FL8tPtx+9T7Ivwt/K38rv31/d395v7b/37/A/8T/zL/S//T/3kAwgCYAcICGAPVAqoCrQKoAtYC9gKxAtACRAM4AygDHwO3AoUCcQLpAZwAfv8g/7v+Xf4G/lz9Bv1+/bj9C/2n/C390/2J/eL8Sf3Y/d/9I/6l/gn/6/4X/8v/GAACAFcAMAFFAfUASAFkAfMADgHHAcUB2gHbApUDsQN+Ax0DoAJWAp0BxgDyAK0A5//b/18AJAAX/8r+9/6v/gz+iv0L/ar8iPyZ/D/9h/3t/RD/Sf/i/nX/HQBh/9P+gv9U/1r+Sv4w//b/FgDhAFACrQIdAg0CLQL4ATACeAIbAg0CngKoAmECVwIbAt4BqAEdAXgAEwBt/8H+G/9S/0P+Pf1u/br9d/2a/QD+O/6J/qz+Yv4O/jP+oP6o/lf+OP7Y/pr/vf8kAKoAbwArAM0AKgGiAGgABgEeAm4C5gHgAUoCfwLrAlcDkQLKATECOAJPAUoAnP+O/+D/uf8f/1L/nP/T/uH9f/2J/Z39ZP0Z/Vz98P0x/sf+uv/A/0T/Tv8h//7+af9j//3+Z/91AMwAJAAHABYB4wHoARACTQL2AaQB8QEFAq8BzgFSArACigL5AasBPAFqAKv/Jf/N/qD+wf5w/tb9Bv6Q/qH+0/2g/Xj+nf6x/Vb9e/4g/yX/j//M/7D/av+Y/8r/tf+a/2sAWQEqAKX/AQGIAUMBtgEtAp8BrAF7AnQCywFHAasBegLaAfIANwECASMA4v+n/6b+7P2Y/jL/aP6l/Rn+3P6A/n/9kv1+/rz+hP4W/4P/L/+2/2YAJwDb/wYAKACJ/4n/KAAXACYAzQCUAW4B2wDmAPoAJgFyAd0BMwJaAmUC8QGpAZkBSgGtAJD/mv7M/uT/FgB//0z/PP+E/pv99f1p/sb9kP1F/sP+SP5M/on/fwBUABwAdwD6/9P+of5h/6z/H/+Y/5wAUwAGAAABmgEoAQoBTwGKAYUBJwFkAXwC4QJ+AsIC1gIzArcBTwGRAHr/Vf62/df98P1G/gj/lP/h/zb/JP7t/dH9MP0J/QH+aP5y/nT/RwBaABcAFACt/xf/U/9s/0f/ov8oAIIAoACmAA0BygEgAh4CbgLVAr0CXAIaAtMBbwHWAX4C9gEHAWsAwv9F/2n/ef/7/qr+h/58/ur9/vwY/Zv9j/1W/f39x/7v/l7/uv+a/w7/hP6n/jn/pP+L/8b/iAB1AC4A0wBWAR0BQAFaAb4AzQDwAfQCAgNJAjwC8wKxAgUCNgI+AkkBaQCn/6T+jP4o/4n/zv9+/3j+Mf5V/p39L/2C/YP9gv3J/Rn+uv6F/zwAXADC/6//8v93/w//ZP94/yv/6f/OAMcAKgEOAiUCjgGkAX8BFwEpAd4AWAF7ApsClwIyA7oCFgF1ABUA7/5g/nf+sP44/0r/J/8+/9b+Nv6s/RP98PxW/cr9T/6+/in/w/8RAGAAtgBtAOP/nf99/3T/lP/M/1UAxgAIAYQBdwFUATkB8AD+ANIAHQHdAZ8BbwE4AmUCTwHPAAABvABhABcA8f+a/w3/sv4m/sv91f0W/k7+HP5K/hr/D/8T/lf+W/8T/1v/ugCzAIP/bP8+AJcAgQBuAKgAsgB9AG0AeACbAIUA6gA0AWsAhAAkApkCKgJYAswBwQA2ALH/Sv/P/4YAbwBJAFMAAABM/9r+tP6U/iP+bf2m/RP+q/3O/X/+If96/5X/UACtAF0ALgBAACkAmf9g/7v/MgA5AJEAUAFLAXMB7AEkAUAAsAAjAfMA/ADmAXQC+QG5Ab0BXwGzAF0ACgB+/yz/Pf84/6z+gP7I/of+9v3b/dr9K/0k/ST+if7w/qf/BABLAE8ADgDc/wwASQAcANb/+f9bABsAvP/7/9IAvgGwAW0BxgFEAZMAEQFjAVoBlAEsAoMCSAIRApgBWwBU/5X/wf8j/9f+L/8Q//r9Ef3z/Cf9o/0l/jz+ov72/hj/AP9r/uj+4/89AKsA7wDCAMAAxwA9AB8ATgANAHsA2ABtAJsAwgEyAooB7gGaAs0BAgF+AZkB/wBxAQMCWwFvAB0ASgBPAID/a/7H/XX9dP13/V39uv2t/tb+If6T/ZP9YP4j/7P/VQBJACEAoAB0AMn/FgC0APUAiQAhAHUAWgA0AIYAuwD0ADMBegHOAQMCxgGAAaMB3gEdAtEBlgG0AdgA9/9A/1/+Jv5h/tz+M/89//r+T/5j/aD8tPxj/T7+5P6X/z8A///L/y0ARwA9AIMASADO/3MAgwDQ/+L/1f9s/5H/sABWAf0ACQF/AU8B+ABMAaIBTAJ7AtEB0wGOAf0AlQF7AR4Akv+b/zz/hf4k/kf+ff6L/if+iv08/TD9Vv3w/fH+1v8JANX/FABqAA4AFwCnAKAAQAAiAGEAMwAdAOYADQG5AK0ASwAbAKYAFQF5AS4CWwL8AQIC6wF7AT0B8QCMAP7/sP+A/0f/ov+9/23/4v7z/Tr95Pyx/EL9Vf6a/uj+nv9Q/9v+gP8BAK//8/+MACwAEwCNAHkAQQAqAGMAvADRAMoA9gCJAcoBLgG0ABkBUAEQAQMBOgGyASYC8wFgAcgA/P9u//z+w/7n/un+4v7T/nf+Sv5M/g3++P1A/rL+rP58/un+ef+t/wYA0wArAe0AfQAgAG//U/9ZAHUAfAAYAQAB8ABMAX4BRAFPAV4BvwCTAOwAYwEPAkECqgEcARMBUAAU/8v+/f7P/rT+wf6A/nz+uv6a/kn+8v38/YL+qP5X/kL+Ev9FAMQA1wDdALAAbQB0AFEA2//p/0YAlADZAMIAdwDVAE8B9wCcAJsAxwDvABoBfwGaAaABSgGAABUAtf+E/5b/lP98/2P/R//m/rn+pf5k/mn+V/5e/k7+iP5K/5L/EAB/AFcAUgBGABQA3f/i/0MAiQBYAF4AvwDOALwAnQCIALwA9gCjAEcAAwGNAVcBRwEoAeAAoQC3ALUAZQD7/3j/NP/g/oX+u/49/xD/t/4p/0X/k/5y/n7+QP6a/k//8P9hALcA1QDxAEUBFgGsAC8AbP8I/0f/9f9EAEMAlQD3ABgBAQEXAQIB3AC9AG4AvADZAK4A8wAdAQABrwBqAMf/JP8a//v+yP4P/1T/B//r/h3/wP4n/iH+mv4o/8H/7//L/0gArgBoAEUAgACqAEIA0//i/xAAaQCqAIQASABFAGEAWwBeAKUAPgF9ARYB+gAWAR0BVgEpAeoAwAAPAJX/bf8I/6n+yP4r/wX/ov5t/pr+1P6j/mL+N/5t/rv+QP9MAOEA9gD5APcArABuAGoAJAAgAH8AtQBrAAAA7v8YAJQA+wCoABgAIABpAH0AlgDlAHEB6AENAogBfQAWAA8AmP/K/kb+gP7k/gj/+P78/v3+4P6Z/ib+DP5d/ub+dv/j/yoAXACOAOQAaQFdAcUApgCZAIYAogBoACwAKAAmAB8AJwBLADEAAgArAJMAtADwALgB1gGBAWMB7ACGAEUA2P8b/5n+xP7w/iD/Rv/+/pj+df7w/Y79L/6x/u/+rf98ALsAoQBXABsAXQDIAOgA5ADLANcABAHvAKMAHQCu/4b/cP+Z/9P/5/9AAI8AsQAiAYoBmAGAAWcBAwFPAMT/c/9H/0H/Zf+e/6n/sf98/+j+bf4M/hL+T/5a/nX+5/6X/xUAlQAEAVMBTAEEAbcAOwAJABsAXgCeAH4AUgBhAKQAcAAeAB8AFgAiAAkAFQCGACcBeAFMAU0BFwGjAC8Aef/L/mn+YP6Z/s3+Fv+N/57/C/+Y/oH+O/4M/lj+I//n/00AyQAdAXsBIQJXAqQBpgAyAB4AIAAPAOL/IQC4ANoAewD8/8v/7/8HAEsAVgA9AKQA9AD6APkA6gDCAIoAWgDp/3T/Ov8X/+P+rP4C/0b/Of8T/3z+S/5L/lb++P6+/5AAWwHZAaUBKQEvAToBrgD0/9T/6/8YAKUA/AC9AFgA9f90/27/Nf8Q/+b/dwCxAKAAcgBzAEMASQCPAKsASQDe/4L/RP9B/0r/tP+G//f+4P6t/mL+gv7k/jf/vP+GADUBgAFBAQABEQH5AI0A6/+B/7j/NQCGAM4A8gDTAJcAcAA+AJz/M/85/1D/bv/B/3kA2QD7AAMBtgBzADAAsP/8/uf+Rv+p/woA/f/w/y4ACgCl/xn/df5r/nz+vf5u/ycA5QBjAWoB+QBmAAEAzv9r/yH/b//o/4wALQEkAfAA9wDEAP//ZP9o/4z/2v/v/yMAmwCjAIUAdACOAHAAEADY/6T/V/8U/2z/+/8TABgAEQDa/wAAtP9e/6n/Mf8w/7n/5/9tALYAuQDXAF4AMABKAKD/Hv86/1AA7ACfALEABwHiAGEA/v8Q/z7/wf8uANAAawCfAIgAKwAIAJL/e/+j/9f/Tv9M/5f/pP+QAFIAgf+i/zEAuv9D/4H/jv85AEAAewB9AGIA3QBRAND/S/8m/8v/JQDB/xQAzQDAAJUAJQCQ/yj/yP/R/9z/igBBALQAGQFcAAcAtP+y/0EA8/+K/0AARQDo/3UA2P/U/0YA3/9EAEcAvP+D/yj/vP5E/5z/7//UAKQAPAFCAbv/CADk/lP+k//Y/lAA7ABlAJEBfwGQAeUA1/9m/4T/Yv89/+j/4P/rAGgBBgEgAaMAEgA8/4f+R/5S/3D/a/9vAEYAfwCTAH0ATv+r/qf+av5R//H+uv8ZAK3/RwBMADMACwDU/73/AgBm/yYA1ABYAEgBKAFxAUwCnAFeASQBHwC+/2D//P5n/3n/2f/yAN8AqQEGASIAUgBc/tf+LP4B/vL+1/5oALwABAGjAOr/LP/U/sj+Zv4P/4D/MgBJAMEALgGdAH4AuP9I//v/AAA2ADQBkwHBAUsB8ADJAJUAAwDk/04Awv+vAHwA4/8bAJf/7P9n/z3/xv/n/6X/of+A//n+Gf9a/r3+5P7M/vz/5f+OAB8AQf/Q/yv/EP9L/67/UgCbAPsBDAHiACwBav/o/1//9f/mAMAAqgHRARgCLQImAfH/kv8C/xr/Mf8y/2r/TQBSATcAuQBPAF//X/8E/iH+Pv0b/s3/jP50AMAAKgBEAdH/Xv8B/+D+1P61/2QAWwCjAY0BvwEcAR0BfwCn/+n/xv9wAHEAdgFcAQkCmgGOAAYBD/9E//H+sf78/7H/PQB0AKgACwHj/1n/2/4z/m7+Wv6O/rr+Yf+o/6z/hACe/28AgwA1/6EAg//7/9gA7P/vAIIA/f+WAEkAVQDOAGkAJwFxAIgAgQCI/3AA2v87AJD/x/9lAOf/ZQDP/28ACABs/8L/2f+x/8z/LQBP/+H/rf8i/4T/Fv+C/5n/sP/c//v/bgDgAFMANQDxAN3/YACJAPL/1P8nALYAzv9WAAgAiQAEAb//hACl/03/ewBi/pX/4v+S/rABnP/y/3sBcv/EANX/g/9nAF3/Rv+kAOb/Kf+dAIAAuf/PANkAjP/G/+r/kf+z/lz/iQBTAEcAfwFuAacA8gADALr/wv+P/1v/YP+6/1cAx//A/54Av/7M/4b/Jf5w//3+qP9X/zAA9AA5AOkAUQDL/xAAff9w/9b/GACmAGAAmAHFAaQAJgHrAEcAEAAsAFf/9f4SAP//sf+wAHsBGwHSAAgB8/9+/1n/Iv6E/pb+Vf6Y/9D/T//gAJYAOf/T/87+o/7f/pz+kP/o/4YApwFuADYB6wEmAE0BmP+7/60Avv8bAc0ATgEeAQ8B1ADr/yIAzv+m/4H/mP8KAD8Asv/eAD4AwP/aAMz+Rv+i/ib+V/8C/hj/l/9P/80A7v8N/0UA0f6o/or/i/7G/1QAuADWAP4AywGKAbIA6ADkAKv/zAAGAAwA1wAlABgBmgCvABEBDQAjADEAm/9q/xz/Zv9K/+X/bAA7/9MAcgC5/z0AeP6i/9f+5P3C/+j+0f6dADwAs/8DAOj/8v/X/7P/zACkAM7/rQELAEMB/wHQ/1ACpAA3AG4AF/+L/5H/UACQAHgAkACvAJ3/Mf83/9D+AP8k/0r/r//BADAAIwB1ANj/QgDr/kr/Sf+b/vH+Wf8UAND+zwA0ALr/lgHx/4oAJgAl/1EAu//8/3QBewB3AakBDgAxAeUAMAClADAA2f84ABj/nf94AFb/NQAFABv/BwBJ/+n+/v+O/9IAQQAJAI0ARwARAQz/w/9x/3b+h/8d/3n/8f9TALcAhgA0AGoAlf/M/17///72/67/+AAWACUBAgLa/wIBNQAjACoAzf+yAH//UQBQAHT/gQD3/ykAJgDY/wQAHP9R/5n/1f9MAEkA7f/2/w4ASP/j/yn/0P7oAHL+Uf8VAab+CwFq/+D/ZAFz/eP/hv4A/2gBxf6ZAf4A9v/xAJv/zQBlAUoAgQEnAHT+oQL1/z7/5QCF/98BCgB4AKcALgAQACj/FACR/qH/fP8xAMD/DwAkAdb98ACv/8v+0wCz/qUAsf9t/9r/L/+VAOX/CgBuAFcA5/+g/nL/uwA9/74AUAJP/8UAvwDx/gkASQBCAAkAtABMAF7/zP+HAMj/u/8TAOr+dv/1/v3+uABvAFEAwwA4AD//Lv/f//7/aABHAccAp/+fABUApv/PAJ7/LwGZAGX/mQGK/9f/WAGt/3cASABZ/5P/bv+y/yAA9P8K/37/zP/P/3b/XP+e/y7/I/8p/p/+2/4eABcAhP+nAdz/tQCQAHz/EQHm/wMAVwB9ACwA+gDIAHUAAgK2ARYCpQElAhMBSgAyAHv/GgBH/k4Adv81/0kB+v6NACP/qf62/8790f4v/gn+R/6F/cP+zv65/mL/2v+i/y3/ZgA6AIr/sQBBAFr/OACO/wMArgC+AG0DGAM8AmkD6gINAnoBggEJAt8AngBxADQA7gAJANf/3f8OAKP/Qf8T/+v9af43/nz99vys/T/9fPwx/qT94f14/g7/Z/9z/wYBbf/b/0kAC/+zAJX/vwDmAS4AQwJkAhkCcANuAg8DTwPlATgDXwJuARQDkwGXAC0B+//Z/7//Rf+4/1//Qv94/rr9mf0c/RH9Hvz2+9H8sfyZ/Ob8yf3I/qz+Fv/b/3r/sQC5AGD/AAA6AJUAugAgASECgALMApoD7gNoBBEEGgNHBEoChAKqAiYBbwN1ACIBRQH5/sEAcP6H/3T/iv11/mb8Ovxb/Gz7YvvQ+rr7Y/yA+7j8q/2I/d3+RP9o/8f/m//c/y4A4/9OABABkgFdAjYClgNCBCIEUQUNBRAFEAXJBOoDZQN9A+ABowF1AUsAUwCD/8L+G/+f/jb++v0q/bj8o/vh+lj6cvkc+tT6I/vR+079qf3B/fb+Wv4d/3T/6P6aAIH/zf+YAOn/MgJOAi0DKwUBBbkGtAdaBmgGTweWBr0FDwUWBA4DpAEVAdIACQB//1T/nP4T/pH9R/xo+9j5BPlH+W74d/j1+K35qPpE+4T8GP3f/Zr+mP7v/qf+bv6D/k3+/P4zAOoADAL6A8IFswacB0oIZAhyCCsIjgeqBqAGbwV1BNkEgQOfA7UCqAFfAfP+nP7m+5H6gPpW90D44vYu9uD3s/ZT+An54fmO++37BP1P/Zb99/0u/ub9M/64/jj/SABBAQYDhwQVBmIHMQiQCd8JVwnxCLsIPwmfCAEHpQbwBeIEyAOJAiUCDAGv/1D+7/y4++L5evif9nD1QPV99Az1nfXE9qD4RPkU+yr8d/xW/ez8l/3q/Yv9Ef70/U//PQB9Ab4D2QTMB+oI9QnFCz8LjgvoCRUJQQlLCM4HSgbBBSsFxgQLBBgCKAFJ/3H9hfwR+jb4CPeH9R71gfS/9In1m/UQ90/4Vvmt+gj7BfzR/B79pP19/bb9V/5g/yUA2gCaAlgE0gUqB68I/QkuC5ALOwtCC9cKbgqBCS8IawcxBtoEhANlAh4CawCE/g39Z/ve+hD5kvdN9ij1/PSp80D0vPSP9Sz3qPeL+uv7Y/xe/Y39e/4s/tb9vP1X/vv+lv+UASsDtQXYBykJkgodC3sMggxLC00L6AonCrgI/gblBc0EqAOcAk0CVgFz/yb+yPxB+/X5+/fu9Xv0aPNM80Hzt/Nb9a32dfhX+p77L/2j/ef9b/61/fr84PxV/WX+of/oAP4CcAXyB8gJIwsoDEoMqAx4C8QKTAo7CfoIGQezBgUGtgQ6BB8CJwGq/7z9kPzg+nv5tPdy9gn1BfS982fzdPQB9en13PeI+S/7wfuI/LT98f3t/Xj99/xO/TX+bf9OAMIBYQR5B4cJnQp3DDsN4gxjDJ0L+wrjCQYJHwhVBj8FwgTmA7sCIwFNAAb/Qv0B/F/68/i59j/1WvQg823zhfNN9Bv2zvca+nv7Jfzb/BD9af12/R39dvx8/Hz9x/5HALUBXAQmB5oJdAscDMQMGQ2ZDE4LiQpiCi0KrwiHBqgFzgQTBLYCIQEwAGb+n/1R/Eb65/gJ98j10vPn8iPz2vLJ8w31Kfcb+WL6K/xy/Tn9P/3Q/VT9e/yZ+7D8Zv5B/4IBtANHBn4JNguWDCENYA26DS4MywoNCuEJ7QjUBgQGcQX+BBQEhgLoAPz+xP2u/Af7/vig9jD1VfQ/833yqfGp8vH01faJ+MX5Afyw/YL9If2//MX8h/y/+/L7LP2a//4BzwOKBmYJNAx3DVENKQ20DEYMWAtmCroJTQmKCDkHBgY+BdAESgPtAKH+v/w7/Gr7DPl49iP1YPWy9OXyxvGB8mP0zfUc95j4T/oD/ET9ev0A/QH9Jf3A/GP8H/37/jUBQQPHBawIjQv1DbAOGw5cDT0N8Qx0C6oJTQn7CJ8HwwXyAxAEhwMQAT/+GPyS/Kb80fme9nf0nfRc9OTxxPDh8BDzhvV69nD4Sfqs/DX+qf1f/QX9DP2M/LH7nvxz/g4BugMYBoYIYwscDkwPvQ5SDTkN4wy4C3sKZgkACYgHDQYbBd0DBgNiAbL+0PzB+9z7dPoe9wf1y/Pd87Py7vC88JvxpfQZ9434PPoV/Cb+v/7F/Ur9/vzA/ID8Gfza/YAANAOaBQ0IsQvpDccOnA4uDrgNDwx0C3sL7ArfCXAHNgaqBdMEnwNfABn+If2y/Mb7dPnJ94v2GvXN83Py9vB98C7xqPPs9Yn3R/pj/Ej+4/5w/iD+FP29/Ef8u/se/YL/QAJVBAcHTAviDdQOEw9sD7EOfwxyC6ELIAsRCQYHswVbBeYElQMGAV3+vP2+/dP8Ufrk9232yvTw8q3xQPDL7wbxcPNz9hf4k/oo/cz+FP+w/Vr9/fz7+8D6NPp2/MP//gEIBKgGMgukDkMPGA+ADnQOfA3DC/IKyQqRCv0INwb+BJIFLAWuAn3+mfzi/N/8V/u59wb1QPR/9HXz3vBA76nwkfOq9T33APn6+zz+fP7I/T39pP0m/Yn70/pN/Jb/xgFnA5kFIgkpDQ0PSA8zDkkOtg6dDTMMTAueC0EKKgfoBD4ENQRNAgP/SvyA+8r8xfyz+cH1HfQ+9ZP0ffGm7gfvZ/KY9CP26vcc+6f+c/+7/u/97/3m/Sj8yPpe+5/96QCsAlYEaAeHC5MP6A/+DuQOYA9KD7gMmgumC24KAwinBKMDMAPqAYj/dPyb+0H8HP3e+kX3T/XY9DH0W/GK7y/vn/Af86f1ePgN+8z9zv8FAO/+HP53/cn8evsL+6f8H/8PAikEowZTCscNlQ9xDxsPsA8eDyENFwzVC98L+QixBA4DmAKrAof/u/sk+xb8k/2R+0X4k/Z69SD1m/Lb7/ju6u/E8sP0OPdu+uD9WgCeADgAav/L/qX9Zvwu+xL7jv2TAEED7AQqCAYNJA/DD3oPzg+uDxgNqAxKDPEL0AkyBTUD8gFtAvIA2PzP+n37AP7O/DP5yvYB9hn11fJY8HPu3+4B8U/07fat+W79oADiAfAA1//0/rv99ft9+iv6avyz/3UC7gS5B14Mew/gD5cP7Q5rD+gNlwvVC6kLZAthB4ADCgOYAmgCQf7r+sP6u/vt/IH6Nfdv9RX1rvRU8hXvre5+8K/zm/aU+Pr7uf47AZYBTgB5/4j+hP0f+xn6sfvR/k8B7AIYBhoKSA7JD2APiA9bD6MPgQ0CDGkMxAuhCY0EtALVAqEB3v4z+7/6Sfvz+2v7x/hn9iv1A/VQ89fvP+5Q72XyV/Xj9zP7/P0CARUCsQF8AOH+LP6r+yX6P/rU/EIAsAGFBIAIZA0UEC4PMw8VEDYQkw7JCwcMnQxkCuoGqQIVAuYBpv9U/b35NfrQ+9z7dPps9hL2dvXV82PxR+4075Dw9vMJ99D5r/17ANoCTgItAQQAjf5//En62PkP+9H9AgHPA8EG9wq5DrQQ2Q96DzkQXg+rDVkLfAsfCykIsQSeATcB1ACX/pP7Svke+gf8Nfug+Nb1ofSR9FryevDz7snvMvO59Xr5U/yQ/30CrwKCAsIAcv8F/mn7EPrg+ZL8rP/AAY4EEgixDMAPtRBoEOUPzw//DgcNxwpyCqAJWwbQAbr/aAFmABb9APrh+R38mvt4+kX3qfQw9EvzOvLr7rfuGfKQ9Z/4QftJ/xYC2QJ7AmcBqv/q/TP89Pkk+XX6JP75AOYCewYRCxAPARClD+QPeQ9rDs4MaAuoC7oKHgiPBOYBVwJ3AWr+Avs5+ZT6Ivv2+e33K/Wz9Bj04vJA8a3vcPHV8wT3W/o3/TsA1QGGAkQCGQGH/6/9U/s1+nn6bvwk/9gAfwP5BswL+A78DuoOuQ/GEBMPbAzPC0gM6QquBt0CFQEoAen/rPzW+R35efv5+9j59fZu9a31PPQk8vHvWu9F8Zn0R/jz+sv9CwFqA5MDRQKwABf/DP3i+p75F/pU/CP/cQEDBHII1QxtDz0PQA+HEB8QjA4BDAoMfgzVCbAFTAEHAYABEf9W+3X4xvnr+6f7mPmt9mT1fvUT9MHxN+8J723yZvX5+CX8Uf/MAn8DKQS9Ar4A7/7R+9/5QPjx+cL86/43AbIETwpGDuUPfw+yD0gQ5w8cDskLeQtXCwoJOwQvAeoAEwFn/lf6B/kC+lD8bvuK+K/1vfSM9cbz8vDo7g3wlvMj94r6gv1EAPwCfQTxA1oCwP+y/SD7G/kh+dT5TvzH/gwCuAboCtgOrQ9fD3AQgxDgD8YMSgvSC1gKeQcJA/oAzgAxAGT+zfoY+cb6ivwa+033DfVB9Z30JPII8EHvTvHg9HX45vsf/hQCnASpBIADEwGz/5r8IPr7+Iz4Kvqt/HsAeQPEBhQLbg5lDzAP5A/TD2sOGAzWC1sMAgtyB/YCOAFlAdsAOP2k+MT3sfpI/Df6l/b/9MP1R/VH84DwAfCT8sT14Ph8+43+NALQAyQE9QLXAEz/xPz8+u34v/gV+7n9xABMAw0H/gruDXYP/A8OEMEPyw47DeoMVQxvCsAFlAEKAQYBGv+c+kf4W/lq+/j7w/ny9kv1XvWo9BrywO8N8CrzL/aI+dT8DgAJA1cExAStAnEApf5j/BT6KvgH+U377/23AOoD5geuCxsOWw/yD6QQFBANDiUNug0FDsIJOARhAVcBPgE4/Vn5gfek+W/8rfsS+VH2m/ZM9uXzL/Gp797wB/P99ZH5Mf0XASQE6wRKBKcCKQHy/kH7p/iA9+j4iPu7/ZIAyAPfCDUNrg6ED+8P4BB6D3ANfQ3IDf4MtgipBFACeAGbAB/9Qvmd94L5bPuE+g34svYc9lz1pfPW8d/w1fCY8/H25vo0/vgAmQOWBPgEZAPcAPn9Mfs5+SD4svi5+nH9nQCvBIUIUgyeDgQQ1xBKEPcPUg5dDUQNBQw6CTIErQGRAQIAwvxo+N73nfmY+hz6Z/dC9ub1gvUJ9JXxNfGT8gb16fcz+xX/zwGTA20EPATNAmQA4P33+gj5Afj8+Mn6U/0AAUsEsgj1C64O0g/xD8sQwQ9zDn4NQw5KDWEIgAPXARsCif/r+h73Qfdi+av60PmK9832Bvd89jf0GvLJ8eryoPSP9z37E//tAYEDuASJBIoDFgEG/vP6dvj39/v4qvqT/Db/tQPXCFMMNg5HDxwRkxEDEJgOUg63Dm4M2Qe9A+QBNwHP/mn6U/eV9+D5+fpR+af3y/bi9h32DvRc8qLxPfO49b/42Pvu/uIBzAO9BCIErwJ+AMr9GfuM+Pz37viy+sX8Z/8iBIsI7Qu3DRMPtRDTEPYPxA4RD1AP4wwnCOQDKwINAcr9NPl49lz3A/rB+lz5qPea98f3Y/YM9DLySfJO86j1kfgG/HT/1AHTA1EEZQQyA4QAOP0Y+r/4QPht+KP5Kvze/1kEpwgrDNkNxw50EA0RXBClDnoOJg/cDKII4wMXAgQBuP2G+RL2bPfW+Zr6HPk/9/v35fep9vvza/Lf8j70bPa/+Aj8vf+hAqUD6wN/A8QCNgDW/Cv6L/gk+En4FPp0/PH/rASeCO0LkQ0iD5MQ3BC0D2MOjA5MDwUN0gd7A80BbAHL/Q35PPb09rz5YPp8+fL3xPdX+AT3wfQL83fzQ/Wd9iL5XfwBAF8CFAPVA6ADjwLn/+b8Yvqe+BH4ZPjl+WD8EQAlBOoH7go0DZcPChHJEPEO8g1xD8QPzAwUB0EDhwLdAEL9Xvie9uv3G/rW+kH5hvij+Kb4g/Y09JPzEPQV9Tv2JvnD/D4ApALTAxkEZAPnAYr/b/yc+bf3DfcV+O35zvwpAIIE8Qi3C2wNtQ5WEIIQpw4cDckN9Q4wDcgHAwONAYUBPP/o+YT2Fvfd+e/6pvlk+Ln3rfc690f2D/Vm9B71k/eG+m39x/9UAZoCRANCA18BmP7q+/v5o/ig9+b3j/nc/IIAMgS8B70KSg3DDvYPRxA5DzgOMg5kD7ANkwhxA9cAlQAT/sP5HvYL9tn4kPqX+hP5UvgQ+IH3ovZI9dT0dvVk9zn6Ef3p/9IBygIpA6wChQEN/4D8Ivr79yn32/d8+gz9yP+zA+YHCwtuDAgO7w8EEFcOHQ0SDnAPdg2eCIkDLgHbAIH+NfpP9l72Dflk+pf63/lQ+YX4PvcJ9zf2jvVy9d32OPrk/Mn/ZwGIAhgDVgJJAcj+Z/zc+fD3lvdQ+Cz6pvzJ/80DjgdzCnYMtw2KDxEQ/Q6PDYoNJA+TDaIIiQNCAasA3/2i+d322vYZ+b766/pU+jT58vgs+Db3Z/ZR9Z311vbc+TP9g/9mAV8CFQOWAgsBSP+y/B362vco90/48/k3/Lz+sgJxB/UKygysDVsPDRAOD7kN8A2cDocMGQhgA1kBxQCq/on6rfYh9yn6IPy++0v65Pkt+Rv4SPdR9lX1rPSD9gj6QP1w/6UA2wGXAo4CEAGE/tb7dPne91H3NfgV+mf8NP8cA48H4AoTDLYMEA5/DzQPUA0JDY8OxQ4zCsYDFAF6AYkALfvs9bX1GPkD/Lf7CPpq+T35VflN+MP20PVD9ZX2E/mK/Dj/OgAiAR0CrwJ/Aff+YfxF+mz4jPcr+Cz6k/zu/j4CVQYcCt0LhQy6DTMP9w5ADUYN2Q7XDlUKiATyAbQBTABC+5f2IvY9+YL8X/z3+tv5mvlu+RD4u/bI9CL0j/XD+ID8dv4MAEcBdAKOAuQAwf4l/Nr5+PdL97D4r/qt/ND+HALrBoIKDQwVDIkMLg5mDsANGw0eDuAOpwt2Bp4C6wGZAIT7m/be9TL5OvwD/CH7kPpm+tv5PPjJ9u70T/RH9bj3j/t5/k0ALwGaASACEAEE/zf8Xvm89xX37fgM+7X8jP6VAaoGRgqgCxwLcgujDQQPew5XDdcNCQ/6DLkHLAMxATwAfvyv9+v1Jfjt+wf99fvH+in6nPla+Kr2SfUN9ML04vey+/L+4v/jAMUB4gH1AAv+nPtB+f73//fg+DX7Of1N/wYCnAVpCagKVwq4CqwMwA4oDjsNCA6TD18OGQn3A1cBdwBZ/Uv4h/VO95z7M/1E/NH6Ufo2+qr4xfaE9JXz5vS493/7DP4bAIkB6QHKASAAAv5W+2P5Wvib97D4FPsL/sj/iAHBBHoIpwpLCvoJlgtADvgO6A3BDa0Pqg/hCh8E4ADuAMH+b/ns9NH2HvwD/3z9dPog+nH6jPl99kHzOPJb86b2n/qX/br//ABWAtEC8AD9/YT6zvgg+Nj3lfhC+oT9XwDHAlIFVQh6CncKogmLCg4NcQ70DYINHg/dD/wM1ga2AeT/rf5L+6n27vWP+dT9bv4K/HP62Pkf+fv2NfSo8rDyofXZ+YD94v+iANIBAgLuANr9G/pD+Hr39veT+LX6Dv4MAYkDgAVBCEoKAgrVCE0JVgy9DgoOaA0jD5kRUg9CCHkCBgBm/2T7g/Zj9ZT4f/2S/jH9QfsT+iD5iPai87zx2fFt9C74X/zF/2wBggIgAn4AhP1m+p/4Afd79tj3TPu9/0MC4gNxBbkHOAr3CaoI8gdjCu4N5Q7bDnEPcxFSEKcKEQT1/0P+kfuJ95H14PeN/Fb/Tv4d/EL6APnY9oTzLPGd8BHzPPeR+yz/dAGfAooCtQDP/YH6wveS9jT2vvey+tr+3wLgBHQGwwd+CdcJVQjTB2kJqgyQDv4OmBCeEhASoAw4Ba0ARf6L+y331PQq98T7Of9I/739o/tz+Z32SPO18KnvHvF69LH55P4FAvECLAIDAV/+p/qJ96r12vVV9+P6l/8zA5sFzQZ1CJkJbwntB20GNQgbDDkPXg/JD4USkBMOD2sGGwDf/Wv8M/kD9qL2C/ts/4kANP46+6j46PXz8kzw+e+N8dL0Xfkl/jMCUQMfAp7/oPzC+Tv36vXw9Q/3cvqA/1ME/gYOB1QHOwjWCOoHUwZRBwELvA53EDsRMBNVFFgQVAjvAPf9lfwE+bD1Afbw+rH/GQFk/+/7tfie9d3yFfCG7qvvh/P++N39awHnAl4CmgBk/dz54/bh9VD2O/cJ+pr+0gOdBhIHRgfhB4QIfwd7BkIHUAoxDlEQmBFFE3UUCBKnCswCWP6d/In6DfdT9qT5tv57AScAAv3T+Kf17PJd8MnurO4A8lL3yPzgAG8C8AHC/+v8WPpj91r1+fRq9iv6Yf73AhYG+waPB9EHbAiiB+8FxwZVCa0NuBAzEtET7xMfE58NmQUF/4r7CPuX+Nn2fPg5/YEBLQEV/gH6NvZx84zwi+5i7grxh/Zy+3f/6wHCAqQB9P1r+rf3aPbQ9QL2gPgT/f4BgwXnBmsHQggOCD4HlQWtBcUI5QvZDp8QbhMNFmgUSg+RB3EB4f3b+r74vvbR9zz8UAA4Aq//r/uF93Dz3/Cn7lDuAfBP9FX6D//bAfYCTwI2/5P67/ax9XX1mvVM90f73wCKBbYH3wcOB1sHOweuBQEFsgY5CzcO8Q8kE3oWFRfnEUAKcwOc/rv7TPkd9/32VPpr/0UCLQHg/VP5zvT28Ezv6u5W7+Dx7PZa/YEBWAOsAiwAtPwV+Rr3k/UZ9S72Zvld/pYCGAZsB/wG1AZjBncGVwWABZoIBAzAD+cRtxSWFrgUgg9rB+EAvfzN+hb5fveV+Ln8hQFHApD/Lvt79nXy/+4m7g/v7PAV9Sz6df9TAokCgQHe/Qr67/aX9b/14/U++BT8gwCoBNAGrgeSBnQFhwUnBZoFsQbfCRoOMhGAFAwWxxVEEk0LOgSq/dn6Ivo8+Yb5EvscANUDlgIs/qD3dvMB8Mntze3j7oXz1fh8/hoC8gLVAuT/0Psn9zj0K/QW9TH36vnw/SADvgbJB40G3gSMBF4EQgTBBLwGHQunDxYTQhU1FnoVURA6CFIBWv0T/LH6AvpL+03+RwK5A/4A1PrD9D7xnu+W7r3ujfHl9t38xQAMAjIBFv9L/Hb47vSK88f09feN+lb9LgHPBKYGCAWNAwMDCwOLA9gDQAbBCZoOnhIMFO4UGhWuE5cMuwOo/lz9Gv2J+rb6x/2YAQQESgKN/nX4VPPB8H7uPu7076f0XPrs/doAiAFbALn97/nK9kr0avTl9mH5kftG/twBYgQqBPACWwKlAkIDGwSQBV4I9As9D6ERuhIpFBIUwRD4CeUC+f8I/x/+qfym/G//ZQJRAyoB0vta9oDylfAg8M/vifK19kr78P43AFkA1v0g+4n4UfZM9T71x/c++uT8nf+8AWoDiwI+AvIB/QFMAxwEZQa1CGYMXBB7EScSMBOcExIQRwh9AuD/CP+k/jv91/2S/6wCLgSGADD7nPWi8tjwc++b8BHzQPcF/P7+YwA8/0v9NPuE9831IfVt9qH4O/pl/ZX//wHwAh4CbgHIACgChQOaBLsGpgm1DfoPsRDlEZESjhIKDlQHKwLY/6kA2/8M/zT/YgEJBJgDEwD5+dT0kPFm8DLwY/HP9K74Bf3K/2wA8v6C+/v4I/Z99Pn0j/Zv+S/7Jv7gAOEB8gHvAJoA0f+gABMDIQXnBp4J3w1XEJMQ8RAVEqwReg0+B0ICXQBnAQEC5ACe/xABTATrA4n/MvmS9B3y2vCb8RrzGfa0+fD8Nv+t/s/8Nvr/9hX1HvS69Xz4c/rH/A7+EwA5Aa4AFgC0/hv/xwBpA10GDgiCCpINvQ9WEAsQ5hCZEMgMMweFArUBaQLZAgwCtwAoAlcEYgRb/5z4XvRy8i/yQPI09Db3M/pc/dL+ff4r/Fv56PZw9JX0W/a6+HT63Ptt/rX/UACo/4H+Ov6S/gcBcAO8BTIIWgpHDRUPow/rD7oPzw+9DHUHiQP6AZ8DxAPCAuUBtwJLBT0EiP8t+c/0lPPK8ufyePRa9zz7O/0l/o79bfuC+fn1MvQe9DL2i/ll+ur7lv11/zgASv7q/X79gf79AOQCAwZrB04K+gy1Dc4OxQ7lD/EO+QvCCK4ELQOMAx4EoANCAosDKQXAA4f/1/n69cfzIfPv8zT18/co+0j9xP3X/Dz7xPiC9Uj05/Tx9nX5tPqU/Hb9rv5E/5D9B/1q/ED+xwCsArAFDwfsCUoMWQ1KDsMNIQ8aD+YMrQk/BUoENgR0BLYD8gGFA8AEbwQyAJr6fvcN9Rr0S/PR9CL40fq3/Mv8oPxU+yP5WvYg9BT1YPfV+Yr6Xft//Xz+//6g/ez87vzu/eUAoALJBKoGVgmxC14MYA17DQIOlw4WDsYKlgVxA6UEqQUQBBgCCgMmBVIF4wEf/EH3XfT380n0QvWM9zb66fy8/Vr9lvuw+Ov14fN09NP2Tfkw++D76vzv/S/+4f1Z/Av8WP0gAKgDYgXaBjoIVwqMDL4MnQwADacOOQ/PCw8HLgSvBIIFqgMUAqMCPgWjBSgCLv08+I71/PP+8zj1g/fZ+tj8Yf07/MD6qfhn9ZvzYfSr97f6LPz2/Pf8Zf0//W78nvuy+wr+zACQA6gFAgcwCNkIXAqvC2EMKg0tDi4PJA1dCIwErwPyBCgEZgK5AqME2QUBAwL+rvg09VT0kPQA9uj3jfrC/Bn9Nvxh+qn3EfWr8031Rfje+rb8u/zM/Df8CPzl+/D6x/vR/U4BPARkBdcGKAc8CJEJmAoQDIwMEw8qELAN0QhqBLgE7gSyA2gCzgKSBSMGnAO3/hr5CPZR9B30dPXK90L7wfzl/DH8g/pl+Dz1uvPZ9O73bfvo/OL8rfyN/M/8yPuq+of7lf0iAUQD+gS+BvoGzgdvCHEK8AsRDIoNiQ49Dl8KjgUBBDIE+wTRA/0CeARSBTkEY/9N+s72fPR39H/1iPhC+9b8ZP1i/MX6z/dF9brzYfQ297D6n/0D/pD98fxP/G776fl++oT88/+zA+0FbgfpBi8HWQhrCVoKjQqQDIYOEQ9MDCcHowMVA4MEDQTwAnkDHwX4BDIBavwM+K30ePPo9Hr4Nvt//A79gPw9+5v4O/aA9Fz0Ivfs+in+kP56/Zr8jPvX+jr6bvpT/BT/JQNOBkoHSAdtBjQHTQhOCYQKfwvZDeMO2AxeCJQENgS+BMwDswJEAxYFqARjAU/9JPkJ9ij0Z/VH+IX69Ptv/Lz8a/sy+Z72mPSf9Kj2cfr6/K/9ev3j/Gb8Tvu++rD63/vm/mcCeAV9BsUGwwbKBsgHiAivCe8KQg1jD64NfAl4BVYEkQQ/AyQCnQJ7BDIFowLL/kb6i/aG9L/0Zvep+cr7tvzC/C/8CvqS96T06/Nf9uD50fyO/Xj9e/2K/MP7tvpb+q37IP5QAigFgAbWBlgGuAbsBgwIQwlEChYN2w4ODswJbQWOBAMEXwM7AuwC9QShBL8C5f4D+7P3TPWP9Uz3KPp8/Lb8H/zs+qz5mvci9Rb1Cvdl+hf9+f3d/Zb8tvsG+1D67vpt/Ar/QAKvBHAGdQbSBegFCQZaB7wIjQoEDRYOVg2tCRUGjATEA0YDgwJAA6oEhgTRAm3/7/uV+BD2CfaV9xn6ofvY+7z7KPtL+k/4J/bo9fP3jvpZ/Bv9VP2+/Ef77/oo+9b7CP3W/ggCbQS6BdgFzQQlBWgG1Qd+CFUJeQyKDiYNMQkXBhsFLAQ1A8ACKAN9A5kDnAL8/6D8NPkY92322fdL+mT7E/uj+h/7zvrN+PP2gvb29zX6Z/zT/Tr9Ofzb+zb8cPzu+6T8ZP6FANsCnASsBYkFBgUPBoIH6wgDCuoKggxjDEkKGQdwBBwEFwR8A8wCCwMvBFQDBgCE/Of5Ofj19mD3pvlO+1L7ufrl+p36FPk89+j2YPhG+hn87vwj/dD8QPzp+4j7/vsK/ZH+rgAeAyYFTAXCBO4EHAZcB/AHRgkQC5YMSwyYCagGmARBBDAEWwMPA20D6gPeAvb/0/we+lj4i/co+Pf5Ifsm+3P6V/oH+u74Ifjk9y35wPpC/CT9nvxC/Pz72/vG+9b7S/0m/9YApgLCA6AEpgScBKUFvgZ3CKYJpgrbC7QLbwovB2wEOwRvBFoE5AKnAoQDZwIoAAn9Q/uZ+bj30ffx+MD6F/uI+lL6JfpP+r354fjg+K/5TvtE/J38yvwo/BX8e/xo/Vj+if6c/zkBGwO6BP8EGAXfBXUHLQmCCcMJkwq1CqoJ8wbrBIsEhQQSBIwCEAKJAgYCLACf/eT7Gvo7+P/3MfnE+tL6GPpW+tL6HPt4+o/5Vvnd+Y77AP1E/er8lPyS/Nz8av0A/hX+dv50AOACigRLBbYFSAawBtwHJgk7CVAJ4QnfCcYHUwUpBUEFyAOzAT4BCwKdAef/hv1R+/D5Tfkp+QP5c/lN+nz6pvpV++X7MPvN+dT51vq7+1D8rfzg/Or8av0e/lb+IP47/hX/gQBRArsDngQ0BcwF1Aa7B24IuQicCAgJCwnkB/AFjgSLBOADMQIIASQBLAGk/4H9JfxJ++n57Pjn+JH5ZPqk+ur6Pvvo+3X8pPuv+q36kPt0/Lf8Nf1s/Vv95/2X/t/+dP66/jgApQEQA0YEDAVwBQ0GgwcvCAoISQiZCDUIqQaeBVAFbAQ6A/IBPwHlAGkAtv/0/UT8Ofto+t75hPnv+Qn6+fnf+uz7fPzr+1/7b/uM+0b81Pzn/Jv86PxN/rX+Pf4//uT+p/8JAF8BLgMCBIoEOgUeBsAGHQeCB1wHlQdMCIkHaQX7Az0ECAQbAtQA1gCuAKP/Rf71/F77SfoD+uP58vmA+i37VvuZ+0z8h/wk/AH8N/xO/Lv8m/3I/S/9S/1X/uD+xf5H/xgAlwBLAcgC+wP7AzsEOQUNBnQGCwenB2AHvQY6BmYFLwSjA3QDRQIFAdEADwHP/3L9VfzV+wv7KPoI+un6I/sm+5n7+/uQ/Lv8mPxt/Gz8Sv2n/V/9l/0B/nb+lv7Y/jX/Cf+U/8cAeAHkAaYC1ANvBJQEbAUpBj0GTgZ2BoMG6gUaBU4EWQPxAmwCOgEWAJL/ev90/uf8B/wo+1D6OPrq+pz7afu4+7v8Hv30/J/8zfzn/Nr8lP1R/pT+Wv5h/t/+If+Q/8j/vv8QABwBXQKpAuECnQNUBNwEPwXbBQ0G8QUEBlwFSgS4A8ADNAOhAbAAbwDv/wj/4v3t/AL8p/uc+w/78PpX+7v73vtN/HP9k/33/Dr9CP5+/iz+Qf7X/sv+5v4f/1P/m/+r//7/RADsAM0B+AE9AvEC/QOqBMIEOQWXBaIFVwW/BF8EyQNlA8ACggGRAPv/p//e/uL9d/3f/OT7K/st+2P7KftQ+1T8Sf24/eX95v33/Qn+Rv5r/mD+2f4R/xz/W/+q/7r/Ef8j/0UASQGfAeIBxwJiA00DYAP0A4YE9QRNBVEF7AQzBE8DTwKkAXkB4QDj/1H/OP/C/m/9YvwO/Oz77fv9+0P8rPwk/Yv9jf3A/Ub+d/44/gH+bf7G/oH+dv7j/nv/sv+g/+X/GQBdAI8AsgBpAWkCJQM0A1YDSATTBI4EXwSDBF0EbwO+AooC6gEhAZUAUwDs/1r/wv7m/Q79g/wX/LL75vuA/OT89vwW/aX96f3b/dn96v1M/sX+Qf9d/yb/Tv9p/1r/ef8RAKUAowDVAHQB+AEUAgwCbgIPA8oDVgRzBKAEvQQ6BPUCGAJbAmkCjAFvACoASgDI/5z+Xv3y/Pf8xfxB/Pz7g/zY/LH8xPyH/Xr+Yv7t/Ub+CP9f/8T+av7m/mb/5/8JAC8AXgBeAIIAawD3AMoB/gEwAqsCogOqA/oCOgPVAx8EfwPzAtICFQI8AXYAFwDx/4//RP+9/kz+9P0t/Wn8/ftB/KP8rfwP/X/96v0Z/hf+Wf54/o7+l/69/n//z/+n/87/OwC5AJEAegDSAOAAJAFsAewBqQL9AhkDtwLnApoDVAPLApIC9gLJAswBggEzAZgAyP9G/3v/P//J/vv9Dv2r/LL8H/0R/df8T/0H/iv+zv3+/XT+Y/5A/vr+1P/i/5j/mf8GAEMAaABnAE8A3wBvAZ8BbQGYAVgCXgInApICgQOsA8ICdwKuAlsCeQEMASoB4wBEAKP/Uv9B/wL/Mv4n/f/8ff2I/eH8hPz//Iz95v1E/rf+B//U/pr+sP4//8D/fP89/7z/vwASAT4AxP8QAIQA5wBKAQoCPwLoAdoBHQLHAuYCjQJkAqoCKgO2ArQBCAG6AHcAAwDc/7H/Kv+M/ir+FP7P/WH9GP0s/ZT9xf3C/cn9MP6b/qr+2v4Y/1L/Xf9Z/5z/vf8IAGAAngCxAHMAcQBqALwAeQHwAfQBnwECArICwgJeAvIBPQKiApYCIQJ8AU4B+QAaAEb/Uf/G/zT/Hv60/fX96P1a/QX97/xU/e79Lv5c/nT+qv68/tb+hP/w/8//O/8y/y8A1ACtACAAKQDLANUAxgDfABMBSQE+AcwBQQJUAikCpQEOAqMCuAInAg0B0QCJADMA8/+M/7f/RP+r/kX+B/4p/pj9E/0Q/bX9df5F/j3+c/6m/vf+I/+R/8P/mP++/9r/OwBxAFEAUgATAF0A+QAVAfcAyAAiAZkBoAHoAfQBtgHDAfMBOQIAAtABxwE6AcwATQDe/3v/G/9V/zD/0P6M/kn+FP5n/Wv9/f0U/m/+5/4h/7z+ov6g/xMAzP/G/xcAHQDh/ygAZwD6/5v/JAC6AKYAoADTAN8AuAD7AEgBOAF8AdcBAgLWAd4BGAKMAecAugDbALgAJgDX/0L/nP6X/s7+wP5K/kb+Vv7b/fn9Uv5w/lD+Qf4W/7v/y/+0/7b/3v/6/3EAowBpAF8AQwD2/y4AmAB1AJMA0AAQAVIBJQFFAUQB4wApAbMB9QGOAWABjgERAaQAXgAxAOX/bf81/07/Kv+y/qL+Ff6j/Sn+iP5a/jj+MP/D/x7/7/5D/6X/hP9l/zMAxAC8AFYAEAA5AAUABgDw//T/mQCJAD0AOADdAKUBFQGzAFIBxwGZARUBNwFFAZsAYQBeAGYAZAAYAKv/GP9S/3D/pv4Z/kr+/f63/oD+Nf9w/1b/Kf9y/7D/mf+j/6f/zf+y/x8AqgDa/1D/rP/g//D/QQDHAJIAQACqANkAwACRAJ8ADQFSAeABCgIYAUMATgDKAJwA3//k/zUAGgCP/0v/Zf9F/wb/p/4D/6X/uf80/+X+T/9i/2L/e/88ALgA3f+e/+L/6f+m/xf/6/5V/40A7wANAM//5f93AIkA/f+PALUAQQBiAAYBdAHTAHUAnwCkAFwAGABmAEgA+f8qACoASf/1/goA5v8O/3H/OgBhAIz/H/9q/5f/vP/H/yUAdgBMAG4AQgALAAwAlv8i/w3/xf84AKH/w/9KAAYAs//b/0kAYwA2AEwAdgBbAOf/yf9DAJwA0wDMAGMAdAB9AN7/of/W/8n/tv/V/+b/rv/L/2IAtwDXAD4Aq//S/5b/vv/U/9b/KQAJAAIAvP/y//D/Tv9g/2r/uf9h/7n+KP/x/1oAw/8mABEBggAOAAIAJwA2ADQAPADS//L/iADmAMEAQwB5AGEAjP91//H//f9l/83/FwEoAYsAJADs//H/4v8vACwAYf+B/wsARADi/x//rP/R/4v/vP9y/3X/7f7z/rL/kv+7/9v/SwCVAHMAtAAXAPf/JQBEAKQABACDANkAYQAuAIz/3v8lAG8AbQDc/wQAq/+n/w0A4//t/yUAJABIAIAAFwAUAFsAWQBaAEcAAgA4/6j/MgBt/x7/XP/K/0j/3v69/2kAOAC5//P/SAAJAOf/MwCRALIA2QC6ACQAAQBdACYAi/9y/+3/IgCL/2v/EABCAKb/Yv/M/8H/uf+9/+//mAD/AN4AFgA1AMsA3AC0AEMApgBFAI7/tP+f/8H/dv+F/+v/v/++/0b/LP/C/wQATgCw/6r/cwDq/7f/JABlAOr/m/9DABEAYP87/+X/SQCZ/1r/1v/P/2b/zP8lANP/SQD+APwAtgC9ANgAUwCDADcB2AAYAND/ZwCPAJH/I/+j//7/i//q/hP/fP9q/3H/bP+//xMA5f/e/77///8KAMn/+/+XANYA6v8z/4j/IACfAHgAu//p/4MAZACh/1n/CQBaAEkAygC2ARUB4/8tAKgAfgACAHAAMADC/5wAzP/+/jH/r/9aAGj/pP+nAB4ALP+u/gAAbgBm/xwAGQGcAMH/2f8+AIz/KP/m/woAzf8qAIQA2f8f/7//RADA/+z/yQB7AOT/DQCkAKAAFAB4AM0AvQCtAGYAHgBx/6r/TgCl/1b/xP/x/4//bP/m/ywAbv9H/08A8v+E/6//1f8/AM//zP/u/43/CQDO/7D/9f/k/0wAs/+h/9z/lf+d/7H/sgCoAMn///+RAMYAFQDL/04AmwCcAGMAIAD+/3n/Yf8dAAYA8v88AFIAMQDm/4cAFgCH/34A1gBFAIr/6f9sAMD/O/+w/8r/TP9q/4X/nv+U//b/WACC//X/jQCb/2X/u/+XAJwAsv+DAM4A6/+g//b/lQBDACgAswAZAAgAiADh/0L/tP8mAccAg/89AOEAUQCn/zkAyAAHANv/WgB6APv/d/+U/zr/Sf/1/7L/J/9F/y4At//V/oX/7/+k/zX/1/92AAMA8P85ADgA+P9iAMMADADr/6cANwCH/ywAugAdANX/nAAPAZwAWwBeAAUASADzAOEAPwAKAKIAmQCW/3f/0P+E/y3/E/+k/0T/gv7e/vv+Kf+F/8//lf8y/xAAfgB5/1//UwC8AC0A4v+AAKoApQBgAFYAuQCBAGsAQwBnAM8AqgBaANkAVgGMACYAkgDBAND/qv9pAL//eP9O/w3/MP+W/gr/If+k/hX/5v7R/n7+6/7m/0P/xf/DAIEAKwAkAM0AcADb/7sALwG4ADUAsgAwAUUAyP92AMQAhAC5AOoAkQA5AFYAZAD4/xcANwDR/8r/6v91/7L+8/59/xD/0v5l/23/3/47/47/qf+f/4P/GQAGABUAVQAyAL4A0gDMAPAA0AD9AIkApAA1AREBygB3AIcAEACG/27/w/8cAI3/sP/7/+H/ov/t/hb/kf+Y/9j/9P/6/9P/P/+W/wwA0f8fAHUAhgAsABkAQwCQ/7X/SQBpAGwAYQCyAJ4ATQBPAFkAHgCZABABPwDZ/0AAOABE/wb/EACH/0H+0v4g/7b+i/7V/mP/dP/V/1AAZAB4AJcAuACQAKMACgG4AIEAIQFIAZ0ArwARAWkAEwA5AKsAgQDH/yIAKQD5/+b/gf97/0r/J/9g/3H/Z//A/jH+1f4S/8n+Ev+B/+r/BQDi/xUAVACNAHwAXwARAS0B0QC0AOQASAHXAK0AvABzAFkAQQBZAFYAMwAWAOT/yP98/y//Pv8d/xz/4v6u/uv+ev6W/uP+2/5Y/7f/FgAlAGsAxgDlAFIBkAHTAbEBbQFPARMBCAHAAIUAMADx/wMAzP9m/0b/ef8//yn/bP8H/3L+jv7c/s3+w/4H/zf/G/8s/0//Lf9W/83/GwBgALkAXwGoAbkBMwIzAjQCqAL2AmYCiwGxAbAB1gAgABQAFwDK/2f/Ef+Y/u/9g/1N/XX9vv3F/cz9Ff6X/nH+0/3P/Rr+hP4Y/23/ov/h/38AKwGpAQwCcwJCA4sDtgPIA3EDXQPJAkcCBAKZAVEB7QCJAP//LP9q/s79n/21/Rv9v/xR/X/9Rv1S/cD98P2Z/W79pv3v/Qj+Sf48/zAArgD6ABQBrgGWAgcDRwNmA5YDogOBA30DOgMaAxoDpgLvAVsBIgGsAM3/4P4T/uT9sP3c/Ir8u/zf/Mr8dPyN/GH8Gfxt/OL8Wv0C/tr+U//M/7IAuAHyAfsBzAI4A0ADIwMfAyMDNQPRAxYEzgNKAwcDngIXAvgBZQHKACoAaf9w/nr9KP2R/A/8FPwI/IL71Pqg+un6Qvth+zL8gP08/vb+9f/4AMMBDAJ1ArgC2wJZA2UDlwPwAzsEtQTPBMEEYwToA4EDGwPbAm8C6wEmAdD/bv6b/c/8vvsE+xP7Hftk+qn5ffmo+aL5Jvp3+/X8dv6P/40ACAF/AX0CWgJcAlMDPQTFBGoEiQTsBBoF6QUyBukFnAVRBYUEAwP/AWgBOgCP/oD9m/xf+0f6Uvmr+Cj4bvh1+AT4+fg8+g77zvuH/ZP/ZQA1AUsC+gLKArgCHgMvA7cDjgRABcAFTwYGB0AHMQfdBiYG2AVgBcsDSwIpARb/rfx5+yb7SPr6+Ez4vPez9h32Qva19tL3fvnD+5D9uP4nAH8AOQGkAkED4gP5A6sEVQWuBagG5AZEB94HmgjZCAMIAAcMBgkFogObAusAKP4v+0z5RPkK+RP4tPYz9rP2HfYn9S71uvZC+Z/74v22/w8B/AExAuQCfAOVAwsEYQQWBdoFygaMB6YH2wg0CnwKsgl2CJMHhwZOBXADqwDZ/fz6ivg390r2rvX+9I30VPTM89TzpPNm9En3WvvN/kIA/wFCBI0FUAWsBOQEzQRHBVoG+QaHB0AI0wgkCbAJ7AlmCVUJQQmFB18FtAMgAQb94Pjd9gL3YvaZ9MfztfMr803yLfIw8k3zh/Yp+ij9KAChAqEEDwcyCLcHUAYpBSsFJQYMCMYIFgkCCjML5AtLCloI8Ac+CPUHAgaEA+4AgP3r+NX0PvW+9lf0AfKO8szzXfKj73TvzvFZ9YD4zvv8/9gDJgZWBzAJFwp0CI8FFwUoBygIEQlsCkELKQzQDGgMQgrxB/QGzwY7B74FGgGC+wL26fPk9V72kvL47pfwJvNH8bHtv+xh8H71gPih+7oALQUjByoJBQzmC3YIkwXPBXkIvwnACEAJlguNDYINuQsXCggJJQhvBysH7wXNAOX5TPWQ85X0w/Q58Sbufe8h8s7vYuss69zuk/My9937mQGWBa0I9Qu0Dq4NWAmIBbAFaQlBCw0KygksDEUOKA5mDAYK1QiQCN4I/wgyBoIAjvql9fPyx/JD8tru4uwV8BDyF+9d6+fpU+xK8ez1YPoPAFEGwAqgDRkQYA+BCyAIXgcsCWYKYwoHC84Mvg65DtEMdApDCRgJlAhdCEsHCgKP+VvzcfLB8hjx5+0x7PXvOfPp7ifomOdv7f/wr/Ic+bMBnQdnCv0MChD8D0MMkAhOCJAKEgujCXoKqg21D/wNvwtZDKAMCAtVCecItwdXA2v7bPLb7kjxXvNx7/LpLOsX8Ljumedc5Vnqge/o8pb4IwB9BwgNQA/nEDISaw/PCVsIDgsBDEQL0gvTDSsPnA57DFcKIAr5ClILuQmmBosDjvyj8mjtsu3577PvR+0d7G7tFe/C60rmCefj7SH0vPj0/vgGzwwQDy0RmxHmDscKHQjACKIKPAvBCqALLg7sDsMMhAraCQgLUAz8C+QJXAUR/u30c+5I7VzvZ/Dd7XPrxez57RfqbuRh5VvsXvLQ9mH9EgZtDRURBxOqE9oRxg2wCcYJaws0C0wKegruDPQN4Qu5CN4H7AkRCwQLrAkxBngAv/fB7obrSO+Z8WzuZuyl7vbvP+wg5srkmOoY8if2nfr5AyIN4xHXEycUMBJ8DWAJ1whHCnkKdAnSCa0L0QwUDHUJcAizCgAN9gt+CWkHMAI9+fjvG+zB7hrx/O6x6/fske/m7NzmuuRO6ubxXfWT+XICyAssEXoT8hQRFKsQ9As1Cc8J6Qm9CPIHqgkYDIcLswi8BsQHGAolC8QJIAegBAj+QPOV67XswfHc8VLuEeyy7RvvF+rc5TLpZ/Cq9fT4cgDOCXQQcxRsFXgVZRJdDFwI0gd0CRkJeAduB2oIWAnYBw8FfgXNCOMK1gooCZkGTgGx93PuXevm7mPxa+8t7tfuCu+I65/mSuc47aLz1Pg4/+0HIw9/E8sVIBaNFEcPlglTB2EHeQjYBwoHbQcICHoH6gS6BCYHggnjCW8I7AdUBAf7fvGL7ubw3PBz7oztM+8S8bnt7Odj5yvstvGK9ib9egWTDEwS1BZwGY0XUxEvC6sHeQaeBdoE8ASiBgYJ2QehBIQD1gRlB0UIvgeuB8YFMQAw967w+PBq8vbwEO6h7nDxqO+k60rpp+qR7+j0B/tLAtQJdQ8vEwcXeRcfE6MM+weqBnsFUATxA1oF8wdWCJYGhQRhBE4GMwg+CW4JkQe1Aab40/GT8PDwnvC57x/v5vC68Lfsb+i16Nvt+vI4+SIBZQgGDuERMhWpFtYUQRC4CyQJ/gbYAzIBbwHcA/0FpwXmA3AEVAZIB20IkglaCb4GsADA9zTxWvEw8w3yk+9e7/Dx7vEf7b7oV+l97uj0QPsdAgYKFhHUFOwWwBZjEnsMJAhgBjcFWAPFAWACQQUbB0EG5wPWAhwF8gcWCXQJmQihBbH/jvda8RTw8PHH8Vvvve5d8HfwJ+3y6XXqA+8p9g7+uwWpDGYSpRXcFkoWFBKPC1kGSQQFBJIDSgPbAyoFtgXbBIkDtwKHA78F7QcXCf4IuQWi/Sb1TfFQ8eTxjvDz7sHv8vEF8g7uxepd7E7xp/cQ//0GxA15EqYVHRcHFcMPzQkZBWsDIwOfAoQCVQMBBdkEZwOzAmIC6QPgBdAH+AmDCXAFd/xv87nwv/G/8vbwSe968LrxgvES7jnrNu1d8rr5tAFjCd4O9BHNFBgWRhQ0DwkJEwVvA5wCmgHwAMsBywPlBB0EpgN1BBUFRwYQCL4J3AhEAyD7q/Ny8Zny7PFH8Gvv0vAU8k/wpO0B7BvupfSq/CsFDQxkECATmxSuFb8TLA4wCCQEpQJ2AUwAe/+9/88BOAPhAwsEXgS9BX4GRAiICtoJOAS8+nv0Z/Pk8znzJfDr7i3xSPL274Xs5usA7x/07PubBDoLCxCpElUUJhVDExIP7QgWBMYBdgAIAOH/tgCdAesBVQIiA/gEHAbnBqcItgpnChQEUvpk8+fyA/UJ8+3vWvDC8v/zX/EG7Xnrbe5x9Ib7lQNkChQPAhOrFdsV4xLNDUwIFgRrAZL/cv+g/5oASgKbAZoA3gA0A7EGNAiHCa0KowqWBhD9BvXG8qL0S/X98Rnwd/Ed8rbwLe6Z7EzuHvMn+t4BuQjbDXMRUBTSFdsTpA4GCcoEZgLXAB0AlQAeARsCmwLLAVYBewIVBW0HcwlhC+QKvAXd/L71O/P/8jDyvvAy8anyH/M+8QXu3e0n8Bv0RvorAU0Itg3kET0VkRVoE7AOCgmcBCQB5P6s/SL+0f+UAQQCAQFWAcYCdAUoCJIJsQu4DGsJbACu9lPzqfON8+TxXfCU8l707fIu8KrtD+8N81/44/5RBeILmxAnFPgVuhOxD+kKKgYWAtj+nfwL/CL+vAAWAsQBqAB4AXMESAfXCKwKcww9CxsFR/to9Ez0b/Vg83zwn/Cl8gTzn/B27artZPFm9p/8TAPTCWoPDRMlFcQU5BADDNcHTwRUASv+nPxJ/Vv/lwGpAf8ABAHgAs0FKwfGCMkKEgsUBzj+tvYh9Jv0pfRz8nTxsfLw8wvzrvA58G3xDvUt+x8BKAeBDC0QWxOoFDgS+wxfB3ID5gB6/gL8D/sW/SwALgJ5AoABmQJcBlYJkgpRC7QLYQmWAt35h/Ql9Ab1QvRp8lzxy/Fd8qXxFvAp8Pnywfeh/lgFAArsDW4R+hOlE/8PnAp7BewBOP9m/bj8Pv0j/z8BDAK5ASMCewMjBu0IPwoODGULtAS7+4z1DPWN9jX1g/LV8Q70mfW0823xJfFc80z38/oyAKEGGwvVDmIRuBG3D+gLQAhVBJcAuf3u+wX9yP6t/4gAOQHPArcEjAYmCKcJ8QtQDKYIagB890X0cfVv9lT0WfEg8k/0GvWF8y/x6vGr9Kb4a/1/AkUI0QycECgTqhJeD1AK5QUbAkP+APuV+S/71f1EALEBjAJEBIsGnggdCRAJvQmZCbsFaf0d9uD0SvcN+Jf1IPOn88f1FPY/893wGfKb9bP5T/5xA1sIfAz6Dx0SaBHRDTQJ+wRhASz+9PuP+0z9lwDAAiQDEwTHBaYHSgjFBxwI4AhrBxUB1Ph69Cf1Ovf29VTzg/Oc9fT2s/VO85/yPvSN91v7/P8iBXMJzw2GEdkSDhG4DKUI1gSQAI38v/mY+e/7Bv/WAOoBnQM2BuEI4gmRCTkJXgmMBpb+1fbg85H1a/f59U305vRe96744vaQ9KDzGfU8+ID7s/99BC4JuA1VEUkSlA9cC4AHZwMF/0n7Ivm8+df8EQAkAt0DOAbpCKoKfgo2CUMIgwYBAr36O/Q98yn2k/c89tv0+PUW+HD42/aV9CD0k/UY+B/8pwCyBWEK/w37EFIRwg7BCsEGQgMw/3v7hvlf+qr9tACwAnAEvgbxCO0JPgmNB30GAQVQAP74j/Nh80D2eff69R713vZC+S/60fhl9vf15feU+kL9twAkBWAJeg0sEOwPlw0rCnkGggIg/oX6AfmE+gT+EQFBA0QF+weQChoLJgpvCKwGPwT+/hn4bfNS84z1M/YO9W30Mfbz+Kz5GPgY9h/2/ved+uX9uQF7BgwLAg+iEToRWg4rCtoF2QHa/Y76Mvlz+r79xQCgAoUEuAbZCOEJZAn7B6IFfgIn/ZH2FvPk87H2gvcw9h/2N/js+nH7vfkJ+KT3zPjC+hf9ZwApBesJug0DECMPCQyuCFgFuQG1/aD6Gfpp/Pz/nQJZBAgGeAi3CqIK6AgsB2EFcQE/+jrzP/F89IP3+PZx9Rb2//jX+pv5YvcT9sT2bPiV+hb+1AKoCBoOnRE4EsgPSgyhCGUE1v+r+4r5C/pW/Hz/GQI6BHcGjAhtCa8IOQfUBcgDTP/l+OXzz/MS97r41PfJ9sr3/vl4+tr45fYq9uv2jfg2+7D+kAM4Ca8NEBDVD6sNjQooB2cDPv8N/L760/vB/uYBUQQhBiIIGAo/CpsItQZDBa0Cy/y79ejx1/Ko9XT2JvVJ9VD4VPuW+2D5hPdk9zv4tfnS+4T/9wRfCqIOmRCbECIPmwtLB1ACPP3J+YH4Nvrt/doB0ATlBl0J9wptCroI3AbYBJkAsPkL85zw+PL29b72Lfbz9un5HPxj+wb5afff9jv3v/ij+6oAEgfpDOAQHxIhETsOAQqfBfwAVvwH+VL4xfok/ywDlgV1B/kJzwsnC9kIrQbEBD0AGPiz8Hzu8/E89jL3ffZT94z6r/xa+4D4jPaj9rn3V/lh/BcBcweKDbgRQxPuEbQOJAp6BdgAv/sL+Fz3IfrP/vMCDgbrCMYLdg0DDJcIjAUxAk38GfRb7pbuv/KJ9jX3zPbS+Gb8Uv6p/PT4gfYs9nT3tvlr/fkCTAlUDx0TJRN7ELUMYghiAwj+O/l59qL30fuqANcEzwdtCpQMWA3kC+wIiQUgAd/6IvO/7Tju2fL99ov3mPbV98D7if7N/KH4cfUV9fL2j/nF/VYD6wlmEAoUFBRDETsNEAlSBNz+d/lH9qr3a/xwAY4FiwgRC+IMBg1jC0QIgQQ0/zH3jO8l7AnuqPKb9eb2kPif+9b+r/6D+1j3ovTZ9Mz2nvq2/2wF4QuvEV0V1xTEEMILdwZCAY775/ZA9pn5NP/vA0kHbgowDZgORQ2SCrcH6QIG+77xHuyJ7DzwfPOe9FT21PlQ/XD+APwP+MD0CPTH9ZL40/x9Ao0JPxGJFo8XUhRVD0UKhwSq/nj54/ZF+Fb8OgHIBH4HBQvyDR8OpQuMCGIFhv+K9gzu9epR7hXzvvS89Nb2SPum/tn9D/pj9jD0RvSh9rD6jv9dBaUMexOTFzkXtBJFDbIHtQGz+1D33/aq+Tn+4AL3BrwKpw1NDy8OMAvdB0gDzfsO8rjq3+kH7mjxffJF9N33KvzV/VT8O/kI9p301PQc+NP9ZwPgCccQuxYmGVIWORFhC0wFI/+J+Xz3Y/hc+wAAkATPCA8MHw6iDoAMlwnwBZX/nfYn7cvoMuuE70zyafPQ9Tj6Dv3c/BH6wPZ19FjzqfXV+iIBAAiXDjoVfBnAGGEUHA4NCLwBGvsN91D2bPlZ/kgDUAiJC7cNPg7nDDALYwi/A6b6de//6MjpFu/58dTxxPPf+Dn93Pyc+TP2fPN88tzz2fjh/4oG9wxiE3kYfhnOFdwPyglOBBj+0fiS9+D5Xf6CAgIGXgnsCzgOGg4ODNIJ4AXf/qvz7ulF6H/s/vBc8YPxtfXZ+q39r/vQ97z0DPL18pz3hv5wBcIKvRCAFqYZRxhxEvILlAWr/6H60PfM+Ez8rAFpBlAJlQs3DXkOxQ0OC7UHggFZ97fsKedh6fHtue8Q8O7xmvdj/KP7KPg69D/y+PHm9L38LARqCjkQbxbkG0Ub1xVkDqQHlAKb/f/5Hvkm+4v/sgPRBpUJawsSDKgLWwo2CEUETPzx8DfoA+je7cHxIfFf8OP0Pfvo/Hb6CvZ28knxT/NV+rwCTQmXDt4Uvht2HVoYJA9aBiYBq/za+NX2YPgs/uUDewjAC64N+A5ODvkMpAu7CJkBMfTc51Xl9+p+8I3voe318BT3lfqi+Pvz0O/f7YjvTPZWAJkJnhA9F54ejCEtHUsU5wozBCj/j/rL9+D3fvz2AtUH+Qn8CikNxA3xC38J1wZ5Afn1B+lG49XnWfAF8/nwW/Fo9rL7d/sl943xF+0z7VTzAf7BB2AO6BSTHJAhByChF2oMwwQmAJj8T/l391H6igDGBoAKKAsNDB8M4Qq+CgQKPAau+LDmQN8k5BruA/F+7tzwn/eO/uj+Fvoi9ArtE+yN8vj8tQYODWETtxotIbwgAxhhDaAEjv6/+RP2pPUv+Qj/XwVnC94PaRG2EYAQ9A8MEDIIXPWz337XgOCv6//tq+tA75P4D/+W/yb7M/T17XTrePJG/3sKfBFsFewcuSMvI1MbBQ/xBMX8Fve08/Hy9/X++a//1wX/C6IR/hGQD8YOmxD1DNH6wuRJ2zHkifCu8UPuW/Cw+Hv/MwD0++j0bu3k6I3tYfoyB2MN4hD8GHEhiCOXHOcRIAag+6r2tfRy9Sn3fvpg/4EE6AtWE30Y+halEeUNIAj8+RfkiNWE2LnmX/FP8O3uafhSBDAHVwGL+dPyPu7S75X4pgRlDJoRdhupJRYnAR2uDW8BSfop9fPw9/Dr9Cv6w/9kBHsJOQ/hEUgRig7yCzcHUPyv7D7gA+K17Z/1uPS388H6HAMxBOn+1Pdy8cHtN+/I9uL/PwcmDmAXwB84IIkX9QpYAkL+X/th+P32QvmG/T8DygcGCjEMlg66D/YNsgoGBfD5DutY4J/il+vv74Lv0PEP+xAEUwZsAkb8ofcL9KXzEvgU/5MGpg5pGK4fwB4+Fl0LoAP5/Xj4WvRn8r7zVPgb/78ENAhMC+4NKBBhEIMORwn6/eHvY+Yx5vbptuu97X/zqP3EBa0GWgOe/fz2f/FO8P30Q/uHAk4MsRj7If0fxhY7DeoFiv+Q91Hx0e7z8a/4i//0BbsKEQ/QERMRGg4pCkEEIfnc61vk6OXC7NHxHvWw+9oDjwdqBM7+xfkk9CLvU+5a9Cr+TAeREBAa5R+5HZQUXQpbAnz7f/Va8Uzx1vWf/PMDLwocD70RoBBoDcwJ5Qa1AIT0z+f+4t/oYfEx9fX2s/trA/sGXAPz/EP3evNz8ArxR/cLAOsJOxSgHf8ghBvkEAUHyv9l+VDzwO8E8bz2e/57Be8K8A1SDVkLOQpGCS4FnfxX81ntkewP7yvyXPbG+lr/KwJmAR3+MvkX9Gjwb/A69Jz5CwGYCwgXyx57HnUXEg9oB/P/dvdA8fnvBPSq+4IDawpwDg8PoA3kC8kJfQX8/pv3evKn79/sTevw7BTybPge/b8AhgLHAV7+Kvrq+Cn6mfyn/ycFqw1YFbcXvBSdDxkJeQI7+2b0JfCd8CH3UP/gBW8JeQtgDiYPWQxcCF8FcQJq+1nysOxk7D/vlPAu8xf5Pf/LAbf/Ff2K+mz3QvWZ9a75X/+aBXANChVRGfwXJRJcC5AEE/6m97vzd/SJ+FT+YAOhB5sLsA3SDCQJBgZPA4D9lvRp7LDqee5R8ozztPXN/MAD+AQ0AZr8hvm99xP30fe2+ssAXQnGEuYZthppFSwNvQW1/3j5ffPV8LXzsPnY/1kF3QosD/QQ3A/oDMgI9wG89+Dt3+lu68Dt7e798kL7yAJkBN4AK/04+sH3mPWc9c350f+EBqcOYxZaGbQVMA8SCX8Df/1j9+70/vaH++8AfAX+CRANXg3rC1IJcgXF/1D4Z/DN63vrV+0F8EL0vforAP8CPAK7/iH8APqS96X2U/kM/+0EwgqLEGwVCReREtkKYgQ4/8r6kPdU9435vfwKAcYF/QnmCxIL4AmlCJ8FnP+N+PHyIu/B7X/uufAB9HL3MPsu/+sAFf83/NH6Jvs/+yr89f+CBsINAxORFa8UGxDPCeUDFf+W+gz3j/b2+Mf8yQAfBd8JOQzAC0EKbQlNBu38a/I/7TTtze2M7RrwP/Vf+7n/3gCjABb/lvxb+rj6VPw6/tQCogkOEe0UWRTGEbwN6AcOAYz7kvgG94n3Uvs6ASUHbQq2C/EMfAw9CJgAE/ls8q3svuqv7G7wgvQQ+Jr8wgHEA+kAyfyJ+1374/mf+GP7RgIYCXwO6hHyE+USlw3AB/4Cyv7Y+kn4NfpQ/1sEnAe+CIEJNwnEBjoCPP0N+VT0MvCC7gHwpfN79135Zvp6/RQAbf+o/OT66vlD+cr6pv1kAqsIXA4HE5IUzRHBDDAIEAQ0/3L7Lfpz/LD/QQHAAt4FwQiKB6QElQMFAm/9KPct8vnwfPIZ81DzgvVT+JT5oPtt/ob+x/yL/Kb+PQGiAq4ErwnEDjQQFg/vDCwKfwY4AbH8zvq5+of7mf0NAuAFnwjNCvQKOAm6BS4CvP1L9yXxJO7d74TylvIe8xT3TftB/HT6FPr0+yv9d/yB/fQC7giYDOIOmhCLEEgO5QoMBjEBhP26+9b8cP8EAm0EpQfACQ4JEgdtA2H/YPvu9371LfPa8bzx6/TB+J744vdd+Wr7XPvf+ef5H/yE/0ACtwP9BoQLdw1EDUAMcAoZCB0FwAH5/0AAugHOA8MEagW9BoMH7QWjAvv/c/2R+i74wfVG9IH0kfUW9033YfaB9sP4Gvsb+m74pvocAIcCqACYAqIIewz8DN8LwguKCwYJQgR5AegBggHTADACvwRHBQwFnAWVBPoBTf6P/Bb9tPt599T07/V59kH0//IY9an4+/ko+Z36jP2p/tj9wP3zAEoEWwUXB3QL1g6uDrcMQQpjB6sDwwBKAAgBtwHsAQYEzwZ0BmgD+QAKABv+YfuV+C329fVp9kj36Pdb9+n2Fveg+KX4q/a89q/52v2PAD8CrwQgCYcNLw7nDP8LNAsSCcEFdAMbAlIBogG7AkoDIwPaA1wEqAN7AtgAM//8/Av6+fYX9mT2O/aQ9sz2Wfbm9dT2j/en91D4i/mu+z///QLZBRsKlA0hDhwN9As1CusGTQSFAocCWgQpBdMFQAe+CAQIXgXbAkgA7f4N/a362ffd80jyf/GD8oDy2fBi87P3PPqs+RH6Xfwx/of/+f+vAtgH+wu0DSoQKhLzDigJSQUAA1UAX/6N/2oDUQepCFcH+gYTB48DSP6u+3r7dvku9gb1s/XJ9Yz0s/Me9FH0yvNw9Gv3kfng+bD7ff8UA3gFIwhVDbwRfBGWDqsMNwsxB18CkAB9AiMFdAXcBZYHjQiyBhMDcQDu/C35OPej9vz1LfSm87P03fYA9j/yWvI69i347fWe9QL6Nv9ZAvsDswfoDaASlRLDEJsP7Qw1CLEDxwFmAQ8B/wITBmgIrgh9B6gFtwIu/7H6p/eB9hL1XvOQ84z1MfWS84PyJPKh85v1WPaV94/75/8bAsoExAegCcoLSA3jDrkPAg4yC3wItgdjBQIC+gH7BOAHRAemBxQIUwVcAt79dfqM90/zTO/R7rbxufCk7zDyIfXU9kv3HvjC+eT9TgA3/88BlQbNCIMJUQuoDhgQjg/nDCIKkAi0BHcBoAG1AnoCgAPWBq0HpQWMAzUC7/+i+7H24/NV88Pw2u2z7/jz1/Vf9KH0Qvju+7b8o/s9/aQAgwIjA1EGZgsjDicOfA6jDzQNSwmDBj0GhgWyApkChwTyBjsFagIjBK0DTf/++Y343vda9GzxYvAU80308/LK84/2WvhF91f4b/p8+6X9sf8YApsFvwiJC4UP3RLFEF8MWgtZC5YIRAM0AE8CuwVABtoEpAUjBwEFTQEe/tr6fvfi89rxbvGe8ZLxpfH784f1qfUo9mn2WPjj+YT7Uf61AdcFDgjIC4QQfhHDD1sNYw3SDMkI5AT+A3cFMQWQBF0F7wZfB9sD4QAb/2785/gw9p30bvHP70fwDvDf7wPxdPMj9Uv3UPiO+a/9MACwAVkE4gfMCpIOAxGsDt8Opw9eDKgHoAVfBnEGJQYWBcUF5AcoBxQEeAE9/xf8jPm79hrz/fH08THuRO3a8QD0IvPb8wv3WfiA+TD8I/7HAW8DHAXmCUYNJQ64DcUQ0BE8DX0KLwrbCu8HKwWBBe4FZgaTAxsCfQFG/j76vPYr9iT0DfIU8qLxBfHt8ETyyPPe9d728Pay+kX+M/7W/uIDfQmMCngK0QtEDiEQyQx9CWkKpAsvCXEF4AV/B3kHAQZqBJIDQQG3/j39kfqS9uXz0/OW86TxgPBR8DvzCfYz9An0LfcF+sP5ufrO/68DzwbjB0YKRBBXEv8PLg2ZDb0MhgjGBSEDAgO9BCEGiwb2BY4FcgOjARX+Tfec8xL0BfR98abw6/KM9V73c/bt9Br3j/nn99T3dv2YAcsBWgMoCCoLnguWCw8LpQt5DLULnAh/Bi0HPAjgB0EG2wV5BhAGPgIy/X76Ifnb9+/2l/b29s73h/cu9D/yyfPc883zMPRd9kf8fP8+/gf/7wRTBqUEQAjcCt4MDg89D4oN+gr5CkUKrQfMAur/2QFxAnQBI/98/Q3/CQDJ/Pf41fj397j2afZh9IT1c/ne97H0zPf++nz4QPgF+xT9vQDLAggEKwjiClcKUAtZDf4KNQg9CWgJHwenBWkF4QWZBQgDVQEdAgwCQ/78+Q/56/h++HL3svZD92r3YvbS9aT2CfYT9a32APp7+937B//tA48HAQfNB4YLhA1EC8cHRAmLCocIAgW8A/wFSAXZA4UCvQJLA2ABQwBa/kr9oPvC+Vz5K/bm9Fj2MPYD9T70D/T49Tr65/nE9pj7jAI5A78BuAKCBgUKAgutCdoKQA5FDUELIQp2Bw0FywOeAiwBDAGrASEBFQH4/h78wvs8+tD3nfVg9IL1ofee9571J/iR+4z6EfnR+Kv6QP33/ksAYAJ2BjEJ5gptC0EKSwsYDDQKWwamBPUFAAa1BL0D+gQMBTUCwv4D/bH7AfiJ9kn3I/hU+c/4KPhn+Hj4aPY+9or3WfWy99/8wf6/ADEEfQblBjIJ4AiDCMALHwwCCpIIaAmBCE4FlgL9APcBywC9/Yf+/wCi/wj9g/3l/Eb6Mvmr9+n1pfVU9c72yPkH+WL4ePzZ/2z+J/7fAHQCRAWhBqUGEAnvCTMI5wWIBiUGtANKA2MDSwTSA0MCgwLSAv0ATP4r/rn+HP1S/FX9Xv2c/BD79viW92n3uPWL9MP4dfyV/Iv8O/9yApACUwGEANUDzQUPBLAGWQoFCs0HtAddB6UEzQIcAOH/FQIWAbr/WAEiA6gAc/1Q/XP9jvyF+WP3h/mh+0L6Zfjt+aD8k/zB+yL7HPyw/4oA0v5q/9IC+wQGBd0EbAUACPoIRQapA04ESwWmA7sACQB8AqUDlwF//7gA/QAk/zT+CP5P/m38HPxK/HH6Nfom+sP7QfxZ+hn62PvV/uX8Afwc/3IAAwNgA5IBRgOGB4sHtgOtA3EEmAK7AWQCtgF4AbcD4QSWBBUEmAGYAGIB//4E+3f6av0J/pj8uPuG/DX+Nfwt+Qn5ifvf/Fz7Dfzf/4YB0P91AOUAPgDHAkkCtAGPA4QEiwSkAwYELAJfAZIDHwO5A8gEFgXDBEMDlwJnAJ3/Yv6r+6P8Nv4b/gj7MPqX/YX9NvqH98L4CPxV/JP56vld//IBqwEMAWsB2QMxBdYESwKdAdEDIgY4BX8B8gPHBs8DiwElAioCBgI/AsD/sf/hAaoAL/6B/W796ftr+737QvvO+k/6gPmg+zz9WfoD+97+kf+F/rYABAMJAsAD+APnAaMCJAKkAC8CTwXdA10DZwijCCcFeQJRAwsE3ACs/lL8wf4eAoD+Svvg/HD9g/q++Zf5Tvg0+UP7Ovrb+tv/KP81/SABKwMLAeP/qQFdA6UD5wI1AcgD/gbLAkkB9QXGBhoDMAJeA4MCMgJDAqMAuP9YAC0Am//S/dL7Lfwy/MT6avmR+pz6Svq4/IX9Xf2D/MH8gv9gAKj+Kf9OA1ADUAFHAy4FpwRNA08DFAP3A9sEFgN7AkMDGAS5Aw0DDgHi/jMAHAAo/aP6aPuC/bb8y/qM+k78ov2A/e77ZfsR/bn92f6a/0T/dwHzA30D7wA5Ai0DKv/9/t4CwAPoAF0COwYGBikERAIHAqcC/gG2/m/9mP8EAJP+6vyx/VIB0gBX+zL6SP5O/nP5//ny/LT9Nv9z/z0AFAL1AMT96wHGAv/7g//tAy0ClgLOBcwE3AHABC4EQ//q/sUB9ADb/nz/nwH5AZH/U//F/sb9Ff0K/LP9b/+q/TT9VQCo/0T92f4//8f8Rf8IAXL+OAE0A/v/KQChAscAtf41AIAAdwGOA+wBYAKvBO0B5v7jAAYB4f5z/x3/K/5pAFACFv/v/hABiv7E/L7+4/7u/Kj9Ov62AL8Aif6a/Tb/NANB/6b97v97AesCowDB/ScALQWV/4L8WQINBbMAnfvbAi8E/f1X/nMBvQOp/9z9DAL5Avf8+PvTA/UC1/rx/M0ESf/5+W8B9v8h+gQCpAKb+/T+1wGl/8z9vv5z/qAA9ABI/TMFAwUI+q7/fAgoAAT5QQDgBL8Bi/8FACYDdgV0/h/9HgW4/834LAJIBA36of4yBWP/Pvxe/9EBBf9q+3v9RgGvAAD7e/1VBBf+wfyDAjIA/PwRAcUBof1SASAE4wFqAEID8QS+AIz+EADQAowBx/6B/3EC+wIt/rf+YQOw/xT5dPv1AwwAHvds/pcF9vxy+g4Ea/7C+o8BFwBD/Zn85gEgAv8AxgD9/1wGTwJB/X8DOQTo/AX/BwWYAHgCjgG1+/MCMARJ+0z9UwVn/3r8RwRIACz8XAFb/WX4uAK/Aor4VfsvAnMBdvw3/Y/9SQNFA5b5LgKHBdz/UQC5AcgCZQB+Asv+yv6ICMUBt/kOAg4Juv8q+soBowEPAD79vPuwAyX/qvmJASUD/vrv+lkExP57+fUBSwG2+FT/7QZ6+lj8UAciApb7//7iCN3/FfpaA9cDhgJN/7D/PgOmAiACMP+HALUC5ABnAAcAs/7J/6sB5f5l+wD8pQIAA9H6IvvEArUBjfoZ/MsBUABt+tj8CgMzAOr79wBvBe8BCP/m/xEBxwQOBDr7ff8mCZoCsPnIA7MFP/qBAtgEvfzf/rABRgBe/KgBTP8g+3MD+v4G/N/+CwAi+577lgVu/ID7IgM1AAn/zAJvAov3tgNeCUj2Ev+JCi3//fflBg0Hive9An0GN/spBQkFevh/AgcJqPjo+bEHX/+s9tj9YwXwARv5Zv1xBT4DkPmW+sAGCAB2+FQAgQjV/ALzUQlrCZv3DftRBi8KMvn0+WkH7AK8/gn/kQM+Amr+wfzaAuoCCvW4AeMH8/qy/t8C4AOs+yr+MAVX/EL+LAHA/k78Df8SB4/9GfzwArX/FgCZAOgC//8u/GQAQgRDATj8L/2uBQEHb/nJ92MFYgvA9pj0+AskBfLxOgAWEOD5n/ZFCDIDX/7t/oT2tP5sDQX69PDoC+sJGvZh/Z4LcABj+RIHp/s5AtIFwfUrBSYEIfq//OUHSQAc9IIIAgWf+Mf7TwQhBvX3w/y6A5wBqfkm+3sGHQES/w7+Jf49BSYAsPquBfAEQfgtAVkN9PrW8uwMXwji88D9zQtn/374rAGwAqr/b/wQALkBsQBz+M37cAq7/UD0jAEHCPP65/mkBXX/2/xkAUoB7P7DAgH+W/yWDRIBL/S3CBMHV/fiAf4IB/1MAD4DjP1cA1wAuvwuAI/+sPwj+7YDYQa2+Ef2DwheBzr0IvNWBugMq/Nt9sAL3QOC+kD/qgdU/5/5QAU6B0MAtfmeAWQGaADIATn85ANpA9P8rgHk/30BSPoQ/9kKpvsP8ysFgwp+84r4kgy5/hb2fALNBUX73v+FAin4kgOQAk37dwOF/egDrQLY++IElv7LALUFS/3e/s0Gov27+0EIPvyX++QCev8XARP6UwJtA0H94QLc/+76EP/9AoP8UP2EBLP9Gv67Brr8iPsrAwcBSP54/PgCrgGU/HgEIgNs+wYE4AFj9xMEHAfY9ir9iwp5/8z71QK9AUkCIv2V/E8HhQWG9zv7owfr/vz32gF8BMD8h/mzAR0G8QAc+4r8VgV4ABn6UvzmAxcDo/uABJUCxPsAAK8IGv5F9QgJ4wJ//MwAqQGSAAL6gQzhAD3zpQfeA0L7fPvUBFgAkPbIAnoHywEf9qL6IgzzAHbywv6TDPb8MfDgCHUMZfWo+SkH+gUr+kT+gwYP/uj+mwHeAn0DN/m//sQH4AAN/Zf8wAhN/jX4nwqpAKX08PyXCdsCOvWn/HUFNQe99+r7kAtO+X35VwUMA8z78PjNCLQBjvhmAVMGrwAK+cwDwwQq/Vb6ogBNCJ7/pflkA+4L5flY8ykLiQz89kzyMgpkCrf0dvsNB5kE5/YD/j0HQv3y+e38aQQWAU351/uFCzICu+yNCAsOtfMC/Q8LjwJ8+an/RgHeBp0E3vK8ASIMwPtF+NcHygdn9kT8NwYJAOb8mfof/6sMsvuO8jcGgQKb9j//CwkH9xb9hgu1+SD/TAWp/gb9qQAHBon7WwGFA6/78AMzA4MEuwAI+1kDDwYiAAP2JgBJCID8r/5FAcT+1ftZAHEDKvaW/AQDXf4LAUv9v/wXAL0GGv8b+bsEiQQNA7v8svogB+IIxvhf+HUTEgb16S8G4xPY+K7yzQN0CEL7Y/lpASwAGf0J/+cDZv+i9Kz/Igj8/C33ZP/DBwkAf/hu/zEIzwLb9vf/KA1gADL36gVDCJT9gP64AxH/+QKJBEv7RwQCBBH4BP/jCGP71/THAmr+ngDmAdz1vPwsBHQBc/gO/3UCv/q0BMsA8/zk/sn/zgfhBB8AQP3u/RIH1wf/9T77IBT9A8vvLwNkC/34BPnOBRf+WfwZBNL72P3kA4n4Y/qWCZ4AefKD/4wJVwCl+3v9qf4CBNwCOPrqA3UHCPezAr4KO/uT+zQCEggpAZ35zgHoBHEAzvkhAaEGbvzx96b+YwwPAETs0wCxDyEA1O5RAEERxvmH97YDEwKj/D37XQMhBe3+YP20BFQGdP5u+9D/tAUb/i/5SQQTBdv8OfsrA/8EyQBC/sf7lQHkAyT6w/rEBeT+0/qoBUkEGfzp+LEFOgtU9s30mwtsCR33bv0ABX/96/+uB0AA7vnaAPv/RwC/AtT6LfkvAw4HowBV/SX+bAHEA9/9nfng/LMAFQDgADsBdvlhB8YL3vR++zAJ2QTQ9Vr8HQsqAKj5JwPRBsn+y/19/x4CpQE6+1UA/gAJ/zgBsvyhABACtP0MAPH9UgIDAoX7iP8UAQr+fPkJAqkFSfy8/RAHCwgi8+PyFhG0Clnx8AEgD7X8efYYBicKkffn+0AGaQRjAk30yPyhCaH9Yfjq/rECfvz//ZsD3f4/+jj80Qa0Ahv33QFHBfX+yAGIAFT7vwF3BOv+JwN+AHL9IwaTBQn8vfloAzEJuP8w974AMAibAHH1YP0nDNT9sfev/4f+PQAWAKf/Svwd/WEAqwBYA/P9JP7nBMkAi/rEAyoFtfaBA9YNY/uT+GMIgQY7/nL+H/gYAgoPP/qw8LUJAAn79OL80Aja+l35ogks/0z0qf54B9T9HvxcA/v7KQNqBjb3w/1pClD9tPdlCRsDjflFBO8F5vz2/YEGJv5E/WUGgf1g+AEGGAVC+KT74gZPBUD8QADE/478c//F/j/86/9YBGL5r/+tDMP2YPdfEAgBtvJBAIIGvP6v+bgDHger/Gb+jQhcAWT4OAMaBr33efvQCXMA2/mwAgsAbf7/CN3/0PVvBP0EGvey+7IIIf9/9TgElQqt+Yj1vAcqCZD5QPS2AfgGBwQr/9HzYARrEIz84fLXA7QNSvlU/A0Lp/zh9N0F0gyO9TD2zAz5Bsb6jfmq/oYGH/9a9YX9tQdl/wb8aAUJ/ZH8CQXO/9r7+Pz7Az8BjfyPAwv+oflECdcHUfbG/fILo/+8+EAE8v7m/V0E1vxWAjYEzvvX/dMDrgMW+HT61QQXAy/8gvvtAlsBOP9RAHf9vQDU/5UDOwNZ9XX+HwurAN34pwFUA7//mgCh/XMAqAMr/j//RQfX/kf01gQ2DcH56fPpBh4H0vkI/4YEIgFs+2L+SQOF/aD87wEsAO38LgMV/iL/uQUK+d/+BAaE/RoAIQOL/V7+vQMS/j//NgZa/g/9yQGIBSb/Bvz6A1T/FP44AQr/3P+6Aab+dP0WATQDW/5M91QBtAZY+wb7bwVCA4f6t/96BWMBB/0w/JsCHQWx/Yr7JAM+Bfv8GQH7BPj7+f7MBykCuvXB/bcKfv/q9+4BigZL/tz7FgEk/7P9Sf4tATz/5PwBAqwAZf4O/gsD5wRq+fn8YwlR/M74RQdTAEj73wSpBWD7nv0SCUkCm/Yf/hkIpf8N+eIBSwOH/aUBbgNT+dv9Qgho/Kj52AHbArP9Bfg4AzEFc/97/qD8owOJBNL6z/y4BST+UvvgBNQEhf6Y/1YC4wDvABr+mv4+AVwEDwFS+HD/jwjZ/8P2iQIOBST6QgCQBYP4Xfo9C4z+w/TTAgMERvwZ/ZUF6v9Z+gwBAAgWAODy+gOTCmP8cPrzAocJB/zc+W0GOgMY+/7+swUEAFT7JgLsAy/95vsdALgAN/6E/xT/3v6vAjD+wv0YAwX9TvqRBK4DLftk/EABQQj0ACb1TASjDbX1/vilE9b+LevbB8AV/fOF7ZITqQrG7j8BvAl3+yH6nwKRAqz4MQAIB8D7I/tgBL4Azfry/4b/bgAWAVX9SAKOAHb+UwHt/zL/3P8iBWYCiPgS/H4JoARd9V3/vQha/3v8lgHOAsn+Rf31AIkC+P3q/boAUQOMAOj4uf6JA3gDyvzX9r4EYAnc+AT3tQguBYT1m/+gClT/yfYfAG8H1Pva+a4HpADT+doI+wbc9q38+AyC/vXxzwcKBiL72P+V/3kDlP8v/cL8YwPHBVT2wPzMBswA5fhU/XMG6QGG+vj8bwVP/qv9aAK/+gcB5QSl/k78vAKFARb8Kwg9BEL1AALiCwX++faSAisG1/t2/0sDVfzF/TgEuwI3+A77/gO6Air7/PvxAnr/cf48Al//v/upANwELALN+l38NAMAA2UDnPZ8ARUOVfdn/ZMLjf9g9ogEUAoD+Cr9LQXqADD/yvw/AfP8nwCAB5/4ufa3CDsH9vOx+AEMWgM28WYAFAwJ+x77ZgXuAIv71f9GA4QAxADJ/4v+LgVFBIz5fvvRCJ0EmP2qAFv+c/39Aq0FXPgp90EHbAes+Uf7bQm8/Cj4+wXdAWn4z/1bBm39zv2eAyb8yQHxAb78NARDACL+qP79AW0E6fr4/2cHPADO/hgCI/0X/zUEefxUAV4CtPqiAicE0Pvg+4sFq//0+nsEX/4O+e4AKwTf/Cr7PAPSA2j/S/9//+IAjwDo/ZMEI/wT+lEJpQOA/OL9RgZrAfH2WgVXBYT5Kf2HBg0DzvTPAowKrPm9+9kES/2c+zEHVP1L8r8Jegm+7lMCQBCs9Hz02Q2mCTbuIP61DHr8LP8AAn77CQP9B9/8Qv4zBfz8jP0qBob8HvYzCtEISfU+/pYI4fqi+twGNP7g/B0AOgIXAd77bv8z/V0D4QDw/O0BWP8DAVP/qP4OAe0A3/93/P0CAAgW/c75pwPNBn37gvdICv4F0fIeAukIV/p++8AEtwFz+xEBYASN/LD6RwTBAt385gC6/SH/iAWe+1v87AVn/3L/8AGb/a3+yQHqAkj+Jv/GA4D9aP+7A8H76/7GBTD8M/vFCz8B9O7DA7cNSPdY9qkLqwRi9V0BKgWE/YT+7QA+/2H+av/n/hADzQKe/CH+VwK8ASgALwLE/hP6ygFWCDz5fftzCyr4aPsnEhn8TO2+B6UPDPOh+LYMPv5D+jUFkf5X+j8HEgHi9/0Dyv8J+ukEjQHf960FYgb79vAGYgir8v4AAA3l+Er1SwoBBk/24f01CEEAtvlVA2wBRfuWAWIE+v2e+C8FwQR3+ncBRgOq/Sz83wDEAdL9TP32AYQF0P2w+kkFJwRJ+lz+5QRyAST8zv6EA3IAKfymABwIWftK92wLhAXz7yz9YhAf+3T1YAmpA3X54QBoBIv8EP9kAXT6RQBwBaH7Yf05CP//U/hfBMMGYvlb+nUJIwGj9W4CHwdJAf/53/22BVoCOfwF/twDCf3S/aEFFQBQ+kkBlAY+/vP7gQB2A2f+xf0sAur7Q/yqBTQEFPqiAUoCE/mJBoMFGfOJ/L4RS/xu7koOHAZh74EDfw3d+qT2dgezA8r43/8SBtr53gC/CtL01/xCDUX/NPL0AcoOK/Z598kMbwBG9t4EOgTw+K7/XAUs/mj9PQT0/f783QIz/YABfAQx+n4BYwPr+TUDGAI8+7AAkQEd//8ApwEB+xD+kAlE/uf4TQEBBM4Dffra/cQENAAsAI/9AQDQA7P/oP9M/WcBcgO6+0v9ywKpAW388/+bA33+aPv0ATQF5/hqAE0FWvpkAEsCjQBH/P8CkwT5+PIA8wSf/fH/IQPl+m7+ZgewALP9ff4gA9P9v/4qCm75Q/WACvsHJvFb+7wOWf2G+AEDggPa+p8AQQct+qn9PwPj/M7/dAWs/tn6lwAtBNcCuPn++scE5gcg+n320g3wBDjylwFrCf0Aifh7/i0EPQGH/gD9YAJcAXf+lf8b//P/CQPAAfL3vADlCYf6JvuhBQMB+fpA/y4G8vwN+5QFhAAk+2wAFgXj+2L7QAmHAaL5UQDYAXcAsP7Q/YEDiwOK/Oz9MQI5Afn8LQBKAYL8bgLaAt398v4//4ABIwEzARf7Zv3WCCz+RPpRAaYBDf+B/VYEfQC++vsCtgLE+9YCcQF/+aABVwXT/038WwM7Awn8JQDFASMAoP4LAYb/zP0yAq3/NP+XAKgBdv0n/cgFGwFD+0z+kgEuAI39HwHMAdb+of0kBa0Bd/gjAkgC6PtPAl0Fovp0/bAIXgA9/IX/N/6HA50DRvgzAKQIO/zd+u4DxgBj+xwDdQXD+yb44ANGB837bPm//zwGkQAq+2QAAwKY/oL+gwNr/iP+FwSCACb///7pA0oCaPe+AqYJhvgE+7gFywNz+5b70wNiA5kAmvom/n4GnP4e+WkCBAbh+Nr8PQo2/X33kwUCAvD61AK2AV76TwAMByQARPiNAjMHWvux/QIEYQGi/Y3/5gNZARz8av02AqEAegCK//z+MgL0/jX/OgDb/m3/WwDc/lX/AwL5/BIAtwR5/VD8wgKjAmX/6QFYAAX8lP6MBZ//5fldBTMC/vwUA3oACf6X/dL/sgLv/ln+jAHP/4QBEwBz+wkBjgIu/bb9dgKZAYH/f/3x/boBNgK5ANT7dQJrBWf7PP8mApj/nAB0/1EBVAJv/ib+egGzA8X8+vtMBDECcf13/IED3QGJ+QgBJgSK/Jv9XgKWADz+qP0o//ECqwFl/XcAu/+m/7IETv/m/DH/pQVsBKT5Rv4uA6wBN//f/RcCuwJT/hv+VAOk/m76twDmA1z/j/ryACsDd/9E/lT+9wAcAV8Acv0O/zADgf85/FMBrAPO/VX/2AIpAS4Aev6cAoAA9PznA/cBvPsL/tAF+gHQ+vYAAAOP/Sf9bQJh/wj8nQFzAAT/DADT/zwAF/7p/gsCFQCf/DUAZgPz/9P8QAB/BNr/Af5GArkC4P/A/l8BngF5/yMAeAG5AFz+av94Ap/9z/tsAxoDMvt7/Z0FPf8c+mUB6wEn/A//NQOw/tn83QACAsr+nv5SAaoB6f+C/3QCQAF+/OkAQgRz/mH/JgNlAd//MQByAFH/uv4k/78Ajf/2/MgAugGC/qn++P8AABr/1P9U/t79IgB5/5P/2v+VAEkBQP8CAIICbwFh/+L+xAHRATj+LQGgAXIALwEmAMwBYwGGAPj+8/4RATL/+f2//q7/WQBo/+v9vP+BAM7/Vv4Z/dQABQAb/f79UAGUAeb8egAZA4b/if/cAZ8CR/9s/sABYQKe/wj/ygGAAgUBegCGAC8AWQC7AIf/7f6JAAX/fP77/23+Cv93AHX/Wf5K/7//i/6F/1D/HP7E/2wAMAASAM3+HAGWAoX/CADnAcsAXwA4AsAB/P4vAO8CEAI5/zcA+QBt/zoBXgCQ/Wr/9gD2/ir9ef8mAI39XP/n/yv/8P+r/gf/mv8I/9X+/P/bAPT/fwDcATQBqf8MAZUBof+kAVgDYQCY/9cBEQHb//0AjgD+/g7/7wBUAAn+kP5aAB7/Vv2w/9T/jv4j//L+6P59/vD+Wf+C/+7/3v8zANkAMgFFAAwApQG3AQEAVwD4AVYBCgAqAW0C0gDN/xACDAJt/77/GwDt/ur/6P9L/tz/RwBZ/2//y/6f/kP+Zf4m/q/+gP+G/iL/RQANAIH/HgDrACoBzf8N/2cBgwFKAFkAqwAbAdQBfAH6//UA2gFfAFAAXgAR/xr/GgAhAJb/4P/Z/6n/GAAz/7P+tv4X/jn+Gf/J/yn/PP8lAAIAn//PAH0B3P+v/wkBKwFsAJcAzQDcACEBmgHEAUMAygBFAdj/RAD4/9n+Af+V/6f/av8k/8T+I/8d/43+tv5J/93+mf2h/nMAyf8w/zgAVgAbAGQBtAFsALsACwJpAYIAEwEoAcoAWgHnAZgBjwAVAJAAsgDd/9r+H//e/9D/2v5V/pT+gP5Q/nr+M/+C/7r/YP+x/l3/k/8L/4r/BwErAUYA4AC6ARcBegBTAWkBrQCKAfUBNwFoAc4AMABJAJH/BgCZANX/7f5h/mj+rv6L/p/9pf2s/vb+fv7P/rT/ff8x/7T/lQBNAF7/RABmAfIAwgC5AeUBqQE7AuIB7gB9AdYBBwGgAIUAtQCgADgA3P8c/6f+9v4n/y3+lf0y/kj+/v3m/SH+f/6h/kj/4/8iAFIAAwARAOoAFAH/AJkB1QGfAcgBlAKFApYBlgHmAXoB9wAKAUEAJP9S/zgAPQC0/tX9XP5b/qT9mv1f/Sz95/0d/vr9rf07/ln/bf8BAEwBxQHnAKQAUgGZAbsB5AFEAnACTAPPA6cCGQLPAW8BPAGRAA8Avv9p/1j/Bv9W/nr9UP0H/qL9Cf05/Wj9B/3Q/OX9/f1v/Xb+z/9gAKEAhgH3AtACVwFPAfsBWQLKAvQC2QKUA9sEYwQLAl0AFAFYAScAtv8s/7r+cv7m/cD9t/zs+xD9yv0J/XD8gPy2+4z7Tv1u/nL+Fv+5ABIB5AAIAsMC5wIhAxQDmgLFAskDSASuAzEDagSNBHIC1gH2AagAfv+C/4X+tPxq/Db91Pxq++b7aPyz+4f7X/sv+177KfwJ/cj9+P6uAK0B3wF1AqsC2AJsAz4DXAL7AmsFdAYmBgIGIQVOBDgEMANKAY4AlwAVAKn+k/yL+1b6y/mH+yv73/kn+778IPs1+c/6+/u2++n8Mf+mAOcBYQNvA6gCPQMdBGsDeQNyBH0ERwQGBhcI6AbeBF0ENwTeAjgBtwDrAHT/4fzi+8/6ofnB+ff5rfmu+Xz6G/qq+GT4zPhG+nb8SP53ABICXAL7AjAEVASJA8kDmARYBXMG1Ab6BrwHSgijB7UFIgUgBdQCfwB+/yP/Vf5h/HT6IPoU+gL5dfjj95L4Yvoh+k/4PPg1+kr71/vH/UwAWQIMBHEEZQRYBHQDMQQCBcIEbgUIB0oJ1AktCH0GbAamBpgEfQL4AMf+wfzc+zr7Vvrq+iL7aPna98f2VvV29dT3SPnR+VL7nf3U/tX9Xv20AIAEBAUxBIsE2wWdBeEDQANFBUkIrAlYCj0KQAi4Bd0DsgLcAXkAM/+h/gj9Yfv/+if7hPoU+CX3Efg8+IP3yfbk99z5U/ub/JH91P7z/1sBaANVBOoDCwN4A8IEwwTDBAUGKAn2C2sLGwkTB+8FlASzAm0BGAHNADb/n/yy+XH45PgN+a74X/hT+PP34vf89on1e/aF+Qj9ev8cAXYCsgNnBOIDnwJsAnEENgZ+BksGyAbYCEUKPQmDBysGIQXdBKEE3gKy/5j9MP1l/GX6+Pjh+b76xvnn+Gr4Z/dB9if23vcB+lz71vwO//kAiAF6AvwDSQTpAsABZAMLBrAGlgVmBZQHMwpjCkUIDQbZBKgEAQVyBGkBJ/51/IP7afpq+az5Evpz+Zz4X/iI91z1SvTi9dz4rvuI/ZH+bACYAssCXwInA/QD7QNFBAUGmQeQB8cG3gaRCAcKhAmJB0gFlgTTBMADFAER/hP8//ql+p765/n/+AH5ivm/+GH2XfSF9M32Rvlv+5v9Rv/LAGsC5AIoAk4CawNYBKQERAUDB1II4genBtUGpgiFCc8HUwXdBNAFiQTwACj+9/ts+oT6bft3+6r5OfhJ+Hf41vb18+Hzuva1+Zf73vwe/pH/bwGkAtUCOwMzBLUEMQTaAxcFQAd+CI8IgggfCYAJBQjzBIMDtgQ/BdsCDf9F/Hn6avn0+SP7yfoZ+S74+vfK9sf0+POZ9Sf5ffwQ/pH+Y/8fAXYCwQI7A1wEYgVFBd8EEwXVBS0HRQjRCEMJSAnZBwcFQgPMA8sEJwRKAUv97vnh+JT5e/p/+337Jvq/+IH33vXr83rzTfZA+yP/FwCD////kwG7AZcAVwE2BOMFvgXIBUEG9AZdB1AHdAdZCC4J5geoBdgEjwU6BToC6v0v+mb4y/ja+ob8tfvU+T34NPZu9Cj02PRG9oj53v1gAA0AD/8QAC8CrAKLAsMDVQUDBt4FMQZEB/MH4wffB1kIKghHB4QGtgXWBLkDtgJQAHn7R/dR9zb72v1l/Hb5I/iN9zb14/IS9Kf3MPrX+7L98/7p/vf+ZwBoAvoDwAR1BM8DAwQZBakGIQhkCccJGQk8CCkHOgarBRsFvATPBLsD+P6R+O/1b/j5+zT8nfoB+sn4ePVR8pDy4fX6+AD78/zS/sP/m//o/yAB4wKIBB0FzQQvBGQEcgULBxMJCArhCQsJswdDBjoF1AT/BI8FYAQfAPX6TPjr+Fj6N/q3+W76hfo+95fyavE29L/3IPpR/Nf+7v9+/6v/+wA5AhYDMwRFBagF7wVMBtQGtgfCCGMJEwkqCBoHOwaKBdYFoAbqBJb/lPl096v5cvxR/Jr6N/rd+b72W/Kg8P7y/fYG+lb8LP7w/qH+1v56AGECjAMMBOMEwgVjBhIHTQckBxsHuAeBCOEIYggUB/4FggWnBT8FIAKS+0j2l/cs/NX9cfxR+3j6K/eP8m3wPPI+9ur50fxw/jf+kP33/bf/4AEPBIoFIwUZBAEEFwXHBjYIzAh/CAEI2wfuB3YHZQbcBSQGGwYjBGf/kfkJ9y753vsZ/Fr7Wfvf+UH1ovAl8KzztveI+tP8k/79/oD+rP5vAL8CYARJBUQFtQSuBOYFuQfhCBAJjggZCLoHIwevBpIG+wb8BjIFXABx+in4+vkD/Iv7V/qJ+hb6vfZJ8qXwlPLw9S35wPuC/Zn+/v4V/8b/uQHgA/YEWgVwBaMFUgZTByIIHQjpBycIUAiOB3wGjgZ9B1gHlAVPAlb9afjR9ur54f00/jD7Afgr9rXz4/AN8RL13fk+/Lz8FP2L/Sf+VP/NAW4EwQUYBq4F+wS+BPEFQAiQCV0JoAj3B70G2gRVBOUFgAd7B64FrQFb+6j1qvTb+Bb+cv9X/Gb4cfUT8kfvWPD29QH8qv6v/lL+WP5Y/oT/ZQLfBKQF4gVOBt8FAAWDBVQHBAnQCaUJlQh6BkcEvgPaBEwGFgc5BrUBLfq39Dn13flZ/U39sftf+Yz1GvEN7x/xYPYl/E3/Yv9P/ur9/P6+AN4CvwQDBsoGqgYYBu8FrgbVB6QIOwl3CV0IYQYpBX4EGgSMBMgF9wST/7z4vvX290D7ZPvm+U75mviU9W7xTvA388T31/th/l7/B/81/+kAiwL6AjsD9ATiBvsGggb+BgcIUgjTBw8ImggUCLoGvAWzBZEFWwWYBBwBE/te9k733vvM/XH7c/hl96z1TPLI8ALz2/bZ+VD8Ev4D/kL9q/68AnEFLwXUBNEFDwbkBPkETwdoCbwJRgm8CJwH4QXPBOQEjgXHBroHogXK/l325fLe9oX96v8B/ZL5W/cJ9MfvnO4t8tL3ovyU/1oAtf7R/Jv9zgDTA+QFpwcQCNAGHwUdBRAHfwiKCIIICQl7CMUFZwPTA3UGPwj+Bu4CmPxW9l/0qPfK/M3+Jf08+rj2XPLv7jLvwfKs99X8CgFiAiEAQv0v/bYAbgQIBhAHAQgACK0GpgUUBvUGFAhWCQ4KYQiPBLoCTgR2BswGmAXqAnb8Z/T68XT3RP4i/5X7Ffmi9yj0qu/S7sryqPgV/tAB2QI7AVv/8v8pAkgD1QPmBWYI8gh4B3wGfAZdBoYGpAeXCDMHWgRDA0EEsgWkBuoFaAHy+IbyFfTh+hf/OP3K+W/4ePdH9EHwFfBo9Cn64P6hAWgC8QD2/wUB0AIfBOUEZwapB5kH8garBnoGzQVIBgUIwgj/BjUE9gK0A0UFiwZABZf/rPcU8/r1KvyQ/j38bvkQ+bP3OPM+70HwPfaB/J4ArAI0AjUA/v5SAOYCcQS0BSsH4gc6ByYGCwbhBaMFVAbxB7AI/AZ5BPACSANkBeEG8AQr/mn2xfMm90n8Gv6U/Kf67PjO9STxqO4B8c/2Df2uAYsDWwIyAEv/aQB/AoIEDwaqBiAHiQfEB0YH4wU+BcAFCAewB6AGzQTBA+oEnwYPBqwBZPpD9G/zLPgo/TP+6Pxf+934S/QH8LHvRfN2+Pv94gLjBO0CRAAIAOwBrwNxBBoF1gW7BuoHZwhNB0oF7QQkBk8G0AQNA2ADLwV+BisHwQVfAb/5rPKH8sv4VP9QAEr9lfof+Fj0D/Ao74DzqPrWAI8DFgO6Af4AHgF9ATECiwMkBW4G4QZSB70HLAfNBSsFUgZsBksENgL7AtkFIAe2BkAFIQFQ+UnyGfPy+S3/lv7K+2L6ofig9Djw4e9l9Jb66f/iArkDRgO/AqcC/AFwAXgC9AQ+B8AHrQfHByUHsAWgBP4EEgXvAyED8APhBecGAwfBBFj+afZi8r31ivtS/RH8bfsg/K35ifOV76vwrvXb+tL+0wIPBQ4FbAP2AZ4BTgG9AlwFYQeJCL8IeghNBqoDKQOxBKcFEwQXA60E6wYeB1oFZAIC/Yz2YfO79sT88f7u/O36Pvqz93Tyge9b8gX4l/yn/7gC7gTqA5EBvwDSATYDKQTdBZwH/AecB64GeAUkBJQD+QS+BbYEiwPOAwAGrAYfBXgBq/t19m30vfex/GD+O/1N+w76vPYH8oDwvvOA+ab9nAB/A7sEcANgAT0BGwLlAgwE5gVCBxAHoQaeBSsELQO+A+MFHQZGBMUCGgTABp8GngRHAav8B/fG86H3SP6YAAf9vPnM+dT3Q/Lf7m/yWvkv/twAJAO3BOED3gHeADABegLNBEwHIAgIB9QFFgUpBHYDGwQABnAGrARfA+YDqwUfBvUEvwGr+wT2t/Qq+b39g/1/+376ePo+903yWfFQ9Nj4Yvw/AGUEuwVmBCMC9gAdATcChAT7BvgHYgfIBS0EBAPLAvADVAXKBRQFuwTNBAEFoAU3BcEBufrk9VX32/vG/ef7Pfvn+0D63/Vw8hvzX/Ue+MX7EwCvA7IE1wM4AjkBpwH9ArAErgVmBtEGHAbABLUDJgT6BL8EEATHA3AEKgX7BcoGbwWsAEf63/bZ93j6NPzP/Fn9dfx5+ZD1ufKm8t70h/it/GUAHAPXA9ACuwGIAbgCAQSBBG8FJAaSBr8FBgSPAx0EWwUbBcgDaQPeA0YFNwa4BmMF7ACy+hn23Pbt+tL9Uf3T+8L7zvrs9hXykPGj9QX68Pzk/3YDmgRyAjcAigDMArsEkwX8BVgGPQZMBWgDEwLxAjsFLwaxBFUDKwSIBYEFpQQABAYCnvxO9xH3jvv1/i79b/oM+mn6Bfi+89HyhPVi+XT8XP+SApQDjQICATcBKwP9BEIGGQaDBRQFwQQvBBYDIQMlBCkF0gSaA1AD8wM/BesFaAU0AuT7+/ZX9yf8wP74/Hv7+ft9+8T2bPIb84D2mPn9+w4AlQOtA4EB2f/CAPAC/QSMBe4EsQTUBN8EwQO8Ai8DswR5BY0EdAN8A7gEswUQBrMFagM7/mH4qvbE+cn9S/7O/PH7A/v193rz5fH18yj4a/zw/6gCugJQAez/SgCpAuwEcgYnBiIFrgSoBIcEYAPQAiEE5gXFBQwESQN2BFEFBwWkBJMDyf/D+Zf2nfiG/JL9Pfzl+9b7avmL9PjxuvNv9yT7iv4xAqADIgJvAJgAlwIfBEMF/gXYBUEFtgR9BI0D2AKJA9wEdQXgBJsEpgRyBLwEugVKBc8AkPrp97z50vtD+6v6QPwp/RT6EfXi8gP0EvaF+LH8wwESBBEDNgG7AN8BewO/BB0F9wR1BTQGeAVrAxsCSgMsBd8E0gPJAyQFuAX4BCcF2gTyARr8AfgN+Yr7Wfyg+/X7Kfy++f/1wvM49L/1W/hQ/GwAqAIsAvYAcgDMAQ0EgQWxBUwFdQVtBWcEBQOFAn4D1gQ7BbcEMQRRBKoEswRJBc4FxwMR/n74Vvgl/Ln9dfsH+qX7/PvB90bzbfOh9hX5Wfp7/VIBawL/AB8AFAJ9BGMFRQXMBPQEFgU7BZAEEQMdA54EpgVtBAgDJwTFBcsFFQWGBc4EwP9l+Zb3tfoN/dL7Pvro+n77j/h09Drzi/Vf+CP6AP1nADsCkQFyAGsBRAPbBI0FkwVeBVIFpQXLBNkCJwKVA88EBQQdA/MDXgWhBaYFFga/BAcACvu/+Y/74fx3/Bj8JfwX+x/4//Qh9HP1//d7+vb8YP+TAEgAef+JAEEDXwWLBVgEQwSaBIQEwQPvAq8D2ASEBfIEwgPEA8wEFwatBq8GDQYEA/j8DfiE+Kr8d/4J/ET6HvuB+ir2+fH48nn3m/o5/BX+HgBpABD/bP/1AcoEIwbaBWwFwQTtA1cD5AIDA90DlgVMBrIEEwNtAzEFBQb5BXUGNgVHAHD6yvhw+w79Avxb+xr8XPs49ynzmPIL9eL3avqk/X8ALgF7/yj+fP/BAsEFqwZxBuEFPAUKBFYCowH8AqEFBgcDBqQE3QSJBQQFygRKBsUGpgJL/Hz55vo0/Bz7cPrG+yv8kPmQ9YjzWPSu9mf5bPyJ/14BsABx/hn+AwGZBOQFJgUWBZ4F7QS8AnEB/wKhBdUGDQbVBL4EMgUtBa8EFQWEBqMFdgAu+ob45fvf/eb7H/rh++b8Z/ih8oPxA/VK+K75JvzA/54BTQBu/gb/DALQBIYFBQWeBDgFgQU7BMsCDQNOBbcGvAWhBEsFpAZ2BrAFdwYzBscBt/up+Sb8g/3a+/D6Lvx1/PH4VvSb8sLzF/a0+JL7df5QAFkA4v4f/ncASgTTBU4EAANuBBQGRwUPA/gCfAUiB/IFDgRLBBsG1AYKBkgGJQdABUT/RPm/+AL8DP7D/Pz6dPtG+933+fIq8SX0WPge+7T8mv4MAJv/Vv7B/ugB/wSGBSUEWgNWBEMFqAReA68D2wWzB/0GtQQkBBsG9Ae7B+gGbwaBA/T8S/gU+nv+tv4j+9r5G/v++Vn1rvF48tn1Bvma+9L9S/9Y/zD+uP2J/24DQwZoBVYDJwPzBEUFGwM4AsAETwi4CDcGXwTBBD8GWQefCN4I1wUYAOD64Pm/+0L9Nv0s/Gr75fkx9+HzevGz8bn0R/k5/W7/Ov+l/bH8sv3uAB0E2gV0BW8EiASZBDAELQO8A3IGeghdCHMGXAXkBSQH6AfxB2cHtAQ6//v4qPf8+03/jP3H+aP5tfpg93PxZ+9F8zL46Pri/DD/CACE/sT8Vf3uAOUEngaQBSgEowScBfYESgOVA1MGrQg2CFQGAQbfBqsHhwdwB/IGRANM/ST5Svos/uf+JfyQ+S/5SPhl9HjwiPBq9K74A/vI/In+yP46/R/8Lf5hAokF1QXCBMcEyQUQBpkEfgPJBPYH7QmqCGEGyAVxB+4IdwjnBtsEoAGg/C35xfrN/iD/5/rz91n4kPcK8zrvH/FU9vz5mvsi/Yf+A/6T/B79AQBqA2kFpQU2BS4FlAWZBa0EDgTYBQoJSgpdCBEGwgaSCKkItgfQBrsEfv/r+Wz5NP1P/8n8O/mj+FH4zfRr8Gbv6fLd9hr5Oftu/V7+/fz9+7f9IAFbBMEFugUCBfIEnwWqBcYEmgQ4B3oKuAq3B68FSgeJCYcJhAfhBRUE2f/a+lD5cfzg/jr8Cfg19z/4vPVh8KbuiPJS9475FvuP/cb+mP2X/Iz+SQLxBI0FLwUcBX4FPAZ/BskFmAUnB5oJtAleBzcG6gfYCQ8JegdOBg4Dzfwl+GX6Ov9u/+X6Vvet9w73AfN77/zv3fNk96b5wftK/Tr9+/sC/Pz+OwPIBfoF+gRwBFoFpQbaBnYFbgVTCMoKGApEB6QGjQhcCckIcgiiB+cC1fum+Bj7UP5U/c35Zve89l/1+vF97yjwpfPY96r6M/yl/DH8PvwM/mYBVQSUBekF1QWWBe8FUgbuBTkFhwYHCikMJwrGBlgGFQgzCaUIHAglB/4BOftW+G/7Xv8v/SP4BPYP94P22fFT7uzvUPS290v5qfor/Kr84vxN/jIBFwRwBXUFUQWbBRcGUAbsBSkGMwjiCqALZAlaB8QHcwkTCngJsAjuBW4AOPsQ+jj83fzw+nL46Pax9VnztfAU78jvy/Iw9v347/pz/E79lv3r/pABggQzBlUGPAYdBpoGLQfeBsoGJQilCnMLYgl/B2QIYwqQCpQJmQh5Bt8ADfof+Jb7Zv67+7P2TfVn9rD0t++b7AjvufNc9jP46/pM/Y/95Pxn/qoBkgRUBhQHQQe9Bn8GtwbVBuYGIwjMChYMzwrPCLMIPAqPCkYJnQeWBWoB9vtW+Qb7F/0P+5L2RvR59Izz3u8R7bPuBfOC9jf4uvm7++v8of1q/4MCUwWoBtkGywaTBnoG5AYyB9MHGgn6CioMxwoFCQAJbQrRCiYJAwi/BncCjfuY9/35I/0w+wf22fO19GPzX+/Q7JXuXvJf9Xn4lPtK/Q/9vPyO/tcB2QRxBrAGQwYLBq4GPgdXBw0HDQgNC6UNSg0CCk0IDArjC9MK/Ac1Bm4Dxv0c+dv57Pxq++/1wfJP87Xy9u7r69vszPCn9Nr3ffr3+7T8c/0q/+8BtgSiBlUHVwcRB9QG/QaBB9EHngjuCoUNsA0SC2UJmQrTC7UKxgg5B3cDEP0B+df6j/3P+tf04fHT8rbyTu807FfsWu9z82P3T/pz+7j73Px5/8YCBgUkBlMGIAb6BesFrQaDB+UHuQhnC4sOdg5EC3cJvAsiDv4M6AlyB6UEU/9r+h/6LPww+zz2NPK98ezxj+/G64rqX+1I8qv2nvk1+6T7F/xL/kAChwWRBmMGgAa7BpwGzwaoBzkI2ggtC0MO+Q5vDFIKdAtoDQUNFAsoCe4FdwCF+5T6gPuv+T31JfIS8r7xY+5X6m/pUOyb8Eb0mfdQ+m37E/wD/n0BfwTNBXIG6wYHB9cGRAchCF8IwQjICgQOIA9TDcoLnQxaDvINyAuqCdsG3wE4/O75RfvQ++D3HPKg70vwfu+B65TorOrH7yD0Jfei+W77Yvza/QgBcwRbBhwHoQe9B3AHdAftB+sHGAg4CqcNkA9WDpUMywzUDc8NhgxWC8oIKwMR/Zv65/vf+4z3NPK174PvOu5164jpUepx7TPxPvWa+Lr6Wfz0/XAASgPPBXYHpAfHBgYGmgbpB3MIEwgNCVQM2w5qDtkMOw1xDgYOwQxADEILfQZk//T66fr7+9L5CPWY8IHuxu3W69zopOc66j/vBPR996v5bPt+/RwA+AIjBbMG/gdqCBMI2gd5CMQIPQjjCKML8w4dELcOUA2HDbkO7g5dDdUKxge3A3H+ZPpY+U75K/cN8kvuWe0/7Mjpq+cU6ULtwfG29eT4dfuM/br/IwI2BHYFjQa3BzoI4weyBzMIVwgxCH4J+wsLDmYOrQ3RDawOMw93Dm0MrQmNBW4A+fss+lX6p/j+8xzv5OwP7PHpveen52Hque4r8333ePr9+8X9WAAzAyoFjQYICNAIzAhdCCkI+wfOB98INAsdDm8PtA7zDUcOLg+3Du8MQQtPCVMFrv8Q/HH7g/pb9r/wYe2H7M/r7+no5/jnzOqS74X0Efhw+oX8y/50AS0ETgZbB3EHYAfeB9UIqgmNCQQJuQkgDHIOCA9pDo4Opg8CEGQPEA6PCwsHXgFD/cD7Qvvz+Ln0efA87THrMOkP52XmUujq7FvytPZp+Sj7+fwz/xYCAwUzB5QIsAhdCCUI8AfbB6AHPQi0CgMOrQ8JDwMOIg7uDhMPyw7dDXQLLAeYAaX9FPyN+lv3n/Lz7j3ts+sG6RbmtOWa6IztCvNH9+X5dPts/S4AUALlA5oFfAe0CLAIZQhbCCoIBghBCRwMxw6WD+oOoQ6TD4cQHRCiDsgMWQqdBpYBdf1R+675l/bq8fbtkut66S7nheUq5oPpeu6383L3dvn/+vv8oP8dAmwEeQbNB+sHMAckB98HzgibCbsKsgxqDkEP3A6XDqwPEBHVETsRYQ/HC+MFgv9D+wv6cPlv9wH0CfBe7AHpMuaa5CTlAOkz7yv1MfnT+q/72Pxl/t4AzQOZBlgIpAhiCAcIKAiACMEIAgoQDGgO2w/kD/cPfxBTEZ0R3BCMDt0JDwQz/+D82PvJ+Yf25vK470Tsc+i+5dfkc+Ye6j7vnPTc9yr5TfqA/FD/zgE2BC0GtgbNBS8FGgbSB+MIawnrCiINuA7pDrgOSg/5DyoRghIhE2UR6gv+BCj/CPxC+9L6I/kR9fbvrOt76Gjl7eIZ4zjnTu6M9L/37fjr+fX7a/6wAPcCIAW+BnoH7wdbCL8IWAkTClQLSQ04D/8PaA/KDtYPYhI1FGwTURB3C4UFEgCN/Fr7Zvox+En13fFb7iLqzOUJ49HiV+a87Hbzn/et+En57vpm/Wz/BQFCA30FyAZeBwgI0QgYCTsJLApLDHYOkA/FD/0PLhH4EnQUpBQPEv8M+QbKATr+0ftj+k75f/eK8wbuZeks5sLjM+Lh4xPqu/Hx9jj4TfiO+c77e/7CAPoCKQUBB3sICgnqCKkIsQigCWYL5A0wEPMQcBB+EBgS5RNrFOoS5A+1CyUGpwBr/BD6DPmn98z0+e/d6krnK+X142TjueXI617yovYv+Gz5fft4/UD/JgHNAycGHQe7B1YI1Qg7Cd4J9QrCC+gMqA6wEA4SgBJ6E68UXBRJEcEMfghDBPT/y/t2+SX5DPgY9NvtQehN5RDkq+Ob5NznZO1M86P3xvlu+oH7Ev5HAV0DKQRnBToHgQi+CGgIAQkkCl4LJw3KDvQPfRBKEe4S4RPGE+MSbhEeDn0IFgN9/zD9avpr9171o/I97gLpkuWf5BXkc+Sc57ntAvQF+Bn6CPtg+wT8Wf6lAb0DcwSMBRQIFQr7CTMJ9gnNC98M0Q1DD94Q7xG7EicUAxU+FAQSqA7jCYcDc/43/M367fdm9Pzxh++e6xvnSeRO5NDliOgA7VTy6vbq+YX7C/w5/Ff9Uv9kAcACDgTeBi8KvAvlCpIJKQpTDDYO1w6tD5MRfxPPFEgVLBUCE7AN2gdtAwEBEf87/CP5zvUD8wTxxe7h6snlSeNN5ZPpEO2276Lzbvgt+4X7efuT/Dr+cP+3ACgCpgOTBT0I2QohDAsM5As1Df8O/g8+EOAQ5hJqFeQWtxWtEdgLfQXLAOf9evsI+fL2L/aS9YPzgO826g7mruSz5nHq5+1U8aH1Zfo3/TT9ePxk/P381f0f/8ABzgQgB5IIuQkKCzwMFg13DQUO5Q5YEHESdxTZFYkVvBLhDYEIRwRJAcP+JfxU+Yv3ovZn9eLy1+4w60TpJekd6jTs7+/H82z2Mvjf+b372vwF/aH8jPzr/ZoABwRsBi8Hygc6CfUK0QsQDOIMCg9rEagSIBPJEwoU+RHWDO8G8gIMAdT/yv1U+z75zvec9m70/fBj7WXrVes97OvtyfB09JX35vj3+Df5CvpW+1n82PyE/dL+RgETBCIGSAflBw4JxQqLDCwOhw/nENMRVRKqEn0SABEMDd0HxQPWAToBEgBb/r78Zft++Sv2G/KN7k7svevU7Bvv4PHd9Jn3MPld+Qj56fjY+Mb4O/k/+0T+6QC6AiEEnAXgBg0ICQnTCegKrAz3DjcR6RJZE2ASFBCxDAcJAAYKBNgCqAFmALX/HP89/Xv5QfUB8hTwJ+/k7q7vjvEd9Jr2/PcF+Jv39PfH+LX4sfdQ9+346/ug/qAAWAJXBEUGvQc1CcEKSgyTDc8OaxAvEl0T5hJXEJ4MQQk+BxsGjASHAsgAxv8G/4P9EPsH+A71cPJr8MvvvPCQ8lH0h/Ug9kj2H/bZ9a/1m/Xy9TX3b/nZ+7/9W//eAG0CHgT8BWAIIgvfDfYPHBGxEdoRSxFoD5kMQAomCdoIVghABwQG3AReA9wAev0b+jv3E/W280fz0fOm9Cz1O/UK9Z30HfSc8zHzKfO38wz15vbG+HL6JPwf/hQAugFsA2MFoAfXCaILZw0gD4IQVhFQEUQQIQ4BDOAKXwqlCUEI4AbMBYEEcAK1/xz94frX+K32qvSm88DzTPR89FL0EvTi857zEfNS8hfy/fLl9Pj2q/iD+rf8GP8eAdcCqwSVBpEIhAp1DCEOOg+3D4gP0w7JDaEMkQuuCsgJ1QjEB7kGoQX/A7MB4P5M/HX6Rfl0+Jj3u/YT9pj1GvVu9MjzcfNY81TzX/PY87T0zfU79wz5fPv4/ef/TgGYAk0EDAZ2B8IIVAoODFgN0w1oDXUMgwvaCmAK5AlyCd4I/QeRBuwEZAPmAVsAp/4N/Zz7qvoV+jf5yPcX9tH0DfTb897z3/PY8+bzXPQ09Tf2ZPfR+Fb6//v1/VwAyALYBGsGagc9CCAJOgohC2ALHAvpCiULaQs7C5cKwgngCN8HrQYcBQoDFQG3/wH/Z/50/ZT82vsv+xn6ofgs98b1hfS786fz/fNi9LL0N/UP9ir3gfj/+c/7wf2g/14B4QJLBKoF8AYtCDEJ3gkiCgsK1AmBCVgJaQmDCX4JLQmVCHUH3AUjBIsCPAEAAAf/fP4J/mf9Xvzv+mD58/fv9gP23vTc81rzo/NM9B71M/aD9/T4NfqL+/38d/4TAKIBPAPJBGsG4wfQCAsJoggzCBEIOgh6CKgI2wgICSsJLwmwCGoHgAWXA0MCOAEUAOz+Ff6W/f385ftg+tb4rffD9rv1y/RR9G/05PRo9SD2Qfft+Nz6ovzq/c/+m/+WAK0BzgIVBIsFEQcYCHoIVgj/B88HtAe8BwwIrwhNCVMJiQhFB9gFdAQTA7sBfAB//9X+Bf7m/Gz75/nT+Pz3LvdS9rD1RfXv9NT0G/Xx9Wf3NPmq+tP7Af1t/vH/6wCeAZ8CHwSmBboGVweEB44HwQf0B/oHBQh/CF0J0glzCW8IIQfMBU8E2QKRAXwAuP8r/6b+2v2v/GD7/PmB+PX2sfUO9e70+/QU9Vz1GvZc98z4JvpH+2H8o/34/kMAYgGHAtUDSQWZBmIHoweyB8AH1wfYB9QHNQjvCJIJuAk+CSkInQYBBXoDDQLQAN3/Hv9Q/mb9Tvwd+9v5pfir97D2qfW29Cf0FPRx9EL1afb097D5UPuu/Nn96/7N/5AAjAH4AsQEhAaYB/oH7wf3BzIIVQhaCH0IAAmgCfYJ0Qn8CJMH8gVjBOsCbwErAE7/m/7C/av8lvuI+kf52Pdw9lb1qfRJ9CD0M/TF9N71RPew+BX6n/sP/Sv+Ff/6/wgBSALRA4IFFAdCCM8Iywh6CDEITwjUCE0Jfgm7CRgKGQoUCRsH5wQYA9oB3gADACT/If4g/Qn8vfpL+RL4LvdJ9kb1WvS/84rzqfMB9M/0WPZU+Fr6EPx+/bz+w/+rAL0BFAONBCUGxgfpCEsJTQlnCacJuwm2CQAKhgrOCoEKvAl/CMgG9wRXA+ABfQBu/8L+A/7W/Gn7K/o/+Rz4k/YW9frzXfMH89/y4PI480P0+vX399D5evsd/ar+2v/aABcCwgOaBTgHdwhQCeoJKQoFCssJyQkbCqIKLguBC10LhAoACSgHYQXZA38CKgG2/2H+T/00/ND6P/n39wL3LfZA9RP09PIh8t7xGPKQ8m3z5/QF91b5XvsK/X3+4f8uAWcCzAN/BVwH8gjkCTwKXgqSCrgK0wr7ClYL0AsWDA8MiAsvChUIwgXTA0MCAgEBAPT+sv07/Mv6gPkR+JL2YvWL9Jnzi/Lx8dLxz/H38bXyOPQr9iv4E/rF+0D9rP5NAAECmANQBTIH7wgDCn0KpgrTChULMQtEC40LKAy2DKAMzQt+CugIHAcjBSIDTgHK/6H+l/1m/Bf75/ng+J33Ffap9HzzhPKi8QPx1PAT8eDxVPNK9Wb3U/kp++z8av6t//UAhQJUBEEGRQgOCjcLhwtiC00LZAuPC6sL1AseDFkMUgysCz8KYAiDBtUEBgMDASv/u/2N/GX7Sfo1+Sn4K/cJ9ov07PKd8dLwdfBz8PvwK/La89z1Avga+uv7lP1H/8kABwJJA9YEpQaDCDAKSgu/C6sLfAuCC8ELBgw1DEEMOgwFDDcLowmiB6kFwgMAAnUAHP/L/Vv8AfvT+aD4ZvdU9l71GPR48iPxgvBU8FbwDfHD8ur0CPcR+TH7Df1N/mj/zgB1AgAErwXFB8wJDAuKC78LxQuRC10Lkgv7Cz4MbgyXDGoMWguDCWEHOgUnAy0Bcf/8/ar8b/tO+jn5Lvgp9xX21PRd8+vxvPAb8P3vW/BN8enyKPWb99n5ufsx/Wr+m//yAIACNQQkBj8IKAp9CwkMBwy1C2gLWwuWCwUMVAx7DG8M6gvWCi0JDgejBFoChgD9/or9Hvz4+g76I/kn+Dn3UPYU9Z3zN/Ib8WzwXPAG8Ury4vPL9SH4k/qH/Mv93f4TAGUB9QIDBV0HbgnpCscL/AuCC8EKcQq4ChsLggsEDIQMbgx5C+wJEwgEBsIDsgH+/3D+6vyQ+4b6pvnM+Ab4XPeL9ln13fOI8onx5PDF8GHxw/Ku9OL2MflU+wv9Qv49/0wAiAEEA+UEEwcpCZQKNQtJC+QKPgq5CcAJYgoOC4YLyAuyC9cKMglYB4kFnQOHAaX/Kf7B/FX7OPqA+e34Ovim9yv3Mfa49CvzEfJk8Tbx1/FK80v1XPeG+b/7nf3n/sb/ygAMApoDZgVIBxAJZAoUCxQLnQr3CYAJhwn2CYMK/gpYC0YLegrlCO0G/AQWA0ABfv/g/Xf8QftD+mP5t/hD+OL3gPfo9tP1TfTS8uPxnPEA8jvzYvXy91H6SfwE/nb/dABJAWgC4wODBUUHHgmLCgALfQrHCVEJCAkLCZ4JggoiCywLygoACowIhQZlBH8CpQDW/k79+fuX+jT5TvgC+P738fez9zL3PPbK9FjzbfIK8ifyHfMR9cT3e/rV/N/+hgCYAUcCRAPEBGoGCwikCc8K/wpqCskJVwn8CO8IiQmCCv8Kzgo6CkIJmgd7BXgDtgECAED+sfxo+yT67PgO+K/3nPeU9173zvbO9YL0TvOG8kfysfLd89z1YPgH+2r9X//6ADcCNANdBPQFtQdACVoKDQs3C6MKqgnfCJoIqwgXCd8JmAqlCuQJkwi/BpYEawKLANX+/vxL+/P54/jz90T3Dvcy92j3Yffv9iD2HvUJ9Afzj/IT85D0tPY0+cv7SP5pABsCeAOUBK0F6wZeCOAJKAvpC88L/grSCcUIOAhYCPwIowncCZ8J5giUB6oFcANRAVn/fP3a+2P6Cvnu9zb31va09uX2bPfM93P3e/Z49Yn0svNE87vzNPVW9/355fyT/70BZAO6BNkF6gYSCGoJwAqrCwAMtwvJCnsJbAj4BwIIVwi+CAcJxgjSB1IGfwRvAkYAbP7Q/Cb7dPn/99L24/WB9cX1bPb79kn3SvfX9gr2SfXb9LL08/Qb9iX4pvpm/UAAygKkBAkGYgfGCPkJDAsUDLUMoAzYC6oKMgm+B/AGBgd+B88H5QeUB6MG+wQEAysBSf9o/ZD73flg+Cf3TvbJ9av17/V39uP2EvcS99X2TPad9Vr1n/VF9m33Wvny+67+RQG/A/IFkQeyCLwJ1QrKC4QMBQ3qDO4LcQoKCdIH6gaWBtsGOgcDBy4G+ARWA1UBUf9y/Z77yvk6+P/27PU79Rv1hfUh9sj2cffc99j3afcL98r2g/aB9un2//fN+XD8f/9pAhoFeAdaCXQKHgvSC5gMHA0UDZoMpAs3CqkIWQd8BjwGaQaEBhYG8wRHA10Bav+J/b/7AfpS+NL22vV39X31yfVp9kn3EviR+Mf4zvhh+JX31/aB9qz2Ufeh+Kv6W/17ALkDmwbACDoKQQvqC2IM8wyDDasN/QyyC0cK0Ah6B6QGWQb9BUEFTwQoA5UBg/9//bT7/flL+OP26/UW9aT0+vTR9a72rve1+DD5GPnf+Mn4c/jN93P31/el+Lr5pfuN/r8BsARZB5oJNAs9DBMNqQ3XDeQNsA3QDAwL9ghIBw8GNwXoBOMEbAQ/A7sBLgCm/vj8PPt5+bf3K/YL9Wb0M/TI9Bf2o/f1+O/5h/qH+hT6cfnU+DT4xPfl96X44vni+93+PQJiBfUH/AmDC5AMQw2tDekN2Q2KDesMyAsSChoISgbCBLMDDANPAjIBvP9M/vj8j/sI+oX4J/fo9er0Q/Qp9NH0Nfbc91f5p/qx+x38zfsT+y76avnx+Ov4cfl3+i/8wf72AVcFcQjeCnQMgQ03DmYOFA6pDUINgQwCCxQJFgchBV4DMwK2ATUBUgAz/9n9Qvy/+ov5dPhC9yz2hvU19Qz1bfW89mT45PlP+7P8ef04/W/8ePtn+oH5Ifly+Vr67Ps7/jgBaARKB8oJtwv2DLMNLA43DssNMQ1cDBYLNAkfBx4FWAPtAdQAGQBd/2H+Ov0A/Hn63Pi098P23PUX9cf0DPXM9Sv3Jfky+7f8vP1Y/hf+Bv3h++n6+vlP+Xn5mPpE/HT+UgGHBHIH1QnGCxgNiA1gDSkN5AxaDKILnwoACQAHGwWKA04CPQFXAFn//f2C/Ef7FvqZ+Df3YPbg9WT1Q/XF9cT2Efi6+Z37PP1J/rH+l/7T/Zz8f/uZ+tn5l/k0+pn7rP1cAGADOwZ/CEgKygvWDD4NVQ1oDTINhQxfC68JewcmBUQD1AGlALD/z/6q/Tz86vrP+az4jPfI9mb2Ava19QP2B/en+IH6RvzR/eT+bf81/zP+uPxa+076ePku+c35T/tU/bv/pwK8BVIIHAqEC6EMGA0lDUwNZw3oDJkL1QnwB8cFkQPpAcEAjf8y/hT9Efzk+rv5wfjh9wz3fvYh9tP10/WW9j/4Kfri+2n9ov4y/wf/Wf4+/QH89Poi+ob5jPmv+sr8Sf/2Ac4EaAdqCeMKGgziDAsNBw0eDfwMDQxWCkcINQZQBHwCygBb/zL+J/0K/Oj6B/pT+Yv4vfch97v2ZPZf9g33X/j/+aX7Pf2B/jz/Yv8K/0r+NP0W/BT7H/rA+Xr6OvyC/vQAtAN0Bq8IMQpDCxUMdgyODKMMqAzzC0cKLAgjBkYEngJMAREAwf5m/S78O/tf+pH5yPgP+Ff3rvZp9pb2Q/eC+Db64vtZ/Z3+Q/9I/7j+wP2S/HL7nvon+gv6ifoD/EH+rQAnA6kFuActCWQKjwtbDJkMrQzbDJ8MbAuFCYAHfgVcA1gBqf8b/qn8qPsF+0j6gvnW+Fn4+Pd79yD3E/eA93b43flm+8r8Cv4E/1j/4P7o/cL8n/uo+hP6/PmA+tD72v1hAOcCSQWpB5oJxwpsC+YLOwxvDIQMQwxjC74Jtge2BccDswHL/2v+cP2d/Lf7pfp++Xj4w/dC9+j2zPb+9oD3Yfio+Tn72vxM/kj/p/85/zr+G/0E/Cv7lPpQ+sb6FPwH/jcATAJOBH4GlggRCvoKkQsVDHwMsAy1DCwMpQo6CNYFywPWARcAx/6S/TH83frY+Sz5Xfhe9672cPZm9qv2n/f1+G36HfzK/QP/kv+Z/wP/2/1+/G77/fq/+nj6mPqH+0H9jv8lApgE1AbMCD0KFgu2C2wM/wwUDaIMvAsoCgcIzQXMA8kBuP///cn80fvC+sn5Bflk+N73YvcN9/32NffF97r4F/rT+6L9If/8/yIAqP+y/oD9UPxS+6b6Tvp6+lT75fwE/2gB5ANJBoEIKQr+CnYL8AtaDJEMnwxuDHMLagm4Bv8DhAFB/4/9ifzL+7P6Ufk9+Ir3DffY9hP3Zfen9yL4Eflv+vb7k/0F/xYAjABVAI3/K/6P/Ef7i/o8+oz6dvuM/A3+KQCyAlYFzgfWCS4L3QtSDOYMSQ0KDT4Mygp4COUF5ANMAm4Aaf7y/Bz8RvtA+mD5mfiI9472R/Z39pT24vbm94f5X/td/T//sgBnASABJADW/oT9OvwX+0L6/PmT+u/72/07ANoCRAVPBwwJdQpaC9ILVAwNDZANIQ2vC4cJ4gZDBBkCKABL/tH8yfvb+uT5B/lo+OD3QvfF9rD22/Yy9x34nflc+zL9Af9kAO8AxQATAN7+V/0V/D37gfoA+mL66PsB/ikAmAJBBW4H2wjmCfcKBAy9DBcNSw0PDewL3wklB1wE8AHf/xj+r/y8+xL7WPpq+Yz48vdp9/L23/YJ9yb3p/cN+Sb7Pv3e/jwAZgGsAdgAh/8X/n38KPuH+nP60/rH+0n9Hv9LAcIDZga1CB8K5gqQCy4MpAwgDUENcwyZCt4H4QRCAhEAFf5H/P36Nvqy+T35x/g7+G73qPaG9hD3ufdW+Er59foF/Sr/IgFsAqwCwgH8/wD+V/wc+0v61fnz+fr63vw7/7kBGgQkBuAHWQmUCowLNAypDAMNFA1tDOAKgwiTBZgC4/9r/YL7e/oq+gH6p/kf+Y/4Bfhc97n2ifbe9qH35/jU+hH9Kv/2AFMC0wJOAhcBiv/l/UH83voo+jr6Ffua/Jz+ugCoApAEiAY/CI0JsgqLC0MMHQ2GDQQNdwv7CNUFqgL5//n9dvwQ+xf6q/lC+bP4VPj99133sfaI9i/3W/i++Xr7f/1S/+QARQLzAmoCzADT/gT9hPuT+ln6rfqF+878l/7JAOECrgRUBsgH2QjVCQYLRQxGDbUNUg3lC2wJbAZ+A7QAE/7f+3X63Pm3+bP5cfm5+Nb3+/ZX9jX2pfaP9/P40voY/Yv/swEmA6kDIwOcAbT///2L/Ev7YvoX+o762vvg/SIAMgIFBNkFnAcTCUYKZQuADFMNnw1aDU4MMAoUB5sDhAAF/tX7Kfpe+Tf5P/kb+an4BPhU98P2efaW9mL35PjW+v/8Ov9YAe0CqANgAxQCKAAw/o38XPtp+t75Qvqk+4j9q/8EAhgEpQULB6MIPQqAC20MIA1dDeIMpwvRCVcHLATkABz+7vs4+jj57/j/+Pn4oPhB+Av46vem90z3bfd0+EP6f/za/vAAbgIxAzoDggIKAQ7/E/2F+3n6FPqD+rX7d/16/3kBZwM6BfoGjQjOCcIKiAtWDA0NHA0EDNUJCAcdBE0Bt/52/Jz6L/lz+Iz4A/k++dj4JPix93/3g/cH+Er5QPuN/cj/pQECA8wDtQOQAq4Asv7j/Fr7O/q1+fr5H/v0/AP/FgEpAzUFFwecCMAJrAqaC30MPg1pDTUMrwnBBuoDFwF+/kr8YvoR+Yr4nPjm+An55/im+F34G/gn+Jz4YPmR+lr8af6IAJgCDgRQBDgDXAF2/9H9RPzk+gf62vmH+gb8K/5kAEsCPQRgBjoIogmyCrQLmwwIDewMEAxjCtEHzwQBAlz/m/w7+vP4mvh7+DT4APjz9/b33fev9/L34vgs+on7F/0B//kAcgIeAxMDgQJOAab/Bf6d/Fz7ffpi+v76HPzF/eb/IwIiBOsF1AenCecKxwuvDG0NSA3KCzsJYQbDA2IBA//V/B37z/nd+Fn4N/gu+BP4v/d795H3APjH+O75YvsD/RH/XgEpA+MDlQOFAuwAJP+X/Xf8xfth+zD7fPuN/Fv+hQB9AisE1wV1B+IIEAoLC+4LlgyXDHgLZwknB/sEdwKN/+v8IfsI+kX51/ix+F74r/cY9/T2K/eX9134lflT+1f9f/+8AY0DZgQQBNcCOQGB/9D9Wfw5+5n6xvrO+4f9ov9xAboCPgQ8BhoIMQmVCSAKKAsQDAEMAAt1CTQHZQTJAXH/L/1f+yD6Mvl7+AH4xfeO9yX3v/bC9lX3VPjO+av7k/2I/7IBvAPMBJ4EuwNOAmUAdf64/ED7HfrF+cD6vfzZ/rUAmAKfBJMGJAhkCYwKegvfC7cLRAt1CgMJyQYgBJ4BWf8u/VT78fnE+J732/bU9h73GffV9vX2rfec+PH5FfyE/oYA4gH5AucDDAQeA34B6f+I/kX9T/zD+577+fsR/bL+ogC8Ar0EZwabB6MIwgnaCq4LFQzPC5AKjwg3BpQD5ACa/sr8BPtu+af4Ufij97L2HfY99r32I/eV93L48PkM/I/+/gDUAgQEkASGBLgD7wG3//L9+fyD/Cf8AfyK/NH9cP8HAcgCrARoBs0HugiLCaEKsQv6CzsL0AkKCBAG1gNWAdr+XPwu+vH4r/hr+Gj3V/bC9Yf1mvUu9lL36fj6+mj96/8BAkkD9AM7BP4DFwOwAeX/3/0y/C77EfvZ+1T9FP+lAEsCHQT4BZ8HxgidCZIKYgunC6ELugpnCGAF1gJCAQcAif7P/EL7APr0+Bj47fZs9ZT0w/Rg9Q/2PfdP+ej7g/4FAU4D4ARGBbcEowP+AQQAZP5v/Z786Pv8+9D8Df5d/6kANgL8A88FiwcJCR0K8gr7C9IMfAzfCqAIWAYcBIQBrP4k/Gb6WfmV+Or3CfcY9mX1DfVG9dz1p/YV+Gn6//xO/zcBmwJtA50DJQMFApoAKv8S/p79af1k/dj9z/76/xwBTwKWAxgF3wY5CKsI0AijCRcL7gtEC2wJZwd4BUkDAAHY/uP8J/vG+eT4G/jp9pT1vPSL9OP0lfWu9pX4Fvuq/dX/SwFzAmUDlwOQAvwA1f8p/8f+W/6+/bb9q/4UAFgBCQK/AjkEBQYUB5cHpgj3CQ8L+gs8DDML1ggDBloDHAEt/2T9//u1+mb5Rvgx9+H1wPSC9NL0TvXz9RP3N/kq/Oj+sgC0AWoCwgJuAnMBBADO/jz+PP5l/mr+sf6Y/ygBswLPA7YEZgUVBjoHjwiACTsKXgvFDOcM1QqWB74EaAKi/6L8cfpe+fT4efjM9wL3Rvbf9cz1D/Zr9g/3QPgt+tT8R//dAO4BAAPBA1QDuAGx/wn+OP0r/Zn9Yf6P/wEBkQIFBB0FxwUoBoEG3gahB9gIOApqC84LDQsOCYcGLQQBAqT/CP36+tP53PjE9xX3ZvZG9Zr0yPRG9b/11/Ya+RH8vP6ZAL8BEQK7AT8BeABH/5L+0v6G/0QAJwFLAjwDjgOEA7UDGAQpBEcECQU+BoQHAwnVCuoLPgtfCRQHzgR3Ah0AOP6T/DH7P/rJ+Q35ZPeY9az0rvTn9PX0V/Xq9pL5iPzn/nQAlQFuAs8CQgIsAVYAz/+A/6T/EgBmALoAaQF3AmsD9QN1BFwFdAZIBycIbAm0CmQLAgu/CfUHvgVTAxYBc//Y/eb7VPqW+ef4Vvdm9SX04PMl9K70xvW+9xD6MfwO/q7/+ACRAYABKAHBAEIA1f+g/9P/kQBsARYCWQKBAhADywNQBLkElAXCBuoHOQlwCvIKngqXCR8IOwbrA98BRwCA/m78Cvua+sr5Nvjz9lj2xPXa9HL0FvWS9oL4nfrX/I7+bf/R/wwAPgBuAHUASQBzAO4ASAF2AbwBPgLCAgEDRQPSA4gEfgVoBkkHSAhFCdYJpgnYCGsHwAUYBJgCXgH4/3T+cf23/JP75fk5+Df3avZ59fv0RPX59cr2QfiB+rz8Vv5t/1YA6wD0AKgAVgBUAJIA2wAiAWUBEgLdAl4D1QOQBHMF5wUWBlgGkAa6Bh0HvgcpCNkHogYfBbgDMwKCABL/7P3Q/NX79foL+gX55vcd97b2p/YO98H3svjf+Vr70/zL/S3+tP5X/0v/QP+3/14A6gCuAcUCsQMQBBkECAShAx8DHgOeAywEOAXGBhYI1ghACWQJQgjSBXsDJAL9AEz/Df7n/eL9//yx+9b67PlO+B735vYD9+72IPeU+JP6AfzN/K39gv6b/mr+sP5e/+X/NgDCAH4BCgKGAuECGAN/A0EELAWoBeAFZwbZBugGJAd3BygHMgYaBS0EEAOkAYkAHADK/+3+B/6E/cv8Y/ul+YX4DPil92X33PdG+Zj6OfvY+8b8av1L/QD9P/31/cL+cv9tANABvQLUAsQCGwOnA7EDVAOVA4QEMwV7BdcFYgbUBtsGhQanBXoEbANdAoIBzAAdAHb//v7//q/+ZP3g+9z6b/rH+db41/im+Rz6Dfqh+tH7P/xA/LX8if09/pb+Dv+7/4wAVAHXATICUgJVAkACSQLYAuMDkgSIBNMEXgV5BVkFVQU8BakEtAPMAhMCigETAWoAAQDJ/3L/mf5K/VD8gfvZ+o36mvr6+t/6i/o2+yn8Y/xV/Nf8Cv7q/v/+4v4h/4n/yf9HAOIAJQFQAaABNwK+AgYDHAP0AkwDzQObAw8DvQKCA2oERwTZA64DqwMDA74BpgCv/wb/tv6K/p3+cf6j/dX8lPyS/Pf7afuk+9P78/tC/G38+fz4/aX+fP4k/qL+jv8cADkAWQCZALcAmADkAJoBagEFAWsBAQLMAvIDOwT4A3QE9AR5BGADeALYAVEBqAB5APUAyQD4/7b/yf8e/x3+N/3Z/Gj9jf0P/RD9S/1W/Rn9H/3P/cz9av2t/W7+JP8v/xT/RP/T/zMAHQAtAJUAEgFuAWEBEgETAXUBhAFCAT4CXQPsAqgCNwNmA4wCUwFQAUwCEQIRAdkAqgDz/4L/sf+x/1X/8P4x/sv9Lf7M/QP97vxC/ZL9Uv1k/VL+M/8t/6H+mP4b/3j/Df+2/gz/Z//h/10ApAAqAewASgDPAF0BpwGvAY4BBwLTAvACQgKsAaUBdgKHAlYB+QCnAZEBdgD8/yMAcv/U/iX/ZP82/9n9lf34/j7+6fyE/Yv+Ev9k/2b/LP9I/0b/Bf8u/w7/KP+9/5r/Wf9gAIkBSwBD/10AZQA3/3j/ywCiAZgBewEpAmsCwwGYAQcCmgEVAScBsAB4APkA3wCS/0L/9v+f//T+if5+/j7/6v4u/ir/Jf+U/kr/Xv+Q/3f/7P6f/xAAHADR/yz//P4T/1r/RP/1/jP/8f+RAA0Ar/+AAFYBXAE2AOUA8AHkAHEA0gBFAsEB7v/GAK8BawGYAPL/OgAtAEn/Qf+H/73+rP6n/8X/sP8pADUA5P86AEQA/f+d/9f+NQA3AXT/w/0g/yEBIQCo/h7/NwAe/6v+JAByAKf/B/86AFIBlP8CAFQCYP+8/QUB7wGG/+z+KwFuAdn/bf9uAQ8C/f7b/UgAIwGs/zP/ZgDCAO//nv8f/xQAjgCE/+v/RgEHAksAv/8oASMBRgAu/3H/OwBi/1r//f8NAOX/IP8HALgAn/93/1kAq/8U/uj/xgCO/6D/BgCoADkAfv/+/yEAy/5r/tz/vv9X/v7+yP8gAOkAoQD+/9gATwH0/qj/lAIjAV3/RwD6AT0CPABi/7YAFQHL/2v+0P/BABv/Xf+kAPkAif8V/wYAov9R/yX/lf9P/7X+RQBaAFr+pP44/2H/5f9j/xf/yv/o/87+jQBVAaX/+P9CAdIBQgBSAMEA1QBGAMT+/QHYAXD/OwKyAaT/vADYAc//Xv77AFsBZQBK/6/+TAE5AF39Ef/zACD/Pf0dADEBY/05/r8A2v/v/vn+XAB2AL7+9P7YAE0AoP/xAEMAof+CAMr/Uf/NAK4Arv8aAFQBkQHJ/9n/IAGRASIBb/+TAEoAvv/3AWMAXP+A/2QAfAGC/hv+IgBz/5r+uv4yAGAAEv9B/mr/AALU/or9AwEcAWT/Ff9EAEsA2P4l/wQB+P8L//QAQQEr/23/JAE9ANX/JQBvATYBTf48AF8CmgCU/+gAdQJlAD3/4gCqAOr/LAGd/9H+aAHL/zn/+P6Y/uQAaP7s/XIAXf8v/9//n/9D/z3/nABC/xn+qwDPAGH+pP+rAtEAZ//O/xEAuwH6AGz+Tv9PAZkBMQBE/rkBVQOQ/qD8cgJQBDT87vuiBR0Fr/s7/jgFyP+U+8YAJwLb/TX9PAGFAUL9of2hABABl/2G/VoBswAJ/wD+IwGOApP+bf8iAaIAjgC4/hP+UAGeAVP+LgCwAgQAeP/v////9f8c/3oAdAK2AZf+YQGEBEz+dv3mAQ4BJACQ/oP+lwLKAT/83f0ZBHD/tvpL/wgCtv81+wj/2AOt/t38uP+3ARgA9vwRApoCWPw//0sCvAAt/+z/3AAC/zUAngK5ALj+XADgAWIBlP64ALACZ/6DAfEBfP8nAIAAdAJ+/ZT+8wID/1r+BADHArH91vstA9z+V/x1AF4A1P+g/Tj+qgLn/0j8cAAqAkn/Av5GAGoBmv5l/0cDagCs/0QBs/2LAYICJfxeAOMDX/8U/6YBiALxAIz+MACdApsA5vssAa4FnPzX/U0Dzv8//q/+a/87/rb/DgAO/tD/XQGaAIn94wANAUD7VgE+AYT/sAAN/tkE/P5r/RsDdv/kAGz9YADyBGn8Dv1vBJkAGv6/AR8BmwBWAB8AWgGs/w0A2gDI/iAC+AHh+wcA4AKU+279FQKf/zn9Gv03AUUAbvyz/1IDuv2c+88DvAJ1/b7/gAJT/wL+dAJKARj+uQAJAqP/sv7aAYsDr/7H/lQE7AEA/A0AogRc/vX8+AEWAwcA5fwBA60BEP1TADf99QOL/uf5yAU0AJH9Yf6X/rQAQP/U/O/+rAQR/kv8IQAeAxMCZfqVAZwCMAASAKH90ASRANr8uQKgACX+T/51AEkCff87/hoAYwNXACj8swBAA+3/RP5PAXoCnwFk/U0AEwTJ/Yv8m/4pBfL9zPi7A68CZftm/DICogLY/ZP88gLmAHz+HwBT/yMDff3Z/5sD3/1ZACkBbf1sAWoD1ftI/uwFTf/A/McCagGEAdD9Fv7kBI3/2f0kBGv+8PyhA2AAIgBs+6cA9AM5+ZQAGQRS/Nn+rQGOAP38cv5dAiL/kP5WAK4AUv3rAHADN/x+AGgD2QEN/Rf//ATb/tP+0QBcAX4BWf4oASoBU/6aAn7+YvvzBD8At/3CANr8bwUA/gn6Ywmt+jP9vQY1/AEDsPxI/t8Gg/mu/E4Gl/6a+qAB1v9XAnT+EvkACQ8Ax/mXBAb/ZgA8BAb8AgHOA6D9fAGX/CgDpAIt+qAD9P/K/ukCRf2OAEEEq/inAS8I2/bJ/vMFQgFb/t78tALp/7/86QAmAiL/9PtZAmMCvvxMAB0Bhv+d/ksAVQCh/4MA/v/y/t4Ax//i/kkDGgDc+6wBswXt/B7+bwMC/5UBywAM/OoAnwJY/ooBR/13AcUCuvkdA67+i/7bAyX7WgA3A1H9f/66ApL9nQG3Af761gSg/EAA6gGa+rkF//0K/n0CQv/UAVr+av9YAhUAFP/r/rwClAAs/2n/c/97BMv8tf67BBb+WgC0AUv9RQHpA8z6M//qAlH/6f9E+2UCmgOt+4D/MAGP/X4AbgCH/awDQvwLAB8GE/pxAQcD5vwCAqYAWAD//z8AN//1/VYDwgB2//H8GgJBBVz71P7+/3ADdQDZ++0DqwBt/lr+7AAiAlD92v1QAcwBPv2G/yMCAv77/8D9YP6VAm39PAGv/978ZgPA/XP/UQOd/JMAOAN/AXD+7/0QBZkBqv7e/b4DpwGD/K4DnP7BAG8A8f2OAZL+bwDyAPX9bP8+A1oAyf9WALQAmAE3/EcBswLk+RECYgA5AKsBW/maAYwBm/6E+0cACQV7+4cBk/wDAdIFS/kPAGABtAKJ/0z8ygIzAgQBjvumAZMD3vvnANsC3v7W/qwBrgLu/gr9ngE8Aqb9EQBkA4j+6ADi/psASQMz+2gAQwKbAET8jAEfA175qAKb/uP+QQBs/i8Dy/ldAX0F1/lz/gMETv7D/eUDigCO/pkBaP+DAbICff6I/TUEhAKb+vUDmQEM/rwBrPw3AlwBqvzgBJL+g/oeB3r/JvwBAgP9nwOI/GH+bQdO+CH+BAif/ML5uQPWAIT8YQJu/Z4Cqf3A+xAHU/3y/TUEP/2x/7YEgfrO//cFyvpqA9cAuP0mBQT+1v5/AaYCNv9T+vYFKwPo+yD/8f+QA2T+MfsUBVMAavvdBLb+8f34Ay37MQI+BP72mQTXBCP3kQOA/0P/qQNT+Zn/SANq/g7/8v8G/1ABqgAu/uD/ov/4Aa8Afv2w/zgFjP7c+84DbwGl/pD/wAFhAOL+bQH3AXn+pP3vAmUA6f6D/gwAbwPK/CL+OwXc/mv7UwKSAP4ATvwgAG4G1fpz/4EASwDBAQn7HwIvAWP9o/7tBYj+C/flCgL8PPwCB2X63gN9/h8AQAZQ+c7+hAqq+xb5BQmO/QYAxgDJ+eMJrvvQ9mkLtQFD9AgDBAqk9u/9qQSZ/XcDLvofA/UGJPYkAoUDUvvfARkBmf69/6P/YgJV/6H4xAUuAoH2/gjT/qz5Awek/y4AU/wFAr0FFP2o+qEDIwrm9VT8xwdsAPr+X/y5AXcBbP6nAhb9b/2YBEsBvfx8/VoDpQDX/H0A8gCbARf+KADcAGMAuf4d/tsFLPyF+8EH2/5Z+gUEeQGY+4kB5P/5/tsAKf78AS8Ci/tSAGgF1ftJ/FEFbASa+Mb+3gjF+fv9LQfM+5j7yQMPAyH9ZP5kAF8BnQFC/vH/4wFX/zz/owTb/Xv9IQKMA3EBEfaFB5MBb/kXBT38LgLT/r/+MQIK+w4CrQNz+rEALgXI+dcB5gET+p8FL//I/ZMDbv2IAan+3f3SBi745v96CPX3iAJwAkj9QQI//K8Dw/8wABABevwmB3L9KfxLBR//3v2aAZcCCvyW/acEcgDY/aX7/AHaBUX59P4qASMAHAEG/GEEu/0P/eMGSftQAGACvf1kAvb+dwBc/lADDABh+6IEf/+ZADT/Fv0bA3YCjPvw/vYHbfsd/rgDVf61A5T73/7HBBwB2/yX+0kHTgDH94MD9wOP+jD/GQE4AOr+Df6SBQD7Uv6+BLf9DAI8/8f+YQJgAO8AY/9dALoARv/yAmz8V//6BGT9j/3hAlkBZ/tT/+EBcQHq+zEByAOr/O8Bgf5sAIYCOP1i/vwFFv4G+wAHe/w8/9gB3PvZA9f7cgAMBoH5vQDSAHYA8QJp+CAFEAMn+oMEp/6i/vYAVgJM/gf+EgS+/OkBnf2C/h0GN/pVAYkBgf1iAmT+jgDeAK7/SQBv//wDof6p+kUHVQF5+W8DZgKo/O0CzP2d/eEHCfjy/NEKCflZ/qsCLf1dBCH55wNQA3n2mQWSAzv7sv7zAqoBPvzn/4EElf47/pX/hAGaAu76hwN8Al/6wgK6AOr/VQJj++cBLwb3934B/whI+EP7BAi9A/z2Uv5jCRn95Pp+AvMBjPzp/nQCCP4dAZD+0f3IAiz/LvzHA+QAvf2XAkT8OwIuARv8tgRG/QQAoAV8+tP+jgSN/p//6f49ApYAhP26AjL+n//wA1z7a/8YBez9Jf6U/0EEY/99+VIG0/+G+uIF0P7v/PkBj/8GAEQBPf8l/LcFtf54/GgE9vu9ARUCP/5Z/xT+3AYA+1T8pQgK/NP/DP8/AXICW/rsBG3/AP1CA1P/CgCT/z39tgJiA2v6b//dBc/9kfrHA8IEgPrc/OwG+f9F++QCSP/TAOYCe/hEBeMEkvYzA60D1/vK/SAFNQEN+T0CWALc/QACaP7Y/AoESALA+/v/aAMG/YABxgKi+mAAggdl/U/4+wYdAi74IAKYA0f7dwARAyL9PQBRAfn9R/8/Bf38gv2dBv794/0lAY4BUQAY/YgCCgA0/YsD1/5o/rkBO/7QATv//P5FAWb/BQFA/lsBfP93/yEDr/yGANMAagCO//T7oATv/6T7qgL7AE380/6oBDv/7vv/AdgBiwCD/sn9LwN+Ax/9d/xjB1IAS/h/B4r+4vvqBub6iAEwATD8ZgZu+SsBMQRg+VwGR/05/JUHI/tl/5gCVP3eA0r8A/6JBRv9uf8NAMX+fABl/+4ARf8S/5D+3ABOBOD7Jv0TCNb8Mfm1CvL9gPpGBo399ALHAAz5eQSjB432c/3iCpv6yP1LA+r9BQH5/R8A7AR6+tL+dAfQ+EUCYgMP9ZsJ5gI09VYHJQCx+ssG0Pt8+nsGwgLF+jj9/QbS/sL43wQlA3j6VgDcAxb/dvxkBMkBhfebB5wCv/doBgH+Cf9LBfT5FAJHAif9GAGJ/+oCkfuI/vUGqfx6/B0CpAKg/x78ZwHJAPEB6P4D+jYHyQDg+Z0DH/78/1wDrvsJAewBY/0+ACUAigG8/dAAIQO1+kUESQA3/bMBg/+eBD75QQJnBh34twINAxX79ATdAGb2owlDAAD4Kgfk+zMCnv4d/uMG3ffg/x8FR/1J/roC2f0K/SgIRvnr/LQIMfvA/zMEsfsyAdYFo/mg/3wG3fpr/lUDYgLj/jn6vANtA+z8fvzwAcMD7P0UAdb+LgLPAB787wGeAF0AZ/+e/8f/qv+kAdL/vPv1ALsEQfzW/ucBr/41A/T8W/3UBpX9uP7EAPT/OQKy/JYBQ/+B/9ACc/2AAJ3/VgBM/sf+dwUp/Kn9sgTw/rn+rf8Q/7cB7wHJ/R/+UQSDAOz8PP8YAbsD9/y6/uIBuwDAAcH62AAcBO39U/6e/sUE1AA2+aoCUQPE/VoBZvxUAHEGX/6h+swAOQfU+XP+cwX4/OoBgPuTAAMHFPou/VMFHP7s/pwDdfopA48BbvzzA0L98v4nBNUBYfiPAwkF0viUAk0BK//s/WEBVwTS+Xz/4gU//FD/Qwam9xMAXAmr+Yz+dgXQ+0UBfQU69woAvQf7+coAygGl/HIER/uxAMgE0/XNBYIEXPXPBhgDofVUCZQB//XmCOf9EvuLCrn5BPzOCin5t/54BRr8ff9H/z4CNQIE+7n9Hgex/Q/+8AMQ+L0GgQNT9mUIJ/80+1MDkAKT/dT7KwbV/of8hgOu/438RQD3AbH9zADAABP/UQB+AT7+cP2kBoz8cvyUBwn9Cv5EA0kA6PwlAdkA/wB0AbH5bQQsAZH6sAJGAvv9av7xAEX/zwOF/Er9mwRo/U0CFwBA/NIEav8G/sECq/38/70Eo/o5AfgEBPqBAaL/WP/FAbz9mv95BTT8lvy7BYD7tAQq/CH8pwsa+k3+pABiAugAcfumAb79lQUB/rX6iQXeADP9Cf6XAyj/fgFw/fn99AYb/FkA9v5sAQkEtfgvBIoA3vkGBWAAKv5RAYP9QgF5AF/8KgKxAMr7/gTpALb7XAIX/sgCdv86/VMDj/+QAHT/UADK/58AX/+7AH0AI/xQBsf/tPiFBKgDWfq8/2AFZvvh/wMDi/6DAV3+ZP7xALkDqfxq/egFVfyV/2kDsfy6/zMB5P/m/SL/ewRSABn77/+EArgC0/17+e0HaAHM+GwGSgAz/VAAegGZAW79WwBAAGECNf6C/pABCfyBA/cA9/x/AfH+1wE6/6z8wQD2AbUBdP8K/iMBsQOI+67+MARBAAr9P/8DBuH71v6NAk/9YAIX/cn/Twba+R3+Dgcn/CUAHwCZ/ukEWP2B/VEDXgBf/qQB7/z+AOQD/vwb/jAAYQJF//P+JADFAG4AIv4pAEsAjP76AG8AbwC7Ajr6OAF5BZj6bv/VAccCQvw7/1AGCfv6/kkDW/+V/iIA5QLw+7T+EQiG/D/+eAHh/UAFXvn4AZQF9vshAB/+mgOV/GgApQSb+FgFJ/7e+4AIPfp9/YoFo/5K/coCTwCk+UwDMwf9+Q38rATLAD4Bz/osAh8B/vuECIj5ef8ABI7/7ABV+UAE7QHc/ggCxv/O/vr9qAMP/cQAswN5+ZIGVAJx9uACFgF6AWoAFvqEA1oC6f3C/1789gH0BDP8f/oXBmQCfPkvBVcAIfovBNz/mP/WAUD9BQLx/qD/cgKc/bMA5v8MAogAR/vj//UDKP8L/vMFQfoQ/e4J5Pc7APAFVvuVA9v8CwNS/in6sglF/fD6gwUXAa/72f5LAf4AoQDB/BYCzwHa/Tr/cP/hAqj+9/0aA0r/v/+wAFX/DAAKABEACgLg/xH+4v7rAHACxv3G/4//4wD1/xH+CAOo/mf+q//XAvz/B/zwAjgDkf17/20AH/73AngB9PwTAbkAKP5qA576qv3BCfP6Nf3tBr/64gDIAeb89QQM+eQDbARD+f4Dv/wOA0kDQ/lj/qAEAgPV+VkCXQC5/p0CQfvGAr8Byf8t/iL+BgWS/UX+1gDfAs0BLfuHAC0BEf60AVUB5P1I/8AAJgHb/tP7lQMGAjr7fwWM/dn8RAWC/KQCk/7q/v8Cqv0VA8f8Sv8pBbr7qP9ZA2r7gQEvBqj6U/yaBRQCqfkdAikFAPkMA7ED8vohAucAzvwFASAF9/lK/1IFbvsHAVsAq/5YAYv+y/8mAPD+7wEgA3z6uf6PBMz/uv8T+/4DzQOr+aYDdf9e/qsB/gA4/3X98wLB/lIC9v88/FADjP2jAgMBVfwhBGn9+gGYAUj6VQCQAsMDvfzQ/LoCcQL6/SP9CAG2AxT+wvqBBwv9Vf0DBlv51wOs/7b7JQkl+Rb9sghw+lUBGADG+zQIa/3z+DEI7/3E+xcIsPfrADEI4/nt/8v/ugIRAPn97wDv/u4BPgD7/NAB0v9Z/p8C9f08AXL/o/2WBCP+Yv5TAJEDuPwU/VIIvPri/WkF3v6X/T0AvAM4+1T+KAja+tH/QwNY+5MFhvr9/dEH+f35/D4AiANC+z0BXgVc+FcD6gAs+8gIDPuE+voHkf4k+2wEjwD3+T8C8wVz/sT27AbxA8z7FQBH/qoDTfzBBgL9pPl4CWX81ACt/C4CuADV/90DsPdBB1v88/3qBMH6CgV8/GoAqgU0+RcA5ACvAQ0C2fcEBA0Frft+/3/+HQFAAbIC6vj6/5EKL/bfAQsHHPb+AiIFc/rUAQEBIv7dAlD9JwEtAkb8CwDmAvn/yP5p/moCFwG5+vsGHv/u9tYJ9f9E+swDtv/ZALT6MwZjAK31kwuB/kv5yQS8AJT+Pv12AEQDzf9M+ooF8gEP+vMBef/oAvL9ovxWBh39tf6VAyX+WP6kApr+hAHTAoL6kAHs/9cBYP+z/r4BHP4lADb/cgLhABf7O/8QBX4AGPtO/6kGOf0Y/9gBzfzfAtj/qf/I/zYA+f5mA/D8sfwtBsD9//xeBHkAnvz6AAgArwTt94cBfAhK+PYCDfxVA6wGWPb2/nUDQQOM/WL+VAG7/9MC3ft+ABcBhwHn/on6cQjb/A780AMsAKYCpvqHAc0BOPt5A68AugCR/Wf99AY4/Oj8XgOj/4f+WgAmA0T95v9g/lkDPQCF/N8CbP0tBMz9S/13BN/+SQDp/hcA8/7YBAAAMfhzBmMB3/ntA6gChfr0/ykGzPsn/3EEtfkZAZcFCPzm/k8B+P8RABsBr/46/0MBZP9f/1D/rwFNAvD9sPzIAK4DeQDW+PUC6QXH+MwBsAKs+8IBxAKY/S/+lgNE/cIB4QKn+yIBTf1kA+kEEfrz/2wAGwKXAi76xv3hA54EMP3c/Pr/5APz/jD7nAUOAGr8I/8iBQQApPqfBE7+TgC1APr65weN/ij3qQfaAJn+bP66/IoFhwG/+a7/xAVo/MMBdP78/DcHD/5X/IUA9QJVAKH9cQIZAFr+ygCe/9oAPf2eAcMDmPngBBH/d/pWB5H9gP12AfUBfP/r+9EEvwCj+coD5QJr/A7/owKp/rf8VAbi/Cn/PwQB+UAF+/7B+noGhgE2/I79hARG/nn+eQcB+cX+GwUz+2EFFP8b/CMDlf/kANv9OQGQ/177+wfJAfD2LAG1BAYES/go/AkJ5vrnAk0CzfjUA7oCav8/+lMCTQOG+lEH8v+E+P8AbwI+AxP6iwFoAVL+YQcA+In+BgOHAG4DfvdiAy8GafnTABYAcf3uA+cBEfp3/xwH9PqHAH8F9vZgAuwE/f60/4H7BQWJATn90AGF/eEAJQHGAL4AyP0D/iQBfQWE+iX+bAZf+QoDNwS4+PoBowJvADf9CQAfAGf9Hgfd/QT7ygLiAe8AHPkFAXEHMvl+/4QFW/7I/R39XQTyAeX6/wDy/0kDHQBh+zkFSfz0/lwFxf7lAIH8sf6XA+0AC/7s/yoBuv5dAAEASP9CAjf9mP6sBRH+7vswAbgFdv0X/lsAvgDPA676fwIaAh/8HwFyAlr8OPywBmz/jfveBB/+7/0+A5D+NwEM+nsEZgWk+AYE3v10AR0FRvfS/1kF2/+f/tH9MQICA8L73f7XAmX/kABx/UQCdgJF+xoAqP9UBYUBlfYWBEcCLfysAaL/cQFA+zcCJQY595b+PAMnANr/PwBOAX7+VwB1/7QBlv97/nICYgCnAI3/+P11A3MB4Pq0A3H+WACNBeT3WgJPAx7+sP4AAAgEv/gkBGkEavndA3v+1v3nA5AAqPxc/zoCAv9pAW7+k/6yAqz8pwCSACb8rAa8/7j3jgJdBVr/Wfpr/lIIxvz0+iIHn/5A/2v/3gAOAuf8ywA8/hwFSwE4+VoAqAFHAyj8KgCqBN/80QHZ/uD9PAD2APkD6v2t/WADDAOa+BQAkgaf/E/9wQDUBJf8of2wAfT9VQLu/Y0CqwE/90AFxwOQ+4kB1/3QBUkBd/g2AVMEWf4j/asD2f0xAlwBOvmpAvcCvf0G/qEC2AFw/1r+Vv8+Avn9MP5gAyYAsv1WA2T+kf6hAfD+UP9v/x4CgPtTA/gCfvgpAvoD3/83+4UC3QE0+kUDfAKo/w0ASf9dAQj/GvxPBJsDnfrX/3QDof6h/HEEUQCT/FID/fxlAYAD8vt0/kACvwLs/fYA8f4l/QkD+gGs/V7+GgSPAMD+N/1jAfb/k/04BSb8XQEGAqH+Yv+m++IDZP70AHMCP/0vAWn/i/3t/3AD4fz5/Z8EaP93/tH9JACqBAf7N/40BR//v//D/IwB7QJV/tv+dv4bBrD96vx/BjL+Y/6yAdgARwEy/UT/9QOc/AgBtgQq/P/+GQLSAUz+bf0J/w8FrP/r+SAH1f0T+ioI+v2F+/oCcwDn/m3+SABv/e0BRgC9/WgCp/5UAg/8mf3/BXX8afwjBb0D3vsLAF4BBQEEADH8hwEKBF4Ah/09AgwAoPyYA/UA7P5QALH/Mf/pAFAAiP57A/78nfxjA5AAN/+2/tH++QAcAvn6/vzaBhX/B/oMA1YCn//K/xAA9AD6/nT/BwFkAQX9EP5DBOUBH/wVAs8CjPy5AEUBTgG2+4YDtwPt/KwCKv2HBFb/EPiiA+oC2f89/dgAeAL7/iT/4PybAC0Btv27/2EBcf8L/FsASQTy/aj9l//IAC8BivuMAicDyPooAXYDev3D/ZMCLAJq/gEAvAEUAW8ATv28ALgEKv80/VoE+QLY+k8BWQO0/Hz/egATAMv/kgCbAbr8y/+oAHr9kgO5/e/7kgN2AF7/NP2zADIDL/w6/ewChAHQ/bMAGf86/1MCFf/W/gQAS/8EAsgDBv7j/x0DaAAd/yj/GgIa/rICwwF2/QoDdf0gAZkCnP2H/Pb+wATx/Qf/if8NACoCUfplACEB9f1sAj3/pv9xAK38OQCIBH3+KP1PA2z/WfzvAe//af5tBeH/tfnBA44Ff/tN/h4Fy/72/KYD5wOX/Of+lgM4ADsAo/2P/r0DoQBv/AgAWAKh/QkApwBA/ZUABv7nAGkAnvxHBMH+CfwYAur/Sf+l/poBCQAkAIICJfwzAf0Cuv14/K4DFQV0+QECWQM9+x0C5QFo/ZT/KASv/1v+KAJ7AMUBHgDH/VcAKQHNAB8Abf4XAMQAov/WAKz8ef7FAen+NP83/lH/eP85ACcAff69ADH/2v6iAHP/wf7aAH0Ahv9PADsAdgHtAMr+zP5GAtz/Hv/DA7D/i/9bAXcA7v/i/5cC1/8MAM4BHf+m/wYCXgDW/U0CFAHz/TgBf/8z/jkAKQBc/gz/BgDk/0f/q/5D/qf+4gAV/lUAHwNW/oz/mQEXAK3+f/9SAJ3/+wFwAOH+3QG8/04BJwEn/HkAygGs/b7/6ALz/kD/gQOB/az93gMDAGv/xwDAAGQC6f94//j+DAHs/7D9lAFH/gj/+P+s/mr/3v4cARv/EQGD/8r9SQMW/xj+PAHpAWoAq/8SAa7+uAEcAUT+TQFnALX/YQDO/1v/Y/+m/18A5f8PAMsAev9IAFcA6P9WAcn/H//7AOQA/P68/wcBg/4Z/wAAFf8I/2b+EgDC/1X+GACZAH7/9//M//f+PQBrAPX/ngC0ATUCLgHkAPD/yv8DAcL/+P+bAZQB5wAs/+L+4f8M/z39ev7QASkAqf+tAZT/4v5w/wkBhwAh/94C9gLG/3wA9wE1//L9+v/x/6j//P8iANH/N/9W/j3++/4i/87+P/+s/5z/bwDO//P+PQA4ANr+VAB6AGf/2AG9/9b95QBF/8L9cf/3/gv/fwHrAPr+pwBVAd4A6AEQAroCqgSGA+MB+AJqAzwCcQDzAEQAp/8NApz/lf0s/1f+5vz7/C/9sP2W/ej84P3W/f/97v29/Zb+EP9rANH/uf/W//7+3P5Z/vX+gv41/4kAJQBnAI7/mv9pATsCxgG2AhUF0gXIBNoDggVQBb4CTAO7A0wCrAHGAdYAxv47/0D/dvxD/Mz7Yvtu+4L6hfvQ+xb8AfyT/BD+jf1h/tz+lP71/yMAOv/T/mL+Rv+h/yH/tv+k/+0AjQFuAIABDQOgAhkDLQXwBP8FZQe7BTgFTAW0BQgEFwK0A60CDwFSACL/qf/F/Z37Tfsw+qH5sfnN+Bf5xPti/JP72Pus/Ab9Nv3P/Sr+Iv/r/ycA+f68/nkAFwB0/y//TQBYAuoBRgKUA6QDUgPgBCEGrAWPBvAH0whTBzwGvgUjA4YCXAKvABkAJABR/x/+PPxK+nX5YPhU9233Hfgh+YP6JPpI+oL8Y/1a/Tb+bf/O/0EAbQCs/4oAoQG7AOr/8/8MAT0BewCaAcMCYgMnBDQFkgUUBhkH8gauB+wIZQggBncEYgTkA4sCwAGUAIn+JP77/E/6ifjE91z33vYI94r3jPhS+aP5fvr0+wj9zvyb/ZT+5v7m/wQAnv8jAGgBzgCU/1wAxwBLARECvAJ1A24EEwYKBiMGXAchCGUJpQkmCV0IewY9BWgE8QPBAm0A+/8I/7/7uvkl+XX3/PWd9Wv0DfW097b4cviB+M76u/3x/Rj9Lv5VAG8BGQGkAJMAJwHxAckAegD7AZUCUwITAsQCLwQGBo8GJQbTBigIbArYCm0JPwn1B0sGCgawBLsBRgDD/7j9+/tn+vn3zPWE9Inz2vKO88n06PVw99j4y/kj/Oz9ov1h/iwARAHkARUCRgG2AdgCDgL9AKcAAQECAkECeQJlBLgFbQZ9B18HKgjXCWoKIQrcCc0JbAihBmkElAKHAaT+8vvd+vz5C/hs9QX0yPK/8kjz9/LT9D/33vgl+pr7jP6pAKMBFgKTAtgDkgScA8wBogHQAQ4BngBAAGAAHwHnAWICKANnBO4FrwflB4kIXQplC4wL2Qo7CS4GXgXnBWgDTf+P/Lv7Rfq69070Q/LU8v/xK/Ar8WXzZfUI+FX5M/tU/xYC2QHWAQcDkAPjBCcFXQPdAhwDdwLuAOb/jP89AHwBZwEwA1cFJAZsB3cHCwhDCQ0LVwwICz8JPghnB3UF1AK8/2X9AfzS+fP2nPRI8wnxXO+a783wtfJk9BD2s/jn+7j+JgFBA8cE6wSHBHMFZAYpBZoCAwI/A+kCeQCq/lj/wgDyAJoA5wFhBc4H2wcFCOAJzQxoDfMK4AjAB3gHCAaNAp7/iv2B+yL4o/W+8yvxwu+p7lbvkfAx8i/0EfaF+QD8Bf9aAkEEYwX2BQMHkgfyB5kGSAQsA0wCCQHh/j79X/3N/nsA/AFrA2UF/wcbCeMIugkODLkNWgwGCckGrgYjBkkCbP2P+m76BfpU9tDxxO+27yjvbe4h77zxxvRV90X6/f31AVUElQWJBt8HagkPCUYHnAXqAysCugAA/0b9XvzQ/Iz+3QCRApMDVgWLB/8JtAoCC0ENRA5bDKQIWwamBSgElwAs/FT6rPmv91D0QPGX7y/vRO/C7ojvIvLE9PH3KfzBAFYEgAakB1gIIQkWCRUIewZ+BOwCeAEOALX+DP1P/O38Hv67/34BWAPgBdMIgQqcC/cMUQ6ODu0LvAdkBC8DEwLw/tz6Y/hc+Jz3vPQ18Q/v3e5J73zvffAD85T2aPr7/TcC6wUZCA4JDAmFCVIJsAcfBesCsAFxAAX/Hv35+637Y/yi/g0BRgMNBSoHaQkoC2oMMg0UDq8N7wrYBgEEnQIFAL37FPhL91f3cfV+8rvwQfCe8LvwuPA18vn0g/jg++z/YgSjB2oJ8Qk+CnkJAAgSBpwDuQE8AOT+sf2U/Pz7YvyD/Y3/fQHyAvAE2Qf5CrcMmg09DnwOBg0sCRgFLQIRAN/8uvig9kP29/U59DTy5vEF8h7y1fE+8i70KffR+o/+4wKWBqAITgmYCZ0JqQj9BmcEwwHt/7P+2f3t/Dj8f/v9+x7+hACgAqUD4wV4CRIMgA1wDgIQ3g9TDLMGvgKmAJf9aPmx9HfzNvXa9Lzy6vFD86D0x/SF9Dj1Xfec+Sz88P/DAwgH9gj4CdwKiwqUCM4FJAN4AFH+/vwv/On7nvuY+zz96v+lAW0CdwOUBlQKwQxZDmoQHxIEEC0KMAQtAar+LPkM87rwjfJo9N3zTvMZ9e/2D/eY9Sn1kfZ1+Lr6uP1IAn4GCgl7CmcL7QqdCAwGQQMzAFf9jfvG+8v8Ev2C/Df9xP/EAV4CLgI2BEAIkgt+DTkP/BFBEhwOmgdPAhX/wPqu9EvvW+5E8VbzD/RR9WT3FPkE+Wz4Ofie+L35CfwCANgDLQejCdIKCwt7CRsHPAQdAb796vqI+o/7cPyc/Cr9gf82AikDeALHAgcGCgqEDEwO1hAEEzoRrAs6BR4Azfuh9Vnvn+zl7YXw2/H282r33/pv/Kb79fqH+qn6bvul/foAEwRhB7EJkQocCpcILAb8Avj+Pftx+dX5VPsH/Lz8Tf72ACoDngOvA7kE+gdgC1gNRQ+SEaYS2A/sCCUCSP3W9/jwMetK6lbtPPEb9K72mPrg/Qj/4v0g/GH7ZvuJ/J/+swGNBasIhwqHChUJ7gZ5AyX/+PqC+Mj4HvtS/Rz+5P7dAEUDSgQQBPcDHAVmBxsKpQ34EbgUPxJAC64ENABV+1fz+erq5yfqcO4g8gL2F/s//00B7QB7/8j9Ovxg+9n79P7vAtcFuQfXCPoIAgh6BXABEv2o+W34d/m5+0/9rP6TADgDYwUxBRwEnwR2B3YKtAw9Dx4SWxOUD/IHywBF+4X1AO6I57rmw+qv8MX1DPp8/uABPAMaAgcA2v29+wb7t/xDAAgEywYtCLQIyQePBR8C8f0P+sz3cPjg+qn9qv8DAQUDeAVJBsgEpgMWBUwIDAtCDZ0Q5xNDEwANhgTl/T34bPGP6RDl6OZy7E/yLvfO/HsCSwWiBDECJQBI/pb8uPuR/Pf/PAQfB/0HLQfjBdcDjQCd/Of4afeu+M/7mv6GANcCSgUyB9AGVgVhBSMHbglEC7wNFxFPEiAPdwjzAdH8Avab7dTmfuV56XHu7vJC+Cn/sARxBtcFCwTGAZ3/n/38/Kz9uf80AgsEYgVkBYAEcAIc/7b7NPnE+E/6iPx6/qEAtgOLBmgHMwcMBwoIOgnLCXoLZQ5DEVoQ2ApdBEf/RPp98h3qyOWo5qjqEe9L9Hn7YQLyBkgIcQcuBmwDlP+a/PH7qP1K/4MAoQEYA4IEDQQXAgj/1/v7+aH5BvsM/R//sAF9BOUGnAd8BzAIiwnEClILsAxAD4oQuA33Bl0AtvuJ9iLvWejv5cTnBuxC8fr3Zv9mBTAIJQjAB9EGjQRxAJL8ifuA/Gf+uf9UATID7wNMAxEBG/6G+8L69fo9+w78t/2kAD4E8wZhCIcJ5Qo8DJMMGw2XDsEPlQ2zB80BLv3y+OXy/+s26MfoN+wa8LD0APuPAVEGKAgiCCcHpwSuAbz+Tf1i/Zz9gP6w/4YBCwNEAw4Clv8z/Zf78voN+837Tv0JAPgCgwU1BzkIzglqC7UMDw2gDbQOIQ4ZC4cFi/9S+v30BO9p6TvnAOlB7Vby4veD/lsEMgiECVAJGAg0BbYBl/4t/Rv9ZP0G/gn/1wA0As8B6P+m/R78nfvj+2783/wG/ooAUQThB3AJ0wkcCp4LoA2gDvEOGg70C1gHlQFt/Kb3R/LO653nnOcD67rvXfQX+uIA5QZ1Ch4LMgpBCAwFxwHP/jz9Af25/Ov8pv0j/ysArP+q/hb9M/wp/KH8t/2o/l0A4QLvBXkIlQlaCj8LXgw5DZMNxg1BDS0KrAR8/qr5MfVd78jp5+Z56FLsKPHO9mb9fQRxCccLxQtkCvoHeATJANT9M/yJ+2b7Fvx0/an+Tv+5/pf95PzL/G/9DP4K/5gAjQLgBMIGHQgZCRQKOQvSC1EMGQ2UDWYMUQjYAqv9xvjq8ibs2+fA59fqIe/H8wv6PgGWB1sLTAymC3oJ9AUWAhv/GP2T+5H6yvop/MD9pf5v/u39YP1A/Vz9eP30/fb+NAHpA5QGbQgiCZEJigrYC1EMZAyNDDEMvQnjBH3/RPtg9y7yfezj6DHpUey18J/1j/sHAksHzwpcDCAMEAp1BoECQ/8c/Yb7SvoE+gj71vwA/gT+if0k/Zj9Gf5L/oD+Of8+AfkDzQaTCJYJnQrXC+kMRQ1nDcwMMgvhBycDFf7s+AP0/O726mHpaOrV7YbyDvg0/hwE7wiOC/0L5gqpCLIF9wGS/qH8rvsi+6X6Lvu2/J/9Zv2n/NX8f/2b/bD9j/6UANoCxwTtBjIJ+Qq1C70LYQzrDMoM0guGCRUGHwEx/AH4DfSO78fqUOmv6yrwdvSQ+G7+6wTqCcELowsPC2MJ5wVqAQD+Sfz/+tP5rvnt+qX8QP0t/VX9tv39/e79Mv4l/6gAlAKvBMMGhQjgCYwLGw2yDTkNjwwnDCYKfQWN/7v6Bfer8p3tMOpk6ivtsfCk9Dv66ACaBhUKrQsRDOMKAQhJBOEAIP7b+z760Pl/+qH7sPx//dv9bv0p/YD9Lf6L/pr+8P/SAgMGnAdCCOAJ4wslDQEN4wwwDRIMhAg5A6X+o/rk9dDwrOwo69Hr9O3d8QL34fxEAqUGLgrIC0sLOgnkBpUEXgEg/tb78Pq/+lD6gvqJ+378cvzA+wH8bf2+/jz/4P+1AToEJAaAByYJEAtdDEkMCQyKDCINIAxBCMYCiP1P+V31lvAD7OrpYuth77TzNviv/bMD6gh1C9QL7Ar+CNIFtgG4/gf9xvt7+q/5vfqf/N/9qv2w/FX8a/zg/GP9Ev5w/1cB7wOdBo4IwgnoCqUMAA6rDZcMwAvRCq8H1QHN+173JfTx74rrJ+qH7N/wyfTa+Hb+oAQZCc4KBAvACiwJHwZuAp//B/5x/Az7XPr4+iz8s/yq/Er8KfwZ/BH8nvys/WH/pAEyBMkGpwggCsYLeQ12DtsNuQy1C8gJ2wUiAAb7ZvcL9M7v6utl60ruLPKf9U75iP5KBG8ILApzCiYKxwjnBakCKwBn/rX89/ps+i37Kfw4/KT7rPvw+0v8ufx5/X/+q/95ARIE/AYGCf0J6Ao8DC0NJg2PDAQMVwr/BQAA3PqJ9xr0ku/W64Xree4j8o316/nB/zQFgwgtChwL/gr2CKcFxwK/AL3+TPxU+un5lPpq+8370fuD+xL7OPsf/ID9vv4WAC0CCAXFB4IJ7gpdDHANOg0GDDgLkgquCHsEGP+q+jD3rvP873/tnu137yrypPVB+qD/8gMlB7sJdwtfC9oInAX2AtEAhv4Q/I76APr2+V/6Efu/+9L7hfvT++L8JP4G/0wAzgKRBcsHSAm1Cj4M5wzNDIMMzAwUDJoITgMb/mP61faZ8p7usOxs7WHvLPJK9qP75AC2BGMHngnECiYK4gdmBW0DWQEd/+L8dfvf+qb6vvr/+jn74PpA+l76jPtF/Sr/SwHyA38GaQjOCRALTgzYDKcMcgyzDBUMwgg9A+P98PlZ9ijyZO787OPtxe8f8vr1h/v5ALUEBQcCCVsKCwoWCLcFwQPPAWv/IP3O+zP7x/po+k/6bfp6+mb6nfrC+3T9Vv+YAU8EAQfVCBQKJgtHDMAMPQwSDIEMFAzyCJYDev5m+nf29vEA7qPsmO1d76rxafUi+w4BnwWyCH0KCAu/CT8H6AQGAyIBof45/PD6tPro+uT6yfqu+nL6Y/rg+vr7fv04/4IBjQSKB7EJzwq0C6sMJA3nDHUMOQwFC4cHPwJO/b/5c/ZA8oLuTu187m7wa/K99dr6aAC1BGkHWwlsCuoJ+QewBdQDuAEU/3D8sPru+W/5Qfl0+d35Mvp7+kf7n/xf/loAygLXBacIUAocC8gLpQy+DBUMtQvJCyILuAdLAlj94vmr9jryFu6T7MrtCPBa8u71WvsIAXwFXAhCCtwKdwkCB6QE7AL8AHH+HfyI+uL5p/mr+fv5MfpL+p/6h/v3/Hz+ZwAqA4wGeQnCChQLgQsbDEwM8wsSDHMMXQt8B88BDv23+SP2tfEZ7vTsl+3R7hfxh/WY+zsBOQVcCNIKpQtNCqYHPQUYA6kAuv0H+3T5zvjg+Ib5fvr4+vf6O/sl/G39df7F/wsCNwUrCCAKiAsBDVkOpg73DWgNFw0zC4oGSgAo+9T3nvSt8F7tr+wt7mbwO/NQ95v8hgElBccHmAkQCs8IngbFBDcDcQEe/4v8gvpH+Rb5Yfl7+S758vjB+aP7w/2O/18B+wNEB80J7QpQCx8MMg18DSUNDA09DaML8wbtADX83/j19A7waezg60ntG+/F8Z325PxEAv0F2gj6CowLEQqKBwsFpQL5/wr9ovry+P336fej+Kj5QPqp+mj7tvws/p7/iwE7BDIHdwnHCtQLJg1EDlAOpA1IDc8MmgrLBfT/SPu09wT01O/e7Gnsge1H7w3yr/aA/LcBvAXPCBYL1Qt/CiEIuwVHAzgAw/zm+eX31/a89mH3ffiI+Y36EPzt/cz/RAH2Al8FHwhaCkgLpws0DOkMFA3cDP4M0gynCsIF+/+N+0v4ZvSp7yfsdeuH7DTuLfFS9uT8pwLbBhgKUQynDMUKFAiVBfACkf/O++L4ZPfb9v32vffh+O750Poj/P797v+PAR8DVAXpB/cJEAu3C5cMag2eDXQNbg0LDacKtAXV//b6MvcE83Puees76+fsSu/I8vv35f36AssG3wkeDFsMZQp7B8EEHALv/o375vhz9wj3S/cv+G75c/ox+y781f2x/1UBIQNgBfkHJAphC/sLugySDaMNCA1vDOoL3glRBZv/Evva90D07e+h7O3rDO3r7sHxsfb1/HwCgQahCScM/gyjC+wI7gXHAhb/S/tL+Iz2y/XZ9dv2e/jx+Vr7Cf1E/3cBGQOCBBoGIgjFCaQKJQvFC28MoQyTDLkMpgyuCgcGDwAo+7T3APS073Ps5OtA7Q/vzPGT9ub8fQJPBmIJBgz7DIwLxAgOBh8DNf/m+q/3K/aV9YP1WPYp+D763Ptt/Yn/1wGZA/IEoQaZCOUJHQoaCr4KogvZC70LPgzPDDALdQa8AFr8JPkA9cjvBewu6zLsr+2S8Of1efwsAlUG8QnQDKkNDQxNCYUGSgMM/6n6k/cW9oj1h/WO9mn4QPrY+7f9BQDrAQsDGgTmBRMIiQndCR8KDQslDJMMpgxQDb0NywvEBuoAbfy5+AH0h+7K6h/qcOtY7bfwZPY8/RwDXQfeCoUNFg40DCUJDQbCAtL+y/q49wL2YPW99RH3vvhf+t77pf3d/+IBSwNmBAgG8gcuCXgJiQlUCowLSgyCDAMNPQ05C2IGmAA0/M/4cfQO7ybroupL7GTuZPGu9nr9XwNuB6gKTQ0rDmcMCgmvBUUCNf7o+Z72BPWh9CT1rPb7+Dz7BP3D/uQA9wJzBJMFxQYVCL4IlAh3CA4JPwoVC30LCQxuDCAL6QYvAVP8pviB9Grvj+sE673s7u7k8RD3JP5nBFMIxArZDKwNzwsbCHMEWgHP/aT5TvbI9MD0b/Xg9jj5u/vT/ZP/nQGvAw0FvAWFBtYHowhUCL8HKghZCTIKsgqiC3kMEAuGBtoArPxe+f30ie/k68Drau1R7zTyu/fL/qsEdgheC6oN7Q1jC5sHRAQBAfv8ofiu9av0yPSP9TL31Pl4/JL+fgCnAo0EdgXFBYUGrgcoCIYH7gZ5B4cIPAmqCYsKTQsDCiUGQAE+/aP5/vTZ75XsXuz77RPwK/NW+NX+pgS6CHoLNg0TDdEKZgfyA6UAwvyj+In1W/Sx9Mn1pvch+sD8Dv9DAWsDIQXLBaYF0QVqBtsGdAb9BZAGEAh+CYoKwwuEDNUKFAZnACL87PiS9DLvrOvG6z3uB/F69MX5YQAkBuAJRwyjDR0NSApDBoUCIv9H+3D3vvQX9An1vPYD+Xv74f3x//gBAgROBYUFSwWzBaoG9QYmBpAFUQYPCGsJKgoMC50LIwqvBRoAofsb+OXz/e7P6xXsoO6O8Rb1XPr3ALwGdQqNDH4N1AwkCi8GQQKj/uT6G/da9I7zefR69vP4ovtS/skAFgMUBVcGiAY8BkkGyQbkBjAGmQUwBroHFAkcCjgLsAubCZwE6/6x+jv3wPLN7RrrIuwd72ryZvYi/NsCVAilC1kN1w2YDFcJEgUJASz9Q/nE9aHzUfOO9Pn27vno/In/+wFfBDsG6AayBnMGkgZaBlkFYQSkBDIGuwfqCEUK0AuUCx8IwALU/e/5d/Uz8DDsZ+sE7WnvjPK696j+EAWHCUgMHQ49DhEMQwghBC0A5Puw9530gPMR9LT1Nvhn+4H++wD8ArkE9gUrBt0FzwUTBtEFwQQwBP8E6QarCBQKnQuiDE8L+wZfAX/8VPiK83buAeuv6ovscu+88wP6ZQGiB+MLhg61D7UOdQsJB7QCdf7k+ZT14PJy8r3zM/Zw+Q39QwCiAngELwYuB8kGiwXCBMYEpAQeBN0D7gTxBtYIWQryC/sMBwuFBb/+wfk29ufxtuyM6W7q2O3n8Y/2AP09BAYKTg0kD8QPIw4iCigF8QAE/dH48vTi8hPzt/Q892/62f15ADECtwNUBRIGoQXlBAYFwQWvBeIErwQTBh8IfQlhClYLegvPCE0DX/3u+Bj1VvB8617pBeun7sTy2/f6/nkG9wvLDgoQNRA7DuEJqQQQAKz7IvcW8zXxuvHf8+P2efpM/n0BygOoBXEHNgiJByUGmwWlBQwF9QPCA38FrwcNCfAJWwudCzQIfgEh+5T3mvTS74jq1uhw693va/RP+i8ClgkeDusP1BBOEE8N/weXAun9J/l39DjxrPDk8Sz0k/cn/F4A5QJjBIAGqAjoCGQHJQZCBs8F6ANCAlsDeAawCGIJaArxC/wKigVl/r35+fb08sbs1uiq6UXtMfG89SD9rgVHDGgPzRAXEUYP+grIBQkB+vtK9nPxrO9N8GLybPXd+bP+0AGYA4AFHAhpCXQI4wZTBhQGegRdAmEC9QSrB/YI1QmQC0EM6wh6Amn80vhV9f7vhupy6I7qqe5384b5bwEyCZoODhGXEWsQSA1YCL8Cdv0/+L7zqvAa8KPxqvSu+B39wQAHA4EEKQb4B1wIDwcNBUgESATfAykDzgOLBnwJ6gpRCy4M0QubB77/nvjw9MfyoO5t6crnaOvK8Zb3aP18BJoL6Q+6EPEPYA5SCyAGRQAE+832VfNU8VHxDvMA9tr5TP64AXgDbQRdBosIygjHBscEkQT4BHgEvgPhBMcHFwrRCjULiQufCe0DtPyO96j0MfE+7Mro2Ol57hv00vl0AIcHDQ3WD4oQxw8mDaAIGwME/l/5S/Vs8nzxXPKB9Mj3AfwBAGcCoAMRBVgHZghpB4MFwgQjBdIE6APwAxwGrggPCqEKegtNC48HggDW+U/2+PMI8PLq4OhT64zw/vW0+6ICWwnWDY8P6g8vD3oMdQeWAVD89fdC9K7x5fD58bz0ufhl/fwAMgPPBOwGzwjcCFwH8AWdBS4F4QP9AksE9AbiCG8JOQp+C0UK7ASv/cn4MPam8v7sxOha6dTtFfMy+NP+jQbfDPYP3hDFEPEOggpcBGb+Ovnh9IzxDvB+8MnyrPag+wQArgJpBG8GsQhQCdoH7QU4BSwFXQRBA58D7QVOCHUJRwq1C90LJAgmAZz6/vYj9GbvGeoV6Mfq0O929dT7dANkCtYOzxByEU4QXgyCBkMA3fo+9rXyuvB+8NPxEPXs+f3+ZwIJBJ4F2AeYCSYJWwf9BW8FfATRAvwBaAPfBaYH0Qh9CjAMwQpkBbL+Fvre9kPyaeyu6GfpEe3p8df3oP+KB1oNWxChETUROQ78CL0C4PyB9wXzOPCZ79Dwo/Pa9+n8VQEYBA4GIwjjCasJrwe/Bf0EdQTfAjIBtgGSBKsHYQmeCkQMUgx8CJsBr/vW9+7zQu4W6RboNet+8DT2//zMBMELGBDCEUwR0A5MCpAEzP4n+VD07PDY77Hw0fJk9i/7FwBxA3YFgAewCZYKWwkuB9gFEQWKA3cBygCzAoQFpwc2CRULUgwxCrgEfP73+UH2XPHv6/HoHupx7iH0iPrAAbkIEw7IEBoRKQ8+CyEGkgAw+yj2YfJ78HPw1vHM9Ff5lv65AggFeAYiCNgJAgpbCAgGjwTCA7ACngHUAekDogaZCOYJGgsVCysIHAKn+yH31/Ow7wHrDOmS61bxqvfZ/XsEHAvvD6oRkxB/DSQJowOQ/Zn3z/Lu73Tv9/DP8wD4Sf26AqQGnwiHCUsKQQptCFsF2gLfAUUBUgAAANgBOQUxCAwKuwsZDacLKAb3/p/5Jfbc8Tnskui76YXuPPTa+YcA6gfvDdcQ7hAiD48LigatABX7MvZW8iPw/O+08Rf10vk8//oDxAZoCLAJtwoGCn4HwwQtA4gCcwFtAPkAiwOFBqoITwrrC70L9we5AQT8Hfjx83zurunO6OzrxfAZ9qL8vAQvDM0QLRJREcAOTgodBJr9tffr8n3vA+4q74Pyrvd8/eQCpwbVCEYKQAsYC/8I4wVvA3ICtAGYAAsAxAExBSkI6gkuCxYMGQpcBAH9xfft9MPxBu1o6UrqHO9Y9UT72gHzCLQOTBHqENIOagueBpUAb/r79DrxRe9h70Dx5vRN+i4A5ARDB18Ijgm5CmkKSQjlBZ4E3wNiAr4A2gBhA3gGXghMCQIKtgl+BrEA1/rP9nPz/u7H6m7pT+yr8dv3jf7eBYQMihCVEUEQeA1mCUsEZ/5j+DrzHPBL71bw9/I198j89QGPBaEHnAmPC+cL/gkeBx8F5QMDArr/Fv8FAVYE5gbbCN0K0AtsCY8DjP2U+Wj2evGx64joKOrm7pn02PoSApAJHA+wEW4ROA+JC7kG7ACX+sj0xvAo72bvZPFM9Rj75ADIBIQG2gfiCTYLXgrdB7AFigQtAwYBlv+TAJcDRwYGCJYJQgupCggGkv92+l/3iPMb7qfpWukm7eHyDfnU/xkHZA0IEY4Rzw+NDIEIWwM6/fb2BvLB75Pv3/C785P4jP5pAw0GfQcmCagKlAqcCHoGJgX8A/0B/v85ANcCEwbvBywJjArNCsAHvwHN+5f3/fNr70frAeqS7FfxGveB/ZUERQvFD4ARcRC2DdIJOQXJ/7v5PPTR8M7voPDj8pz2pft5AOkD4gWFBxAJsQnKCBwHxwXuBJ8DzAEGAXECSQVyB9oINAocC2AJGwRU/f73wPQq8X7s0+hF6fntkPRC+x8CgAnqD4ETShNGENkL7QZOAS/7Y/Uq8Xrv0u/a8Ub1R/rI/w8EbwaGB3EI9AhFCFEGRQQuA8UCBwJrAToCHwXWCDcLDAzuC7QKvgbC/1v4FfPT75zsfOnk6Hrs9/IQ+tAAigfSDRASAROwEI8MxQfYApj9H/iF8yvxOfGQ8pb0r/c8/M0AuwPuBO8FbwdWCM8HkgYBBhoGywW/BCsEFgUUB5UISQnMCYEJjwZzAJj5uvTW8aPuw+rB6AXr1vCK9+X9gASXC2ER/hMUE80PYAtCBnIAWfoG9XjxQ/Ce8Bvy6vRC+Tv+AQIRBF0F+gaBCMUIxQfVBqMGmga9BbsEOAUtB9QICwnzCCEJxgf9AtX7tvU48pHvG+wE6SzpO+148136mwFiCQ4QzhM1FPwRUQ6GCcMDSP0M92XyGfDM7+nwS/Mq98r7wP9DArUDZAUkByAI3gdJB30H1wdvB3MGdAYFCIgJswnwCCsI0gYGA/D8DvcL8zbwy+yk6UXpkuxN8nn4Hv9mBkoN9RFtE04Siw+PC4oG1wAT+yH2nfIF8QbxefIh9Y34LPwi/3YBpAMYBj0IIQn4CN4IGAncCJEHNQYPBscGLAfyBgsHSgeaBfIAQPsp96v0O/Fx7DDpC+pd7qfzvfkWAfUIHA9SEiQTRBKvD84KiQQM/kz4tfO88ODv6fBm89X26PqR/jYBEwMVBRIHMgghCKIHygcCCJ4H1gYLB3AIjQl/CfYI0winB2YDlPxG9ljySe8l64DnUedw6/Lx0fhHADIIGQ8+E14UehO1EAcM8wXB/zj6jvUr8srwSfH08m/1tvhW/CT/GgHwAlkFbgdQCHwI+giPCQ8J7Ad5B3sIaAkpCZcI4QicCDAF7P70+Cf1w/ET7VLoyeYx6f3t+fOL+4oElgzCEfETYhT9EkgPhAkCA7v8LPcy8z3xBPHG8brzFPcm+2X+eABsAuUEGgcKCHgILQn5CUoJnwfZBs8HHwkkCdkIYAmhCfkGTgFu+zT3DPON7Q3ou+WZ567r/vD99zEBbQotEeMUHhYGFWMR0wuYBTr/A/nV85zwmO/67+TxLfUC+UD8e/7oANoDkQb/B7IIrQmeCkAK2wj5B24ICgmYCDYIughbCV0HYQKr/Av4F/QO77Dpgubi5ibqPO859ur+nge3DoUT/xXbFY8SOw03B0kBg/vz9e7x7O8M8Kzxb/TX9wr7p/39/6gCcwW9BxcJKgr/CksLQQqoCPAHNAieCEIIIAiaCIAIjgWz/2z51/QK8XrsFOgu5gnos+xG8277qATvDI0STRXFFfQTkg89CXcCfPxa9zXzdfAo8NjxdPRb92f6Mv1X/0IBKwMrBZQGlwejCKMJ/gmDCScJmAmVCuMKbgrWCQgJTwb3AIL68PS88IHsYOg35u3n7exp86P6lgKKCqgQzRMXFCsSRA7TCNwCrv14+R/2EfSq87v0X/Ys+BP64vtd/Yn+CAAaAmwEiAZxCEEKjAu4CxwLsQrVCsQK7gktCcwIdQdIA738d/YV8nfuTurm5p/mIurn77n2hv7yBkAO5RLEFFkUexFkDGgG1QAK/K/3YvT18lXzi/Tr9Zr3kvl1+wf99f6CASQETQZcCNwK6gwuDdQLjgp0CucKjAqfCf8INgh5BRQAtPkS9FDvZ+pA5vLkeOer7Cfz6PqwAw4MExInFawVmBPeDrUIkwJh/fb4Y/VL89zyuvMk9QL3Cfm2+vT7hP0cAEAD0wXFB74J9gtmDSoN8wvOCkwKtwnTCD4I7geQBn4Ccvx/9q/xSe3p6Bvms+aH6lrwWveF/z0IVg+cE/YUGRQiEe4LlAWW//H6e/cQ9fjzHvQf9aT2Wvgj+o/7sfw2/nkAfwNqBgEJMAuoDB8Ncgx8C6oK3AnHCNQHjgcnB70Evv+4+ab0gvAv7E/oneZk6AftcPNe+0YEcgzXESQUOhR9Em4OWgjHASD8z/fH9FTzfvOu9Dr2E/ho+qr8UP5t/7kAvQIEBTYHJAnXCvALvgvuCmIKQQqgCWMIhAcwBwEGGgIM/Bv2kvG37ejpgOd16Mfs2vKn+TQB0gjKDkQSRBNDEhcP8QkxBBD/9fqY93L14PRC9fn12/Zk+Dz6l/uP/Aj+ogDAA2MG0whaC2YN4w28DG4LpgrnCVYImwb+BbYFjQN5/nD48PPM8ELtdunt53Xq0e8v9gX9WgRlC3cQ3xLcErwQnAxAB7EBjvxF+E31KPQ/9LH0jfVl9wX6F/wY/W/+GwFZBLIGPwgzClIMRQ1QDKsKwwlhCZYIYAfvBuUGaAUrAYT7yfYP8xnv3+p56K3pz+1u8+/5LwF1CCUOjBGdEj0RsA2KCEcDkv58+jn3JvWJ9LD0XfXK9ub49foy/EP9R/8pAiwFigeGCU8LfQyUDGYLKAqCCRIJPAhGBwwHyQZiBCD/tfjI83LwTe1L6gPpTOtp8Nf2m/2GBAwLjA93EeMQlw4BC10GRwF4/Lv4j/am9VT1aPUi9sf3m/kT+zz8iv2A/yQCQAVECMcKlwxzDSsNJAzsChcKLQmYBwIGDAVZBLMBZfx/9i7yv+987XDrW+tY7rDztPkAAHgGGAy+D9kQ8w9jDU8JbgRT/876Xfd/9Sz1nPVj9mb3//je+mf8Vv1h/kkAswIQBTwHvQlGDJcNIg26C+8KwAr7CVsIrgaVBYkDGP8t+Uv0rfF178TsGOtr7J7w8/WB+x0B1QbPCxEPLhAGDxYMKwjrA97/Ffzm+Kf2WPXi9Cz1mfag+Hv6x/v5/P/+tQGeBBYHPQljCwYNhw2bDBYL8Qn7CMQHWgZPBX0EFwKq/Z346PSy8jDwWO3F60XthPGq9gT8nQFrB2kMcQ9BEPIO/gv5B2cD1/6N+kj3YvV79E70AvXv9nL5j/vt/Df+RwDcAlcFWgc6CT4LswzcDOQL8AqWCvsJtwhBB1UG9QRMAaf7Lvbi8svwM+6a6zDrQ+5v8w75kv4hBJgJBQ6LEG0Q5A0uChMG1QFP/RT5Jvae9BL0JvRq9eH3WfoF/AL9tP6FAZME+waMCCgKxwuRDCIM7QonCr0J+QgGCDQHkAZuBMX/6/kp9Wny4O/P7MPqleuH7w/1CfsLAaEGgwvRDjgQTg9rDLcIagT1/2r7vPeV9Xn0L/Sc9FP2zfjp+lb8v/35/5oC7wTxBuYI3Qo8DGkMnAuuClsKPAplCfgH5Qb0BWsDYf6S+F30yfFV70nsoOo27MDwlPYn/LgBSQczDIQPKxCXDowL7QfuA1b/7fqF96v17fSz9Gr1HvdK+fP6Evya/dX/iwIkBYIHzAnfC0wNPw0EDLQKHAqrCZEISgdDBqIE+AC4+zH3ZfT/8aPumetf64HubfNr+G/9zAIaCJ8MbQ8jEFkO3wr9BvgCtf5Y+un2wfSc86bz8PQZ9yz5pfo6/Hb+fQGfBA0Hzwg2CqoLewxGDGULlQpHCpAJcwiyB04HmAX2AAj7V/aH8+rwW+2S6nrqke2v8jn4+/2KAwIJ2A3mEFQR7g5fC1MH+QJY/ub5nfZq9HPzjvPE9MP23vj1+vv8Q//JAWUECQdFCfwK7AsKDIgLuQpaCgUKUAlZCJ4HMgciBXQAbPqG9ZTy4e/T7Jjq7+pA7lrzYvmR/00FbQqBDg8RFhGEDowKJAbQAS79vfhc9VXzu/Je8yv1ffep+b77wv0bAJ4CAQVVBzQJwwqKC4kLAgtbCmgKhgoJCuYIDwhqB9MEYP+u+LTzFvEF73HsgupK60XvJ/Vv+1IB4gbVC3oPChEGECgNeAmkBUwBhvxN+Iz1QfTP8y30hPWe98n5qvt3/aL/KALOBEEHVQn6CrsLtQscC7MK4grzCmoKPAkRCGYGwgIy/Xj3m/Mo8WPudes76n7savH+9nj8zwFyB4UM2Q+kEOcOEgzNCEcFAAEg/A/4i/Wh9FH0mfTA9ZL3k/kg+8n8KP82AlQFvgehCSILBwwdDIELBQvkCskKMQo+CWYInQZ8Akn8V/aV8i/wUO1D6njpfuw58mf4/f1VA6MIUQ16EE4Rsg9JDFkIcQRPAAH8JfjF9Zj0JfRn9J71jfcd+Tf6uvt1/hoCiwUuCC4KnQt7DGsMvwtCCycL2ArTCd0IPgiWBi8Ce/tm9cPxi+/i7AzqpenG7F/yZvjz/ZID9gikDZ8QNxGPD0oMngivBHEAL/yk+GL2EfVW9FP0O/Xn9pv4G/oF/LT+KwKFBTsIWArdC60MfAzHC2ULUwvvCtwJxwgxCFsGrAG7+oz0FPG+7h3snem66VDt2vIH+Rn/IQWGCrIOUhHGEf0PeQxhCFoEDADH+zL42/V49Izzm/Pl9P/26PiC+sH8yv8GA6AFywfYCX4LAww5C08KIQqSCqIKHgqpCesIawYgAZz6X/XH8ZfuCOvf6PjpMu4u9BX6y/+DBQwLfw+rES8RYg7RCl8H0gO0/0D7sfeX9ar0nvRg9cT2Dfj6+DL6XPyC/3wC3gQVB4QJvQudDD0MqQvdC2YMJwxPC2kK0QjLBPf91Pbg8f3uV+xe6Vzo1+ox8HL2Ovz0AbEHBw3QEAkS3BATDtsKTgcUA4j+b/qK95/1ZPQq9Pv0dva397n4Ovqi/K3/egLtBCYHWAkgC94L8QvSCwoMMgzACxgLYQrZCLUE//1t9/vyG/C07Bvp7+eK6u3vrvVD+z4Bngd7DTsRlRKeESAP3Qv3B60D6/6B+jX3CfXh84fzKPSD9Qr3sPij+lb9aAAYA1EFWAexCa0LcQwVDJILtguoC+sKIAq6CV4I8wMa/eD26vIk8KLsSemA6CjrQvDb9c37KAJyCMwNXRHzElMS0Q8nDK8H5QIO/vv5CvfI9EPzrvKA8xv13/br+FX7M/7AABQDlQVdCMcKzwuzC04LRwtNC9EKBAqACQ8JQgf8Avn8Wfde8yHwvezu6arpjOwy8Yv2P/yPArkIsA0REXMS5xE6D0QL8QZoAvj9vfmG9nj0XvNJ8w70vPW+9+75hfxQ/xcCPgQbBhMIwAmpCp0KkwrcChML7Ap7Ck0KaQl4BjsBXvum9pLyXu5x6tPoX+rM7TbyhvcL/twEuQpND0QSNROmEVEOcApRBgMCUf0B+dH11vP/8hTzEfR49RH3N/kP/Ef/FAJRBHwGrAiOCpkL1gvPC5sLMQuKCjkKKgroCEgFjf/a+ZX1LPKg7kHr7ula69nudPO1+Kb+jATaCVIORBFSEhERSg7OCsEGbQLW/cb5cfYU9Afz/vK887b0XPb0+Bj8Wv9IAhAFXAcSCYsKtwt5DF0MsAs1C98KaAo6CS8HvAOV/uH4EfTf8CfuS+um6eDqEe9V9KT5Vf9vBT4LgQ8PEscSVxEeDtcJqwWJAR39xPgu9RLzIPI68kfzGfV19/r5Bv2bABMEtgY2CGgJ3AofDJYMGAyFCxMLlAr0CQEJPQdnA6T9tvdp86DwtO2X6hPpwepg7zf1TvtnATgHigzMEFYTohOYEeoNOwkcBAP/YfrF9h/0T/Kb8VXyaPTB9gH5bvt5/tIBmgTvBrYIBwrBCgkLbgu7C8sLTgvFCoQKmgnIBpQBmfuI9pXy8u5M6wPpUulZ7AvxavZV/HgCpwgqDhQS1BMSE40QzgwaCK0CXf00+dv1EPMn8fbwe/KA9Kb2I/mB/BsAHQPCBRgIIAo7C6sL/wseDN0LCQtsCnoKVAqHCEYE3P7T+bL1FvKR7rrrN+q16nvtPvIL+Lb9OgP8CJsOkRKOEwkSGg90C/AGxwHq/LL4GvU88tTwRvHS8sT0A/cL+q79YgG0BFsHQwlcChgLyQtLDDQMdwupCmEKVAqTCUcHXwNQ/uH4OfTD8Abuceu76Yjqcu5P9D/6nv8xBQEL8g+BEpkSABEFDtcJzQTj/2X7Tffu87HxI/HV8W3ztfWC+ML7D/+JAtkFeggRCtMKgwsYDD4MnwvHCmYKVwowCvYIMwawATX8P/dz85jwju2v6rLpzOuf8DD2kPv0AMYGgAyBEFISCRIYENIMTQhiA3X++vkr9kHztPFY8TvyE/R29j35ZfxQAFYEkAekCcsK2AtwDDMMQwtMCtYJiAl6CUgJEAjNBH3/Vvqj9tTzYPA57KHpBOp57X/y0Pcz/aECbgjJDb4ROBPfEbkOqApIBpIB2fya+OD06fFR8M/w2PIr9XH3SvpI/r0CewYACaAKzAtjDEsMrQvZChEKeQkiCesIWAjCBqIDDv8H+rf1bvJX7xDszela6mLuIvSx+aP+7wMUCoQPhRKPEoIQaw11CdcE5P8o+wL3kfN18QDx9/HC8+X1f/is+3n/bwPhBmcJtgpuC/oLNAzSC9wKEgqnCXYJDAn/B/EFCgLD/LL3G/RM8dntgep06SPsQPGd9qr7AwEiB+sM9BCpEgISeA+GC+oGHgJM/cb48PRg8inxUPHI8g71wPeT+tv9xwGsBZ4IKwoHC7ULBQyPC4EKswlLCfAIlAhbCLYHJQUuAKD6ifbM86Twnuzj6W3qFO4N8wr4bv13A6MJoQ6MEUISwhDTDeQJTAVFADn7F/fx89DxwfBL8WjzA/a2+LP7h/+8AwkHCglUCs4L4QyhDDsL1wlJCRgJ4QiACMoH6gVBApL9PPnP9Xryqe5O6zXqSeyr8Mf1x/rz/9EF1wuBED4SQRHPDsgLAAgCA5v92/hP9djyVfFH8Y3yr/Qk9xH6z/3MAWUFIwg0CrQLVwxGDJ0LuQqiCXkI6wfyB/YHxwYJBF0AXvye+P/0ZvHw7XjrWesQ7t/yGvj3/BUC1wdsDd4QZBHLDzIN/Qm6BdIA9vu99230KPJu8SjyDfRY9rv4pvtI/0oDoAbGCOQJngpWC5ILAAvHCbYIfAjKCNsIBQgZBhQD7v5G+gT2mfKv78vs/eoJ7FjwSvaZ+z4AQQXiCp0PmxHsEHIOOAs5B3oCtv0/+cb1R/MQ8lnyu/P79VD43/rS/TUBygSEByAJywlcCg8LQQujCmYJnwiLCLkIdggpB68EwADt+2D30PMF8Rfum+tv66zuSPS/+VP+6QJKCIYNlxDaEOgO8gttCFAE5v9p+5D3q/T68rXyovN39ZD3qfkW/Bz/tgLSBbQHzAjVCSALtAsmCyIKfgl/CTAJeQhbB2MFDAIs/W/42fRC8pzvyOzN6+LtvPIb+Lf8FgHnBQkLzA4aEAwPpwzXCVsGDgJf/Vr5h/ad9I3zlfPq9N/2rfiR+k39GgGpBO8GRAi5CWELIQx+C1UKtAlwCQAJJggvB60FwwJn/rr5KvZ+8+vwQO7U7Cbu+fHv9oP7mf/0A7MI/wxFDwkP/ww3CioHZAMu/yv79vfM9Wn0JPQJ9Yf29/di+dT7Uv/pAqAFdQdRCQgLBwzGC+oKHgpQCaYI4Ac0B/gFbgOk/1L7r/e/9Bvyju/q7XDuTfG+9V76lv7VAk4HcgvvDSQOoQxGCmkH0wPT/wz8DPnu9nH14fRY9YH22/dR+X/7hf7pAeMEOgdBCfcKEAwhDFELKgonCWoIngeLBvwExgKx/+77PPg69bbyVPCQ7pnuQvGR9f75zv2xAUoGrgpzDcENVwxDCtoH6QQOAfj8e/k79xD2ZvVd9eT1Mvcf+XL7O/49AWkEJwdKCesKCwx8DOgLqwpNCSUIKAcsBvQEBAPn/+z7L/hs9Tvz4/Da7rbuUvG39Rv6z/2GAcEFDgrnDG8NDAzGCYEH3wSPAZn9/Pm894z2+/Ws9Qr2YPdD+XL73f3YABUE4AYeCeAKVQztDEIM1go/CfIHqAZxBTkEbgKo/977K/hN9SfzKvF471TvafFZ9fD5Q/5NAhcGmwkjDPsMDgzmCWAHhQQzAYn9UPoy+OT2PPYJ9ob2vPda+Un7of2CAHcDHwaBCKYKRwzGDCEMBwvhCaEIGAd+Bf4DAALo/uT6Qvfm9D7zjfH279nvOfJy9i37N//CAmIG/QmrDDYNuQsaCV4GsAN/AAb94fni9/D2cvZk9s32DvjI+bX7+f2bAKQDiwYeCS4LdgzVDFAMVwv8CUUIeQbrBJUDbwHQ/YD5GfZJ9Ony//BB74vvtPJi9/f7zv9wA1sH6AoNDfoMJgugCAwGWwP//1z8WfmV99P2XvZN9vj2cPhC+gX8K/7tAAwE9wZTCUYLmgwtDfQM/wt0Ck0ILQZ1BLYCDgAS/AX4OfXQ85zy2/DE79vwc/QP+TP9vQBLBBkIOwuiDBwMXwpeCCkGQwO0/yn8f/np9/P2Pfb89a72Qfg8+mv8+v4KAkQFLAiAChgM9Az7DGQMUAulCaQHfgWQA2IBSv5d+pr2LfTC8o/xTPAY8BfyAfaT+qr+RwLZBTsJywu+DAIMKArMBzUFOALu/rz7OPmY95b2DPYW9uj2XfhD+p78bf+dAr4FnAghC+sMtA1eDXoMPAt1CTgHsARuAuT/cfxd+Nn0BPMj8j7xSPCl8IHzA/iX/E8AoQNNB74K5QzjDD8LNgkLB44EMAFu/UT6L/hD9532J/Ya9vH23vgq+7n9aQCOA+wG6QlKDIQNxg0vDTYMwAqKCP8FfwNBAVH+L/rb9RHzV/IO8ubw1O8z8Zj17foR//oBGQXxCB8MRA1KDHUKlwhhBlsDkv8D/HX57vf19i724PV49v/3DfpZ/Pn+HgKcBegIYgvmDK0N3Q1VDagLLAmRBmkESgIi/936sfYX9BHzSvL78AXwKPHd9H75df1xAJMDaAfcCo8MGAywCiYJRweIBN8AWf2U+qf4J/f+9X/1xfUN9//4Zfsn/kQB1QRgCFsLNw0DDiUOsQ1wDCkKfwcOBeYCSACB/BT4Y/Rn8qnx8/AN8EDwyvJM9/z7zf8SA6IGMAqEDPsM9gtZCksIhAUEAmX+Wfv4+Dr3CvaQ9en1B/fA+Nv6Xf1TAMIDRgdTCnoMsQ0fDt8N0gy+ChYIiAVeA+IAMP2Q+Ij0lvIe8nDxFPCu7yTyu/Ze+7n+mAEpBQYJAgz9DFYM3gr7CJ4GaAOx/wb8Lvlc90P2pPWp9br2qfjo+kb9EgCBAy4HOgpKDGsN3Q26DeQMKwuaCMUFSgMHAfT9kPkF9UnyoPFu8YPw0e+F8db13vrB/o0BnQQcCCwLmQxIDOYK+Qi9Bu4DigDy/Mb5qveD9vr15vVy9v/3WvoX/ef/6AJeBuIJtQwMDvMNRA2QDHMLJwkEBgMDhwDI/QH68/UL89LxSPGT8FXw4vGT9RX6Bv5CAYIECggLC7UMkwwgCx8JyQb/A4QA1/y4+Z73efba9bz1WfbW9xn6yfzH/wkDawaVCQYMdA3aDasNGg3RC6MJ0AYQBHgBMv7s+aL16vLv8WfxgvAd8MDxZPWd+WD9sQA/BM0HsQo9DEQMMwtNCQQHTQQdAb/9pfpi+OH2/PW09Tb2mfee+Sr8J/+SAjkGnQkxDIgN0g2XDQINsAtbCakGPAStAQ3+dPmb9ajz2fKy8Sfw8O8Z8t71svnv/CgAwgN0B1oKwAuzC6wKQgltB78ENwGG/bf64/iC9032lPUJ9q/3/vmE/DT/awIBBoAJEAwpDWENZA1cDVQM5AnjBj4E4gFz/rv5NfWW8sbxFPHz75/vo/HT9Wb6Nv5WAYoE7QevCvoLngtNCpgIlgYTBPQAr/3f+uX4ifeP9g72W/az9+v5lPx7/6gCQgbtCZoMug25DZYNbg1YDNYJkAazA+UAEP1W+Df0GvI48S7wKe+y78PyM/dn+8/+EgLJBTIJWwvYCxQL2Ak3CBUGMAPd/8r8T/qF+Aj3+vW59Xb2Sfip+nb9lgAlBOIH9Ar2DM8NNw5fDpgNfwuPCOAFOQN8/376wvUK8/Xx9fCK7x7vIPHx9Pb4TPx5/yUD7wbeCUcLWwuwCpUJ9geKBWMCA/8G/Kr5uvc19mD1sPUr92P5Bfz5/nsCbwYVCowMqw0pDoEOOA54DIkJlwYrBEgB3/yW94bztPH/8O/vr+4D7+DxgvYO+57+ygFFBeUInguoDCwMsArUCJUG0gOMABn9NPoM+Kr21fWg9XH2V/gb+w7+HgGSBFEIpwuiDUsOQg4IDkQNXAt5CFUFAQLv/SH50PQ58gbx5O+N7pPuQvHK9RH6Rv1jAGQEdwgeC+ILggvjCrwJjwduBAwBD/5F+7P4mPaE9cb14/aP+J36U/3TAJ4ELAjdCqMMxg1wDo4OdA0cCzUIkAXtAgP/xPm/9M7x4/A08O3uWe5g8M30gvko/TIA1APIB8QK+Au2C98KpAnWByIFyQF6/qb7e/m794X2BvaF9h/4dvol/fz/NAPQBjcKjwyYDfcNKg61De8L9gjNBcQC9v4B+h31BfLD8PDv8u7+7nrxvfXL+e/8AADzA/cHpgqKC0gLrwqRCZYHnwQxAQj+R/sU+Uz3LvYk9iD3BPk2+9L9EAHHBFgI0ApCDC0N8g0aDuMMiwrLBzIFHALh/Qn5//Sm8irxlO9T7uvu8/EY9sf54vxYAIsEYwjdCrULaAt9CvwI7gYtBO4Amv29+sD4cPe19rz2y/fU+Ur8+f4BAnQFuQjzCh8M0wxzDYMNMgzLCSYHpwR6AcD8gPeZ8/HxKfGp70zuNO8w80D4Dfyd/oABrAWeCYoLGwu5Ca0IowezBX4C/v5W/LP6gfkm+Ar3J/fc+Gj7zf0VAOwCigbxCeMLbAybDBwNHQ2RC64ImwXAAj7/h/q+9Y7yNPGL8MHvzO/Z8aL1w/k8/WsAzQM3B6wJkQopCgoJuAf+BYgDfgCM/Wj7/vnS+N73v/cV+V37uf34/4YCwgXBCJ8KZQvNC0cMCwyrCmQIBwadA0QA6ft592D0nfI/8QDwxu+/8VP1E/k+/EH/2gJ4BhkJDAq0CfMIBgigBkEEOgFf/jv8svo4+db3QfcS+CT6qvww/+UBDAU9CKIK7wuNDPQM6gzIC6AJ+wZIBPoAm/zr9170jfJK8cnv8O5s8EP0jPi6+zz+eAGgBR4JgQr2Ce4IRQhcBzUFvwE7/u37tvqg+TT4S/cA+Dn67vxI/58BpgQgCPgKMAxSDGYMkAznC7wJ0AbMA40Aavy79xL0JPI68Szwie/v8KD07Pgi/JD+owGcBQEJUArDCcwIIwg1BwIFlQFC/hT86fq7+T74hveO+AP7jP2R/88B8wRaCKUKeAvjC5gM9Ay6C/8ICgZSAxcAjfug9iLzj/Hd8BHw6++68WH1gfnd/ND/IwOWBicJDQq8CQYJIgipBiMEBgEo/v/7YPr8+Bn4Mfhl+SD7AP1h/5wCGgaRCKsJXwqjC9sMnQy9CkAIGAaZA7P/v/p49iD0xfIu8bPvA/DC8ob2ofn5++/+EQMZB1QJYwm4CIAIOgirBn8DEwC6/Wr8Hfth+TX4oviH+rr8hv6BAGcDsQYGCQcKswq3C0wMQQvUCGoGWwSLASP9PvgE9dbzUfMf8gLx+vFd9X75aPwT/jsAwANvBzAJlghFB6kGSQadBEkB2/36+577R/sv+j35xvkP/Mv+1QBpAnsEMAdoCWYKlQrjCjALZwpgCNsFbANbABz8sve29IvzvPJ08dvwnvKY9pP6/vyb/isB6wTiB4IISgciBtwFWQVnA0QAqf2E/CH8ePt5+k76ovvK/cv/dAGSAzoGgQilCfEJXQq+CioKUggDBv4DhwHV/Xz5P/bc9BL0zfLD8cLyDva0+RH8o/3//4UDiwaSB7YGpwVRBdMELAOBAGz+lP0b/SH84fqz+uT7rv1K/7MA0gJqBYcHnQgqCfkJmApCCqoIhQaDBCoC8f7s+mr3a/V49ILzdfKp8hT1sviZ+079Kv9CApoFTgfnBt0FiQV6BUEEjQG6/kD9/fye/K37+fqm+4T9c//6AGMCUARkBvQHyQglCXwJaQmYCAAH4ARqAlb/5Pu6+Ir2QfUU9BXzK/M29Yv4afs5/d7+kAHABJsGUwYaBa0E5wQ6BO8BPf/E/YP9Jv3v+8z6Fvvv/D3/AAGAAm0EvgarCKwJJgpDCuAJugjhBqwE4AFr/qb6nPfq9fv0//P78h7zQfWG+F37Pv0R/9MBzgSKBm8GmgVQBUoFlwSzAoAABv8v/k/9C/wp+2z7p/we/lv/5QAIA28FcQebCF4J4gkUCpUJEgjNBbUCRv/c++f4mval9DzzufKy8971O/gw+gn8pv7pAZoEzAXUBewFRgbpBTIEzgEkAGX/sv5x/R78sftI/IH96/5pABMC1QO7BYoH2Qg/Cd0IcggdCCYHpATSADX9x/oz+Tf3n/S88rzyl/TW9o74L/qX/Of/AwPmBJkF3wVWBmgGgQW5A7wBNgD5/t/98Px9/Kb8GP3x/Vr/QQEWA2AEjQUWB7cIawnOCLIHBAdeBk0EWQAN/En5Cfi29m70gPLG8kv1FPiq+dz6VP0hAXkE0gXSBeIFcgZqBgUF7AIpARwA9f54/U78GPy0/Gb9Of6j/8cB9QNpBWUGdQeuCEkJrQhLB9sFcARUAg3/YfuM+AD34vVp9C3zgPPR9az4u/o3/Gv+sQGmBP0F5QW8BR4GIgbxBMYC0ACa/7r+rv2Y/EP8xfzI/fj+SAD2AcUDjwUbBzUIwQinCCsIVQf3BbcDlABI/Wz6KPgj9jX0DfNN8+L08fbR+N36nf3wALIDMwXLBT8GugaKBmoFxgNHAukAXf/Y/dT8m/zM/CH90f06/zYBEAN1BKsFHgd0CPEIaAhrB1oGxQQRAon+ZftH+Zb3dfVn89HyHPQv9s/3U/nm+6T/IAPnBHoFJAZBB54HSwYkBHYClQF7AJP+sfzx+278GP12/SX+zf8kAikEkQXBBiYIRAl5CdUIvwcaBl0Dtf87/Nj58veI9fjy3vEi84b1RPdi+Gf6K/4rAowEOQXFBUAHgAgSCPMFtAN1AnMBvf9v/dT7tftW/OH8ZP2v/uMAEgOkBNgFUwfxCLEJUQlBCAcHJgX6ATX+9Pqe+EH2YfNA8UPxM/NH9af2lPgU/HsArgMTBe0FXQflCPoIcwdgBfcD7wJCAfD+5PwI/Az8XfzY/NX9jf+PAYADSQUaB78IrQmcCdAIqwfvBRIDXf/b+0T5C/dc9MLxwPAE8jf05fVL99f55v32AXEEogXXBowIrwk0CYQHsAVLBNQC2wCm/tz8FPwv/Nz8rP13/rD/pgFeBOUGbQgYCX8J2wlMCRcHowMQAB79Zvo+9+TzXPGX8FXxo/L/8871uPh9/AgApgKqBL8GqgifCUQJPggsB8gFrAMqAUj/Tf6c/dP8bPw9/f/+tgD7AW8DtwUfCIkJowkPCT4InAavA+X/SPxU+X72r/Od8SHxEvJb88T0xvb++bz9wgDaApkEpAZlCO8IPwgjB1MGSQVjA+QA3/4D/sb9dP03/dL9kP/SAdUDZQXSBmwI3Ql6Cr8JoQfJBOoBLP8O/Cj4MvR08arwOfHn8ZDyDPRH93/7IP+BAW8D+wWbCOsJWAnmB/UGZAYeBagC+/9V/r/9cP3//PL8zv15/4cBpwOwBWMHqQi4CV8K1wl5B/gDuwA5/nz7pvee8xPxufB38QbypPJ49PT36/v//jQBmAOmBikJ4wnwCMQHTQerBuEEEgK+/8v+fv7s/Sr9WP3U/rwAVALVA+QFCghiCbwJiwnrCA0HswPg/6H88fnB9iHzefD27x7xc/Kk85L1+/gA/TAASwI9BL4G9QiZCaQIZwfpBmUGpATTAXr/jP5u/hT+iP3L/Tz/RQEoA98EpwZOCFcJiAn5CIMH6AR1Af799fr996z0kPHi7yPwivEW8+j0z/e4+5n/ZgJpBF0GSwhwCTIJLggzB0gGzgSOAkcAu/73/YD9QP2U/Zv+KgD1ASMEfQZtCHwJ5AkSCo4JcAebA1r/7fsf+eP1I/JT78zuHPDB8TTzdvUy+Zb9GgF8A8QFYwhmCrYKnglvCJYHSwbpAxIBHP8v/nj9iPwh/CT9Ff/cAEUCNAT1BlAJOwoXCv8JoQlrBxIDQ/7C+ij4xvSp8OLt+u3z78XxSfPk9Wb6N/+XAo0Edgb9CNQK0QpUCfwHTwcmBsUD8gAo/3r+7v0R/ar8sv2w/48B/QKzBBAHLQnyCXgJoAh6BxcFIQG7/Ef5pvbM86/wtu4F7+7wGvNB9TT4MPxJAFYDbwVFBwYJHgoJCikJAAihBuIE5wIYAZD/Qv43/dD8Nv0c/nP/TwHfA2UGGQgCCbYJRwqlCRQHHgMf/7T7Rvh+9BzxS+8/7w/wIvHi8gP2Gvr3/d4AQQPlBWwIzAnRCVwJNgm3CO4GOgQIAjEBqQBT/6H9Ev06/uD/6wC2AWMD8wXyB3kIAQiUB/sG+QREAf78jfny9jv0gvH172zwLfLj86n1WfhA/AIAgwImBMIFhAdhCB4IbQf7BoQGIQUjA5QBCwHMAAoAK/8X/xwAhwHVAiAEmAUMBxMIYgjaB18G+gP/ANv9jPrx9mTzxfDA7wvwCvGJ8vn0gPiH/BoA5wI+BW0HFQmdCR0JMQhaB1MGoQSNAvYAPwDP/yX/jP6w/qn/swCGAaUCfwSPBsMH/AfuB7oHiQanA+7/mPzE+ZD20fIg8K/v3fAR8vDy1PSL+Ab9cgBxAlcE3gYrCbcJowiJB2UHWwfoBWoDfQEXAU0BoQBX/9L+6/+sAbICHAP8A9wFoQcICAMHXAWTAzwBB/5Y+s/26PPL8anwqfCq8XDz4vUd+av88/+WArcEogYLCG8I1gf4BksGiAVLBLMCfwEGAeMAgwD//wYA4gAlAjIDEwQxBZIGrAcTCJMHKAa6A3kAIv0N+vf2i/Ok8IzvSvDO8TjzPvXa+FX9EAEVA40ElAalCDwJ4gdGBpYFVwVDBBsCUwDp/3MAmwD5/6f/lACOAlQERgXRBbMGBggFCdIILQdsBEcBMf7u+h33EPP378buI+8I8DrxffNw9xr8AQCnAu8Enwf9CdwKGQrhCBgIOweGBf0CwQCN//v+Wv57/SD9yf1O/zYBKQMsBSIH0AgKCnsK6wnoB5YEpQC4/Pn4C/VG8aHu8u327pfwafIP9ST56P20ARYE8gUjCOUJAgqoCDkHmAbwBT0E1gESAKn/0v+R//r+J/95AD4CvQPxBGIG9wcdCUMJVwiNBtsDZACb/Ab5v/WC8rPvdu5V74TxrPO29Z744vxDASoEvAVBByUJTAqZCcMHVAbBBfME7QJmALj+Uf5B/tb9mv1T/icASAIoBOkFtweiCesKFwvCCfkGawOv/wH87/d584vvUe0W7QXukO8A8sT1ePrf/lsCMwXqBz4KYwsZC+4JzQjBBy0G4QOCAQEAQv9+/pL9OP0l/tr/eQHOAmAEXwYnCC0JUQmjCPgGKAS5ADj9uvn49Unyx+/t7k/vRvDs8dP0zPjP/BUAzQKfBT0I5gkLCjYJYwitB4IGaAQnArAAEgCq/wv/wv49/2YAygEyA8IEQgaEB3MIIwkRCWIHIwRyAEj9JPov9u7xEO977kjvIvBF8fvzjvhX/dcARwPeBcwIvQqrClkJOgiWB28GHASLAQgAlv80/3j+LP4C/54ACgIwA8kEzAZjCNMIqgiICNIHdwWOAbP9vfrG9+3zF/BX7gHvY/BV8bryM/Ym+3H/AgICBMMGcwluCo4JYQj5B20HnwXjAtsAKgDp/0L/cf6d/tb/QAFlAroDvwXEB+4IOwk1CakIpAYoA1T/EfzP+Lz0qfBQ7lfugu+b8C/ybvUj+qD+vAEaBK0GOglXCrIJbgieB+oGRwXMAqUAqv9V/8/+Xf7I/hgAfwGUAvAD6AXkB+MIDQkZCfcIgwceBOL/RfxE+bX1c/Ee7lntvu6W8B3yfvTE+Pb9DgJ+BI4GDQnvCuAKFglXB1cGOwUUA1sAqf4//jX+3v3P/ef+vwCEAucDiQXAB8IJtAp7CrwJdAj0Bf4Bfv2L+TH2tvIs7wPtLO0Q70/xufM89xL89wCOBO4G/gjeCrYLyQrOCPEGcwXBA24BKv/F/UD9F/0B/XT9zP7MAOoCzwSjBmwIIQpRC20LDwo5B7MDDwAm/Lb3EfN+78rtmu0q7mLv/fEb9un6Fv9XAjoF5AfsCaYKIwrvCIMHAgY+BEgCbwAt/4v+PP4p/m3+Pf+PAFQCVwQkBpIHtgi3CVcKyAmKB/EDLgDa/HT5U/UK8VruCu4674jw8/Gp9Cj5J/7xAUEELQZeCDMKmwqGCf8H2Aa3BfwD0AEAAP7+d/77/aH91v2+/i0A7gHrA/QFvwdHCYcKJQtnCskH9QP3/1H8b/jk85fvHu0u7Y/u/O+w8eP0zPng/pAC6AQUB4YJKwseC9QJdghiB/IF5wO+ASMAD/8o/p/9rP0//hj/SwBQAt8EMQeUCGMJPAqPChsJYwXQAPv80vkj9pbx6e3v7GDuZvD/8Tz0UPiV/fMBdQQgBg0I3wlKChYJkgetBvEFYwRBAqoADAC//y3/zv5E/00ASwE/AskD9AWzB2gIigjNCJYInQbGAo3+PftP+In0W/DP7Q/uDPD28a3zb/bp+sL/LAP5BHMGVgiiCS8JeAf5BWcFsQQEA/oAw//G//b/qf9Z/+n/QwGKApsD5QSqBk0IGgkqCYsIDAcwBFAAY/zd+G311/H17tXtrO6J8Lryj/V3+f396wGiBKYGfAjDCZMJJgibBqYF0wREA1wBGADW/+r/gf8j/4v/yQArAioDUwTnBaIHxAgeCQEJJwgVBowCdv7D+kL3nPMP8PHtCu6W76Hx9PNy9xb8lQC0A54FkQdyCQkKvgi5BpwFEwXRA3gBZf/n/nP/s/8v/wz/MgAWAqcDpATlBawHRgn6CaQJnwi7BscD9P/y+0L4tvRf8dTuBu4J7/vwSvNI9lD6vP5KAqcEgAZhCJAJFwl6BwoGYwW/BB0D/ACa/3P/sf9v/wP/OP9JAJEBsgIFBLgFcAeVCDAJbwnPCMYGXgO8/338Gfke9T3xEO/97vfvFvHF8v71Z/pp/iwBTAPfBWoIhQnICFUHqgaFBpYFbwNCAXEAhgA1ACb/mP6D/wkBGQK2Ah0EZwZOCP0IxgiDCLgHSwVbAUP9F/oi96TzYPDk7qPvPvHU8vf0lfgb/cUA8gKWBK4GjAinCCcHlgUWBdwEeAM6Acb/9/+lAHAAuP/1/58BYAMhBIUEtgWpB98InQioB8cGcAWdAqv+FPti+Mv1vfI18MjvUfFV8w31Wff4+vX+3AGFAwEFygbXB0wHowVVBNEDRgP7AV0Asf/3/18AaQB8AFQBvgISBPMEzAUABxkInAhqCMEHSAaIA+z/fPy++f72v/Pd8NTvw/Ba8tXz8fWS+d79DAHIAj4EZAYzCDEIeQa+BCYEyQOKAqIAjf/t/5QAjQAfAJgAKwKeA04ErwTRBVEHEgjoB3QHCgedBZgCzP6G+yT5g/Z08ynxzPAH8mvzz/Qk9+H6yP5XAbgCHgQoBpUHGgdJBdEDkwNPA90B7f8q/wQA7ADPAGEAEAHrAowETAXBBe4GbggFCY0IkAdeBjwE2gD4/Jr58PZR9OjxlvD/8LDyo/S69pv5Tf3FAAEDQwRnBaQGBwfkBQ4E0gJYAq8BUAAf/yb/KQDyAAsBQwFNAr0DuARLBSAGUAcoCCcIrAcHB5oF5QJd/0L8/fmV96X0LPKu8fzycPRk9df23Pmg/TkAVgFJAjQEBwYeBq8EaANkA54DzAI3AVsA4wCbAX0B4QAWAXACqwMjBG8EqgVzB3EIWAi7BxEHhgVjAnn+K/vi+HH2evNJ8Tnx6vKa9NX1yPdK+zf/sgGoApQDTgWxBiwGTATXApwCiQJfAb//Lf8FAPgA/QDDAIABOAOrBEAFxwX6BnAIAwlyCHEHRAY4BM0A4fzW+b73ovUm82jx0fHl8wD2cvdP+X/8AgBFAg8DpgPZBLIFNQWNAyoCtQGEAdgA4f+p/zEAzgAgAWoBNgJaA38EewVrBm4HLAiFCGMIrQfuBe8CVv8X/JL5LveN9GLyyPHN8l701fWz96r6If62ACECNQO6BPcF4wWiBGED7AKCAnYBIgCk/zYAvwCyAHAA+QBOAnsDOQTfBBQGYwcQCCMI6QdcB6QFjwII/wT8rPkW91P0c/JI8mHzgPSr9bH3/fpt/q0A4QENA9gEMwb5BY0EXQMwA+0CpgHy/z7/6P+WAHQAEwCmAD4CwAO2BIEF2AZRCAIJ4whJCEkHGgWnAeX91/p2+Ob1UvPL8Sry2vOG9fn2Hvlo/Nj//AHTAngDpwSGBfMEYQMoAuEBswHJALT/jP94AGMBlwGgAUUCegNuBOsEhQWIBnoHrwdWB9sG3QXNA5wAev08+1f5+PZX9PHygvMH9Tn2GvfX+Mn7p/5SAAwBIgLIA9gEkwSJAxMDTgM/A0kCGwHlAHEBuwE5Ac4AgwHMAqcD3wOKBCwG2AeECAgIQwdKBkgE9AAx/Vb6MvjX9ULztPFC8hL0zvVA92j5u/z7/98BowKPAwoF1AUhBZkDqgKgAkUC+QCT/2z/YAAPAfAA5QAdAhkEdQXVBTIGjAcnCYsJVAh1BrMEhQI//277S/hS9rD02/La8b3yJPWC9zv5HfvO/bYAdgLvAjED4wN4BNQDWwI/AS8BZwHnACYA8f+yALEBFgI5AtkCVQTKBYMGzQZgB4EIHAlRCBwGWAOvAPr9B/vw91r1vPMY8zjz7PNf9ZX3QvrO/Lz+VgDUAUQDGgQQBIsDEAPvApwC5QEhAegAWAGaAXgBNwGFAXMCRAPYA1wEaQXLBsgHQAg7COAHkgbuA4wAQf2n+hX4UPUW8zfy5vIR9EX17faQ+cH8Tv/iAAQCfQMCBWsFnAR9AyADFgNVAuMAuf/L/1oAZgAPADsAjAFDA5QEeAXCBqoIEApUCp8JhAiqBmIDHv8t+2D4AvZr80Px0/Bz8tn0xPaa+FX75v7TAR0DZgP+Ax8FTAXVA+4BAgE3AQkB2v/X/jz/xQC3AZ8BkQHKAtwEDAZXBr4GGgiBCXEJHQg6Bn8EEwKK/vb6QviP9tf0O/O98uTz+PWj9wT57/qK/fT/MwHNAZgCsAM3BJMDmwI4AoACaAJuAY8ApQB9AbgBKQEMARECqwOXBA0F/wWzBysJRQleCAQHKAVWApn+Ivt8+C723PMC8sbxDfPJ9Fn2K/jt+gr+WgCQAX0C9AMiBSMFEwQnAysDHAMkAqYAEAClABoB3QCDAEsB+AJbBBEFyQVkByQJ7wmVCbYIhQclBWoBd/1o+vv3R/Vy8tTwYPEr88r0JfZW+Mb7If8zASACNQPaBO4FggUaBBQD4wKhAnEB9v9Y/7P/NQA+AF0AbwFXAxQFMAZJB8YITgrwClAK3AjMBu0DJAAg/M74Ovbq89nx+PDr8fvzF/bw9176dP0qAK4BSAIUAxAEYgSlA4IC/wH5AboB7QA6AD4AvQAYASABngHVAkMEWwUyBlgHtgixCdkJRQkiCBsG9AIk/7P7C/me9gf08fFy8ZLyP/S49YP3NPpP/Y3/oACJAREDiATPBA4EcQOLA4gDlAIvAYoAvgC4AB4AuP99ABMCZgM+BFIFMgcYCREKCgqcCbwInAbwAsL+VPui+Mr1qPKW8K7wSvLt80n1e/fu+mf+mwClAc8CegSiBU8FEgQ9AxkDxAKKATEAoP/B/+b/vv/5/xUBygJQBHsF3gavCGAKEgvACtgJSgh+BWYBFf25+R/3afSn8Q7wmfCW8q/0nPYk+X78vv/JAdECzAMDBaQFEAXeA/cCmQIYAicBNADL/+L/2v+6/xQARQHpAlIEiwUKB+oIeAoMC8EK3gkfCAMFqwBc/Pj4Svai8yPx/O+w8JryqPTB9nz5zvy1/3gBnwLwAzgFhwW7BNMDbQMrA0AC2gDn/7z/7v/K/5r/IQCfAYAD9wRbBhQI5gkoC1gLpwo4CdMGGAOi/o36c/fg9EbyZvBT8AXyN/QS9iX4KvuH/tgAtAFXAqkD6wT4BOkDFQMyA18DlQIoAVQAigDgAIYAAQBnANQBZAONBLYFagdLCXIKlgoICvUIogbkAp/+A/s/+Hv1nPKl8JrwEfLJ80f1W/dg+pj90P8VAWoCIgRyBYYF5ASFBIEE+QOcAjgBiQBBAJ3/6v4V/0oAzgESA5UEyQY4CdkKVAtdCxcLkQkOBk8BEv3h+fT2lvOW8HvvX/AW8qfzh/WI+Df8J//QAAUCqQNLBd0FQgWBBGAENgQlA4MBcgBHACIAdv/o/pP/PgHDArgD9wRAB6UJ6AruCpUKGwpgCLUEEQAY/EX5dPYf83rw5+9D8QfzffRM9kD5xPxq/8AA0gGJA0YFswXRBOcD6QMGBB8DewFHADAAUwDU/0T/6//MAXwDawRtBXwH9glICx4LSQpMCUcHdgO8/tT6Jvit9bvyVfAC8L7xDfTd9br3e/rZ/W4AugGdAvUDRAV0BY4EogNyA1EDbgIQAS0AFgDv/2b/P/9UAEUC3wPiBCsGXAibCqkLaQuHCjEJhgYtAnX9xPkt95r0tvHi7z3wRvJ39D/2afiF+6/+ugC4Aa8CEQQZBf0EJAR7A1ADAgMQAugASQBEAC0Awv+v/7gAiQIfBFUFvAarCIoKWwvrCqsJxAe1BIIAMfzb+Gv2F/Tr8QLxB/IZ9AD2offZ+cj8Vv+EAP0AAQKLA4QEPQRsAzoDkQNtA2ACLQHGAOQAmADa/6L/nQBRAuEDJAWuBqgIVgoMC7cKtAmrB1UEGQAW/P/4f/YN9OzxSvF78oX0RfbF9+L5wvxD/4MADQEGAnoDZAQrBH0DWwONA0QDSQJAAbgAXADe/4D/0v/wAF4CwwNMBUEHQQmeCgALiApTCfUGMAPK/uv6Gvix9SzzQfEQ8bby9vTH9nv43fqs/dP/0ABdAVoCpwM/BOoDWwMxA0MD5QIXAjgBrwBbAPj/0v9RAIMB4AIxBMIFsgejCcoK7gpACsMIKgZkAhv+YvqP9yf16PKJ8eDxqfPQ9a73n/kP/Jz+SQAHAacBtAKtA8wDMgPOAv4CJAO4AtQBIwHjAJ4ALAAEAKgA2AEjA3AECAYDCJ8JZQpWCpgJ7AfXBMUA0fym+RH3kfSD8szx0PLa9Nn2q/ij+gP9Jf9TAMQAKQEBArcCugJeAlsC1gIWA7sCEQKyAX8BAAGEAIwATwFZAkgDdgQ0BjQIrQkvCvgJLQlbBxkECQBK/GT55fZW9Fby8vFA80b1HPfU+AP7aP03/xEAjgBaAWMC+QLVAogCrAIZAyIDmQLpAXkBLQGjAB8ARQA7AWkCaQOcBG8GlggFCkoKxwm7CK8GPQP2/hz7Pvjg9avzLvJN8u3zH/Yj+PP5+vsn/sf/iwDnAIwBTgKmAl4CHwJ1AvECAgNoAtIBtwGlATUBpgDWANsBEQMVBDsF9QbNCAkKNAppCdEHMwWlAdf9YPqN9zD1RPNf8vDytfTj9tz4r/qz/LD+GgC2AAwBkAEwAnwCTQIxAm4C2gL0Ao4CBQKHAS4B7gDoAFoBKgJDA54EQgYCCHIJRQpMCmkJUAfhA9P/+PvZ+Dz27fNl8ibyX/Nt9Zf3pPms+5D9DP8OAKYADQFnAbUBBwI7AmYChwKrAsMCsgJ9AgACWQHjABQB4gHHAsADAgW/Bp4I2AkbCpAJRwjXBTMCB/5t+r73kPXG89vyW/MP9TL3L/n/+sH8T/5O/9b/LwCbACUBmQHvAWQCAQN3A38DPgMGA88CNAIqAVIAWgA+AWACYQOlBH0GkAgJClgKnQnfBxwFbAF7/QD6KvfK9ALzgfKG8271VvcQ+fD6Ev3P/qX/4f9IABwB1AESAh0CeAIaA4EDVwPZAm8CEQKLAdsAdwDaAOcBMQN6BBIG/QepCYcKVgo0CfQGfgNn/4/7h/jz9ajzTvKb8m30pvaK+Fz6g/yh/vT/UABNAH8A9gBGAWwBpAEFAmwCrgLJAscCkQIOAncBNgFsAfEBjQJiA8gEmwZaCFsJhQkECcoHfQX3Ae39ZPqf9171l/PX8q3zsvXs97/5dPtR/fr+2v8JACwAhQDFAM4A8gCVAU0CowKTAoECrwKwAjQCggFLAbIBUgL2Ar0DEwXMBlIIMAksCVoIlgbQA18A6fzL+Qv3qfQl8yrzmvSb9m34Kvo6/Gz+8v98AKcAGQGfAcIBdAFWAckBVQJ8Aj4CEQIWAuoBbAEAAScB0AGMAkQDWAQZBgQIYwnbCZIJlAhVBsgCvv4h+0T4p/VN8xXyrfKx9Pz29Pju+kr9f//fAFUBbgGlAc0BpAFBAQMBGwFDAVIBUwGPAecB7gGcAXMB8gG2Ak0D4APqBI0GGwjpCO4IaQgLB1wEsgD6/O/5Tvfe9DfzI/OS9JT2cfhM+oD8uP44AL0A3wAnAYcBrAGBAWIBqQEVAkICMQJJAp8CwwJEAnoBLQGHAQcCSQLKAjMEMAbLB4AIrgh+CB4HHgQtAKz83vkY90D0cvKz8mD0Kfae93X5Avyb/kcACwGUAVAC+QIVA8ECdAJwAnECHwKdAWIBewFiAeYAhgDpAOgBswI0AyoEKgZbCHsJhAkUCTUI7wUFAsD9U/qs9/D0bvKJ8cjyG/Us9wX5ZvtB/osAoAH+AVoCvgLHAlIC4AHQAeYBzQGXAZoB7gEtAv4BmQGRAQcCjwLQAiQDPgT8BYsHOwguCLIHhAYrBMQALP0A+hH3b/TZ8uryOvTZ9Yb3nvlC/Kv+IwDZAI8BcQLdAn0C7gHkAVACawIGArABzwEoAi4CzQGNAcwBNwJkAqIChgMGBXcGWAfrBz0IpgdvBQUCrv7v+yf58/U881jyVPMG9Zz2bPgC++b9JQBdASEC7wKHA2QDwwJEAh8CCwK1AWsBjQHxARcCvwF2Aa0BJQJXAkYCvQITBLsF4waDBxcITggtB0wEoQB+/cT6u/eA9FvyRvKJ8xf1wfYK+f77zf6vANMBzQKfA9MDUwOZAiUC7gGaAREBugD5AJIB/AEEAv0BQgLJAiQDWQMEBFEFlwZCB20HYAe2BqoEPgGi/a76Hfho9f3yIvJM84X1p/e1+Tr8HP9/AdsCcQPIA9kDRQNVAqcBYgErAbwAfQDbALkBdAKqApUCnQLNAssCogLGAnkDiAR3BRwGkwaOBpoFbQOXAMX9Bvsu+IL14PO+87T0Ffan95n59vsv/vH/PwFIAvECCQPTArIC0QLVAnQC+gHkAUACgQJPAt8BtwHoAQEC9AEqAgMDKwQzBTAGJQeMB5gGQwSEAfH+Qfzx+In1UvPe8q3z4PRm9p74ePtE/nwAHQJKA/0DJATbA4cDRgPQAhoCagEkAVUBkAGUAX4BtAFFAtgCFAMmA6ADmwSuBVQGjQaGBgAGdQTcAfv+dvz2+R/3dvQi86zzQvXi9oL4vvqL/QkAmAGGAj4DuwOlAxwDhQIZAq0BLgHUAMIA9wAgASABRQG5AVsC2AIeA4ADPgRJBTcGuAa6Bi0G4ASoAtX/8Pw2+pr3LvWU82LzafQV9gv4afoW/ZH/ZQG2ArMDOQQJBGsD4wKNAjYCogEkARsBaAG8AcwB0QH9AUoClgLEAgkDhAM0BPcEpgUeBhMGGwUnA7gAcP46/K/58vbe9Fn0E/VC9nz3CPlC+779yP87AS4CwQL8Av8C+ALRAnQCBgK2AaMBqwG1AcoBywG1Aa8B4gE9AqgCSQM1BGkFoQZ1B4YHkQafBP4BM/8//Af5rvUA8+3xcfLR85H1yveb+tb95QB9A0kFIAYpBtcFfwW6BDUDXwEfAL//rP96/2n/+f8SAR8CvgIwA9ADkwRGBeUFeQa9BlEG8gTuAp4ARf7Y+zz5uvbc9DL01vRS9gz4x/mu+/D9EACfAWkCnAKHAkIC4wF1ARIBsAB4AMEAkAF2AgEDVAPWA40E3ARlBKoDZwO8Ay8EbQSPBJkEVQR6A/EB8v+m/UH76/jb9mH1s/T39AT2hfc0+Rb7MP1c/zYBkAJzA/UDLwQ3BBUEwgM6A4MC8AHHAdUByQGLAXcBxgFGAq8CFgPJA8YErgU0Bn4GfQapBYwDlADF/Wn76/gj9tXzCfPH8wr1cvZa+P360v0dAM8BJQMhBJEEXQTgA0cDkAKiAaAA/f8DAI4AIQGBAeMBjQKHA3EEDAV7Bf8FjgbYBrwGOQYKBd8C7v/2/Gn6APiB9XXz3PLQ84H1Svc7+bD7bf7bAI0CjAMMBCEE1QNTA6wC5gEXAWAAGQBkABEBzgFkAvoCoQM6BJsEvgTnBCwFbAV5BV8FNwWkBDQDBgGi/mv8Nvrc97P1efSI9GX1kvb699r5G/xB/v//XAFrAiwDeANrA0EDFAPIAj0CswF4AYIBlAGSAbgBLwKxAgUDUgPwA9wEvAVgBssGKQcYB+cFYgMrADH9f/qx9670T/KD8WfyUPSA9t74mfuI/h4BJQObBHQFhgX3BCcEZAOeAnQBEwAp/w//fv/h/zQA9QBfAhoEcgUmBqcGXAcaCFcI6QfQBgIFcwJT/1n8zPmX92/1lfP48u7z8fUv+E/6aPyI/lsAowFJAoECcgIqAuEBsQGeAY8BYAEyAVkB3gFzAqoCngLbAp4DfgTWBNoENQUfBgEHMweyBpwF5gNxAZf+1fs7+Yr2/PNn8o3yC/Ti9bX30vmO/Ez/TAFfAu0CcgPFA4IDuwL1AZUBaQEVAaMAlAAIAYsBxQH8AccC8QPoBHgFDQYOBykIuQhOCBwHXgX4At7/e/xk+bb2Y/So8ivyF/MI9VT3hPnC+yr+cQADAsICGgNfA2oDEgNzAv0B2wGnASkBsQDCAEkBpwGzAfUBxwLoA9EEfgVuBr4H3ggeCYIIPgciBQ0CPf6B+i33L/Sp8TXwkPB78hL1u/ej+u/9KQGMA98EaQWXBUQFPATfAqQBwwDZ/9D+E/4l/hP/MgAdAQMCSwPkBGAGbgdACC0J7gkBCioJgAceBe0BK/53+jj3gPQl8pfwovB+8mb1JfiC+gz9//+kAhgEXQQzBAkEsQPRArQB6wB7ABQApv+X/xoA4QCKAQoCtgKxA7EEfwVFBlMHjQhtCZEJzAgwB7sEbAGu/fX5kPaE8xvxEPDk8BLzmvX893X6eP2JANkCEwSnBAQFBAV+BK0D0gLcAbkAmP/J/pT+wf4l/8f/ygBCAr4DIgWIBhoIpwm5Cg8LgAoMCYYG7QLD/rn6LPfm8//wYO/O7wby2/Rz9yr6b/27ACgDZwQBBVwFQwV9BGoDjQLbAe0Avf/O/pb+8/5s/+n/qgDzAX0D4AQdBmoH2ggVCqcKXwpICVIHTARdADr8mPiE9cjyr/AF8C7xqfNi9uD4gPtl/ioB+ALLAzEEdASABBIEOwNhAq4B3gDX/wT/1f4p/4H/yv9vAMABawP/BIkGMAjQCegKPQvFClsJwwblAmf+Lvqf9oDz2PBh77nvyPGi9Iz3b/px/XgA7gJeBO4EGQULBakE3APlAvsBIwE0AEL/zv4F/3P/yf85AEAB/QL7BMUGQgikCdwKeQsRC5MJAAdTAwf/wvod9z708fGP8InwNPID9eD3Z/rI/Eb/gwH6Am4DSwMiAxkD7wJ+AvcBggEoAc8AZgAhABgAOwB4AOwA9AGRA10F7wZUCNMJJguYC5AKPwgSBT4BF/35+G31pvK38AXw1/AJ89r1jvga+8P9UwBRAloDswPQA+IDygNgA7QC9AFMAbIAFgCT/0n/Zv/a/4wAoQEhA/MEwAZ3CAgKOgu4CyALVQlIBlgCJ/42+r72sPNJ8TzwAfE389r1PPip+lr95v++AbUCIANQA2IDPgP1AqACRQLGAS8BnwAmAMf/jP+j/yUAGQFcAt4DjAVtB1YJ2gqeC28LRArtB3QEMQDi+yj49PQ88l7wFPCR8Rj0yPZP+fP7s/4kAcUCnAMNBD0EEgSfAwADXwK9AeEA9/85/9/+yP7k/m3/hAACApcDPgUcByMJuwp3C3ILyQokCQYGsgE8/YT5cvaR8xnxG/Aa8WTz4fVE+Nn6kP0AAKsBrgJWA7kDuQNhA/YCuQJ2AukBJAFuABsAIQA4AEcAsACqAfUCMwRsBeoGlgjkCWIKLQpJCVAH1QN3/4H7bfjI9Q3z9fCx8HHyIfWQ94r5ofsW/k8AzAF6Ar4C+QI1Az0DBQO6AmoC5gExAaEAawBHABEABgCsAPkBXQOgBPEFtgeVCcwK+wo+CqYI2gXsAaz99fn79mv0SPI68eLxCPSj9uv43/rt/C//HAEjAmICdwK8AvIC4AKjAnYCRQLJASgBxgC1ALUArwDmAKgBwgLqAxkFjAYuCGIJzgl0CWsIYAYhAzn/kvu1+Fv2SPTb8svyMfRH9jH43fmI+0j97v4pAAIBigH3AVwCuwIYA1IDNAO6AhICdgEGAb8AiQBxALcAegGbAvIDfwVEB/gICAotCokJGQh6BbMBq/1Q+sj3nfW587/yTPMP9Rf3zvhM+vD7v/1m/64AgQH9AWcC1QIqA0YDEQOUAtkBJQGwAIMAegBsAIcANgF0AtUDMAWvBmQIyQlACsUJewhTBjcDYf+2++v43vY/9SD0EfRi9V33N/mn+uT7T/27/rv/PACJANkALwFzAbgB+QEeAg8CzAGaAZ0BqAGWAXkBmwEvAhEDGAQ0BXoGwwezCPsIdwgwBwAF8wF0/j/75vgZ94X1a/SC9PT11PdC+VT6oPtG/b7+kP8AAIIAJgGcAeIBJQJpAnYCLgLSAZIBewFzAV4BdAHdAZACdgNzBJ8F7gYwCPAI6QgxCMQGaAQJAW39lfqa+OT2M/VK9AH18fbl+Bn6+/pc/Az+NP+W/7z/JwC+ADoBhQHNARoCOQITAu0B6wHpAboBhwGxAU4CBwOxA4kEvwUpBzUIlAhpCLIHCgY6A+3/8/yE+k/4KPbD9MH0+PWV9/L4Nvqr+1z91/7A/yIAWAC5ADUBhgGtAa4BogF/ATsBGAErATwBEgH7AIcBmwKUAzwEDAVjBtEHhQhuCP4HFQcbBfUBmf7l+775ofel9a30SfXb9l/4hvnN+oz8S/5u/8v/3/8uALUAKAFaAXABqAHgAegBxwG0AcAByAG2AbsBKgLqAo0DFwTtBCkGXgf9B/cHegdTBhoE/QDn/W77QvkT90v1yfS29T73mPjL+TP73fxb/i3/dP+p/wwAjQAEAT8BVwGRAeEBAQLnAdUB1AG1AYgBkgEQArECMAPkAxoFsQboB1QIMAiYBzAGnAN2AKT9NvvZ+KD2WPV09Yf2zPf3+Fv6DPy8/RH/z/8RADEAZAC3APUABAEZAV4BtwHdAeEBBAIpAjACHwI7ArYCQAO2A1kEdwW7BnoHlQc5B0wGWQRzAXX+APz1+d/3CvZn9Uj25fcs+Qv6SfsC/YD+Mf9G/1f/ff+2//X/QgC2AB4BYwGyARICTAI9Ag8CDgI/AoYC0AIZA5oDcQSjBe4GuAfFBzwHHAYMBC8BYf4Z/P75xffz9X31ivby98D4Yfmq+pz8Pf7r/iH/gf8FAHkA7gBkAccB/wEYAj0CfwKKAkkCFAIXAkgCgwK2AgEDtwPwBFYGXAe1B3MHmwbzBH0Cpf/x/Jn6d/iz9tr1SPaZ99/45PkL+5D8Mf4w/2H/MP8l/1//nv/o/10A0QAbAVoB3wGdAg0D8QKkAr4CMQNpA0kDcQNHBHAFRQatBvcG7QbrBcMDEwGm/nf8P/oE+Ev2vvVm9q/35vjh+RD7tPxZ/kX/Tf8u/33/GwCFAKEA6wB/AQICQAKEAvgCNgMYA+UC/gJnA5EDZAOMA28EnQV4BuYGMwcrBxAGtAPWAET+BvyQ+RX3bfU99TX2Yvdy+J35K/v9/G3+Ff84/2v/8v95ALMApADDAFcB8QEyAkgCjAL5Ah8D6wLDAuUC+wLkAh8D+AM6BUEGxwbzBqcGawUaA3gAEP6w+yn53fa69ef1p/Zu92b48fnO+279i/4+/7z/EwBNAH0ApgDVAC4BtgE5AocCywJFA7QDyAOGA0IDOwMsAwsDMwPtA/MEuQUeBiUGmAUSBK0BOv8d/QP7qPik9vT1pfbO96v4dPni+tH8ZP4j/27/4P9VAIEAawBnALcAJwFzAakB/AFvAtsCJwNSA1cDRANHA18DmQMNBNQE6gXVBhUHhQZRBXAD3AAn/sP7jPli96z1KPUR9nD3U/ge+Zj6sPx1/j7/nf9PAB8BagFPAV4BwQEpAkQCOQJvAsQC6wL5AgYDBAPeAsAC9QKdA4sEhwV2Bi0HQwdYBl0EyQEi/4/8Evqv9+T1SfXv9UL3dPiV+R77Cv24/nr/rP/w/2IAhwApAPr/fgA7AY4BlwH0AcsCgwOtA5IDpgPKA5YDNgNdA0EEXwUsBrYGQgdRBwsGawNZAK79PfuZ+P71RPQV9BD1Zvau9y/5PPt9/T3/HAB8APcAdwGZAU8BIwGRASoCVAIpAlsCAwNwA2sDUAN6A6kDaAMrA7ED2gTSBVkG7QavB44HjwVbAlP/3fxG+i/3ovSR89zzwPTi9Zb3yPkG/Ov9Uv9gACoBswHsAc4BmgGIAcQB+QHOAaYBBwLRAk0DLwMRA2QDuQOVA4cDTwSgBacGHwd/B68HxAY1BNcA8v12+6b4ofWR80jzA/Tm9Af2+/et+hf9yv4ZAFsBXgK/ApcCUwI5Ak0CVQIiAs8BvwE2AuYCKwMRAxkDewPbA+8DRgRNBZ0GaAeUB5AHBwcaBdYBhP76+4j5h/a485vyVfOZ9Kj1/PZN+Sf8cf7s/xQBNAL6Ag0DvwJsAjsCJAL2AZcBQAFOAcUBLwI6AkQCqwJQA9EDRQQ8BakG5weJCKwIVAjaBu8DVABF/dP6/fe59HTyQ/Jv86f0qfVF99z5ofzE/lAApAHTAoMDmQN1A3cDgwNMA68C8AGJAa4B6AHLAaAB4QGqAnQD/gPDBDkG2wfFCNQIZQheB/oENAFt/ZH6LPhv9cfy2/EI8wX1kPbG98D5gvz7/m0AEAGhATYCgwJWAhgCTwLKAtQCTgLdARQCkQKXAi0CAwKOAkkDwwNoBNgFqQfeCD4JRgn5CEAHrQNr/9r7EPkN9vHyCPEi8bPyd/Qu9kj4BfvZ/fT/NQH6AaQCJgMmA9ICngLgAjAD7wI+At4BMgKHAisCngG8AYUCQQO1A7QEowabCIYJlgmCCesIjAZFAtT9i/rP95X0bPEi8CrxDvOe9Fj2NPmY/DP/mgCEAZkCiQOxAyoDqQLDAi0DPwO4AgYC3wFQAokCHwKcAdYBpAJWA+oDJgVAB+kIcglWCf8IpwdlBAIAK/xZ+W32JfPq8Ozwj/Io9IX1qPe9+rz9kf+VAJwBugJaAzADxQK2AiYDlQNwA9ACWQJzArYCmAIkAvcBXgLpApoDzQSqBl8IIglECRAJDghNBSMBKP0F+iX37/NY8c7wG/Lu84D1e/dg+mv9rf/aAKEBfgIuA1oD6gKQAsECOgNRA8ECHQIBAlUCWQLbAYcB5gGYAicDDgTVBfkHSQlvCVUJEQlgB40D9v5k+8L4+PXj8vrwYPEo87v0/PUF+An73P13/ykADwFcAm4DqgNTA0ID3gN3BC4EIwNKAh8CJQK2AfwA6wCuAZUCfgP8BDsHNAkHCvkJkglLCCUFlwA3/P34S/ZK8/nwpfA58l30EfYT+Nb6yf37/wMBtQGKAl0DnAM2A9UC5gI/A0kDngK6AVABaQFeAQMBBQGjAX4CVQOzBPEGNwlVCj8K8AkzCc0GfgLC/Uf6pfe49MPxb/CC8XnzAvV29tL47/t2/uT/0wDqARADsQPPA8ID9QNlBJMEFwQQAy8C2QGiARwBhACfAHMBVQJQA/UEXQdbCQAKsQkzCf0H3gQ5AOP76/h69pnzN/H38K/yqPQC9qD3SPpW/YT/ggBCAXgCkgMDBOADwgMJBGwESQRNAx4CiAF2AUcBrgBNALMArgHIAjMESAZjCL0JBwq7CeIIgAZcAsX9WPrT9yD1ZvIl8SHyCfSN9eD2+fj1+4X+BADjAOUBGAPXA/EDsgO/AysEYgTjA8YC4gGYAXsB8wBNAEoA6ADiAQ4D3gQ0B/MImwl/CSwJwQc8BIv/g/sF+b720/OI8YXxjPOV9dL2OPjP+vD99/+0AEUBfALaA0gE7gOUA+0DhgRNBB4D0wFaAVUB+AA4AM7/VABJAVUCvQPbBSAIZQm0CXwJxAiPBmoC3P2G+lr40fXt8pPxqPLv9Ij2dvca+ej7oP78/3AAGgF9AqwD2QONA50DMwSKBAEE2wL0AbwBiwHpAEAAKgCyAIcBuQKUBMwGcAgxCYMJgwkrCLMELABz/Oz5VfdM9BnyIPLe84L1qfYn+Kr6hv1h/0kABgFGAoMD8QO2A24DyQNZBBkEBgPXAVwBSwHnADEAy/9EACEBFAJ0A5IF0wcoCYUJawnlCMQGsQJd/iD72fhB9n/zLfIF8xD1i/aD9zn59PuJ/s//SQATAWQCfQOiA1QDcQMXBHAE1gOpAr4BcgE4AZoA+v/u/5UAiwG8An8EnwZQCCoJewlUCQcI7wTXAD79jvr49xL16PKy8iP0xPXm9jv4fPoQ/er+0v9qAGcBjwIUA/8C8AJiA+kDwgPfAtgBaQFeAfgAOADT/0sANQE3ApcDmwWsB+UIdgm1CUQJIAdLA0H/LPy++db24fNx8h7zyvQY9kT3Gvmg+wT+fP9dAD4BRgIVA1MDMgMtA48D5wOIA3ECdAEwASsBtgD//9//jgCRAbsCQARSBiUIIAl5CWgJcgiwBbAB9P0q+6/4zfVv89jy7fN89b/2N/ha+t381f70/7wAlQF/AhADJQMOAysDogPUAywD+wEoAf8A1QA4AJX/w/+2AOQBNwP1BAsHpQhpCY8JNAnCB5cElAAa/YH66vcG9SjzL/OL9AH2LPe2+Pb6Sf3i/tr/rACZAXoC7QIcA1kDuwMJBNQD/wL8AV8BCAFuAKb/Uv/V/+AAEwKiA8UF3gcWCXwJpAk0CQgHHAMD/wT8j/mn9s3zkPJx8w71UvaZ97P5WPxp/oz/WABdAWwCAQMbAxIDYgP1Ay8EpAOQArIBXwENAUkAdv9t/z4AOwE8AsIDAgYBCPMIHgkNCUYIugXPART+Vvvy+CP2yPMw81j02/XS9g34PPrS/IP+Mf/a/wwBUgLeAs8C9gKuA2wEYQSfA8gCPALCAQwBSwDs/wkAggBTAcECvASWBuoH1AhhCQgJLAcGBKUAnP2m+or35/Sh88PznfSq9Qr37Pgc+xf9lP6b/4QAiQFdArMCyQIYA7wDIwTQA/wCYAIeAqEBwwAiAEMAtQD6AJYBMwNzBQ8H0Ad3CBsJtwhOBrQCYf+1/OL5p/ZI9KPzUvRE9Tn2xPfU+ef7gP2v/sD/tgCaAVACzgIPA1oD4wNCBPcDGANsAkAC/gFUAbYAywBAAakBUgLHA94FVAfdByMIZwioB9kE9ACo/T77t/i99bPzmPPG9P71DPfA+B/7a/3Y/qH/ZQAtAd4BRwJ9AqwC9QKBA9YDbgOGAugB3AG+ASYBfAB/ACcB6wHRAjwEEQaKBzwIYAgUCNsGKQScAGP91fpZ+Mn1NvQ39Dv1T/ZF98P43/rd/B7+2/66/8EAnwEUAm8CEQPEAyoEDASPAxYDvwJuAt4BQAEBATEBugGAAp0DMQXEBtAHMQgoCJMHvAWfAhL/Dfxu+dP2nvSI8wr0PfVp9sH3q/kb/P79B//S/9kABgKaAqgCuAJFA+wD7QNcA7sCdAJHAuIBeAFUAZMB2QEfAsQC7QNlBYcGMweaB6QHzgaqBLMBs/4A/HX54PbQ9Prze/Si9bz27/ee+cv7nf2J/in/NgCVAWgCawJ6Ai8DCQQNBEkDswKoApsCGwKeAakBCgJTApECbwP2BEQG9wZhB94HvwdKBqQDvAA2/rf79/hK9pf0OPS39I31jfYJ+AH68vt5/X/+bv+LAJ0BKAJMArECgQMhBPMDRQPbAukC5wJpAuwB9gFfAqgC9QLmA2wFsAYiB1sHtweFB7wFmAKJ/zv9APsm+Hz1RvSL9Eb1w/Wi9mH4c/of/Cr9Mf5//84ArgEaAo4COgMLBHsENASfAzADEQPNAjMCwQHdAVUClgLqAgQEvgUNB2MHcweqBzcHBgWVAbD+pPxp+m737/Rh9C317/VA9iT3IflF+338Cv3o/Vj/pABLAZQBJAIiAwgEVQQBBJIDbANdA/sCUAL5AUYC0gIvA7YD9gSMBpAHxwfOB7wHpAbyA5sA+/3/+4n5gvZt9FT0VPX49T32Rvdg+WD7b/wR/SP+mP+vABYBcQFLAmgDHQQiBL8DYwNRA0AD0gInAukBdQI5A8sDagSzBVcHMwgkCMAHYAcLBgwDa/+g/Mr6lfjP9fbzKPSD9V72t/at97L5v/vI/FL9Rv60/9sAawHXAagCvQN0BHIE2wNZAz0DPAPgAjwCEAKjApQDYwQvBYIG6wedCFkIpwfGBtoEmgHz/Tj7YflD9+j0mfM29Ln1sfZI94n4uvqI/Df9k/2R/hwALwGbAQ8CAwMjBJsESASVAxAD3QKxAlUC3AHpAcYCFARMBVIGhAe0CEAJrwhuB/YFvQN2AM/8EPpW+KX24/Tl83/09PUB96H3qfhV+sb7j/w3/XH+CgA1AQwCBAMqBPoEEQWiBAIEXQOwAh8C0wHVAT0CQwPKBF0GsAfBCIMJlQnACBgHxgTIAUD+8/p/+M32R/UI9PjzJ/Wx9qj3avjH+V77ZfzU/KT9PP+6AJEBMwJ5A/QEiAUJBT0EtQMlA0oCiQFgAdoBnwKgAwoFuQZOCFYJtAlbCU4IjwbtA5sAG/07+k74tfYl9Rf0cvTX9ef2Uffk93P5EPvD+wv8Ov15/0AB7QGLAhwE3QUxBiUFEQS5A3IDmAK9AbcBiAKLA4IE3AWhByIJ0AmwCfcIoAd+BX8CKf/2+1H5UPe59ZD0GvSY9LH1tvZq90P4oPki+y78Av1V/h0AoQGVAnADnASMBX0FkwTJA2kD6AIZApoBHgI1AxYE5AQ0BgsISQl2CfcITwgpB6UEJQG6/TT7EPnE9tz0BvR79E71//W79sb3J/ld+lX7T/yJ/Qn/dACqAawCwAPYBHkFPwVzBNoDoANJA4gCCgKRAr4DygRxBXEGFwhYCWcJkQikB1UGnwO6/yz8Bfpv+Fb2SPSy89f0N/bZ9kv3g/hZ+pX7JvzL/CH+yf/zALQBigLHA+4EPQWlBLwDSgMrA9ECFwKzAXkC7wMyBQoGOAfiCOgJmAlPCCEHswXOArn+JPtD+fr3FvZA9OHzMvWZ9hf3ZfeL+Df6Wvvr+678P/4gAHEBTwI1A1wERgVSBX8EgwP/AsICQwKpAcwBAAOZBMoFyAZLCNkJQgo9Cd4HrQahBP8A1vwB+o34IPcv9ffzm/Qo9kf3r/de+MD5CPu/+z/8T/3l/lcAYgE1AlQDhgQqBc0E0AMRA+UCxQIsAqIBIwK0Az4FJQYRB6wIIAoiCsgISwcFBrMDz//G+2D5Y/gG9wX18fPx9ML2evdl9wP43Pl0+9z7Efxi/Z7/OQG3AR8CWQPUBDAFSgQ4A+0CJAPvAkoCEgIAA6AE3wWuBsIHOQkVCpEJFQiSBvAEMQJ9/i77PvkD+G/25vSW9Jz1uvYe92z3ivgh+kz75vvC/G/+QwBhAQQC3QIPBNMEiwSkAwcD+QLPAjYC0AFzAukDHAXjBesGnQjeCbcJpAiUB3cGBAQuAIj8YvoV+UT3TfWF9G31wfZP95b3bvjb+QH7gvsT/Eb96/47ABMB5wESA1UE2gRYBHEDBwMqA/0CQwLQAZICHARVBRoGKQfZCPQJkwlQCCMHygXvArr+Dfsw+VT4xPbY9EH0lPVo9yD4Q/gW+c76IPxj/Jz8w/2d//EAXwHaAQoDeATlBBQEBgOxAu8CyAIuAggC+AJ6BJwFgQbPB2UJBAofCYwHNwasBLoBsv1Y+r/47PeB9hv1CvVW9rT3L/h7+Hv52Pq0+wL8tvw5/v7/KwHmAd4CCQS9BHcEnwP8ArgCYwK4AWoBOgK3A/sE3AUFB9wIOwoWCroIWAcIBoYDqv/a+6r5cPjO9g/1ffTJ9Wv3G/hY+Cv51foJ/Fj8ofyk/Un/gAAxAeIBEgNKBIIEtgPZAsMCAwObAr8BqAEDA8AEqAUSBigHAwn7CTUJsgeIBh8FHwIL/uv6fPly+Iv2xfTJ9F32s/fZ9wb4TvkX+wL8E/yv/GL+OwAsAYcBZwLPA6gEMQQiA7EC5QLMAgsCnAFjAt4DDgW7BdMGkwjACXcJSQhOBxQGkwPf/338iPox+YX3yfVK9S/2LPeY9/j3GfmI+kv7mPtX/Oz9iv99AP0A5AFaA2AEPwRnA+gC8gLQAisCmAEKAkkDXwQBBfkFugdSCX4JdgiQB9EG9ARoAZ79Ovvs+VD4OfZJ9S72gffu99H3mPg/+lD7UvtS+4n8Zv7L/3cAQQHQAlAE3AR4BOMDugOGA8sCzQFyAToCYwMuBM4EJwYdCFwJLAkxCHUHVganA73/U/yV+jv5Jvc09Qf1hfam97v39Pdt+Tr77vvQ+0388f2S/z0AqwDJAV4DRQT+A1gDNwN4Az4DXQKhAfgBRQNWBO8EzAVrBwEJQwlfCH4H1wb4BF0Bp/1++2j6svhj9kL1IvaI99z3vved+FH6cfuM+9r7TP0R/+z/QwAcAboCGAQ+BJkDFwMxAysDegKgAYEBcAKPA18EKQWbBkwIAQmGCKwHQwcsBjsDOv8q/MT6evld93n1b/Xq9gP4H/hf+Lj5QPu0+5v7N/zh/WL/CACRANYBiANrBDcEswOjA78DOgNBAqoBGQIVA+kDnwS/BZEH+QgtCYgIwwffBrMEJwGJ/Rr7pPnX99r18/S+9S333fcs+Bj5uvri+yX8cfxv/dD+p/8YABYBqQICBEQE2QOrA9ADnQOqArQBjQFRAloDEgT+BH8GOAgcCeIIQAiIBxwGAgMu/0n8qPoe+dj2JvVJ9c329fcO+HD40/lY++L7z/t0/Oz9PP+7/1QA4AG2A6oEXwTGA7QD3QNoAzcCTAF1AYACmwN7BLIFgwf+CFUJqgjzByoHAAVDAVH9/vrO+ST43PVp9DP18/YE+EX42vhm+qb7JPx3/HX99f7X/10ATwECA3kEqAQABIADlgOMA78CvQF4AUICYgNdBGsF4QZsCAkJuggxCJwHDwbDAtH+3ftM+uL4uPb89PP0UPaA99T3Svh0+cb6Wvuu+7z8Sv51//L/vQB3AkIE/QR4BN4D4QP3A2YDPwKMAd0B1QLcA8QEIAbRBxIJNwl9CMQHugZqBHkAjPxd+k35x/dy9Tn0NvUE99P3tPd2+FP6vvv3+xz8lv2g/7gA5wDHAcQDXgVlBW4E5QP0A7oDuAK1AaoBaQJkA04EkAU9B6wILwnoCGEIcQd/BSgCcf6Q+5j5v/er9W70pPS49a/2T/di+Nj5/fqS+yf8bv3S/sb/awCIAUADmQQLBdIEnwSaBCMEOANKAvwBXgLfAn8DjARUBhYI2Qi4CG8IUggOB9ADnf9k/Kf6tvgV9vfz2vMz9R32d/Y+9yL56fqe+/77Hf3m/hcAjABNAd0CkQRUBSMFsARzBEAEsQPCAvIB6AGsAqIDiASTBT0HAAmyCRoJBAgNBzIFrwFW/R36j/gX9/r0XvPD84X10fZE9/33xfmh+3z8zvy5/Wn/2ACmAV4CpgPwBGkFGAWQBD0EvQPmAvUBnAE1Ah8DAQT7BKYGhAiACUYJfgi5BwYG1gLb/pv7gflt9xf1fvPL8yn1GPaW9rH3tPlt+y38pvzB/Vf/eQAmAQoCcgPFBEsFCAWVBFgEEARVA0ACkAH0AQ4DCwS4BNgFsAcuCXUJswj0B/oGbwRaAGX8DPpl+AT2l/PU8iL0oPUo9qv2QPiO+v37lPxn/Qn/sABiAeYB/AJ+BG0FVwUABbAEXgSqA7ECAQLVAUoCAAMEBG0F+QZaCAcJPQnqCMsHhQUSAnn+RvvN+JL2Y/Qr80Pza/SG9Yv2CPjW+Yb7jfxa/W3+qv+fADcBLwKYA+UERgX2BNcE2QRyBGkDggJkAuMCbQPSA8oEhgYrCNQIswiMCFIIzgZoA1//Q/w4+uv3DPX68uDyGfTt9EH1QfaH+Nf67/ts/Ij9ev8AAXEB5gE2A/UEywVqBd0EzATkBEAE9QI1AogCYAPVAzQEYAVCB8oI/wh7CBoIeQdDBVQBdP3f+vr4Zfag83vyMvNs9Pj0nPVo98z5lfs9/Av9qP5XADQBkAGVAiQEUQV3BRgFAgUIBZ0EmQPTAgQDuwMuBGYEVwUnB5MIzQhsCGoIJQgQBiwCV/7d+7D5t/a083jyPvMn9FT02PS49g75f/ox+1X8IP6i/1sA/gA/AtoDFwWIBXMFawV6BV8FqQShAxMDaQNWBNIECAX1BcQHLwnxCAQIcAeoBvkDhP/N+8n5Tfik9bny+/Fo8wH1NfV19WX3Kvra+xz8p/xL/hUA8gA/AUYCFgSMBckFSAUPBVsFagWwBMIDmwOMBJQF+AVHBmAHAAmfCfwI3gfIBggFgQFT/Rn6Jvg59pfz3PEU8ovzofQe9WX2n/jM+vz7s/wG/rD/CQGtAXEC2AMfBasFdAVeBZUFpAU1BW0EEwReBBUFngUWBgEHGwjPCJAI5QcXB4oFtwLT/mX7+Pjw9sT0tPIZ8v/yU/RI9Sj25PfU+Uj7Jfz8/EX+gf9cAAwBRwIDBGYF/AXqBQMGUwZWBsUFAwXWBEwF/AV2Bu4GzQe7CAsJjgivB4AGVwTEAKr8fvmK96r1XPOd8aLxHPNw9Cb1FPbm9yn6jPth/Hr9If+2AHcBPAKeA1YFZgZQBvsFFwaHBmIGgQXnBDEF9wV1BrkGeQeUCB8JkgiHB44GCAUSAvj9cPo9+Hn2TPQv8qfxofLq89H0xPWI93f52fq6+7j8VP68/5cAQQGEAmsE2QVSBisGVAbvBhEHcAacBaUFewYNBxAHVweMCKMJTQn0B9kGEwb8A9L/Q/tr+PP25fQp8prwe/F682r0tPQG9pn4ufpK+5n7B/0b/3kA3ADEAdAD9wUOB+EGrQYiB4wHJwcRBncFxgVxBs0GvwZIB4MIXgkDCY8HLwacBM4Bk/19+Sj3lfWh84LxyvAQ8tPz+PS69Wv3tflc+yH8xPwo/q//pgBwAa4CeAThBXEGkAbQBiwHGAdzBtIF1gVXBtUGKwe9B8MIgglmCWoIUQfkBQMDAf/8+kP4PvbF837xh/B28d/ywPPS9Kz2C/mg+mP7h/xI/gYA3gCCAc4CmgQzBqQGewaPBg0HZgfDBr4FWwX6BZcGnwazBpUHGQmNCZkIVgeBBgYFbAHp/F/5S/dD9WnyhfC18C3yWvP+83r1z/cs+oD7Jfxm/RT/dwArAdABGAPcBE0GrgafBtQGWAdeB3QGjQWuBYIG1QaiBgwHTQhgCUEJTAiIB10GmAN8/5P71PhM9mPz4fAK8PDwCvLV8tvzwPVd+F36vvvd/D3+CQB2AaECsAMBBXwGJwcSB94GJgd0B7wGZwXDBGwFgQaUBkQG8QafCL4JGgmpB28G1gSQARL9S/kT9yz1WPL979TvmPFT88/zpfQI9xX6GfyW/Fb98f7IAP4BkQKmAzoFtAZGB/wG0gYEBz4HkQY9BbQEiQXVBkUHEgeoB0cJTwpjCX8H/QUsBKgAyPuv92v1tvMm8f3uP+958bfztfS09Q745fqz/EX9/f2l/0YBPgLdAioEKgZhB2wHJwdFB64HVgc/BksFMgW9BRAGcgY+BzwI+wgHCacIjQc7BaMBoP0V+uf2FvSY8ezvg+8e8IjxW/OM9bL3uPm3+139qf6V/2wAbAFmApAD6gQtBtgG5Qb/BlwHWgenBscFiwXvBTgGTgbvBioILwk5CbMIEQjGBikEBwDT+4H4wfUd84PwKO9/7/DwqPIs9Cf2o/gQ++n88/0M/2gAxQHFAo4DuwQABvkGPgcRBxkHKAfsBgUGAgWwBDcF0wUFBmIGYQfVCIAJsAgRBzsF6QIl/676CPee9NjytvBY7xjwRfJr9Lf1V/e0+TX8+v2W/jP/VgClAbQCigOKBLkFvAb9BsUGjQaCBkcGbwV/BGUEVgVRBq0GLQdWCLwJRwpNCWEH1gRjATX9FPnm9WLzNfHL76zv+fC/8nD0Q/Y4+GX6LfyF/ZX+ZP9zALwBSAPWBAgGBweQB6MHZAcMB6UGxAWYBPADFQTdBIkFGgYiB5IIzAnkCbgIiwaBA7D/lPvc9+v0p/K98LnvIPDY8SH0/fWM91v5fvtg/Tj+qv5e/6UAOwKMA7oE2QXhBnoHWgfvBm0G8gU0BSQEdAOsA7UE9AXyBu4HTQmYCqgKDAlBBswC9P7f+un2q/OB8Rbwg+9B8CbyxfQf9+H4cfoL/JL9iv4K/6j/nwAlAsQDMQWFBkIHdAdoB/UGTgZHBTkEggMrA2sDFASHBWYHsQhmCc4JGQr2CGwFfwBO/If5nfYi80vwk++38A3yRPMY9fP3fvq8+3D8gv0C//3/LQCpABsCSwQfBtAGugakBvAGAwf/BVQETwNnA9QD6wNDBOYFNQi6CfUJ1gmpCQsIWQRv/0z7aPiL9Wjy3u9a77DwmfJm9Bv2QfiX+m78v/2L/mX/cABvAXkCswNDBZUGNQcoB7wGfgYOBhcFxAOsAnUC/gKeA04ElAV3BzIJ8AmDCU0IVwYxA9j+hvpM98n0a/JD8LDvWfHW89n1SPdG+b77vv3Y/i7/wv+yAIUBUgJnA+cENwa6BncGBgbFBVUFUwQYA0MCXAIpAygEQwXMBosI5wmJCv8JVwiCBYcBPv1E+QL2OPP18OTvUPAb8iP08vXz9wT6Nvy9/a/+if9SAEIBEwJiAx0FfgY2BzMHAge5Bv4FnAQuA0IC7AHhAToCYQMnBSUHqgirCU4KwwmjBwcEtf/O+zn49PQu8ljwJvAN8b7yw/TL9hr5VPs9/Yj+O//2/6oAdQFfAooDBwUkBqIGxgajBikGBwXLA/wCfQI2Ah0C0AJkBC0GrQfjCA0KXQoxCWMGjgKi/qv61vZc89zwnu+e7/nwAvNg9dr3W/q7/HH+kf9tADsB+gF7AjcDWAR8BUgGhQZuBkUGiwU8BAADTwIWAs4BzQHRAuwEWAfjCMMJjArlCo0J9AVKATr95Pkl9jXygu8L7xPwN/GZ8vj0ZPhk+x/9Zv7h/2YBIgIKAmIClQMhBQYGBgbvBRYGPQaZBSQE9gKXAucC5gKvAmgDWgWpB9wIJAl4CbAJTgh4BJj/rfvJ+GH1lvED79juafDo8WHzzvUk+Vf8YP7X/xYBLQIOA1MDegP3A7cEjAXgBaIFMgXrBKEEpwNjArIB3wGIAvYCrAN7Bc4HaAnXCb8JkAlCCLcEjf/e+qr3r/Qp8R/uZ+1W7+/x7/MD9jb59Pyd/8MAewGsAuwDIASqA78DqgSwBbcF7QSNBLMEkgR2AzQC+AGSAiIDJgPnAzQGtAjaCbYJpAluCX4HFwPm/ez5uvZE86/vm+3w7ajvqfHK8+P2nPrU/RgArgEGAxMEegQ/BOwDMQTlBEsF/gRMBAwEQwTaA7wC5gEuAhwDgwOqA94EaweoCTgK6wnGCUQJ0gYVAhD9MfkI9oLyAe9q7R/uAvDr8QH0I/ff+hP+NACfARQDYgQXBRQF3AQmBasFzgUwBUwE7gPgA2cDQgJxAdUBzAJMA6wDFgWJB6kJPQrTCU0JKAg1BZEAFfx8+Cn1l/Go7gruZe858enyOPW8+D787f6wAAkCYwNABHgEawSNBOAEEwXfBFEE7wO1A1cDkQLAAaMBRQITA7gD5wT9BggJNQqhCs4KRwrHB28D3/4j+4n3O/N079Xtae6v79vw0/IY9rn5m/yU/oYAYwKhAx8ESgTSBHIF3wXjBYwFRAUHBZsEjQMkAmIBoAExAmsCvQJUBAMHLgnuCRQKaQrNCc8G+QFo/fD5kvaL8hvvF+407/XwivKe9Mb3Ofvo/Zf/5AAtAgcDXwNyA7gDWAT/BFUFKgXfBMUEjQTXA6IC0AH/AaQCJQO1AzYFtQf1CdQKjgoOCvwIBwYdAe/79/fc9HDxMO4p7cfudvG488D10viH/G7/zwB5AXECOgNTAxMDMwMoBAEFGQXWBK8E1ASsBMwDngL2ATsCuAI2AyQEygX6B8QJrArcCjAKHghaBKn//PrY9hHzuu/P7e7tje+b8QP0MPer+r/9vf8WAWACPgNcA9wCtAJYAzYEqQSIBG0EsATZBFwEZAOOAmICngLgAm4DygT3BuII7QmACrsKCwonB2YCqv2v+Qv2+vGQ7nDtW+458Bnyp/Q4+NL7l/5BAJYB6wLRAwUEsQO0A0sEEAVnBeUESQQnBBYEdwNUAp4BxQElAnUCVQMyBZIHSwkbCpMKlAo3CakF/gCp/K/4yvTs8GvuNe5470/xcfNX9tH58/xz/wABCgLyAnMDewNDA1QDxwM0BCcEzAPDA90DhQORArABnwEvAqICAwNfBMYGDwlgCvYKiwuFC1oJBwUpAAn8A/hw833vju327UDvlvC78vz1xPnS/Mr+fwD6AVUDKwRwBK0E8wSTBfUFrAUPBX8EJwQ8A7YBlwCCAB8BhgHfAXMDUwb2CDsKygp1Cz0LmwjlAxz/L/te9/fyOu/q7bXuUvDq8SH0e/cO+/D9qv/kABUC7gJyA4cDqAMaBLsEOwUWBawEYQQKBFQDAwL8AOcAeQEcAsgCXAT2BqMJLQt7C3cL1ApTCJ8DL/7A+R32SvKH7tvsCO5y8LDyyPQL+A38Ev+ZAGMBdAJgA3gDQQNSAyEEBAU+BTIF6wSiBE8EcANPAkgBBwFZAcsByQJhBKIG1AhVCkcLRgvtCbEGOgKe/TX5D/UR8V7uxO3J7pDws/LQ9X/5+vx8/xwBggJjA8IDlQNKA3wD3AMzBDMEBAQBBPwDoQPKAvgBqgHKAQcCcwKYA5gFwAc0CS0K4ArJCvQICAWIAGr8i/hq9E3wIO4v7oTvJfEU83H2fPrp/SMAhgElA28E3ASPBD4EkQTTBLkEPwS4A5oDYAPYAvABNgFFAaUB7AFTApEDrAXDBxkJ0wlfChcK1wfFA4D/zPv597TzA/Cl7m3vtvAb8m30Nfj3+6X+iAASAngDFwTiA4sDZwOJA6YDoQNtAy0DJgMEA4QCzwFqAbkBJQJpAhgD2wQYB58Ilgl8CiMLIAqtBkYCUv7S+rL2SvJ17+/u8e8Z8azyZvXI+Br8aP45ANMB6AKqA/EDPwSEBMEEHQUOBYQE1gNvA/wCEwL2AHMAzwBlAegB3gILBbUHgwlfCsYK3gpeCXMFigA2/NX4N/VC8eHu5e6W8FryEfTn9ov6uP2O/6MA6wH3Am0DXwN2AwkEfATABMMEbwTwA08DyQLiAbAAEABgAE8BKwJVA4EFSghmCgsLHwvYCgcJ8ASE/+X6VvfS8yHw4O2G7tzwOPNQ9UT4HPxA/9EAfAFNAkEDjQM8AwYDagMrBJgEdAQdBMQDTwOUAqUB2wCFAMQAawF3AjMEggbOCHsKaAujC8IK+weAA5P+H/pA9mryHO+w7YHuofDs8pL16/iW/Ij/LgFGAj4D0gPSA3IDgAPzAz0EFwTAA68DpANGA4MCsgGAAbcBDAJvAjID5AT6Bp8IdwnzCRkKzQh+BQUB1fwr+Xr1t/En7+ruM/AG8hr0x/ZC+mj9kP/PAOABKAPlA+wDzAMgBOYEPgX+BH4EGATCA+MCpwGpAFcAvABLAQgCdQOhBQEIsgmKCg0L6QroCKMEeP82+7r3AvQt8PntsO4C8UTzRPUL+J77i/7+/7AAjQGJAu0C1QIhAxEEMQWsBWQFEAXBBCkE3AJLAWUAUQC3AP0AtgG5A4sG9AhTClYL6QvxCpIHhgK0/Yn5j/WG8Wfuqu367jjxofN69uj5Hv1t/+MA4AGZAvQCCwMnA6MDhwRCBYQFTAXxBLoEAgSiAj8BoADfAAwBLQE0ApIEZQdBCVcKNQtfC7sJsAXEAE38PPgs9FjwTe5r7t7v6fFR9Hf3yvp8/W3/wADeAaYCKgORAwcE0gSFBfUF6AVqBfYEYQRwA+8BmABHALoAHwGRAdwCSgUbCNcJjQrHCkcKCwiOA5P+SPqE9rvyTu8t7mbvl/HB8wr2Pvl1/BD/uACJARcCeQLmAjwDjgPqA4cEOwVHBd0EOgSyA+gChgGNAJ8AdgEUAp0CQATmBnUJ1QotCxcL1Al8BnYBbPxP+IT07/B07j7u9u8w8mr06PYC+jX9av/NAKUBYwItA6kDJwSdBEsFCgY/Bt8FBgUxBDEDpwEtAIb/6v+rACIBOwK9BPgHSAoiC1sL/Ar1CKMEUP+S+qf26fKL7xru4O7j8CLzf/WQ+Nz7if46AEwBPgLVAi8DcAPLA2EEFwXXBQcGiwWsBLgD2wKpAYEA+P9LADUBOwLhA3QGTQk9C6cLSAtECpMHvgL4/Dn41fSk8XHuDO2J7qPxgfSl9nn5AP3i/2AB5AGGAhUDPANSA6gDlgRrBb0FyQUwBT8EQwM9AkUBQwD3/4MAjQHvApcEIgfeCZUL9wsgC1sJ2gW2AEH7r/Ys897vc+1H7VnvHfKS9F/31vpY/qAAzQHUArcDHwTrA+4DoARXBZwFdAUDBVsEaQNEAjsBZgAPAGEAIgE+AtsDbQZKCR8L2wu/C/IKUghUA9r9Nflt9YjxDe7j7APuPfBq8hn1yPhp/Bv/0ABQAp0DSQSPBL0ESwXTBRcGOAa5BcAEogOaApEBWQCO/6//ZgAsAWUCmAShBzcKYwu6C30L/wk2Bt4Az/uQ97/z8+9o7WTtOe+l8QD00PY2+nD9GQDDAcoCiwMJBF4EpAQWBXIFqgWDBfEEZASPA1gC+gAAAPz/lQAyAeQBfwMzBvgI4wrMCwQMagv/CJsEZv+P+iv2+/Gn7g7tkO1G71/x5PPy9nf6q/3z/7oBGQNMBDMFwgVpBu0GQwcQB08GUwUQBKcCBwGE/7z+5P7K/90A+gHtA/4GGQrbCzkM8gu+CosHUALE/C/4Z/TA8NPtKe2M7r7w4/Jk9c74U/z//poA5wFDAzQEwAQuBdkFmgb6BugGOgYkBc8DVALdAHn/xf4J/xkAewERA3YFfAhAC8QMxQzLC6QJugVSAKX6EPaM8nLvCu2d7JTunfFv9Pf2Kfq5/V4ApAFeAmADTwTJBBMFqAWVBiEHyQboBcUEZwPtAXoAb/8N/33/iQAPAkwEAwemCasLzAzoDGsL9AffAn39tvho9JrwqO197GPti+8m8v70T/iw+7r+AQGLApQDIgSpBDsFwAUnBlIGUgb7BSgF4wNvAgEB1f9F/3P/MgBRAf8CggV0COcKQAy3DFQMbwqMBhIBsvtF93rz6O8G7Ufsr+008NTyivX++LT8vv+yAekC/wPqBKIFIAabBhkHFgeWBrYFrgR8A+kBWgBB/xf/oP9jAG8BQwMBBssI5wr8Cy8MZgvYCGgE+v4P+gj2VvIJ7/TsMe1I797xcfRS9+L6Lv6qAGUCkANlBMsEGAWPBRsGNQbEBSoFgATRA6kCHgHZ/13/xf+XAJsBBgM7BQ8IdgriC3EMNgytChcH7QGv/Eb4bfS88PntQu167n3wmvId9Tj4R/vV/cH/cAHXAqIDUwQ8BXQGYQeVB1gHygYABssEMgN9Af7/PP9s/zUAYwHhAhAF0gcqCnILpwscCx8JAgWY/5f6v/ZY8w/w3u0Y7hjwTPJV9ND2DPoh/TD/iACrAdICnAMqBPkE+QXHBvQGsgY3BmYFLQSPAggB9P+I/9n/ygBaAncE/gZzCT8LMQwnDNUKwwfvAoH9pviw9ETxce4Z7dbtJfDf8pn1aPho+xL+6v8jASECDQPIA18EJQURBscG/waiBv0FFwW7AzYC4wA9AEMAtgCqAT0DqAVDCBYKCgt2C0gLSgnzBG//dfqA9tHyhO+f7eLtqu/V8Tf0EfdE+u381/5iAMMBzwJbA/8D8ATVBWUGmAamBlwGdQUXBKsCbwGSADMAcgA5AYUClwQzB5wJGAudC6YLtgrLB/MCfv0N+Xf1AvIH767tmu6E8Gvye/Qy90X6vvyR/jcAyAEFA/QDIwV3BlYHgwc5B+4GUAYMBWYD3AHaAFAAZQAFAQoCfAOSBfYH1gnYCvsKcwp8CK8EzP8U+1v37fPT8N/us+4t8A7yEfRi9tj4M/sY/Q//7gBEAkQDXAS2BbwGGgfUBlsGtAW2BJcDTgI1AYEAhQBkAZ4C+wOXBbIH2QkZC10L7wqpCdsGWQJo/Tr54/XC8gfw5u6N7yjxzPJ59JH2rviL+k78K/45AP8BoQNoBRYHZwjMCJEI3AejBicFdQPzAb8AEAA5ACMBjAI2BPsF/wfWCdcKywroCTcIBAVdAHb7pvfb9EPy/+9A74rwc/Lu8zj1Gfd6+Wn74vx3/nMAgAIgBLoFPwc5CF0IyQfzBskFTAS3Am4BwQCdADUBZAL4A9MFxQemCfcKaAsLC7IJDwcHA0T+9/lh9lTz1vBu74HvsvBK8vXz1PW396L5a/sm/QP/2gDKAqQEPQaLB0QIbwj+B+sGrgVABMQCmgH5ADwBGAJRA8cEgQaPCCAKyQqqCvIJdwhUBcsAF/xK+FD1lPJ28KfvVPC38STznPRU9kH47/mj+3j9g/+jAZUDbQXpBvcHdghGCI8HZwYMBawDaAKEAVMB5gEGA18E5AWwB4MJvAr1Cj8K/wjeBlADxf5Q+hP3iPQX8j3wze/58FryVvNs9C/2Yfg7+uv75v0yAJACpgSTBgkIvQi6CBUIJQfIBTUE0ALFAWgBwgHTAisEfgX2BscIWArkCnUKcQnyByQF6wBs/Mb4HfaF8zPxJfCJ8Mnxz/LH8yn11vac+ED6Y/zK/h4BUgNWBR0HKAh7CA0IMwcRBrsEmgOXAuYB4wGzAisErAX3BmoIAAovCxULAQqoCKoGawPp/qn6tveA9S7zDPFq8C3xPfLo8oLzw/RL9ur38PlP/Cb/1QFqBN0GkgiRCXcJtwhzB9sFaAQZAx4CkAHlAQ4DrAQ4Bq8HPgmvCn8LGgu6CesHfwXKAQ/90fg89mj0VfJ/8DnwfvGK8tnySfOu9JP2VPhh+ir9UAAtA4gFuwdWCcoJKwnmB5QGIAWnA2YCoAGuAXwCAwS2BVIHCQnACvwLBAwKC5kJfwc3BLn/UfsG+H31MfMw8UfwW/Dd8H3xN/Ji86v0QfZi+CL7V/6EAYgEKAcwCXMKqwrbCVYIfwa3BBIDqAEFAWcBnAJFBBUG9QfECWALaQw8DA8LSQnpBksDdf7e+aj2YfQE8gTwUO/e753w+vCv8RzzDPUB93/5zvxiAMMDgQa/CDoKrQo2CuUIMAdVBaIDXAJjATQB9wGMA3cFGAfNCIsK/gtyDJ4LLwpNCJQFawG0/NL4F/a+80Pxke8e76/vV/DI8LPxKPM49bb3jvrl/VgBzgSvB7QJ+wokC0QKlgi4BgAFNQOuAd0AFgEyArYDcQVGBx8J/QpUDI8Mzwt1CncIJwWMAOH7K/hM9Z/yQvD+7rfuAe+k74rw1vFN81v1O/iu+1X/vwLvBaYIggpYCyUL/glBCDcGPASIAjIBsQAeAW4CQAQxBhYI6AnDC/gM1gybC+sJpQftAwf/WPrw9k/0qfFz72LuiO4h777vlPDV8bfzIvZS+fL8lAAYBDUHvgklC30L7wqUCcAHvAXQAzYCIAHVAJ8BDAPCBJEGbQh0CjYMNw3oDHALhwnPBuEC9f1j+WX2/PNU8dru4u2C7hzvS++270/x1POp9uv5mP2BAREFHAh/CrUL0wvnClAJZQcoBRQDZAF6AIwAhwFGAykFFwcVCUsLEg26DTUNuQu2CYEG6wHn/LT4ufXA8uPvBe6Y7T7usu5H74rwb/Lk9Mz3jft8//QC9QV8CFQKIgvhCqoJ6AfZBegDWwIfAYQAygALAvkDBwbwB98JFgzeDSEO/ww6Cw4JqQXIAOD7Ovhw9W7ype9j7nLus+6u7hPvbPBV8rL0zvd6+17/BgN0BlsJFgu/C18LPApnCC4GMwR3AhsBYwCxAAgCywOrBacHugnnC4QN3w3VDAoLxAgrBTMAKvuJ9+/0JPJU78ntEu6w7tbuMO+P8OvyiPWK+DD8KwD8AyMHwAlfC7ALAguqCe4HoAU8A1MBKADA/xcAjwHHAx8GUQipCi4N5g4LD7MNlgvWCNQElf9k+nz2ufNA8dbuTO1v7ZDuhe8+8GnxsPOv9uH5Sf3sAH0EbQeHCeMKOgt0CuMIxgagBG4ChAB1/0D/HQDJAQsEcAbECHQL3g0uD/cOkw2qC5QI/wOW/ur5kvae87HwSu6K7Sbu3O5O7wrw3fFK9CT3Zfr//cQBGgX6BwQK7wrNCtMJZQh2BjEEBQJZAGX/Uf82AOABDQRyBgkJwgsDDiMPwQ5sDWILMgiLAwj+kvln9oTzV/DA7TDt0+1/7uXu3u8P8tv0Gvii+0P/3gIkBgMJuwocC4QKUAm/B3wFDgP9AI//6v4U/1kAUwKpBBMHyQmuDMwOoA/1Dm8NNQuXB3MC7PzG+NP15fLP77Htne2M7j3vtO/L8AXzyPUU+ZX82//9AuIFWgjBCesJMQkCCHoGdARqAsMAmP8Y/3P/xwDLAvsEVAcFCu4M7g5ED4sOOg0uC2IHGAIw/YH5ffYk8yrwue6Q7t7u8u5177jwmfJf9Yb4FfyD/9ACMgavCC4KVwrGCcQI+AbWBIICmQA+/6v+Df8nAA8CdQQrB/4JqAy1DmcP4A5vDQ0LNgftAfb8OflB9hfzB/CR7oDu2u7+7nvvFPFR8xD2Ofms/BwANwNGBqUItgmfCdQI7AdcBhkE0QEeAEv//P6H//cADAOLBTYICQtWDbsOGg91DgINNAoEBhkBbPzS+Nf19PIq8Ifulu4575DvsO/I8CnzFvYd+VT8+v+LA4gGyggPCiEKJwmfB9sFwQNVAVX/df6W/lv/zAALA80FsAiYCzcO6g8rEEwPsQ3oCmYG7AAh/I74jfVV8o/vTu5i7ujuQ+/272PxwPPW9hb6W/1lAGcDRAZKCCUJ0wjkB4UGwgThAg0Bw/8T/0f/egAmAjUEbwb7CKELuQ3sDu0OHA5oDIsJbgWXACL8jfi29dryaPBS70/vs+/f72bw7vEc9LT2iPnq/GAAWAMDBhkIMgn0CPoH5wZvBWYDQwHs/2v/V//k/3cBvwMfBpwISgu1DQQPCg9lDgANCwpsBUYA/vun+J31hfIX8DTvdu/t7w7wb/DI8R304vbQ+fT8PwBXA/cF7AfNCKEIngc3BpsEnQKlAFP/B/9y/3IAJQJeBO4GmglEDIQOsA+XD3gOmQxsCa8Ehv8z+xf4GPXh8Z/vF++d79Pv1++E8D7yrfRW90L6av2nALUDPgb/B5cIKQg9B+4FSgR+AsUApP8+/5//3ACvAs0EIQfkCdEM6g7BD3YPlw7jDFkJZQRB/zf7B/jO9MvxmO/h7hPvXu+9733wIfKX9I73xfrz/TQBJASMBiMIjwghCPEGawWyA/sBgQBX//j+bf+uAGMChwQOB+IJ7gwaD/EPlA9+DpkMFgkaBLr+jvqF93z0n/Gk7yLvee/F7w/wzvCv8jb1Bvgc+zf+MgH5A1kGvAcQCH8HcQZUBdwDFAKGAJf/Zv/+/zgB5QISBaMHXgopDUQP3g9SD1QOlwwKCZkD//0d+rf37vR/8RrvsO4+72TvYe8h8NvxlvTP91z7wv62AaUENwfQCOwIEggZB9gFGQQEAlgAbP8S/3z/3gDvAjUFkQd6CqMN8Q94EH4PHQ5CDL4IMAOq/cj5C/dR9Cvx7e5e7tPuVO+U7z3wvvF39Bf4ePur/tgB9QSNB+sIHAlRCBEHYwV9A7gBEgDu/ob+QP8MAVgDtgU4CF4LoQ6rEAcROBD8DuwMwAjeAnX9mflJ9gXzT/Dc7qzuv+7h7l3vNPCi8TP00fdk+3v+tAEXBfgHWwkQCTAI4wb7BPACOAHY/7b+e/5a/zgBlAOnBRUIjgseDzMRYxGkEFEPBg2/CM4CU/0i+fz1E/Mv8PLtJu2l7STuqO6i72jxT/Qe+CH8of/QAtAFPAihCasJqQgRBxAF9gIyAc7/u/5l/jv/KwGtAzEG0AjXCxUPQRHIER4RiA8VDckIEQOW/Tj5tvUg8g3vau0J7SftQO3t7WHvgPGG9Dv4XfwuAJwDrwbgCPMJiwlUCMEGzgS2AqQAL/9d/kH+7P6bACsDAwZHCawMtQ/MEWESnxEGEJoNHAniAhD91fj69fLymO907Rrth+217TvuXO8d8ejzhPc4+67+CQIRBa4HQQlMCXsILAdoBX8DtAEQAOz+tv5z/xMBdgP7BcQIIQxXD6MRcxLDETQQ0A3SCSoEdv4H+pb2R/PX70ftUOx/7PPsM+0n7h3wFfPd9tT6x/5OAmsFAgi0CT0KZwmoB4gFawOJAeL/ov5T/gL/kgDaArYF3wj9CwEPQxE0EssRWhDgDQkK3QQ0/3r63vZ68y/wye3U7L/s2OwE7dnt8e+n8ub1zfnf/bMB8QScB2AJ7glGCbwH7wX7AxMCZwAr/+b+z/+TAaUDAAYPCW8MbA9ZEe4RsxFxEKsNpwnrBLL/0PoF9wj0WfHM7gftk+zX7Pnsdu087wnyMPXR+Ov8DQGdBAwHrgijCUMJvQf+BWsEpwLAAEX/v/6T/1cBXQPLBdMIOQw7DzUR3xGMEWwQ5A36CSsFOwC2+9H3UPQh8cjuMu1Q7EfszOyF7dDuRfHn9OT4ofweAJUDoAaDCDMJ9QjtB0cGegTYAkYB3/8l/7r/YgFpA5sFXwi1C8UO+hD0EdURqRBODtQKXgZoAY78Ufj59C/yuO+v7X7scuwL7eLtAe8e8Wb0Rvga/HP/jwJcBXYHjgh/CLEHUQaRBMoCPQEKAFT/ff+eAMMCmwVgCBAL2A1cELoRrBF5EFAOcwt7B8kCHv75+XP2hfM98Rnvdu3n7EHt/O3e7izwiPI/9lH68P0wARwEgQYICHEI5QfJBh4FIgORAbUAIgDr/3oAJQK4BFwH0AlvDCkPGBGVEegQXg/MDDUJ1gRGAOX73/d19MHxtO/W7YbsROza7OvtX+/V8Xf1j/lK/X8AiwMpBrcH+gdeB2kGLgWKA/MB1AAvAPH/ZwADAmEE9wZ3CQ0Mtg7UEJ8REBGvD2oNJAobBpUBIv0W+cL1CfOi8Gbuqexf7CPtCe4e7x7xePR2+Cz8Wv8nAp4EYQY5BxQHIwbcBHoDPwJIAZ8AVgChAAcCXgQLB2oJXQuvDRgQVhHzEJ8PwQ0jC5MHUQMV/w37Lffx84Pxau+g7drsOO397ffuj/BN8wL3gfqA/ZYAaQNYBUkGjQYqBiAFygNhAm0B9ACnAOkAAgLhAwMGRAiKCt0MLA+XEN8QIBCVDjoM7Ai/BCsAHvyl+Hb1svJh8I/ud+1S7evttO7e7w7ye/VF+XD8MP8HAngE4AVkBlMGmQU9BOUCJAKZAcsARQArAU0DZAU8B0sJ+AtzDtcPUxDtD8QOpgyqCUUGTwIr/kT6FveR9C3yAfBo7gTuau7u7s7vZ/Em9Jn3+foX/u0AiQNaBToGYwbKBXkE8wLsAUQBzQB9AK4AAwIbBCwGAwhCCrEMog7bDwgQfQ8lDs4LgQirBKkAcfzO+Av2hvMF8fLuye297WruIe9J8KPy5vWX+QL94v90AosEzAUuBggGOwW8A0ECOwGsAE4ADgCXAF0CxATtBuwISQvCDboPphBYECoPNg1iCrEGmQKK/pP6Ivdi9CnybPAT73furu5h73XwPfL89BX4FvsD/u8AdAPgBGgFtwWFBVUElQJKAbIAIwC1/wQATwErAywFfwc+Cr0MWw6ND2IQLhCvDiMM8wgdBfIA6Px3+YT2gfMS8d7vdu8j7/Huie8r8X/zEfa/+LT7q/5sAb4DOQXUBcMFQAU+BOoCqQGIAL//gv8ZAKcBfwNMBY4HXgr1DMgO3g9UEAsQiA7RC0sIDwSq/9P7uvix9aTymvDX78Xvj+9D7wnw9fFC9NP2tPnK/Kb/SQJsBKcFBwZ8BYUEXAMAApkAdf/4/gL/8/+XAZYD+gVzCBMLmw2VD3cQURCUDwgOkQvmByoDsP5E+334avUz8gvwX+9z72Xvae9u8Ffyw/TZ9zj7Vv7ZAA4DFwVPBlsGZQUuBPkCdgEUAB7/vP4G/+P/nQHeAzQGkAj5CpANoQ+DEDMQSw8CDl4LOweHAoH+LPun9zr0ofEw8GTvy+7a7qzvCvH98rX1//gE/JH+BwGBA0IF2wWyBf4E5QN/AgwBxf/q/oH+qP7b/8YBFgSNBvsIlAsQDuEPeBBCEKAP7A3UCo0GDQIm/tL6gPcH9HvxNfDe79zvxe8j8GfxgvM29iz56/tn/vcAbQMNBasFbAWxBMgDfgILAbL/qf4o/of+3f+rAc8DDAZuCCgL3Q3TDz8Qpg8LDwMOowthB4wCrP53+0v44vQx8oDwku9R75LvSvAz8drylfXj+OH7hv4vAacDWwUiBi4GYwXNA/oBiQBx/17+eP2J/QT/KQFUA3wFFAgZC8wNvA+cEMIQAhBDDsoLNgjAAwT//vrm9wz1P/LR76fup+4s7+/v7fDe8tr1Xvmh/GT/FQJfBLwFHAbGBRIFwwO9AdD/ov7q/Tr9Hf0q/l0AEgN/BfsHywqcDdgP/RADERoQnw5LDM0IhQQRAO37Wvgf9V7yZfAr78buPe9Z8JrxevM69jb5HPy+/kEBgwP5BIQFPAWZBGIDvgElAJj+g/3l/Nj8bf3//oEBVwRlBz8KAQ2KD1kR8hEyEYIPAw24CYYF9ACp/Ob4e/Wo8vDwE/Cu77/vtPB58n70ffbB+JD7hP7sALwCAQTCBBkFqAR8A7YBr/8K/sz8Hvz0+338+f1QAIkDyQauCUkMoA6sEIgRJxHHD78NAwsqB+8C1P48+w34IfXk8m7xqvBe8K7wqPEh8yT1bffm+bf8dP+YARADIwSPBFUEfAMPArYAVP/z/cX8RPyN/HD9Kv82Aa8DpQaNCQIMBA6iD0QQDBAiD48NJwtpB/AC/P6i+0L40/QP8obwMvCi8C7xJfLt82j2SPn7+1H+dABQAqEDWQSiBBkEoQLrAG7/Pv7j/Jb7F/uC++f80v5KARIE0wbRCasMEg9pEOAQxRDED4wNNQpaBjIC+P0J+tX2QvQs8vbw8PDU8ePyE/TD9d33GPpF/H3+ggDeAYsCEgN3AxEDtgEHANb+/f0e/TX8z/tE/DT9yf4YAewDowYRCbQLTg4WEFwQog+KDsQM6AkjBjcCvf6R+5H46/Uo9FHz4PLR8ijzP/T89bL3Sfkc+1b9Tf+mAIMBFwKaAooCwAG+AMz/2f60/aX8EPxB/C39av5AAOUCtwVMCKwK0QyLDlkPTw/3Dg8OpAu9BwAE4wCV/bf5J/Ys9Gbz5vJx8tvyPPTY9aX3fflk+0v9BP9hADsB5wFZAnYC3wGhAJ//+f4G/pL8b/tp+yL8Bv1R/oMAcQMlBpQIQgsBDsgPEBDUD3wPJQ7jCmAGUQIY/+77JfjK9CPz9PI783vzSvS09aH3mflQ+x79uP7x/6gAJAFxAR8BPAAE/0L+GP6i/bD8CPxw/FX9E/4U/6sAvQLnBEAH+QkqDCsNiw0wDo8OkQ09CzQIFAX1Aev+D/wg+UX2YfQ79Pf0ZPWv9aX2f/hP+oz7pfz3/d/+9P78/nj/0/9r/6D+Vf6Y/pL+Bf6l/cL9EP6C/l3/nAAcAsUD0wVVCI8K+Qv0DCgO9Q5qDngMrwnOBpYD6f9A/BH5pvb89GP0ffTE9En1SPYl+BX6f/uE/Jz90v6L/9D/rP9M/+z+s/6Z/lr+z/1I/WP9qv2b/aD9if48AOUBmgO0BW4I8woHDGgMIA3+DcAN4At5CTkH7gS9Ae/9F/sy+VD3cPWu9I71y/Zl95b3fvhk+uX7ovz8/F39E/7t/q//pv8W/8P+z/4L/7D+0P1E/VX9u/01/jj/mADuAZsDuAVRCIEKnAsNDI0MMg3hDGILuAhmBVgC/P8d/uD7c/mZ9wn3lPf69+X3C/j0+C/6FfvM+5L8Tv2I/V/9a/2M/VP96vz9/HH9q/2O/YH97f2s/lv/BQANAYACVwRHBvQHMgkvCi0LwwuzC/gKxQkuCDoGPQQGAo//+fy9+o75Dfm0+G34u/i7+d36z/ts/PP8bP2P/Y/9fP0V/U38aPvG+mL6NfpH+r363Pse/Vr+7v+tAT4DTwQwBfMFrAZZB9IHdwj3CAIJxAibCF0IYQfMBcsDyAEcAJT+I/29+6j6IPo2+r36MPt/+8H7JvzN/Fj9dv35/E/81vuF+2P7QPtT+5j7uPv9+6f8q/2K/gX/m//bAM8CagRMBfAFnwZgB8cHvQerB98HEgj1B6wHGAfdBTMEQgJDAJf+9vyD+3r6B/oc+mj6uvqS+qD6jfuv/Db94/y4/Cr9yP3C/QD9g/xi/FT8MfwT/D/8l/we/dP97P5VAK0BDAOgBCYGLQfCB0QIvwjyCMIIVQjbByYHCwa0BEUDmwHf/2L+If3Y+5r66vnO+fn5HPpZ+g777vur/DP9rf0H/vH9wv2m/aH9l/1L/SD9EP0Q/QX97fw1/c/91v4XAEkBdgKVA9AE0QVnBrcGDwfVB4UItghdCKIHzAaqBRQECQIFAD7+o/xx+3z6tvlU+ZD5P/oe+x/8AP3X/X7+wf63/lH+qf0L/fP8Df3i/K38ivyV/Mz8MP25/Vj+If8nAJAB5wKlAw0EjAQnBaAF3QUiBpgGIQdpBz8H1wbzBZ8EGwOgAUgAt/4J/YP7i/o/+jL6c/rz+sH7xPzW/Qf/t//C/yv/Wf6w/fH8Tvyl+1D7Svtj+7P7+ftv/Dz9mP4ZACYBAAIcA6YEyAXzBZ8FjAXxBRIG6gXtBQoG3gU2BbUEegTHAy4CXwCH/zf/Vf7o/KD7IfsX+x77NPti+8D7Qvwk/Qv+Sv4G/qj9oP3H/aP9If1h/OP7xvvT+wn8Kfx1/Dv9pP6ZAGgC2wPcBN8FBAefB5kHFQfIBuAGDgfaBgoGYQXqBE0EOQPKAYoAYf8Y/p/8Zfur+hL6hPlN+d/5Cfs6/CL9/v3//rj/zf9d/+j+hP7a/cr8qvvs+nf6A/q6+TD6jftZ/RP/0QDVAskEOgYhB9UHZwhrCOoHVQcBB7gG9AXxBB8EwwOGA9sC1wG6AMj/0/6S/UD8KfuQ+mj6sfpA+6j7Kvzm/N39mf6x/oz+ef5q/q/9i/zi+3X7yfrn+ar5Svo4+xD8CP3b/gQB1wJfBOcFZAcpCCUItAduB20H9QYEBjcF7gT5BPAEpgQEBCsDTgKFAa8AW/+X/fT73PoS+m75M/lx+RL61Prj+z39Pf53/jn+Sf6j/pb+wP2U/Nn7h/s0++H6o/qd+hf7Svwe/gAAggHZAqIE2QaQCG4JwAnJCagJPwm5CBAI5AY6Bc0DUQP1ArsB5/+E/gH+g/18/Df7g/qB+o/6s/oH+5P7HPyE/Cb98P2D/lj+uf1h/Tn98/xV/Jb7+Pql+sX6Svv1+4v8Rv2b/mwAKgKsA/kEJAYwB/8HmQjHCIUIGwjSB5oH9QYnBoYFzgTPA3QCUwFuADz/pP0V/Dv7lPrJ+VL5Zfkc+sf6Rfv9+wX9Hf6J/ob+cv5D/gv+cf23/Bv8sPtT+/36H/uF+yv8I/1w/vr/gQEBA10EqQWUBvkGTQfCBxYI+wfOB88H1we1BwMHFQYZBdgDOgJsAM3+GP1r+/v5Cvnl+BP5YPkM+nD7/Pzt/Vf+pP4a/yb/bP6B/dD8KPxN+8j6zvq8+n76gfo/+4f8ov2L/rn/XQETA5UE/AULB7UHDAgcCBII4AddB44G6AXjBQEGpwWzBKIDGgOSAmMBiP+7/WX8R/ts+tX5o/nJ+TL6Jvtw/If9Cv4h/ir+Dv7G/T/9h/y4++D6lPrn+k37aPuC+x38I/1Y/mr/cACBAYACpAP2BCUGngabBrwGEAdPBxgHugaYBrcG2ga2BloGhAVGBOwCqAFcAGH+9vsF+hn5yfhc+DX47Phn+u77LP2X/un/SACe/8z+d/4B/sj8Ifsb+gD6B/rn+f/5jvqD+7n8K/7C/1cBuQL3A2AFlgZLB58HngdoByIH6Qa0BnsGLQa1BbgFJwYGBvkEfgNsAqMBQgAD/q/7X/qx+Rf5s/is+E/5Zfq0+/r8CP7d/kr/cf8r/4H+z/0g/U38Vfuv+o36hfpf+l/68/oc/Ff9aP7U/88B0QNRBWwGdQdKCJgIQgi4B2UHDgeNBhgG6gXkBaAFDQUtBDMDLgLdAE3/g/3G+3b6pvlR+UD5h/k0+kn7zPwy/hn/Yv9F/xH/p/74/RP9R/yo+zD7GPtA+177Q/tK+9376fz5/bD+ev/TAJoCLQQ7Be8FjAY0B5gHkwdVB+wGawYgBksGmQaVBgwGHAVMBJwDcwJzAAf++PuU+pX5n/j29yH4B/kv+n377/xI/jn/nv+f/3L/B/8g/gr9Q/za+6n7Y/sb+xj7afvk+178Hv0V/iH/PACOASYDlASlBW0GBgd9B5sHdwcsB80GggZLBmUGewZVBuMFLAVNBEEDAAIvAOb9sPse+jP5bvi094f3cPgF+lv7a/yU/dL+hv9w/yb/CP/O/t/9tvxM/Hn8U/yV+/v6GfuS++T7CPx+/Hv9w/5pAFQCLQS6BQEHNgg1CcEJtQk4CcEITAi+ByUHdgaqBZoEgwOtAvYB1gDx/gr9xfvs+vP5uPgF+E34ZvmZ+n77cfyB/Xv+9f7N/lD+uf0n/X38zftU+xr7CPv9+hj7mfth/AT9g/09/mH/pgDGAeECNQSnBd8GsgdPCMcI+gjOCIAIUAgfCKsH7wZPBsUFBQXPA30CdwFbALr+rPz8+vH5FPkO+Dr3Y/dz+Kn5o/pq+1f8bP0t/lP+I/4M/vr9uf1B/cD8gfxM/O/7mfuK+7H7z/st/OP82/0y/9QAswJ5BAgGeQcICXQKAAvvCsIKtApcCkcJ4gfBBv4F5QQqA6IBtwDc/2b+mfxY+6v6/fkM+Wf4kfgy+dr5dPoo+wP83fx7/bf9sP2Z/Yn9XP3g/E/89fvl+8H7bPs2+2v7E/yi/CD98/1B/8gAFwJEA6YEPwaZB3kILQnNCSQKIQrUCYQJMQmRCLoH0AbPBZMENgPgAVcAjv7K/GP7TvoV+eX3Q/eL9zj4s/hG+VL60fv8/HD9rP0b/o3+Yf62/Tf9GP3+/ID83fuY+8n7DvwS/Bv8e/xK/XD+nv+yAPEBjQNkBfcGCQjmCMcJigrLCqEKVgrfCTgJWwhKByIGwwRbAw0CzABa/6T9GPy1+m/5Svhn9yX3Xfe39zj4Sfnl+kj8Ev2G/RT+2v4o/8X+Q/4X/hD+tP0g/Z38afxN/PD7qvvh+3D8+vyk/b/+PwDdAWYDDgXhBpQIxQmRCjQLdgtVC9UKGAokCfoHsgZMBewDjQIhAbz/T/7W/Hj7bPqT+bb43/d49+P3p/gz+cH50fo3/D79u/0r/sf+P/8S/4X+Qv4e/qX91/w5/A/8B/zP+337tfuJ/Gb9Gv4U/5oAbAIUBIwF+AZQCDoJoAnwCSQKGwqnCQIJdQj5B0YHGAasBFkDIgLjAGX/pf0F/K/6lvmM+JX3Cvc39+v3lvhY+Z36MPx6/SP+lP43/8j/sP8U/7P+n/5j/qn93vxy/E/8//ta+wf7WPsX/Ob81/04/wEB7gLBBHUG+gcpCQYKlArKCroKXgrUCQoJFQggBx0G5gRSA8IBjABb/8L93fta+oT57Pgp+IH3t/ec+IP5QPoh+2v8pP05/mH+0P54/5D/Cf98/l3+gv4g/i39fvxm/GT89Ptp+zz7sfuM/GH9Wv65/34BfwNdBecGGwgqCfkJRAo+Cu8JkQkICTQIRAdjBo0FawT2AnYBLAD3/oL94/uA+o75+fh7+Cb4VPj9+OT55Pru+/j85v2w/jr/kf+x/4v/RP/p/nz+Ev6P/fb8c/wR/Mb7gPtX+3/7+fu7/I/9rv46AO0BlQMABVcGogeoCC4JPglKCVMJCQljCJQH/wZyBn4FKgTrAvEBywA7/3b99Pvn+u/53/ga+A/4lPg/+e355/pN/Kb9l/4n/8T/WQB7ADgAxv9l//v+Vf6T/eb8efwR/JH7HPvu+iD7m/te/FD9fP7o/5YBfQMrBWMGRQcGCKgI6QjVCIcIKwjaB28H5gY9BkEFEgTxAuQBtQBL/979pPzD++76+PlB+Rb5ZPnZ+WL6Mfs+/Er9+/2G/jH/sv/P/43/aP+F/33//P4x/qf9af3//EH8ivtB+2X7lvvb+4H8rv0g/6MAPwLUA1gFlgZpBwIIfQizCIsIIQi9B5AHWgeYBmkFUASHA6QCQwGQ/wf+8vzq+7T6nfkX+Sb5XPmo+VL6dfuy/Jv9Vv4Z/+H/VQBIABkA/P/i/43/8v5E/qP9Hv2d/P77T/vQ+r/6FPuR+yP8DP2Y/moAFAKLA/0EdgZxB7UHsgf8B0gI5wcjB7cGzAbDBskFVgR7Ax4DRwKPANr+yP0E/er7fPqV+Xj5mPmx+RX6H/to/G/9JP7R/qr/LwAoANv/r/+r/3v/C/90/vP9lP0x/af8//t1+zb7Pft+++D7h/ys/SP/qgAxAr4DNAVUBhMHlwcLCFQIKQi+B3cHVwcWB1wGSAVMBG4DXgLnAF7/Ef7i/Lj7ifq3+XX5Zflw+cz5v/oL/B/96P2l/p7/bwCdAGQALgA/AD8Ay/8f/5r+VP7h/Rv9Sfyl+zv75Pqy+vP6svu2/Nn9Nf/wANICbASgBZIGegc0CGcIQwgOCN8HmQcgB4IGrwWvBJ4DmAKFASUAkP4h/Sf8XPtr+pD5Pvmf+UH61PqT+778+v3H/j3/0v9+AM4AfQADAAQAJgC8/87+Cf6//XP9svy7+y/7I/sf+wT7RPsv/HD9qv4KALgBegPJBJEFVwY0B9gH4gemB6kH1gfDBzwHeQbCBe8EwQNRAuUAlv8k/on8Mftc+uP5fPkk+VH5Nfps+2n8J/0T/kL/RwCwAIsAeACmAKcAPACi/0L/+f51/qP92/xU/K773vpJ+lL61fps+x/8Uv0d/wEBigLtA18FoAZ6B/UHUwiLCFkI2weKB2oH1wafBVoEegO5AmoBsv9N/nT9n/xp+2r6Ifo++jP6I/q5+vT7B/2F/QH+A/8NAGsAMAAOAFIAcgAGAGn/CP/H/jr+Yf2i/Br8hfvg+nn6mvoZ+6/7fPzO/Zr/agHxAlEEvgXrBpYH5Qc3CIcIdggACJYHdgcUB+4FaAQ6A3ECRAFm/6v9rfz/+/T6x/lV+cD5Pvpc+sz6Ifyx/af+D/+v/7AASwEBAWIASQBdANz/6P4l/tf9i/3M/N/7XPsW+7X6cPqh+mH7Vfxc/az+XQAyAr0D/wQwBikHwQcOCE8IdQg0CLAHRgcCB2kGLAXdA/QCHgKzAOn+lv3j/CT86PrX+bX5JfpP+lX6Avtl/Kv9P/7A/s//3gAkAbEAZwCeAJcA2v/f/lD+AP5o/Xj8ofs0++b6dvpB+pv6VPs2/EP9v/6cAGkC2AMNBVcGdQcTCEUIZQiQCHgIDAiNBy0HhgY6Bc0DsQLJAWsAk/4Q/UL8s/u1+rb5gPkA+nD6k/on+3v85f26/iv/9P8AAWsB+wBxAGsAaQC2/5z+3f2e/Tn9RPxQ++D6vvqG+j36efpm+4z8r/0G/9YAyQJcBHwFewaMB08IfQhkCE4IQgj5B3IH0QYcBioF5gOmAocBPQC7/kT9JPxW+5j64/mB+a35H/qk+mb7dvye/aD+ev9KAAQBSQEhAeQAwAB5AML/4P4Z/oX99vwf/Db7kfo1+gb6A/pv+kn7Zfyq/T3/IgEBA40EsgW/BqsHSgiJCIQIfwhiCBQIowcXB00GHQXOA5wCeAEjAIz+E/0W/Fr7fPqq+WL5sfk4+sj6lPvC/AT+7v6Z/1UACgE6AdgAdABQACoAiP+K/tX9bf34/Cv8UfvW+qH6fPpd+qv6lPu5/Ov9SP8BAe4CfwSbBZUGqAeOCNQItQiUCIoIWwjJB/oGGwYKBboDZQIhAcz/Uf7j/M/7C/tn+tP5g/mx+Sj6y/qa+6H8u/2t/n//PQDZAA4B4wCnAGwAFwB5/7D+/v1l/cX8B/xL+7H6QPoO+jT6tPp2+3z83f2Z/3MBKQO5BCYGSwcMCHcIygj9CMsIRQjJB4wHEgf5BZgEbQOYAoIB0P81/i79Yvxa+0H6tvm1+dP58Plr+n37p/yB/U7+S/9DAMIAwwCnAKYAgQD3/zj/jP7v/Tn9ffzU+0L7vvpC+hj6UPrb+p77l/wJ/tb/rgFjA+4EWwaGB0MIogjdCAgJ3wheCOEHkwcdBxAGoQRmA3YCSgGf//n93PwW/Cv7JvqN+Zj51/n++Xr6hfu1/JP9QP4p/yYAuQC5AI4AlACHABoAYP+x/ij+g/2u/OD7R/vE+jj67PkU+qP6bftz/Of9xP+5AYEDFgWEBrwHgAjWCAYJLQkRCYcI8geuB0cHKQaQBDwDXgJTAar/5f3K/Bv8PPsf+nH5kvnj+QH6UPpR+638p/1Q/i7/TAD4AOEAlACTAJgACQAG/zz+z/09/Vr8fPvs+pH6HfrN+Qr64vrw+wn9ef5fAGwCFQRiBZEGlQdCCGwIfgisCK0IYwjlB6MHWAeJBjgF0wO8ApMB7P8L/pv8ufvW+rP51/jK+D35pvkn+ib7lPzQ/aj+f/9+ADoBRQHyALIAhAAOACb/P/6k/Rr9SvxM+5f6Ovr7+cX53/mm+uD7P/3O/qgArwJ8BN8F+gbnB3cImAiZCKUIjAgrCLYHcAcOBx0GowRLA2kCWAGo/9r9t/we/D/7H/pz+Z75Dfoh+mP6WPuz/Kv9L/7l/uP/igBhAAUAEwAuALH/vf4Q/sX9W/14/IL7+vqs+kH63vkR+gH7Lfxk/eH+2wD9ArgE/gUWBw0IowjFCKgInQiHCDYIwQdVB+MGGgbzBJ4DXwIkAa3/HP65/Lz78/oP+kj5D/l3+Q/6m/pR+3D8yP3N/mf/9f94AL0AhAACAJj/SP/S/v79Jf2Z/CX8jfvT+lb6O/pj+p76LftX/Nv9jP9IARkD1wQ5BkYHAAhsCIwIfghyCE4IAwicB0YH4gb4BZQEOgM8AjoBqP/e/aT8AfxG+yn6XvmF+Sv6ffqm+nD76PwV/ob+4/6t/3MAdQDe/4n/mf9g/4X+lv0o/eL8Mfw0+5T6evpv+lT6fvpR+6j8BP5u/zEBLwP2BDcGHwfaB1cIlgirCK4IgggrCN4HoAcPB+gFigRaA0EC1gAS/4/9lvzE+7X6wvmA+cX5FfpL+u/6HPxC/fX9a/4t/wAAQgAAANP/5v/E/xn/Pv6t/Ur9sPzT+yj73Pqo+ln6PPq2+rP7yvz3/Yb/egFlA+IEFQY1ByMIhwh+CIAIqQiWCBUIhwdCB+UG3AVNBPwCHgICAUv/hv1w/Nz7/frU+UD5pvlR+o/62frS+zr9Nv6N/vn+1P99AF0A1f+j/7f/Y/90/n79//yq/Oz79/pr+lr6Ufo6+oX6ffvo/Gz++//DAaIDUQWSBmcHAghXCIMImAiFCDYI1AegB3MH1AaXBUEEQANFAsUA3P5L/W/8p/t6+n/5VfnB+R36VfoR+3T8xf1z/uX+rf+FALMAMgDX/9r/pv/h/tf9Pv30/Gb8hfvG+of6b/pE+lf6/Pob/Ff9pP5EADoCCwRXBU0GPgcLCFgIQQg0CFIISAjjB2kHHgenBowFDATQAs8BgwDJ/ib9IPxk+2/6c/ke+Zj5NfqS+h/7Rvyu/Zz+Fv+p/2wA0ACFAAAAvf+R//j+9v0j/a78Nfxs+6L6N/oq+jz6Yfrp+hb8nf0u/8gAiQJgBPcFIgfWBzcIawh+CHcINAi5B0sHEAe+BucFqgR+A6kCvgE6AHv+Ov18/J37efqp+aL5BvpL+pf6evvn/B3+q/4j//j/vgDGADEAw/+1/3f/nP6U/eb8gPzq+wr7W/oh+ib6L/qD+lr7nPz+/XP/PQErA9wEGQYCB8gHRghRCBwI/QfpB5sHLgfYBqEGBAazBEoDVwKFARcAMP7B/A38a/tZ+lv5Y/lC+vT6NPvR+yH9gv47/27/3f+LAMAAMAB9/0v/Mv+R/oP9rfxM/PT7TfuW+kf6bfqw+gP7pvvO/F7+CwC3AVQD3wQ6Bj4HzAfsB+cH9Qf8B7sHNQfHBp4GZAaDBS4EFgNcAmYBvf/y/c/8M/xg+zj6hfnR+Zb6A/tR+0380v32/lL/g/8ZAMQAsgD3/17/O//9/iT+G/19/Dn8yvsF+2b6P/pf+n361fq0+w39lv4UALQBcAMGBS8GAAelBwEIEgj1B+EHvAdXB+QGigZABocFMATLAtwB/ACA/5r9NPyS+wP7HPpq+bH5rvp7++f7pPwG/lD/wf/M/zEAxwDDAPP/Kf/x/tL+Gf75/D78DPzL+xD7Ufoo+oH69/ps+zr8mv0+/9UAUwLPA0AFXgYNB2UHlAelB5IHZgcUB7IGaQYqBrUF1AS1A6sCvQGKAP/+if2G/M77+vo4+gH6b/oY+5n7J/wt/XH+WP+s//r/fwDNAHEAwf9L/xX/r/7J/dj8Rfzr+2T7qPo2+jf6efrB+kr7Yfzh/Xf/8QB5AikErAWgBiMHgQfCB88HpgdsBzsHBwfIBm0G2gXqBLQDeAJaARwAhv7z/M77Lvuq+hT67flz+lf7Ivzd/OL9E//y/0IAZgCtAMwAWgCD/9P+eP4H/jH9SfzS+7r7dfve+nb6k/oU+5r7K/wj/ZD+HACaAQ0DbgSjBXkG7QYTBxsHHQcHB80GdQY3BiYGBgaDBYIEcgOXAqgBPQCB/h/9UPyk+7/6EPoy+vP6pvse/Nn8IP5a/+r/EwBoAMYApADq/x//uP5o/rP9vPwI/MX7ifsM+4v6a/qn+gH7e/s//HH9+P6DAP8BfAPcBPQFuAYVByUHGAcWBw4HugZcBkMGYQY0BloFPARXA4cCOAFp/8z9xPwD/P/6AfrE+VH6/vpr+xf8Y/3d/rb/CwByAPwALQGfAMb/Of/Y/if+Mf1k/Pb7pvsv+6r6Yvp0+qv6APua+5/89P1e/9UAZwLnAyIFDQapBv4GCAfuBtkGyQaXBkUGGgYtBgQGMwX5A/MCLAITAWL/pP2O/Oz7F/sg+sH5Vfo/+8r7VPyB/R//MgB4AJMA9gA/AboAp//O/m7+/P0R/Rf8n/uV+2X75vp9+qH6JPuK+wL88/xg/vP/XQG9Ah0EaQVgBtIG2gbKBt4G3wa7BmgGLgY4BjgGzwXUBMwD+gISAqEAyP5R/XP8wvvU+vj55/mb+lz7zvuN/O79Yv8nAF0AqQAkAT8BfwBt/7/+av7X/db8/vuc+3b7Cft1+kL6gPr6+nT7IPxG/df+hQATAoID2gT4BbQGAgfxBrYGdwZKBiMG7QXTBe4FEQa1BbQEkwO2AukBmwDL/jf9Tfyc+6/67/n6+dH6vftM/Pf8Mf6k/2IAZQBdAJ0AoQDa/8b+Dv7L/XX9svzt+437e/s7+7z6bPqd+jL70vuF/Jf9Iv/wAJgC+gMuBUAG9gYSB8wGfAZrBnMGPQbhBboF+gUXBoUFcwR8A8oCyAElAEn+9Pw4/Gn7X/rJ+SL68Pqn+zv8Of2h/sr/QQBDAHcAtwBvAIb/hf75/aL9FP0+/KX7dvtU+wL7kvps+rf6SPv4+9n8If60/2cB9wJaBIYFZwYBBykH6gaJBkgGJQb/BcUFrgXhBf0FeAVXBEoDmwLHATcAQv7G/PL7PvtT+rr5CfoB++H7evxa/bb+9f9xAGsAcgCQAE0AXP9J/q79dv0P/Ub8m/th+2f7Lvu5+oz65vqe+1z8I/1I/uH/qgE2A4UEqwWYBiAHEAe9Bn8GcAZeBgYGpQWjBf4FDAZaBTsEWwO6AosBrv/N/YL8r/u++sP5bvkO+gH7vfts/Jn9Mf9GAJoApgDRAOMASAAs/zj+0f2A/dH86ftM+zX7Kvvi+pD6kPoK+8b7h/xh/bT+VwABAm4DpgTYBckGTwc8B+AGrwa2Bo8GDwbABd0FMAbuBd4EwgMMA3sCLwE0/3L9cvzL+8j6v/mK+Uv6J/uo+038mf0d//v/KgBSAMAA6gA2AAv/Of7r/Y39tvy++z37Ofsf+5v6MPpl+h373fuP/IL98f6tAFICtwPvBAcG1gYqBwcHzga4BrUGkQY6BggGMAZlBg0G/ATVAwoDPwLNALn+4vzk+037Vvpc+Uz5O/pF+9D7ePzV/Yv/nQCsAJAA0QD+AEgA7P7n/Zb9W/1y/E/7vvre+hH7uPo/+lr6O/s8/P380P0x/xgB0QIYBBMFHgYtB6UHWQfDBpoGyQa7BjYG2AUlBpEGOwbpBIgDxwIZApIAU/6I/LT7OvtD+j75M/lA+n77Kfy6/AH+o/+JAH8AVwClAOYARwD4/vf9tP2N/dL8q/v3+v76Fvuv+hL6IvoB+wb81/yx/Sr/PgE5A5MEiAV6Bl8H3QeMB+gGowbJBs0GPAbFBfYFYQYMBq4EOANzAvABgwAl/i38Z/sm+0j6HPkD+U76yfti/Ln88v3R/wEB2gBWAHgA2ABVAOP+rP1g/W39uPxm+6H6zPow+9P6FPoB+vf6NPzx/Ij9yv4CATUDkwRiBVoGkAc1CN8HBwfQBjcHRAeBBqsF0wVdBgcGcwTWAjgC1gFkANb9zvsl+wT7HPq/+Jb47vmK+z78qfzu/dj/IQETAbYA5gBPAckAS/8S/q79lf3R/Hv7lvqi+vD6jvrW+b75pvrh+9L8ov0E/ygBTAPKBLEFowbBB1YI/AdEB/YGLwcyB5YG+wUSBl8GzgVABMICDwJxAcP/Rv1e+7r6cPqC+Y/4xfg2+qX7TPzr/Ff+IwAiARkB6wApAWwBwQBj/1v+Bv7B/cz8fPuo+pf6lvoh+q350Pm++s37pvyV/R7/VwFSA5MEdgWEBpYH9QegByEHJQdeBx0HcwYRBoMGugbeBTME6QJfAmcBav8C/ZH7/Po9+gv5NvjQ+DT6T/sG/B394f6AADoBJAFPAc0B1AHyAKj/3f5z/tH9ofxw+9D6o/po+sv5avme+Xn6hft0/K79Sf8/ASMDtATwBfwG2gcnCNwHWAcoB0AHEwdxBvMFDwZDBpoFFgTQAjUCkwHl/3z9vPv8+mf6S/lX+Jf49vk3+8/7s/xu/mEAbQGOAaYBAgIqAloB7v/J/kL+yv2t/Fn7hfpq+nL6Dvql+dP5y/rZ+5b8bv3p/hQBFgNoBFkFdQavBzoI2gc6BzEHfgckBzEGlwXeBSIGUQXTA84CiQLFAcz/e/0L/G/7bfoC+Sj4lvjE+a36Tftt/HD+RQBIAZ8B+AFvAl0CigE7ADz/mf7R/aH8T/uI+kH6MPrN+X/5zvmT+or7Z/x1/eL+wwCmAiwEdQV4Bm8H8wfuB5UHQgdEBxQHdwbSBckF+QWCBTkE3gI7Ar8BYgAe/i38N/uh+pn5gPht+Hb5o/ph+0b86/31/2oB6wEWAnICtAIgAs8Aif+2/g3+5Pxo+176JvpN+hf6xvnn+cL64Pux/IP9wv6TAGgCwwPCBMQF5waKB3YHGwcEBz4HGQd1BtAFxQX1BYwFbAQtA3oCzgFkAE7+dvx7+6D6Z/lS+DH4E/ko+h/7Mvzi/cr/OgEFAnUCAgMiA34CMwHm//z+Gv7+/Kv7wvpj+kH6DfrU+RT6zfrC+6r8l/3B/kkA/gFaA4MEiwWABiMHQAcMB9UG7wbvBpoGDQboBSQGvQWeBDIDcQIFArwAo/6D/F77q/qr+ZH4NvgA+Q760/qX+yT9Rf/xALcBDAKfAhUDxgKmAV0Afv+8/pT9D/zh+mn6Y/o++uP56fms+sL7q/x3/Zv+MwDYAS4DKgQYBQ0Gqwa4Bm4GWQamBsAGZgYABhcGcgY3Bh8FxQPrAksC8QDG/rf8bvuS+n/5Xfj998749fnG+qn7Lv05/9kAxQFhAv0CZAPyAsgBfQCS/7b+cP3y+8H6SPoc+uT5zPkY+uD62fvL/Kr91/5WANIBFwMNBPYEtwVBBoMGaQZMBl8GkQZ/BjgGJAZxBogGuAVlBEEDkAJ5AV//+PxT+4D6i/ld+Lr3Qfh0+XD6ZPvY/O3+4ADnAVAC0gJYAyYDEwK0AKP/xv6i/Sn8/vpv+k/6Mfr8+Rz6t/q0+5D8Uf1r/sz/SgGdArMDngR4BRkGXwZ+BmgGagaDBnQGOAYABjsGZAbKBYsEVwOGAocB0/+n/dr7xvra+bz49Pcy+Ez5dvp5+7z8jP5vAMEBhwILA2gDNQNEAvAAu/+7/pL9RfwZ+176H/oC+gL6Pfrd+rr7lPxm/Vj+iv/WAC8CWwNRBCEFxQUzBl0GZQZmBp0GxgaRBjoGKwZXBvgF5AShA7sC2gFCABD+D/zf+gL6+PgL+Oz32fj3+eP6Evz0/RIAhAFLAtcCewOmA+wCjwFCAFb/Mv62/FP7efol+gP65vnz+X76YftB/Az9FP5i/68A5QH9AgYE5ASBBfUFNgZLBlkGXQZtBm8GXwZbBmwGRQZzBTYEDgMNArIApf50/M360fn9+CD43veH+Mz5A/sm/L/9v/9/AXUC8gJeA3kDAAPPAW8AUv9M/hr9wPu/+j/6FPrx+ev5Y/os+xf85/zM/ez+NACjAdECuwOUBFsF6wUJBgMGFQZYBooGaQY9BlkGzQbCBtsFmQSjA9YCSQEL/+P8bvtT+vz4z/eE90z4Z/lU+pP7g/2d/x0BCALEAosD1wM1A/gBzgDd/6z+Nv3Y+/76gPoT+sj5uvkr+s36hvtq/Hb9xP76/ygBaAKpA74EdAX5BU8GXAY2BhoGOQZhBkoGJQZEBoEGKAYJBdMD8wILAk4A3/3W+5/6tfmJ+KD32vch+W36V/un/LH+zAAUAqICIwOFA1wDRgK+AHX/ev5+/TL8Cftv+kP6LvoO+kb65/rV+7f8fP1v/qH/AAE8AkQDOAQTBcIFHAYVBuQF3AUZBjUGEwb2BS8GmgZyBnQFKwQ+A2sC1QB9/kH82frd+a/4tPez99D4RPpo+5n8ef6MAAgCqwL6Aj4DEAMuArkAUf9B/lD9QPwe+2T6Ifou+jj6Xfrq+rL7jvxg/UX+Xf+fAOYBBwMRBOUEiQXmBfUF2wWqBbcF9QUKBvYFFwaBBqQGAQbLBLQD2wKVAV//2PwL+wr6GvkF+Jn3aPjh+T37efwG/gkAzwGqAtgC5ALiAkQC8wBx/0D+af17/HT7pfpq+qD6xvrc+hb7vvub/F39G/78/ioAbgGWApEDXQQkBbEF5gXGBYMFeQWQBaIFkgWwBREGZwY3BjsFIgRFA0kCewD6/en7lvqS+YX40vct+Gj59PpI/Kn9af8yAWQCvwLTArkCNwISAYj/Mv44/Yj8zvsU+6/6rvro+hn7WfvT+3X8OP0B/sn+vP/uACoCRAMnBOcEhgXjBeMFkAVdBXcFtgW5BaAF6wVwBpUGxwWJBJoDwQJJAdb+XPy++tD55vj199T31vhq+tr7J/3L/q8AFwKZAokCegI/Al8B5f9W/k79rPz9+zj7tfq6+gn7QvtX+677W/wd/d/9nP6E/6EA5QEbAwMEtQQ8BZ0FsQVlBQIFzwQMBWcFjwW7BTYGvgaOBoQFPwRCAyQCHgB1/S/76/lB+YH4+/d8+BX64ftF/X/+9v+PAYoCiwIIAoQB/ADy/2j++fwy/PL7oPsO+6v66vqS++v78fsw/Or87v3I/l//NQCFAfcC7ANZBMQETAWiBVEFrgR0BNEEPAVGBW4FGwb/BiYHHwbJBNQD1gLnADH+wvtP+ob5pvgA+Ej4sfl+++38P/7E/1ABUwJ+Ah8CpwEdAQ8Afv4L/Tf80/tq++n6tPoC+4f72vsB/Er8/fzg/aD+UP8nAD8BZAJfAxEEjwQBBT0FHQW1BGMEhQTnBEgFnAUyBgAHUwfABoQFVwQ8A38B9P5a/KH6qfnm+FD4Yvh5+R37s/wQ/oD/BAESAlUC8AF3AfcABQCe/ir9QfzD+2z7FPvL+vL6dfvt+yb8Z/z9/MD9f/4g/+X/3wADAhsDzQNOBK4E+AT1BKAEXARmBMQEJAWBBSEGCAefBzAH9gXBBLkDOwLp/zb9FPvp+TD5jPhh+EH51vps/MP9DP+DAL8BSwIWAn4B5gALANr+cv1S/MH7Vfvx+qj60vpY+9P7IvxU/M78m/12/i//y/+MAJUBsAKRAx8EigTfBPEEuARqBG0EzQQ/BZQFBgbGBnYHeQeLBkwFLgTBAogA1P2w+2T6efmg+Cr4u/gi+rX7C/1W/vf/dwE4AikCxQFeAbAAgP/z/av88fuA+wT7h/py+tz6bvvY+yL8nPxX/TX++P6m/34AiAGZAm4D8wNVBK4E5QTCBFwEJQRiBOoEWwXPBZUGfwffBywHuQV0BG0DvQEY/1T8f/qa+fn4b/h9+Jn5Wvvt/CH+Wv/MAPQBRAK+AQsBfAC1/3L++Pzy+4n7U/vh+nf6qfpP+/L7Hfw8/Nj81f3S/nL/BQAEAVkCdQP/A0cEoQTxBOIEcQQjBFgE4wRNBZ8FVQZqBwcIgQdABhIF8gMxAoP/vfzQ+sD55/gg+CL4Pfnr+mX8nv0R/7oAAgJPAuABZQH7AEYA8/52/XX88ft++9/6f/qt+jf7qvu/++H7b/xp/WL+HP/U/8sAEAImA/ADfATUBAwF6AR7BBIEHASkBA8FUgXVBd4G0gfGB7oGWAUqBLoCbwCR/Tf7DPpn+bX4QfjW+IP6SfyQ/ZX+9v+EAV0CIgJjAecAfQCA//X9mvz6+8n7VvuP+kD6x/qX++f7zvsH/Pf8Mf4D/4v/VgCuARMD1AMlBHoE+gQnBacEFgQFBJMEGgVXBc4FzQbSB9YHvQZwBW4EKAPUANb9b/s0+pL5zfhP+Nj4U/r2+y/9V/7i/3IBPwIQAoUBKgHgAPz/eP4m/Wn8APxn+8H6ivrQ+kf7avth+6D7cvyX/YL+Rv8xAGwBwgLTA38E2QQbBSsF2QRTBAoEXgTvBFUFrAVjBmoH5ActB6AFPQQWA1QBrP7x+0v6u/lV+c/41vgH+tn7UP02/iX/hwDEARECgQHJAG8AGQAM/5X9lPw8/Aj8a/u8+qH6LvvD+8j7ofsT/Dv9b/4o/73/ugAuAmMD+gNBBJQE/gQBBZEENAR5BB4FiQXYBWwGXAfVBywHxgVcBDADgwEB/2P8pPrX+U/50vjP+Lb5Sfvb/BL+NP9/AK4BNgIGAokBEAFlAFL/D/4B/WL84/tB+676kfru+kn7d/uf+yD8Cf0M/vf+wf+yANkB+gLWA1cEuATzBO0EswR5BJME9QRvBdYFXQYdB3kH8AaoBV0EPQOvAV7/zPz9+hP6n/lC+SD5v/kI+4z81/0I/04AUwHGAbABaAEcAZMArf98/m39uPw6/Kv7Gvvm+gT7Qftg+4P77vur/J39gf5k/2QAlwHPArcDSgShBNwE+QTgBKMEogToBDAFdwX5BdEGcAcgB+cFjgSIA0UCIgBv/Uv7QPrF+UT52vhG+af6W/zK/eT+EABIASICUgLsAV8BvQDc/7r+kf27/C/8nvv8+pX6pfr4+kz7iPvd+4v8gv2G/m7/UABWAXsCdQMUBIkE5AQMBfYEsgSpBPgEZwWuBecFdgYCB+AG0AV8BIADYwKHABD++vvc+kf6pvkR+T35UvrM+xr9Pf6M/+AAyAELAuIBowFCAXcAK//M/dX8P/y2+wD7b/po+sv6QvuJ+9z7f/xx/Wj+O/8YACEBSQJBA9YDOASoBPME6QSpBIwE3ARSBaQF4wVfBg0HIQc1BswEqwPAAiwBtf47/L/6IPqo+SX5FPn0+XX77/wl/ln/qwDCAVACQALyAXwBsQCJ/yH+DP1h/ND7F/ts+j36hPr6+kf7mPtG/FP9ZP48/w4AHwFQAkQDygMYBF8ElwSXBFIEQgScBCMFeAWzBUoGEAdcB60GVwUYBO0CSwHu/pr8Bfsg+nT52vja+KT53/o0/Jf9Mv+9ANYBWgKAApYCWQKIAS0Atv6U/bz89/sX+2P6F/pA+rj6HvuO+yP8C/0Y/gX/AQAAAfkB1QJ/Aw4EbwSwBLMEeARNBHsE9QRhBcgFQAbbBiQHmQZmBRkEEQPMAcj/WP1f+0z6s/k2+fz4b/mm+hj8W/2V/gIAWgEuAnsCigJsAt0BrAAb/8n97/xC/Ff7YPrq+Qz6hvrd+jj75vvt/Bn++P6w/5sAygHrApID8gNRBKcEyQSbBHkEqwQOBVsFigX9BbkGNAfeBrcFewRtAxsCFwCv/bD7dvqs+fj4qPgQ+SH6hPv0/HD+8/9UAVACsQLEApgCBALaAE7/9f3t/B/8S/t5+v75+/lY+sH6NPvT+7b8zf3X/sf/rgCtAbQChgMKBF4EnwSqBH4ESARKBKUEHgWABesFjQYgBwAHBAa9BJ4DZgKMACj+7vt4+rT5Ivm9+AT5Afpe+9L8N/6q/wQBDAKJApACZwLuAf4Apv9E/jr9cPyh+8H6LfoS+ln6vPoR+5D7aPyK/aL+lP+GAI4BqAJ9AwMEUgSMBMQEqgRsBFUEiATsBEgFtAVGBtMG1gYCBtQExAOwAg0Bzf6b/Aj7DfpI+cT40Pia+fb6cPzk/V7/yQDoAZIC3gLKAlkCagEIAJb+Wf1z/Kj78Pp0+kf6bPqs+gf7ivtG/D79Q/5E/zcAOwFQAksDEwSEBLwE2gTZBLQEdQR1BLgEGQVrBdUFcQa9BkIGFgXlA98ChgGG/yP9OPsr+qL5M/nz+GX5m/op/JP93v4zAGoBUAKtApECJgJtAV4AE//a/eX8KPxt+7X6S/pY+rT6Hft5+xH8EP0m/hH/4f+8ANAB6wLJA0MEiATSBP8E+AS+BK4E2AQVBWIFqAUQBk0G8gUNBe4DDgP9AUIA9P3L+4n64vlm+QL5IPkZ+pL7D/1b/qL/+AAjAtEC1QJ0As0BzwCJ/zH+H/1L/IH7u/pA+kD6dvrM+j378fvw/Pn95f6q/5AArgHGAp4DCwRDBHkEqQTIBL8EwQT3BE8FkQW6BREGYwY4Bl0FLQQjAxUChwBf/lX8/fos+oj5CfkS+cz59/pH/KL9Mv/NAAQCogK2ApsCPAJVAfL/cP5H/W38qPvW+jz6Ofqn+iP7ePv2+8z84f3h/qD/WgA+AUwCOwPaA08ErwTuBOoEuQSsBN4EKQVhBYgFygUlBhsGZAVABC0DPwL6AAv/w/wF+yT6v/l7+WP57Pke+378yv37/jwAdgFWApcCUQLgAS8BHwDV/qz97vxH/G/7mvo8+nH63fpD+6j7Vvxk/Xr+Xv8oAB0BNAIwA80DHwSCBNcE/gThBL8E1QQPBVQFfAWlBd8F6QVvBV4EQQNdAkABhv9j/Zv7kfoC+pv5a/nI+cL6Efxk/bT+FgBfATMCagJLAg0ClgGbACv/3v36/F38tfv1+n/6ifr5+mr7tvsr/A39Mv4t/+H/ogCtAdQCvwM8BJME9QQsBRwF4ATTBAAFJQUtBTgFigXSBXYFYAQZAzYCYwH1/8/9wPuB+vz5vfmG+bf5o/oO/Hz9qf7T/w0BFwKCAlEC7AFmAZ8Aa/8m/kv9wPwe/Er7s/qm+v36Svtl+7P7m/zo/QD/tf91AKEB9QLVAz0EfwTOBBIF/QTKBMkEBwU5BTwFWwWyBfUFkQVyBCsDIgIgAZX/nf3f+8X6P/rg+an55vm7+uz7IP1T/o//xQCzASICTAJAAtwBEAH6/+T+7/0f/T78YvvD+pf62PoY+0n7pPt+/LH9w/6b/04ANwFhAmgDIASNBPYETAVTBSsFLQVlBWwFTgVNBYYFiwXfBLwDsQINAlQB8f/w/Rr8HPuy+k/65/nk+Z/6xPvY/Nn9AP9BAFsBBQJAAi0C4wFNAWMAaf+I/rr9ufyk+/f6wvrU+sv6yfo8+zP8bP1f/hH/9v9FAbgCrgM3BLkERAWnBZ8FbwVmBW8FVwUUBf8EKgU7BbsEoAOVAuUBLgHs/zD+m/yR++n6Tfrh+e75jvqe+7r8x/3e/iEAWgEWAlcCPgL2AWABdACB/5X+v/3a/Oz7N/vd+tz64/rr+ib72/v9/Av+6P7K//AATQJ5A1kE/wSABcIFtAWTBYMFiAVpBSUFGQVGBUQFkARgA2gC0AEOAZL/uv1M/If7EPt5+gT6O/oJ+wP86/zb/Qz/RgBCAckB+gELAtoBWgGQALH/3f70/fL89/td+xf78PrM+rH6+/rD+9T81P2q/pv/ywArAkwDIgTQBGAFygXVBboFrgWvBZcFZQVHBUAFEgVgBEoDTwKUAbIASP+f/WP8wPtC+536KfpL+hn7HPz7/Ov9D/9TAFQB5gEvAkUCLAKmAcoA2v/g/uj91vzr+1T7BPvh+r36zvor++D7wPyU/W/+Yv+VANsBBQMDBLoEUQXFBQQG/QXMBasFjAV0BUcFJQX0BFYETQMmAkgBegBa/8L9HPwX+7r6m/po+m76K/tj/JH9dv5h/4AAnAFcAnMCHwKtAT8BuADl/+D+2f3t/B/8g/sr+/z62PrC+ur6kvuF/G/9Sv45/20A2wEnAx8E5QSlBTEGWQYyBgAG9AXKBXsFKwXxBLIEDgQaAxwCRwFnAAj/Z/0P/F/7+/pz+gb6Mfoq+4H8qP2b/pn/wwDPAVcCXQInAuoBcQGOAHn/ef6s/ef8HPxv+wP7yvqe+pn60/p4+3D8bP1g/nz/6gBjAo4DZwQVBbMFBAb/BdUFqAWdBYMFWwVBBTsFDgVZBD4DGQItATcAxP4C/Y773vq2+or6X/qq+rX7Ev0y/gL/2P/4APYBTAIJAqoBdQEXAUgAMv80/nb9svzR+xL7vvrC+rD6m/rk+rX74Pz5/eD+5/86Aa8CvwNkBPkElAUGBvsFtQWUBakFnwVMBQoF+wTqBE4EDwPNAeUAEgC1/uv8fPvo+tv6o/ph+q763ftr/Y7+Uv8mADkBJAJoAg0CkAFAAcgA3f/E/uf9Vf23/On7TPsd+yD7APvO+uz6rvvN/ND9uf7W/08ByALZA4gEGwWzBfwFzgV3BUEFQwUsBQcFAQUQBeoEJQT8AuIB9wDl/0T+ePwy+8f60PrV+uz6a/uO/P39MP/0/44ATwH5ASECtQExAeIAhgDM/8j+7/1e/db8Ffwu+5v6iPqe+qT62fqx+//8Nf4e/ygAmQEZAzUEsQT1BE0FoAWpBWsFQAVWBXgFXAUnBREF3wQlBNECbAF1AJ//Uf6o/E/70Pr9+in7KPt0+4r8Df46/+T/bwBIAQQCMALWAUIB3ABbAJz/q/7C/Sb9j/zy+1n7Bfv4+tz60foJ+8X73vz5/fL+8v83AacC4wOmBBkFigXjBeUFkwVLBUUFTQU2BQoF8ASaBLoDkAKJAcUA0P9D/oT8XPss+1T7M/sD+237rfwV/hn/2f+oAJwBRwJdAvcBaQHqAE8Afv+F/pv96vxY/Mz7T/sB+9z6u/rJ+iX72vu+/Kn9sP7q/2kB3wLwA6wEPQXCBfIFswVRBRsFHAUBBccEpwTKBNkEOQT2AroB8gAoAMX+6fxq++D68/r/+uL6QvuN/DL+bf8ZAL0ApgFtAngC4wE5AcEATQB7/3v+qP0X/ZH8+/t++zH7Gfv4+t36Evu4+778yv3L/vT/SQGhAroDhQQJBVYFiAWCBVUFJAUUBSoFNQUuBRcFywQRBOwCsgGcAJT/SP67/ID7Cvsz+2b7cfu7+7z8SP6G/yEAjwBEARUCUALgATUBuABUAKL/ov6n/RT9ufwt/H77/vrg+tr6y/rm+nr7jfyw/bf+wf8MAZEC0QOWBPQERQWVBZYFRAXbBM4E5gTiBNkE4gTmBGMEPAPoAeYAHQDr/h/9gvvs+jb7eftM+137X/wQ/on/NgCTADsBGQJ0AuoBGQGSAFIA3f/p/t79L/3f/Hv82fs3++T61PrD+sz6M/sd/Ez9Zv50/7YARQKsA4wE/gRTBakFqwVQBdcEnQS7BMMEkQRrBHIEQwRhAxwCFwF9AL3/RP6a/Jf7ivvR+7f7hvsB/GP91/6s/xgAwgDVAZACXAJ3AbQAXQDw/xT/4P3//KL8W/zV+yT75PoK+x77/PoU++/7N/1d/jv/NQCyAUgDVAS6BPcEagWrBVkFrgRXBHYElgR8BGMEeQRmBLYDiwKOAfwARgDc/vn8tvuY+/j78vuL+8r7MP3v/vb/KwBnAEYBSgJyApkBnAA3AA4Ac/9R/kr94vzC/F/8n/v0+r/60frk+hP7q/u9/Pj9Cv8RAGEB2QIEBJkE1gQlBWAFPgWiBA0E/gNUBJMEeQRjBGkELARRAxICIQF+AI7/AP5t/LT75fsx/Af86Pur/DL+d//m/xYA1wD9AXwC5gHcAEQAOwDa/7/+iP3q/OX8sPz2+zH7APtT+3H7S/tV+wb8Lf0x/gf/+f9lAfcCCgScBPkEXAWABRgFcgT/AwEEIQQqBDMEQAQ4BLADrQK7ASQBgAAp/1f9+PvH+z38UfwB/CT8ZP0d/y8AcACmAJsBsAK7AqcBcQACAOz/Rv8C/uT8iPyY/GD8tvsz+0n7nvum+3D7mPtj/HD9Rv4E/x0AlwEHA/4DpQRcBe4F4gU/BYcERwRRBCsE2QPDA/8D9gMuAxcCZgEhAXwA/f5B/UT8SPxz/CP8yPs6/J/9Af+y/wQAqAC1AWoCQwKGAeAAnQA0AE3/LP5Q/ev8kvz8+177GPsp+zT7Hvsq+7T7svyq/W7+Xf+vADkCgQM7BLEEOwWOBVcFtQQ2BCgEUARdBEQEVARyBB8EMQMUAncBJQExAG/+vvwX/FX8bfz3+777ofw2/lr/of/L/8AAFAKVAugB4gB1AIkAJwD2/rf9If0P/dP8Gvxt+037mPux+2H7Mvug+4z8av0Q/vT+UwD1ATkD8QOeBFsFwgV9BckEVQRNBF4ESwQ4BH4EwQRUBEsDVgLuAYIBQABD/qr8M/xF/BH8jPuL+6P8If4D/0f/v//kABsCYQK1AfIAogB9ANj/tf7L/XD9Q/3X/C/8rvuX+6X7c/sh+yb7nvtZ/Bj97P0j/5sA7AH5AtsDwQSRBcsFawXgBJUEfwRbBDoEaQTjBP8EPgT8AgYCqwExAez/KP7L/GX8cfwx/Mr75vvi/DL+Ev9t/9j/twCbAfYBnwHqAGoAGgCd/9T+Av5t/Rf9u/xP/Ab85PvF+3v7Tvt2++z7i/wZ/cv96v5eAMgB2AK4A5oEVgWWBV4FCwXXBM0EmARFBC8EbQSRBBAEEgNBAv4BrAGEALv+Ov24/Mz8ifzt+7r7ZPyM/XX+5v5f/zoAJgGfAZwBUgHuAG0AxP8S/2H+sP30/Ez89/vU+7P7e/tV+4b7+ft8/Pf8iv1b/lz/dQCAAWICMgP7A64ECgXvBKAEgASmBM4EywTABNAE2AR7BKYD0QJBAp0BcwDY/n796fy2/EX8wPvF+4n8nv1s/ur+hf9iABMBMgHQAGAAJADa/0j/iv7s/ZD9Wv0g/eL8tvyC/Er8H/wB/Ab8Kvxs/Oz8sP2n/rv/0wDUAc4CwANwBMIEsQSKBJoEwQS4BH8EewTdBDUF2ATKA98CgAJCAk8Bgv/o/T39GP3Z/GP8OfzG/LD9Y/7K/kH/6P9lAHsAVAA1AA0AqP8E/3n+S/4f/rf9Rv0J/R79Kf3N/Eb8Dvwr/Gj8nvzW/Gv9UP5A/ygACQEGAhAD2QNMBIYEkgSgBK0EmARpBFoEhAS5BK0EJwR6A/gCewK0AXwAHv8X/nv9+PyG/Ff8kvwo/cr9Wf76/rD/NQBfAEcAIQD0/5D/C/+g/m7+Uv4W/tD9uP3J/bn9XP3W/Hz8b/xm/Ej8Vvy1/H39gP57/3QAeAF9AnADIwR1BIQEbwRcBEkELAQQBBUEaATHBKUE5gMVA60CgALZAWEAwP7J/ZD9hP0Z/Z/8s/xX/Rf+nv75/kr/lP/B/+n/KAAoAK3/Av+l/sT+z/5H/pP9SP1t/YP9FP1n/CX8Xvyh/MH89Px7/VD+I//q/74AlgFfAvkCdQPZA/sD6APZAwUEVAR5BIUEsgT3BOAEKgQ7A44CHAJXAf//n/7U/ZT9Xf38/M78Iv3Q/V3+nv7q/l3/sP+3/5H/ff96/zv/zf6X/qX+ov5a/v394/0H/vb9cP3d/Kn8vPy5/If8lfww/ST+Bf+8/4YAegF0AjIDogPgA+wD3QPdA+oD8QPXA88DLgS9BNMEKwRFA8ICngIMApgA9/4B/tX9vv06/b785fyf/UL+gP6k/vL+Tv9a/0D/Vf9j/zf/x/51/qr++v7H/jb+5v0W/jr+xf38/JH8ufz2/N78v/we/f/94f6S/zcAIAEvAuYCRgODA7sD3QPfA8wDzAPgA/EDJwR4BJ4EUgSUA+UCgwIJAhUBwf+W/gP+0v2I/Rv98fw3/bb9Kf55/sj+Ev8r/zr/Xf9v/0H/yf5v/or+1P7G/ln+Ff4+/oP+YP7L/UD9B/37/Nv8s/zn/IT9R/4S/+r/5ADYAY0CFQOCA8QDwgOiA5oDqQOuA4oDiwMABIoEjwTqAyEDxAKUAuIBkgA4/2D++v2j/Tr96/z6/GT91v06/pn+5f4T/zf/ZP+L/3L/Cf+5/rz+6v7c/mv+Cf4Q/lT+Sf7O/Uz9Iv1D/T39C/0O/Xn9KP7H/lP/EAD3ALUBRwLNAkYDhgN6A24DuAMjBDAE6QPrA34E/ASOBHEDlgJuAlECVAHC/5v+UP5s/iP+lf1K/XT9y/0P/lr+ov63/p7+qv4P/2X/Jf+U/mL+yP4x//b+Zv4q/nj+wv5v/sT9T/0+/Ur9I/30/Bz9kf0e/sD+f/9OAAIBmAE1At0CUgNfA0kDaQPAA/kD0QO6AxoEpgS0BBMEXAMaAwADWgIWAe//Vv8B/2n+nf0y/VX9k/2n/cb9Jv6h/sv+sP7I/iP/Uv8K/53+m/7o/v7+pP5A/kv+kP6N/h3+sf2o/an9d/0z/RP9Wv3O/Sv+ov5e/0MA9QBmAfMBpgI3A1MDLANJA7YD+APLA6ED9AOUBMUEJgRaAwkD8wJbAiEB4f84//D+a/69/V39e/21/a39tf0V/or+tP6W/qT+DP9e/zD/wf6v/gP/Kv/k/oj+l/7l/tr+ZP7o/cP9xf18/Qr96PxG/cn9Hf54/iH/BwDYAF0BxgE+Aq0C4wLjAgkDXgOvA88DzgMLBIEEvARuBLIDDgO7AlgCfwFMAFr/4v6X/jT+tv2C/aT9yv3Y/fD9N/6J/p/+kv6t/v3+O/8L/8P+4P41/0z/Af+w/rL+2v68/lP+7f2+/af9dP1P/Wb9qP30/VD+8f67/2kA4ABNAfQBoALyAvgCCgNpA9AD5QPHA8YDCQRcBEkEwwMhA7QCZwLqARoBKwBw/wD/q/4+/r39Zv1O/WP9mv3P/f/9Hv5B/pf+9/4u/xb/4v7h/hL/Pv8o/+3+2v7q/u3+tP5I/vj9w/2T/Wz9Xv2M/df9N/69/lj/+P94APEAiwEWAmQChQK1AhwDhQOcA3wDgAPnA2AEbAQABGwDFAPnAogCzQHFANz/R//r/oj+/v2B/Vn9jv3j/Q/+EP4V/kb+k/7K/uX+8v7z/gH/G/8r/zL/Jf8H//b+8P7l/rL+Uv4D/tz91v3L/ab9qP3u/Wv+4/5H/9X/eAAaAZMByQESAn4C0wL+AhcDVAOlA84D6AMBBBUE7QNvA+gCgwIYAmMBdQC3/1H/DP+O/vr9oP2b/bD9tv27/dn9C/4o/kv+hP7H/vj+9v72/hj/Pv9S/0P/Jf8c/yL/HP/g/ob+O/4K/vz9+P0E/in+Uv6K/tj+R//X/04ApgAIAZABIgJwApECyAIjA3cDhwNvA3sDuQPgA5YDEAO1ApMCYgLLAesAGwB//xv/x/5p/gL+qf2R/br9/f0a/u391v0c/qH+BP8B/9b+3f4i/3j/mP96/1D/OP8//zj/Av/D/oz+YP4+/iX+Hv4o/j7+b/7N/kf/xP8hAG0AzwA0AYYBvQH+AV8CwALwAvgCHgN3A8kD1QOdA2YDNQPQAjcCpwEqAagABwBc/+j+q/5Y/tz9hP2d/QT+Qf4x/iL+Q/6M/s/+2v7d/vL+Av8R/x3/QP9L/yn/C/8a/1P/YP8T/7T+hv6V/p/+af4z/jv+hf7l/in/bf/P/0oAzQA3AZEB4AEUAjkCYwKmAtsC1wLFAvUCXQOVA1ED1AKAAmsCPQKmAc4ACQCR/0n/8f6G/h7+2P3P/f79PP5G/iH+HP5k/uH+Kv8R/9T+yP4F/0//Wf8i/+b+4f4N/yn/DP/N/qP+r/7d/t/+s/6E/o7+2f4n/1v/fv+1/xYAlgAEAT0BYwGrARICegKvAqkCpgLZAioDVQMdA8gCiwJkAkoCAAJ8AfgAlQBRAAUAkv8I/5j+Uf44/iz+E/7w/ej9C/5N/pT+sf62/tf+DP9A/03/Kf8B/wD/J/9N/0P/EP/n/uL+8P7s/s/+rf60/uf+Jv9g/3n/lP/X/zsAowDnABEBPwF9AbwB2gHkAfYBFgJKAoUCnwKtAq8CpAKJAlICBQKcASQBsQA/AML/Qv/l/sb+vP6g/nL+Uv5t/pr+sf6j/pf+tP7V/uT+6P74/hv/OP9Q/23/gP94/1L/I/8Z/yD/Dv/p/r/+vv7l/gr/Lv9S/5P/5/8oAFwAigCuANAA7AAGASMBPAFiAY4BwAH7AR0CPAJgAosCmQJkAgwCrQFrAUMB9QB4APr/pf+C/2r/Mf/o/qb+if6X/qf+n/6H/nT+if7G/hL/Q/9X/2H/ff+k/63/j/9c/0b/Uf9K/xv/6v7d/vT+B/8J/w//Nf9t/5f/uP/W//j/GwBKAIUAwQD2AAYBBwEhAUgBbwGBAYIBkwHJAfsBFgIUAvYByAGUAWQBHQG1AEgA3/+F/zj/Av/h/sf+uP65/tP+Af8X/yL/L/9X/5P/rv+d/3//eP99/3n/bf9i/13/Xv9U/0f/SP9L/07/Rf83/z3/Tf9m/4D/mP++/+v/DgAuAFcAfACMAIEAdQCMAMwACQEaASYBXQG4AQcCFgLsAawBgQFWARIBxAB6ADQA7v+z/4//gP9w/1D/L/8e/zL/Tv9T/03/Uf9j/3//l/+g/5//mP+J/4X/jP+Q/4D/ZP9f/2n/aP9X/0j/Tf9T/1L/VP9p/5T/uv/H/9T/7P8LACMAKQA6AFIAaACFAKMAywDqAPwACQEgAUQBVQE6ARABAAEKAQkB5wCiAGYARQAqAP7/sf9W/x7/Hf9C/1r/Yv9v/47/yP/1/wQACwD1/9L/tv+m/53/i/9u/2v/j/+8/87/xP/E/8//3P/g/9H/y//Q/7//nv93/2r/d/+H/6X/zP/l////LABjAJQAsgDJANwA7AD7AP0A8wDhAM0AwQC6AK8AiABIABYA8v/N/6n/hv9w/2z/b/9z/3z/i/+d/5//qP+9/83/1v/e/+v/+f8LACAALgA3ADMAHgD1/9H/xv/A/7D/of+n/8n/7f8EAAIA9P/y//7/DAAIAO3/yf/B/+j/EgAqADMAOABYAIQAnwCnAJEAcQBnAHEAeQBuAEMAGQARACQAJwAFANj/t/+p/6L/kv+K/4b/jv+j/7f/0P/h/+P/2f/M/9P/4P/j/9n/zv/L/8r/0f/n/woALwA3ACkALABBAEsAMAD9/97/5v/5//f/4f/e//v/IgBIAGkAhACFAG8AWwBQAE8ARAAjABgAMQBGAFQAVABQAEkANgAqAAwA4/+4/4j/aP9e/2P/c/+J/6r/zP/m/+//+//3/+X/1//H/7b/rf+t/7D/uv/H/9f/8/8HABMAGgAXABQAAwDu//f/DwAeACMAKAAuAEQAbQCOAJ0AmgCQAI4AiQByAEcAIwASABQAJgA0ADAAJwAhACIAIQAQAPL/2P/E/7P/pf+a/4z/if+V/6H/o/+f/57/pP+e/5j/oP+2/9D/0//O/9P/4f/2//f/7f/x//v//v/4//j/9//q/+H/7/8OACIAFwAEAAcAIABBAFAATwBQAFcAYABiAF4AVQBHADwAQABTAGIAaQBiAF8AZQBtAGoATQAlAAIA5P++/57/jP+B/4X/jv+Z/6r/uP+3/7j/xP/Q/9H/xf+1/7T/wf/g/wEAEQAXAB0AIAAhAB0ABwDx/+P/2f/X/9v/7//7//n//v8QADQATQBFACkAEAAMABIAEgAOAA0AGgA1AFcAdQB8AHAAXgBPAE0ATQA4ABYA+v/5/wsADgD+/+z/5f/h/9T/uv+j/5r/jv+E/4H/gP+F/4r/kv+r/8n/4v/1/wUAFgAWAAUA+v/z//X/9P/v//H/+P8AAA4AHAAmADAAOAA3ADAAIAAMAPv//f8CAP//+P/w//j/BwAWACUALAAvADgASgBUAEwAOwAvAC0ANgA+AEIARgBEAD8AOAAsAB4AAADd/8X/sf+b/4P/df9+/5T/ov+n/7b/zP/j/+r/8v/9/wQACAAKAA0AEQAgACwAMgA+AEUARAA/ADoAMwAfAAUA8P/l/+L/4v/f/9b/0//a/+f/9//+//T/4f/W/+H/5//t//r//v8PACoAPABKAEUALgAeACEALQA3ACYACgD8//z/+v/p/9f/xv++/8D/wv/L/9H/0P/S/9X/2f/d/9j/1P/T/+H/+P8FABEAIAAtADgAQwBMAE8AVABWAEQANgAxAC8AKQAKAOn/3P/g/+3/5//M/7z/vv/S/+T/7//x/+n/6P/s//b/AwD9//v/HABJAGoAcwBkAFMARQBEADQAEQD8/+n/4f/k/9z/yv+1/67/sf+v/6H/of+r/7H/uf/I/9L/0v/N/83/5f8DABQAGwAnADkASABKAFMAXgBPADUAHgAQABMAGgAKAO//5v/1/wgAEwAPAAIA+P/2////AwD+//j/8f/2/wQADgAOAAgADgAsAEgAVABVAEwAPAArABwACwD+//P/5f/Y/8f/w//F/8X/x//C/8T/zP/K/87/xP/K/+P/6P/p//3/HAA3AEQASQBFAEAANgAqACgAJAAYAA4A/f/4/wcADgAJAAEA/P/y/9//0//R/9D/zf/O/9P/7f8QAC0APQA/ADsANQApABoAEgAMAAUADQAhADMAQQA+ADEAJwAfABAAAAD7//n/9v/s/+3/8f/x//P/8P/t//z/DAAIAAQACAABAPP/5f/j/93/2//k/+n/+P8NABQAGAAcACgAKAAUAPj/3f/X/+D/3f/L/7v/uv/N/+P/+v8NAAwABgAAAAwAIAAnACYAJQArADgAOwA0ACYAGQAPAA0AEAANABIAGgAjADQAOAAoABIAAADx/+D/0P/I/8P/y//P/9H/0f/L/87/2//r//H/7//r/+P/6P/q/+X/4P/l/+7/9/8AAAIAAQACAAQABgD9//D/7v/0//b/7v/k/93/5P/8/w4AGwAnAC8ANQA+AEMARAA5AC8AMAAyADUANwA3ADgAOAA1ACsAHgALAPv/7v/q/+b/2P/J/8T/wf/C/8n/z//S/9//6v/u//D/5//X/8P/uv++/77/wP/E/8r/1//u/wIADAARAA8ABQAGABQAHAAXAA8ADQAQACIAJwAYAAoAAAACAAoADwARABgAGQAiADIAPAA6ADgAPABFAEsARwA0ACEAGQAWABUAEwAOAAgAAwAHAAcABwACAPf/8//x/+f/3v/T/8f/wf/B/8T/w//D/8n/0//h/+j/7v/y//b/9v/t/+H/3//j/+r/8P/4/wUAEgAcAB4AHAAUAAkAAwD///j/9f/4//P/7v/r/+3/9P8CABEAFwAjACkAJAAhACAAIgAbABIADgATABQAFQAXABQADgAGAPz/9//0//H/8f/z//n//v8AAAEA/v/x/+n/7P/u/+3/5//Z/9H/1//j/+7/8/8BABEAIQAuAC0AIQAWABoAIQAgAB8AGQAVABQAFQAUAA4ABgAKAAkABgAGAAMA///7//v/AAAAAPz/9//6//j/9P/t/+j/6//z//j/9P/v/+3/5//t//v/AQADAP///P/8//r/9v/x/+//7v/w/+//5//i/+X/4//m/+7/8f/1//f/+f/1/+//9f8CAA0AFQAdACEALAA1AD0ARQBFAEMAOAApAB0AFQAQAAwACAAFAAEABwAHAP3/8//n/97/3f/i/+X/5v/l/+b/6f/2/wEABAAJABcAHgAeAB0AFgAIAP3/9v/z/+//7v/0//f/+P/7//z//f/7//r/9v/u/+r/4v/b/9n/3v/f/97/4//p//f/AQAIABIAHQAmADIAMwAuAC0AKgAnAC0AMAArACMAGgAQAAEA+f/1//D/7v/u//H/7v/u//L/9v/7//7/AQAFAAQAAgD9//j//P8BAAUACgATABwAHwAnACwAJwAXAAMA+P/x/+z/4//Z/9f/4f/p/+z/7f/s/+//7//w//L/9v/2//D/7//r/+v/9f///wEADAAXAB0AHAAWABMACgALABMAEQATABUAEgAJAP3//v8EAAYAAAD7//P/7v/w/+v/5f/o/+7/+v8GAAkAAwADAA0AGAAXAA4ACgADAP3/AQAEAAQABwAHAAAA/P8EAA8AEwAUABEADQAEAPn/7v/m/+3/+f/9//z/+//9/wAAAAD9//v/+f/5//r////9//n//v8HABIAIAApACcAHAAdABgADQACAPb/8v/0//f/9//2/wAAAAD4/+//5//p/+3/8P/z/+//6P/n/+3/9/8AAAYABQABAAwAEQAIAP//+P/7/wAADwAeABsAFAANAAMA9v/w/+X/3f/b/+P/6//p/+3/7v/q//H/+/8AAAAABQAKAAwAFQAXABQAHwAoAC0ALwAxACoAIgAhABwAEAAJAP7/6f/c/+L/8P/1//n//v/+/wQACgANAAUA/f8AAAcADAAGAP3/8f/o//H/+P/7//v/+P/9/wMABQAAAPP/7P/r//j/AgACAPz/9v/3//v/+P/z//H/6v/l/+///P8CAAIAAwAGAA0AGQAgACcALAAvADAAKwAqACUAGAAKAAAABAANABIAEwAIAP//9//s/+L/2//c/93/4P/q//L/+//9//X/9P/3//z/AAD///7//P/3//L/6P/g/+H/4f/h/+D/4P/g/+P/7v/t/+L/3v/d/+L/6//z//n///8FAAgABAAGAAsAEwAbACAAJQAlACcAJQAkACEAHAAhAB0AJAAnAB0AFAASABEACwASABUADQALAAYABgAEAAcADAAJAAgACgAKAAoADQASAAkAAgADAAAABAAFAP//9//y//L/9v/z/+v/3v/R/8b/w//K/87/0v/Y/9r/2//g/+r/7v/v//b/8//w//b//v8EAAYACwAVABUAIAApACIAGwAaACMAIgAaABkADwAJAAsABgAAAAAABgAOABIAFQASAAwABAACAAIAAAD8/wAABQANABYAGQAXABQAEwARABEAEQAKAAAA9v/w/+j/5P/d/9v/3v/h/+P/5P/n/+r/7//s/+j/7v/0//f/+/8BAAMABgAMAAkAAQD//wAABQAMABYAHQAbABwAIQAZABMAFwARABAADQAFAAAA+v8AAPf/8v/2//H/+P8AAAUABwAEAAMA///8////AgAGAAoADgAUABYADwAGAAQACgANAAgAAAD3//T/7v/m/+D/2//a/97/5P/t//D/9P/z//P/+P/3//j//P///wAAAgABAAAAAAAAAAkAEgAaABoAGwAfACIAJQAdAA4ABwAHAAgABQAJAAQA/v/9//3//f/5//j/+P///wgAEAAVAAwABAABAAQACgARABUAEgARABEABQD3//H/7P/s/+j/5f/n/+f/4//e/9f/1//a/93/5f/s//T/8//3//z/BAAIAA8AFAAQABMADAAHAAIAAwADAP//BAAGAAwAFwAeACIAHAAWABMAFAAVABMACwABAAAAAAD3//z/BgADAAQABwAIAAoADgATAAcABAAGAAAAAAD+//7/AAACAAEACAAHAAQAAwABAP7/9f/x/+z/7P/v/+r/6f/u//X/+//4//P//P8IABUAFwATABUACwAEAAMAAAAAAAAAAAD//wEAAwAAAPn/9//7/wcAEQAUABEADAANABEADAAEAPj/+f///wAABAADAAsADQAIAAMAAAD//wEAAQD0//D/7//w/+z/7P/v/+7//f8BAPz//f8AAAEA+v/y/+//9f/1//T/+v/8/wEAAQD+/wEACQAPAA0ACwANABIAEQAGAPr/8f/2/wAA/v/9/wAAAAAGAA8ADAAKAAkADgAPAA8ADwALAAgAAQAAAAAA//8DAAQABAAGAAoADAAMAAEA/v/6//f/+//4/+//6v/m/+P/5v/n/+j/7f/x/+//7v/y//z/AgADAAEA/f8DAAAAAgAMAAMAAAD+/wAACAAQABAACAAHAAUACAAEAP////8HAAUA/f////v///8GAAsACwANABQAEwAUAA4AEgASABEADwAJAA0ADgANAAsAAwADAAoAEAARAA8ACgAGAAkABQD+//b/6f/d/9r/4v/s//r//P/8//r//P8GAAQA/P/7//v/+P/4//b/7f/r/+//+/8BAAkAFgAWABYADgABAP7//P/7//f/9P/y//r/AQAJAAoABgAEAAUABgACAAIACAAMAA8ADAALAA8AEQAWABgAFgAYABUADgAHAAUAAAAAAPn/8f/2//X/8//w/+v/5v/m/+T/3f/V/9D/2P/g/+j/5//l/+r/7P/4//7/AQABAP//9//u//P/7//u//P/+/8AAAUADgAOABEAEgARAAYA+P/y//L/+f8BABAAHAAkACwALwAlACMAKgAoACYAJwAiAB4AGQAVABgAGQAfAB4AFQAOAAAA+//1//H/6f/k/+j/6//s/+r/7f/s/+v/7v/t/+3/8P/4//H/7P/p/9z/2v/g/+7/+f/9////AAAAAAAA/v/4//X/+v/5//r/AAD4//f/8//2//z/AAAQAAcABQAPAAsAEQAXABcAFgAVABMAIQAkACEAIwAcABMAEgAYABIAFgAdABMACgAMAA4ACgAGAAEA+//z//T/8P/x//f/9f/3//T/7P/w//X/9f/y//X/9f/t//P/7f/s//H/7v/v//H/9P/7////+P/2//H//f////7/AAD2//n/9/8BAAQACgAPAA4ADAAHABIABwAQABAABwAHAAgAFAAHABQAGQARAA0ACAAMAAIADAD8////BwAAABAADAANAAUAAwD5/////v/0/wUA/f8GAA4ADAAMAAkABQADAPj/9//8/+f/5v/m/9//4v/n//b/5v/2/wEA/v8AAPL////t/+z/7//w/+z/9P8AAPv/BgD//wYA//8BAAAACwACAAYADQD+/w4ABAAPABQAEwAUAAwAAwARAAwABgAEAAAAEAD//xgAGQANABsAEQAKAAQAEAAOAAYACwAOAA4ACQADAP3//v/q//b/+v/o//7/9v/8//v/+f/2//r/AAAAAP7/6f/4/+L/7v/3//b/AADz/wAA9/8BAAcADgALAAIACAAHAAQACQAQAAAABQD+//b/8P/7//X/6P/1//r/AQD9/wkADgAKAAsACgACAAAAAgD8////9v8AAAIAEAAWABEAHQARABcAEwALAAkA/P/8//r/9v/5//z//v8FAAQACwAOAAgACgD8//b/9//4//3/AwAAAAAABQAHAAkACgALAAIAAgAAAP//AgD+//3/9f/5/wkADQAOABAADAAJAAYACwALAAQABwAFAAEA/f8DAAoABAADAAoAAwADAAUAAgAIAAoACQAAAPv/8v/y//r///8BAAAAAQD6//7////8//f/8//6//H/7P/z//T/9v/7//v/+f/9//3/+P/t/+j/6v/q/+3/6v/v/+//9v/+/wEACAAFAAYA/P/9//7/+P/7/wAAAQD5/wMABwAMABUAFQAYABMAEwARAAwAAgD9//T/6v/q//P//P8AAAUAAQAFAAcAAgAEAAIAAQD+//P/8P/x//b/+//9//7/AQAIAAsADwAOAA4ACQADAP///f/5//f/9v/z//v/AAD//wQAAQAAAAIAAwAAAP3/AAAFAAIABAAHAAUABAAAAAAAAAAAAP7////+//r//f/+////AgAKAAsACwALAAcABAABAAcACwAJAAMAAAAAAAMABgABAAMA/v/8//7//f8BAAUAAgD9//n/+f/6//v//P/9/wAAAgAFAAwADwAMAAUA///2//P/8v/w/+v/6v/r/+f/5//v//j//v8BAP//+//5//X/9v/0//D/7//z//v/AwAHAAoADQAPAA4ADwAPAA8ADQAKAAsACgAKABIAFAAVABkAGQAaABsAFQAOAAYA/f/8////AAADAAYACAAHAAYABgADAP7/9f/v/+7/8v/7/wAAAQAAAAAABAAJAAsACAABAPv/9//y/+//7//y//T/+v8AAAUACQAJAAoACgAHAAQA/v/5//n//P///wAABQAKAA0ADAALAAkACwALAAwAEQAOAAoACgAFAAAAAgACAAMABAAAAP3/+f/4//r/9v/z//D/8f/y//H/8//1//b/8//1//T/9v/5//n/+//7//v/+////wEABgAIAAgABwAEAAEAAwADAAAAAAD+//v//f8AAAEAAQAD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type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ipd.display(ipd.Audio(demoaudio, rate=sr))\n",
+    "ipd.display(ipd.Audio(audio, rate=hparams['audio_sample_rate'], normalize=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#f0_gen,_=get_pitch_crepe(*vocoder.wav2spec(wav_gen),hparams,threshold=0.05)\n",
+    "%matplotlib inline\n",
+    "f0_gen,_=get_pitch_parselmouth(*svc_model.vocoder.wav2spec(wav_gen),hparams)\n",
+    "f0_tst[f0_tst==0]=np.nan#ground truth f0\n",
+    "f0_pred[f0_pred==0]=np.nan#f0 pe predicted\n",
+    "f0_gen[f0_gen==0]=np.nan#f0 generated\n",
+    "fig=plt.figure(figsize=[15,5])\n",
+    "plt.plot(np.arange(0,len(f0_tst)),f0_tst,color='black')\n",
+    "plt.plot(np.arange(0,len(f0_pred)),f0_pred,color='orange')\n",
+    "plt.plot(np.arange(0,len(f0_gen)),f0_gen,color='red')\n",
+    "plt.axhline(librosa.note_to_hz('C4'),ls=\":\",c=\"blue\")\n",
+    "plt.axhline(librosa.note_to_hz('G4'),ls=\":\",c=\"green\")\n",
+    "plt.axhline(librosa.note_to_hz('C5'),ls=\":\",c=\"orange\")\n",
+    "plt.axhline(librosa.note_to_hz('F#5'),ls=\":\",c=\"red\")\n",
+    "#plt.axhline(librosa.note_to_hz('A#5'),ls=\":\",c=\"black\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 ('diffsvc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "5cf89e54348a1bdadbb0ca2d227dcc30cc7e2d47cc75a8605923523671b5b7c7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/models/genshin/__init__.py b/models/genshin/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/genshin/config.yaml b/models/genshin/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0fbb2e59828810a8fbc7f1c1119702cef1973931
--- /dev/null
+++ b/models/genshin/config.yaml
@@ -0,0 +1,445 @@
+K_step: 1000
+accumulate_grad_batches: 1
+audio_num_mel_bins: 128
+audio_sample_rate: 44100
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_hubert: true
+  with_spk_embed: false
+  with_wav: false
+binarizer_cls: preprocessing.SVCpre.SVCBinarizer
+binary_data_dir: data/binary/raiden
+check_val_every_n_epoch: 10
+choose_test_manually: false
+clip_grad_norm: 1
+config_path: training/config_nsf.yaml
+content_cond_steps: []
+cwt_add_f0_loss: false
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_std_scale: 0.8
+datasets:
+- opencpop
+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+decay_steps: 50000
+decoder_type: fft
+dict_dir: ''
+diff_decoder_type: wavenet
+diff_loss_type: l2
+dilation_cycle_length: 4
+dropout: 0.1
+ds_workers: 4
+dur_enc_hidden_stride_kernel:
+- 0,2,3
+- 0,2,3
+- 0,1,3
+dur_loss: mse
+dur_predictor_kernel: 3
+dur_predictor_layers: 5
+enc_ffn_kernel_size: 9
+enc_layers: 4
+encoder_K: 8
+encoder_type: fft
+endless_ds: false
+f0_bin: 256
+f0_max: 1100.0
+f0_min: 40.0
+ffn_act: gelu
+ffn_padding: SAME
+fft_size: 2048
+fmax: 16000
+fmin: 40
+fs2_ckpt: ''
+gaussian_start: true
+gen_dir_name: ''
+gen_tgt_spk_id: -1
+hidden_size: 256
+hop_size: 512
+hubert_gpu: true
+hubert_path: checkpoints/hubert/hubert_soft.pt
+infer: false
+keep_bins: 128
+lambda_commit: 0.25
+lambda_energy: 0.0
+lambda_f0: 1.0
+lambda_ph_dur: 0.3
+lambda_sent_dur: 1.0
+lambda_uv: 1.0
+lambda_word_dur: 1.0
+load_ckpt: ''
+log_interval: 100
+loud_norm: false
+lr: 0.0012
+max_beta: 0.02
+max_epochs: 3000
+max_eval_sentences: 1
+max_eval_tokens: 60000
+max_frames: 42000
+max_input_tokens: 60000
+max_sentences: 48
+max_tokens: 128000
+max_updates: 260000
+mel_loss: ssim:0.5|l1:0.5
+mel_vmax: 1.5
+mel_vmin: -6.0
+min_level_db: -120
+no_fs2: true
+norm_type: gn
+num_ckpt_keep: 10
+num_heads: 2
+num_sanity_val_steps: 1
+num_spk: 1
+num_test_samples: 0
+num_valid_plots: 10
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+out_wav_norm: false
+pe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
+pe_enable: false
+perform_enhance: true
+pitch_ar: false
+pitch_enc_hidden_stride_kernel:
+- 0,2,5
+- 0,2,5
+- 0,2,5
+pitch_extractor: parselmouth
+pitch_loss: l2
+pitch_norm: log
+pitch_type: frame
+pndm_speedup: 10
+pre_align_args:
+  allow_no_txt: false
+  denoise: false
+  forced_align: mfa
+  txt_processor: zh_g2pM
+  use_sox: true
+  use_tone: false
+pre_align_cls: data_gen.singing.pre_align.SingingPreAlign
+predictor_dropout: 0.5
+predictor_grad: 0.1
+predictor_hidden: -1
+predictor_kernel: 5
+predictor_layers: 5
+prenet_dropout: 0.5
+prenet_hidden_size: 256
+pretrain_fs_ckpt: ''
+processed_data_dir: xxx
+profile_infer: false
+raw_data_dir: data/raw/raiden
+ref_norm_layer: bn
+rel_pos: true
+reset_phone_dict: true
+residual_channels: 384
+residual_layers: 20
+save_best: false
+save_ckpt: true
+save_codes:
+- configs
+- modules
+- src
+- utils
+save_f0: true
+save_gt: false
+schedule_type: linear
+seed: 1234
+sort_by_len: true
+speaker_id: raiden
+spec_max:
+- -0.4759584963321686
+- -0.04242899641394615
+- 0.2820039689540863
+- 0.6635098457336426
+- 0.7846556901931763
+- 0.9242268800735474
+- 1.0596446990966797
+- 0.9890199303627014
+- 0.8979427218437195
+- 0.8635445237159729
+- 0.8591453433036804
+- 0.6987467408180237
+- 0.717823326587677
+- 0.7350517511367798
+- 0.6464754939079285
+- 0.6164345145225525
+- 0.4986744523048401
+- 0.3543139100074768
+- 0.2876613438129425
+- 0.3467520773410797
+- 0.27083638310432434
+- 0.4002445936203003
+- 0.4222544729709625
+- 0.4776916205883026
+- 0.5299767255783081
+- 0.6194124817848206
+- 0.5802494287490845
+- 0.6222044229507446
+- 0.6124054193496704
+- 0.6688933968544006
+- 0.7368689179420471
+- 0.7275264859199524
+- 0.7448640465736389
+- 0.5364857912063599
+- 0.5867365002632141
+- 0.48127084970474243
+- 0.48270556330680847
+- 0.45863038301467896
+- 0.3647041916847229
+- 0.3207940459251404
+- 0.3352258801460266
+- 0.2846287190914154
+- 0.2674693465232849
+- 0.3205840587615967
+- 0.36074087023735046
+- 0.40593528747558594
+- 0.266417920589447
+- 0.22159256041049957
+- 0.19403372704982758
+- 0.29326388239860535
+- 0.29472747445106506
+- 0.3801038861274719
+- 0.3864395320415497
+- 0.285959392786026
+- 0.22213149070739746
+- 0.19549456238746643
+- 0.22238962352275848
+- 0.15776650607585907
+- 0.23960433900356293
+- 0.3050341308116913
+- 0.258531779050827
+- 0.19573383033275604
+- 0.2259710431098938
+- 0.2110864669084549
+- 0.24603094160556793
+- 0.05981471762061119
+- 0.17803697288036346
+- 0.17807669937610626
+- 0.18952223658561707
+- 0.053435735404491425
+- 0.1157914251089096
+- 0.026514273136854172
+- 0.16326436400413513
+- 0.22839383780956268
+- 0.08631942421197891
+- 0.23315998911857605
+- 0.162082701921463
+- 0.1533375382423401
+- 0.12668564915657043
+- 0.08644244074821472
+- 0.02113831788301468
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+- 0.22695019841194153
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+spec_min:
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+spk_cond_steps: []
+stop_token_weight: 5.0
+task_cls: training.task.SVC_task.SVCTask
+test_ids: []
+test_input_dir: ''
+test_num: 0
+test_prefixes:
+- test
+test_set_name: test
+timesteps: 1000
+train_set_name: train
+use_crepe: true
+use_denoise: false
+use_energy_embed: false
+use_gt_dur: false
+use_gt_f0: false
+use_midi: false
+use_nsf: true
+use_pitch_embed: true
+use_pos_embed: true
+use_spk_embed: false
+use_spk_id: false
+use_split_spk_id: false
+use_uv: false
+use_var_enc: false
+use_vec: false
+val_check_interval: 2000
+valid_num: 0
+valid_set_name: valid
+vocoder: network.vocoders.nsf_hifigan.NsfHifiGAN
+vocoder_ckpt: checkpoints/nsf_hifigan/model
+warmup_updates: 2000
+wav2spec_eps: 1e-6
+weight_decay: 0
+win_size: 2048
+work_dir: checkpoints/raiden
diff --git a/models/genshin/raiden.ckpt b/models/genshin/raiden.ckpt
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--- /dev/null
+++ b/models/genshin/raiden.ckpt
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+version https://git-lfs.github.com/spec/v1
+oid sha256:1da7f6c45f8651131386c946f1bd90010b1fd568792d8d70f7025ea3b9a9dda2
+size 134965595
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diff --git a/modules/commons/__pycache__/ssim.cpython-38.pyc b/modules/commons/__pycache__/ssim.cpython-38.pyc
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diff --git a/modules/commons/common_layers.py b/modules/commons/common_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..192997cee5b265525b244e1f421b41dab9874b99
--- /dev/null
+++ b/modules/commons/common_layers.py
@@ -0,0 +1,671 @@
+import math
+import torch
+from torch import nn
+from torch.nn import Parameter
+import torch.onnx.operators
+import torch.nn.functional as F
+import utils
+
+
+class Reshape(nn.Module):
+    def __init__(self, *args):
+        super(Reshape, self).__init__()
+        self.shape = args
+
+    def forward(self, x):
+        return x.view(self.shape)
+
+
+class Permute(nn.Module):
+    def __init__(self, *args):
+        super(Permute, self).__init__()
+        self.args = args
+
+    def forward(self, x):
+        return x.permute(self.args)
+
+
+class LinearNorm(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
+        super(LinearNorm, self).__init__()
+        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, x):
+        return self.linear_layer(x)
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
+                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
+        super(ConvNorm, self).__init__()
+        if padding is None:
+            assert (kernel_size % 2 == 1)
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(in_channels, out_channels,
+                                    kernel_size=kernel_size, stride=stride,
+                                    padding=padding, dilation=dilation,
+                                    bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, signal):
+        conv_signal = self.conv(signal)
+        return conv_signal
+
+
+def Embedding(num_embeddings, embedding_dim, padding_idx=None):
+    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
+    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
+    if padding_idx is not None:
+        nn.init.constant_(m.weight[padding_idx], 0)
+    return m
+
+
+def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
+    if not export and torch.cuda.is_available():
+        try:
+            from apex.normalization import FusedLayerNorm
+            return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
+        except ImportError:
+            pass
+    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
+
+
+def Linear(in_features, out_features, bias=True):
+    m = nn.Linear(in_features, out_features, bias)
+    nn.init.xavier_uniform_(m.weight)
+    if bias:
+        nn.init.constant_(m.bias, 0.)
+    return m
+
+
+class SinusoidalPositionalEmbedding(nn.Module):
+    """This module produces sinusoidal positional embeddings of any length.
+
+    Padding symbols are ignored.
+    """
+
+    def __init__(self, embedding_dim, padding_idx, init_size=1024):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.weights = SinusoidalPositionalEmbedding.get_embedding(
+            init_size,
+            embedding_dim,
+            padding_idx,
+        )
+        self.register_buffer('_float_tensor', torch.FloatTensor(1))
+
+    @staticmethod
+    def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
+        """Build sinusoidal embeddings.
+
+        This matches the implementation in tensor2tensor, but differs slightly
+        from the description in Section 3.5 of "Attention Is All You Need".
+        """
+        half_dim = embedding_dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
+        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+        if embedding_dim % 2 == 1:
+            # zero pad
+            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+        if padding_idx is not None:
+            emb[padding_idx, :] = 0
+        return emb
+
+    def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs):
+        """Input is expected to be of size [bsz x seqlen]."""
+        bsz, seq_len = input.shape[:2]
+        max_pos = self.padding_idx + 1 + seq_len
+        if self.weights is None or max_pos > self.weights.size(0):
+            # recompute/expand embeddings if needed
+            self.weights = SinusoidalPositionalEmbedding.get_embedding(
+                max_pos,
+                self.embedding_dim,
+                self.padding_idx,
+            )
+        self.weights = self.weights.to(self._float_tensor)
+
+        if incremental_state is not None:
+            # positions is the same for every token when decoding a single step
+            pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
+            return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
+
+        positions = utils.make_positions(input, self.padding_idx) if positions is None else positions
+        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
+
+    def max_positions(self):
+        """Maximum number of supported positions."""
+        return int(1e5)  # an arbitrary large number
+
+
+class ConvTBC(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, padding=0):
+        super(ConvTBC, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.padding = padding
+
+        self.weight = torch.nn.Parameter(torch.Tensor(
+            self.kernel_size, in_channels, out_channels))
+        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
+
+    def forward(self, input):
+        return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding)
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
+                 add_bias_kv=False, add_zero_attn=False, self_attention=False,
+                 encoder_decoder_attention=False):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim ** -0.5
+
+        self.self_attention = self_attention
+        self.encoder_decoder_attention = encoder_decoder_attention
+
+        assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \
+                                                             'value to be of the same size'
+
+        if self.qkv_same_dim:
+            self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
+        else:
+            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
+            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
+            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
+
+        if bias:
+            self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
+        else:
+            self.register_parameter('in_proj_bias', None)
+
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
+            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self.reset_parameters()
+
+        self.enable_torch_version = False
+        if hasattr(F, "multi_head_attention_forward"):
+            self.enable_torch_version = True
+        else:
+            self.enable_torch_version = False
+        self.last_attn_probs = None
+
+    def reset_parameters(self):
+        if self.qkv_same_dim:
+            nn.init.xavier_uniform_(self.in_proj_weight)
+        else:
+            nn.init.xavier_uniform_(self.k_proj_weight)
+            nn.init.xavier_uniform_(self.v_proj_weight)
+            nn.init.xavier_uniform_(self.q_proj_weight)
+
+        nn.init.xavier_uniform_(self.out_proj.weight)
+        if self.in_proj_bias is not None:
+            nn.init.constant_(self.in_proj_bias, 0.)
+            nn.init.constant_(self.out_proj.bias, 0.)
+        if self.bias_k is not None:
+            nn.init.xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            nn.init.xavier_normal_(self.bias_v)
+
+    def forward(
+            self,
+            query, key, value,
+            key_padding_mask=None,
+            incremental_state=None,
+            need_weights=True,
+            static_kv=False,
+            attn_mask=None,
+            before_softmax=False,
+            need_head_weights=False,
+            enc_dec_attn_constraint_mask=None,
+            reset_attn_weight=None
+    ):
+        """Input shape: Time x Batch x Channel
+
+        Args:
+            key_padding_mask (ByteTensor, optional): mask to exclude
+                keys that are pads, of shape `(batch, src_len)`, where
+                padding elements are indicated by 1s.
+            need_weights (bool, optional): return the attention weights,
+                averaged over heads (default: False).
+            attn_mask (ByteTensor, optional): typically used to
+                implement causal attention, where the mask prevents the
+                attention from looking forward in time (default: None).
+            before_softmax (bool, optional): return the raw attention
+                weights and values before the attention softmax.
+            need_head_weights (bool, optional): return the attention
+                weights for each head. Implies *need_weights*. Default:
+                return the average attention weights over all heads.
+        """
+        if need_head_weights:
+            need_weights = True
+
+        tgt_len, bsz, embed_dim = query.size()
+        assert embed_dim == self.embed_dim
+        assert list(query.size()) == [tgt_len, bsz, embed_dim]
+
+        if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None:
+            if self.qkv_same_dim:
+                return F.multi_head_attention_forward(query, key, value,
+                                                      self.embed_dim, self.num_heads,
+                                                      self.in_proj_weight,
+                                                      self.in_proj_bias, self.bias_k, self.bias_v,
+                                                      self.add_zero_attn, self.dropout,
+                                                      self.out_proj.weight, self.out_proj.bias,
+                                                      self.training, key_padding_mask, need_weights,
+                                                      attn_mask)
+            else:
+                return F.multi_head_attention_forward(query, key, value,
+                                                      self.embed_dim, self.num_heads,
+                                                      torch.empty([0]),
+                                                      self.in_proj_bias, self.bias_k, self.bias_v,
+                                                      self.add_zero_attn, self.dropout,
+                                                      self.out_proj.weight, self.out_proj.bias,
+                                                      self.training, key_padding_mask, need_weights,
+                                                      attn_mask, use_separate_proj_weight=True,
+                                                      q_proj_weight=self.q_proj_weight,
+                                                      k_proj_weight=self.k_proj_weight,
+                                                      v_proj_weight=self.v_proj_weight)
+
+        if incremental_state is not None:
+            print('Not implemented error.')
+            exit()
+        else:
+            saved_state = None
+
+        if self.self_attention:
+            # self-attention
+            q, k, v = self.in_proj_qkv(query)
+        elif self.encoder_decoder_attention:
+            # encoder-decoder attention
+            q = self.in_proj_q(query)
+            if key is None:
+                assert value is None
+                k = v = None
+            else:
+                k = self.in_proj_k(key)
+                v = self.in_proj_v(key)
+
+        else:
+            q = self.in_proj_q(query)
+            k = self.in_proj_k(key)
+            v = self.in_proj_v(value)
+        q *= self.scaling
+
+        if self.bias_k is not None:
+            assert self.bias_v is not None
+            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
+            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
+
+        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+        if k is not None:
+            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+        if v is not None:
+            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+
+        if saved_state is not None:
+            print('Not implemented error.')
+            exit()
+
+        src_len = k.size(1)
+
+        # This is part of a workaround to get around fork/join parallelism
+        # not supporting Optional types.
+        if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]):
+            key_padding_mask = None
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            src_len += 1
+            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
+            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
+
+        attn_weights = torch.bmm(q, k.transpose(1, 2))
+        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
+
+        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            if len(attn_mask.shape) == 2:
+                attn_mask = attn_mask.unsqueeze(0)
+            elif len(attn_mask.shape) == 3:
+                attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape(
+                    bsz * self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights + attn_mask
+
+        if enc_dec_attn_constraint_mask is not None:  # bs x head x L_kv
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.masked_fill(
+                enc_dec_attn_constraint_mask.unsqueeze(2).bool(),
+                -1e9,
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if key_padding_mask is not None:
+            # don't attend to padding symbols
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2),
+                -1e9,
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+
+        if before_softmax:
+            return attn_weights, v
+
+        attn_weights_float = utils.softmax(attn_weights, dim=-1)
+        attn_weights = attn_weights_float.type_as(attn_weights)
+        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
+
+        if reset_attn_weight is not None:
+            if reset_attn_weight:
+                self.last_attn_probs = attn_probs.detach()
+            else:
+                assert self.last_attn_probs is not None
+                attn_probs = self.last_attn_probs
+        attn = torch.bmm(attn_probs, v)
+        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+        attn = self.out_proj(attn)
+
+        if need_weights:
+            attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
+            if not need_head_weights:
+                # average attention weights over heads
+                attn_weights = attn_weights.mean(dim=0)
+        else:
+            attn_weights = None
+
+        return attn, (attn_weights, attn_logits)
+
+    def in_proj_qkv(self, query):
+        return self._in_proj(query).chunk(3, dim=-1)
+
+    def in_proj_q(self, query):
+        if self.qkv_same_dim:
+            return self._in_proj(query, end=self.embed_dim)
+        else:
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[:self.embed_dim]
+            return F.linear(query, self.q_proj_weight, bias)
+
+    def in_proj_k(self, key):
+        if self.qkv_same_dim:
+            return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
+        else:
+            weight = self.k_proj_weight
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[self.embed_dim:2 * self.embed_dim]
+            return F.linear(key, weight, bias)
+
+    def in_proj_v(self, value):
+        if self.qkv_same_dim:
+            return self._in_proj(value, start=2 * self.embed_dim)
+        else:
+            weight = self.v_proj_weight
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[2 * self.embed_dim:]
+            return F.linear(value, weight, bias)
+
+    def _in_proj(self, input, start=0, end=None):
+        weight = self.in_proj_weight
+        bias = self.in_proj_bias
+        weight = weight[start:end, :]
+        if bias is not None:
+            bias = bias[start:end]
+        return F.linear(input, weight, bias)
+
+
+    def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
+        return attn_weights
+
+
+class Swish(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, i):
+        result = i * torch.sigmoid(i)
+        ctx.save_for_backward(i)
+        return result
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        i = ctx.saved_variables[0]
+        sigmoid_i = torch.sigmoid(i)
+        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
+
+
+class CustomSwish(nn.Module):
+    def forward(self, input_tensor):
+        return Swish.apply(input_tensor)
+        
+class Mish(nn.Module):
+    def forward(self, x):
+        return x * torch.tanh(F.softplus(x))
+
+class TransformerFFNLayer(nn.Module):
+    def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.dropout = dropout
+        self.act = act
+        if padding == 'SAME':
+            self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2)
+        elif padding == 'LEFT':
+            self.ffn_1 = nn.Sequential(
+                nn.ConstantPad1d((kernel_size - 1, 0), 0.0),
+                nn.Conv1d(hidden_size, filter_size, kernel_size)
+            )
+        self.ffn_2 = Linear(filter_size, hidden_size)
+        if self.act == 'swish':
+            self.swish_fn = CustomSwish()
+
+    def forward(self, x, incremental_state=None):
+        # x: T x B x C
+        if incremental_state is not None:
+            assert incremental_state is None, 'Nar-generation does not allow this.'
+            exit(1)
+
+        x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1)
+        x = x * self.kernel_size ** -0.5
+
+        if incremental_state is not None:
+            x = x[-1:]
+        if self.act == 'gelu':
+            x = F.gelu(x)
+        if self.act == 'relu':
+            x = F.relu(x)
+        if self.act == 'swish':
+            x = self.swish_fn(x)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = self.ffn_2(x)
+        return x
+
+
+class BatchNorm1dTBC(nn.Module):
+    def __init__(self, c):
+        super(BatchNorm1dTBC, self).__init__()
+        self.bn = nn.BatchNorm1d(c)
+
+    def forward(self, x):
+        """
+
+        :param x: [T, B, C]
+        :return: [T, B, C]
+        """
+        x = x.permute(1, 2, 0)  # [B, C, T]
+        x = self.bn(x)  # [B, C, T]
+        x = x.permute(2, 0, 1)  # [T, B, C]
+        return x
+
+
+class EncSALayer(nn.Module):
+    def __init__(self, c, num_heads, dropout, attention_dropout=0.1,
+                 relu_dropout=0.1, kernel_size=9, padding='SAME', norm='ln', act='gelu'):
+        super().__init__()
+        self.c = c
+        self.dropout = dropout
+        self.num_heads = num_heads
+        if num_heads > 0:
+            if norm == 'ln':
+                self.layer_norm1 = LayerNorm(c)
+            elif norm == 'bn':
+                self.layer_norm1 = BatchNorm1dTBC(c)
+            self.self_attn = MultiheadAttention(
+                self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False,
+            )
+        if norm == 'ln':
+            self.layer_norm2 = LayerNorm(c)
+        elif norm == 'bn':
+            self.layer_norm2 = BatchNorm1dTBC(c)
+        self.ffn = TransformerFFNLayer(
+            c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act)
+
+    def forward(self, x, encoder_padding_mask=None, **kwargs):
+        layer_norm_training = kwargs.get('layer_norm_training', None)
+        if layer_norm_training is not None:
+            self.layer_norm1.training = layer_norm_training
+            self.layer_norm2.training = layer_norm_training
+        if self.num_heads > 0:
+            residual = x
+            x = self.layer_norm1(x)
+            x, _, = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=encoder_padding_mask
+            )
+            x = F.dropout(x, self.dropout, training=self.training)
+            x = residual + x
+            x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
+
+        residual = x
+        x = self.layer_norm2(x)
+        x = self.ffn(x)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+        x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
+        return x
+
+
+class DecSALayer(nn.Module):
+    def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'):
+        super().__init__()
+        self.c = c
+        self.dropout = dropout
+        self.layer_norm1 = LayerNorm(c)
+        self.self_attn = MultiheadAttention(
+            c, num_heads, self_attention=True, dropout=attention_dropout, bias=False
+        )
+        self.layer_norm2 = LayerNorm(c)
+        self.encoder_attn = MultiheadAttention(
+            c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False,
+        )
+        self.layer_norm3 = LayerNorm(c)
+        self.ffn = TransformerFFNLayer(
+            c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act)
+
+    def forward(
+            self,
+            x,
+            encoder_out=None,
+            encoder_padding_mask=None,
+            incremental_state=None,
+            self_attn_mask=None,
+            self_attn_padding_mask=None,
+            attn_out=None,
+            reset_attn_weight=None,
+            **kwargs,
+    ):
+        layer_norm_training = kwargs.get('layer_norm_training', None)
+        if layer_norm_training is not None:
+            self.layer_norm1.training = layer_norm_training
+            self.layer_norm2.training = layer_norm_training
+            self.layer_norm3.training = layer_norm_training
+        residual = x
+        x = self.layer_norm1(x)
+        x, _ = self.self_attn(
+            query=x,
+            key=x,
+            value=x,
+            key_padding_mask=self_attn_padding_mask,
+            incremental_state=incremental_state,
+            attn_mask=self_attn_mask
+        )
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+
+        residual = x
+        x = self.layer_norm2(x)
+        if encoder_out is not None:
+            x, attn = self.encoder_attn(
+                query=x,
+                key=encoder_out,
+                value=encoder_out,
+                key_padding_mask=encoder_padding_mask,
+                incremental_state=incremental_state,
+                static_kv=True,
+                enc_dec_attn_constraint_mask=None, #utils.get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'),
+                reset_attn_weight=reset_attn_weight
+            )
+            attn_logits = attn[1]
+        else:
+            assert attn_out is not None
+            x = self.encoder_attn.in_proj_v(attn_out.transpose(0, 1))
+            attn_logits = None
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+
+        residual = x
+        x = self.layer_norm3(x)
+        x = self.ffn(x, incremental_state=incremental_state)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+        # if len(attn_logits.size()) > 3:
+        #    indices = attn_logits.softmax(-1).max(-1).values.sum(-1).argmax(-1)
+        #    attn_logits = attn_logits.gather(1,
+        #        indices[:, None, None, None].repeat(1, 1, attn_logits.size(-2), attn_logits.size(-1))).squeeze(1)
+        return x, attn_logits
diff --git a/modules/commons/espnet_positional_embedding.py b/modules/commons/espnet_positional_embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..74decb6ab300951490ae08a4b93041a0542b5bb7
--- /dev/null
+++ b/modules/commons/espnet_positional_embedding.py
@@ -0,0 +1,113 @@
+import math
+import torch
+
+
+class PositionalEncoding(torch.nn.Module):
+    """Positional encoding.
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+        reverse (bool): Whether to reverse the input position.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
+        """Construct an PositionalEncoding object."""
+        super(PositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.reverse = reverse
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.d_model)
+        if self.reverse:
+            position = torch.arange(
+                x.size(1) - 1, -1, -1.0, dtype=torch.float32
+            ).unsqueeze(1)
+        else:
+            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.d_model, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype)
+
+    def forward(self, x: torch.Tensor):
+        """Add positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x * self.xscale + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class ScaledPositionalEncoding(PositionalEncoding):
+    """Scaled positional encoding module.
+    See Sec. 3.2  https://arxiv.org/abs/1809.08895
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Initialize class."""
+        super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
+        self.alpha = torch.nn.Parameter(torch.tensor(1.0))
+
+    def reset_parameters(self):
+        """Reset parameters."""
+        self.alpha.data = torch.tensor(1.0)
+
+    def forward(self, x):
+        """Add positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x + self.alpha * self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class RelPositionalEncoding(PositionalEncoding):
+    """Relative positional encoding module.
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Initialize class."""
+        super().__init__(d_model, dropout_rate, max_len, reverse=True)
+
+    def forward(self, x):
+        """Compute positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+            torch.Tensor: Positional embedding tensor (1, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x * self.xscale
+        pos_emb = self.pe[:, : x.size(1)]
+        return self.dropout(x) + self.dropout(pos_emb)
\ No newline at end of file
diff --git a/modules/commons/ssim.py b/modules/commons/ssim.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d0241f267ef58b24979e022b05f2a9adf768826
--- /dev/null
+++ b/modules/commons/ssim.py
@@ -0,0 +1,391 @@
+# '''
+# https://github.com/One-sixth/ms_ssim_pytorch/blob/master/ssim.py
+# '''
+#
+# import torch
+# import torch.jit
+# import torch.nn.functional as F
+#
+#
+# @torch.jit.script
+# def create_window(window_size: int, sigma: float, channel: int):
+#     '''
+#     Create 1-D gauss kernel
+#     :param window_size: the size of gauss kernel
+#     :param sigma: sigma of normal distribution
+#     :param channel: input channel
+#     :return: 1D kernel
+#     '''
+#     coords = torch.arange(window_size, dtype=torch.float)
+#     coords -= window_size // 2
+#
+#     g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))
+#     g /= g.sum()
+#
+#     g = g.reshape(1, 1, 1, -1).repeat(channel, 1, 1, 1)
+#     return g
+#
+#
+# @torch.jit.script
+# def _gaussian_filter(x, window_1d, use_padding: bool):
+#     '''
+#     Blur input with 1-D kernel
+#     :param x: batch of tensors to be blured
+#     :param window_1d: 1-D gauss kernel
+#     :param use_padding: padding image before conv
+#     :return: blured tensors
+#     '''
+#     C = x.shape[1]
+#     padding = 0
+#     if use_padding:
+#         window_size = window_1d.shape[3]
+#         padding = window_size // 2
+#     out = F.conv2d(x, window_1d, stride=1, padding=(0, padding), groups=C)
+#     out = F.conv2d(out, window_1d.transpose(2, 3), stride=1, padding=(padding, 0), groups=C)
+#     return out
+#
+#
+# @torch.jit.script
+# def ssim(X, Y, window, data_range: float, use_padding: bool = False):
+#     '''
+#     Calculate ssim index for X and Y
+#     :param X: images [B, C, H, N_bins]
+#     :param Y: images [B, C, H, N_bins]
+#     :param window: 1-D gauss kernel
+#     :param data_range: value range of input images. (usually 1.0 or 255)
+#     :param use_padding: padding image before conv
+#     :return:
+#     '''
+#
+#     K1 = 0.01
+#     K2 = 0.03
+#     compensation = 1.0
+#
+#     C1 = (K1 * data_range) ** 2
+#     C2 = (K2 * data_range) ** 2
+#
+#     mu1 = _gaussian_filter(X, window, use_padding)
+#     mu2 = _gaussian_filter(Y, window, use_padding)
+#     sigma1_sq = _gaussian_filter(X * X, window, use_padding)
+#     sigma2_sq = _gaussian_filter(Y * Y, window, use_padding)
+#     sigma12 = _gaussian_filter(X * Y, window, use_padding)
+#
+#     mu1_sq = mu1.pow(2)
+#     mu2_sq = mu2.pow(2)
+#     mu1_mu2 = mu1 * mu2
+#
+#     sigma1_sq = compensation * (sigma1_sq - mu1_sq)
+#     sigma2_sq = compensation * (sigma2_sq - mu2_sq)
+#     sigma12 = compensation * (sigma12 - mu1_mu2)
+#
+#     cs_map = (2 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2)
+#     # Fixed the issue that the negative value of cs_map caused ms_ssim to output Nan.
+#     cs_map = cs_map.clamp_min(0.)
+#     ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * cs_map
+#
+#     ssim_val = ssim_map.mean(dim=(1, 2, 3))  # reduce along CHW
+#     cs = cs_map.mean(dim=(1, 2, 3))
+#
+#     return ssim_val, cs
+#
+#
+# @torch.jit.script
+# def ms_ssim(X, Y, window, data_range: float, weights, use_padding: bool = False, eps: float = 1e-8):
+#     '''
+#     interface of ms-ssim
+#     :param X: a batch of images, (N,C,H,W)
+#     :param Y: a batch of images, (N,C,H,W)
+#     :param window: 1-D gauss kernel
+#     :param data_range: value range of input images. (usually 1.0 or 255)
+#     :param weights: weights for different levels
+#     :param use_padding: padding image before conv
+#     :param eps: use for avoid grad nan.
+#     :return:
+#     '''
+#     levels = weights.shape[0]
+#     cs_vals = []
+#     ssim_vals = []
+#     for _ in range(levels):
+#         ssim_val, cs = ssim(X, Y, window=window, data_range=data_range, use_padding=use_padding)
+#         # Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf.
+#         ssim_val = ssim_val.clamp_min(eps)
+#         cs = cs.clamp_min(eps)
+#         cs_vals.append(cs)
+#
+#         ssim_vals.append(ssim_val)
+#         padding = (X.shape[2] % 2, X.shape[3] % 2)
+#         X = F.avg_pool2d(X, kernel_size=2, stride=2, padding=padding)
+#         Y = F.avg_pool2d(Y, kernel_size=2, stride=2, padding=padding)
+#
+#     cs_vals = torch.stack(cs_vals, dim=0)
+#     ms_ssim_val = torch.prod((cs_vals[:-1] ** weights[:-1].unsqueeze(1)) * (ssim_vals[-1] ** weights[-1]), dim=0)
+#     return ms_ssim_val
+#
+#
+# class SSIM(torch.jit.ScriptModule):
+#     __constants__ = ['data_range', 'use_padding']
+#
+#     def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False):
+#         '''
+#         :param window_size: the size of gauss kernel
+#         :param window_sigma: sigma of normal distribution
+#         :param data_range: value range of input images. (usually 1.0 or 255)
+#         :param channel: input channels (default: 3)
+#         :param use_padding: padding image before conv
+#         '''
+#         super().__init__()
+#         assert window_size % 2 == 1, 'Window size must be odd.'
+#         window = create_window(window_size, window_sigma, channel)
+#         self.register_buffer('window', window)
+#         self.data_range = data_range
+#         self.use_padding = use_padding
+#
+#     @torch.jit.script_method
+#     def forward(self, X, Y):
+#         r = ssim(X, Y, window=self.window, data_range=self.data_range, use_padding=self.use_padding)
+#         return r[0]
+#
+#
+# class MS_SSIM(torch.jit.ScriptModule):
+#     __constants__ = ['data_range', 'use_padding', 'eps']
+#
+#     def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False, weights=None,
+#                  levels=None, eps=1e-8):
+#         '''
+#         class for ms-ssim
+#         :param window_size: the size of gauss kernel
+#         :param window_sigma: sigma of normal distribution
+#         :param data_range: value range of input images. (usually 1.0 or 255)
+#         :param channel: input channels
+#         :param use_padding: padding image before conv
+#         :param weights: weights for different levels. (default [0.0448, 0.2856, 0.3001, 0.2363, 0.1333])
+#         :param levels: number of downsampling
+#         :param eps: Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf.
+#         '''
+#         super().__init__()
+#         assert window_size % 2 == 1, 'Window size must be odd.'
+#         self.data_range = data_range
+#         self.use_padding = use_padding
+#         self.eps = eps
+#
+#         window = create_window(window_size, window_sigma, channel)
+#         self.register_buffer('window', window)
+#
+#         if weights is None:
+#             weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]
+#         weights = torch.tensor(weights, dtype=torch.float)
+#
+#         if levels is not None:
+#             weights = weights[:levels]
+#             weights = weights / weights.sum()
+#
+#         self.register_buffer('weights', weights)
+#
+#     @torch.jit.script_method
+#     def forward(self, X, Y):
+#         return ms_ssim(X, Y, window=self.window, data_range=self.data_range, weights=self.weights,
+#                        use_padding=self.use_padding, eps=self.eps)
+#
+#
+# if __name__ == '__main__':
+#     print('Simple Test')
+#     im = torch.randint(0, 255, (5, 3, 256, 256), dtype=torch.float, device='cuda')
+#     img1 = im / 255
+#     img2 = img1 * 0.5
+#
+#     losser = SSIM(data_range=1.).cuda()
+#     loss = losser(img1, img2).mean()
+#
+#     losser2 = MS_SSIM(data_range=1.).cuda()
+#     loss2 = losser2(img1, img2).mean()
+#
+#     print(loss.item())
+#     print(loss2.item())
+#
+# if __name__ == '__main__':
+#     print('Training Test')
+#     import cv2
+#     import torch.optim
+#     import numpy as np
+#     import imageio
+#     import time
+#
+#     out_test_video = False
+#     # 最好不要直接输出gif图，会非常大，最好先输出mkv文件后用ffmpeg转换到GIF
+#     video_use_gif = False
+#
+#     im = cv2.imread('test_img1.jpg', 1)
+#     t_im = torch.from_numpy(im).cuda().permute(2, 0, 1).float()[None] / 255.
+#
+#     if out_test_video:
+#         if video_use_gif:
+#             fps = 0.5
+#             out_wh = (im.shape[1] // 2, im.shape[0] // 2)
+#             suffix = '.gif'
+#         else:
+#             fps = 5
+#             out_wh = (im.shape[1], im.shape[0])
+#             suffix = '.mkv'
+#         video_last_time = time.perf_counter()
+#         video = imageio.get_writer('ssim_test' + suffix, fps=fps)
+#
+#     # 测试ssim
+#     print('Training SSIM')
+#     rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255.
+#     rand_im.requires_grad = True
+#     optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8)
+#     losser = SSIM(data_range=1., channel=t_im.shape[1]).cuda()
+#     ssim_score = 0
+#     while ssim_score < 0.999:
+#         optim.zero_grad()
+#         loss = losser(rand_im, t_im)
+#         (-loss).sum().backward()
+#         ssim_score = loss.item()
+#         optim.step()
+#         r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0]
+#         r_im = cv2.putText(r_im, 'ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
+#
+#         if out_test_video:
+#             if time.perf_counter() - video_last_time > 1. / fps:
+#                 video_last_time = time.perf_counter()
+#                 out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB)
+#                 out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA)
+#                 if isinstance(out_frame, cv2.UMat):
+#                     out_frame = out_frame.get()
+#                 video.append_data(out_frame)
+#
+#         cv2.imshow('ssim', r_im)
+#         cv2.setWindowTitle('ssim', 'ssim %f' % ssim_score)
+#         cv2.waitKey(1)
+#
+#     if out_test_video:
+#         video.close()
+#
+#     # 测试ms_ssim
+#     if out_test_video:
+#         if video_use_gif:
+#             fps = 0.5
+#             out_wh = (im.shape[1] // 2, im.shape[0] // 2)
+#             suffix = '.gif'
+#         else:
+#             fps = 5
+#             out_wh = (im.shape[1], im.shape[0])
+#             suffix = '.mkv'
+#         video_last_time = time.perf_counter()
+#         video = imageio.get_writer('ms_ssim_test' + suffix, fps=fps)
+#
+#     print('Training MS_SSIM')
+#     rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255.
+#     rand_im.requires_grad = True
+#     optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8)
+#     losser = MS_SSIM(data_range=1., channel=t_im.shape[1]).cuda()
+#     ssim_score = 0
+#     while ssim_score < 0.999:
+#         optim.zero_grad()
+#         loss = losser(rand_im, t_im)
+#         (-loss).sum().backward()
+#         ssim_score = loss.item()
+#         optim.step()
+#         r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0]
+#         r_im = cv2.putText(r_im, 'ms_ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
+#
+#         if out_test_video:
+#             if time.perf_counter() - video_last_time > 1. / fps:
+#                 video_last_time = time.perf_counter()
+#                 out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB)
+#                 out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA)
+#                 if isinstance(out_frame, cv2.UMat):
+#                     out_frame = out_frame.get()
+#                 video.append_data(out_frame)
+#
+#         cv2.imshow('ms_ssim', r_im)
+#         cv2.setWindowTitle('ms_ssim', 'ms_ssim %f' % ssim_score)
+#         cv2.waitKey(1)
+#
+#     if out_test_video:
+#         video.close()
+
+"""
+Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim
+"""
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+from math import exp
+
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+    return window
+
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
+
+    C1 = 0.01 ** 2
+    C2 = 0.03 ** 2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1)
+
+
+class SSIM(torch.nn.Module):
+    def __init__(self, window_size=11, size_average=True):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = 1
+        self.window = create_window(window_size, self.channel)
+
+    def forward(self, img1, img2):
+        (_, channel, _, _) = img1.size()
+
+        if channel == self.channel and self.window.data.type() == img1.data.type():
+            window = self.window
+        else:
+            window = create_window(self.window_size, channel)
+
+            if img1.is_cuda:
+                window = window.cuda(img1.get_device())
+            window = window.type_as(img1)
+
+            self.window = window
+            self.channel = channel
+
+        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
+
+
+window = None
+
+
+def ssim(img1, img2, window_size=11, size_average=True):
+    (_, channel, _, _) = img1.size()
+    global window
+    if window is None:
+        window = create_window(window_size, channel)
+        if img1.is_cuda:
+            window = window.cuda(img1.get_device())
+        window = window.type_as(img1)
+    return _ssim(img1, img2, window, window_size, channel, size_average)
diff --git a/modules/fastspeech/__pycache__/fs2.cpython-38.pyc b/modules/fastspeech/__pycache__/fs2.cpython-38.pyc
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diff --git a/modules/fastspeech/fs2.py b/modules/fastspeech/fs2.py
new file mode 100644
index 0000000000000000000000000000000000000000..085448a8dd44cdd8a8e106e9bf1b983fae29cb55
--- /dev/null
+++ b/modules/fastspeech/fs2.py
@@ -0,0 +1,255 @@
+from modules.commons.common_layers import *
+from modules.commons.common_layers import Embedding
+from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \
+    EnergyPredictor, FastspeechEncoder
+from utils.cwt import cwt2f0
+from utils.hparams import hparams
+from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0
+
+FS_ENCODERS = {
+    'fft': lambda hp: FastspeechEncoder(
+        hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'],
+        num_heads=hp['num_heads']),
+}
+
+FS_DECODERS = {
+    'fft': lambda hp: FastspeechDecoder(
+        hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
+}
+
+
+class FastSpeech2(nn.Module):
+    def __init__(self, dictionary, out_dims=None):
+        super().__init__()
+        # self.dictionary = dictionary
+        self.padding_idx = 0
+        if not hparams['no_fs2'] if 'no_fs2' in hparams.keys() else True:
+            self.enc_layers = hparams['enc_layers']
+            self.dec_layers = hparams['dec_layers']
+            self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams)
+            self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
+        self.hidden_size = hparams['hidden_size']
+        # self.encoder_embed_tokens = self.build_embedding(self.dictionary, self.hidden_size)
+        self.out_dims = out_dims
+        if out_dims is None:
+            self.out_dims = hparams['audio_num_mel_bins']
+        self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
+        #=========not used===========
+        # if hparams['use_spk_id']:
+        #     self.spk_embed_proj = Embedding(hparams['num_spk'] + 1, self.hidden_size)
+        #     if hparams['use_split_spk_id']:
+        #         self.spk_embed_f0 = Embedding(hparams['num_spk'] + 1, self.hidden_size)
+        #         self.spk_embed_dur = Embedding(hparams['num_spk'] + 1, self.hidden_size)
+        # elif hparams['use_spk_embed']:
+        #     self.spk_embed_proj = Linear(256, self.hidden_size, bias=True)
+        predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
+        # self.dur_predictor = DurationPredictor(
+        #     self.hidden_size,
+        #     n_chans=predictor_hidden,
+        #     n_layers=hparams['dur_predictor_layers'],
+        #     dropout_rate=hparams['predictor_dropout'], padding=hparams['ffn_padding'],
+        #     kernel_size=hparams['dur_predictor_kernel'])
+        # self.length_regulator = LengthRegulator()
+        if hparams['use_pitch_embed']:
+            self.pitch_embed = Embedding(300, self.hidden_size, self.padding_idx)
+            if hparams['pitch_type'] == 'cwt':
+                h = hparams['cwt_hidden_size']
+                cwt_out_dims = 10
+                if hparams['use_uv']:
+                    cwt_out_dims = cwt_out_dims + 1
+                self.cwt_predictor = nn.Sequential(
+                    nn.Linear(self.hidden_size, h),
+                    PitchPredictor(
+                        h,
+                        n_chans=predictor_hidden,
+                        n_layers=hparams['predictor_layers'],
+                        dropout_rate=hparams['predictor_dropout'], odim=cwt_out_dims,
+                        padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel']))
+                self.cwt_stats_layers = nn.Sequential(
+                    nn.Linear(self.hidden_size, h), nn.ReLU(),
+                    nn.Linear(h, h), nn.ReLU(), nn.Linear(h, 2)
+                )
+            else:
+                self.pitch_predictor = PitchPredictor(
+                    self.hidden_size,
+                    n_chans=predictor_hidden,
+                    n_layers=hparams['predictor_layers'],
+                    dropout_rate=hparams['predictor_dropout'],
+                    odim=2 if hparams['pitch_type'] == 'frame' else 1,
+                    padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel'])
+        if hparams['use_energy_embed']:
+            self.energy_embed = Embedding(256, self.hidden_size, self.padding_idx)
+            # self.energy_predictor = EnergyPredictor(
+            #     self.hidden_size,
+            #     n_chans=predictor_hidden,
+            #     n_layers=hparams['predictor_layers'],
+            #     dropout_rate=hparams['predictor_dropout'], odim=1,
+            #     padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel'])
+
+    # def build_embedding(self, dictionary, embed_dim):
+    #     num_embeddings = len(dictionary)
+    #     emb = Embedding(num_embeddings, embed_dim, self.padding_idx)
+    #     return emb
+
+    def forward(self, hubert, mel2ph=None, spk_embed=None,
+                ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=True,
+                spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs):
+        ret = {}
+        if not hparams['no_fs2'] if 'no_fs2' in hparams.keys() else True:
+            encoder_out =self.encoder(hubert)  # [B, T, C]
+        else:
+            encoder_out =hubert
+        src_nonpadding = (hubert!=0).any(-1)[:,:,None]
+
+        # add ref style embed
+        # Not implemented
+        # variance encoder
+        var_embed = 0
+
+        # encoder_out_dur denotes encoder outputs for duration predictor
+        # in speech adaptation, duration predictor use old speaker embedding
+        if hparams['use_spk_embed']:
+            spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :]
+        elif hparams['use_spk_id']:
+            spk_embed_id = spk_embed
+            if spk_embed_dur_id is None:
+                spk_embed_dur_id = spk_embed_id
+            if spk_embed_f0_id is None:
+                spk_embed_f0_id = spk_embed_id
+            spk_embed = self.spk_embed_proj(spk_embed_id)[:, None, :]
+            spk_embed_dur = spk_embed_f0 = spk_embed
+            if hparams['use_split_spk_id']:
+                spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :]
+                spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :]
+        else:
+            spk_embed_dur = spk_embed_f0 = spk_embed = 0
+
+        # add dur
+        # dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding
+
+        # mel2ph = self.add_dur(dur_inp, mel2ph, hubert, ret)
+        ret['mel2ph'] = mel2ph
+
+        decoder_inp = F.pad(encoder_out, [0, 0, 1, 0])
+
+        mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]])
+        decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_)  # [B, T, H]
+
+        tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
+
+        # add pitch and energy embed
+        pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding
+        if hparams['use_pitch_embed']:
+            pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding
+            decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph)
+        if hparams['use_energy_embed']:
+            decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret)
+
+        ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding
+        if not hparams['no_fs2'] if 'no_fs2' in hparams.keys() else True:
+            if skip_decoder:
+                return ret
+            ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs)
+
+        return ret
+
+    def add_dur(self, dur_input, mel2ph, hubert, ret):
+        src_padding = (hubert==0).all(-1)
+        dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
+        if mel2ph is None:
+            dur, xs = self.dur_predictor.inference(dur_input, src_padding)
+            ret['dur'] = xs
+            ret['dur_choice'] = dur
+            mel2ph = self.length_regulator(dur, src_padding).detach()
+        else:
+            ret['dur'] = self.dur_predictor(dur_input, src_padding)
+        ret['mel2ph'] = mel2ph
+        return mel2ph
+
+    def run_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
+        x = decoder_inp  # [B, T, H]
+        x = self.decoder(x)
+        x = self.mel_out(x)
+        return x * tgt_nonpadding
+
+    def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph):
+        f0 = cwt2f0(cwt_spec, mean, std, hparams['cwt_scales'])
+        f0 = torch.cat(
+            [f0] + [f0[:, -1:]] * (mel2ph.shape[1] - f0.shape[1]), 1)
+        f0_norm = norm_f0(f0, None, hparams)
+        return f0_norm
+
+    def out2mel(self, out):
+        return out
+    
+    def add_pitch(self,decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
+        # if hparams['pitch_type'] == 'ph':
+        #     pitch_pred_inp = encoder_out.detach() + hparams['predictor_grad'] * (encoder_out - encoder_out.detach())
+        #     pitch_padding = (encoder_out.sum().abs() == 0)
+        #     ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp)
+        #     if f0 is None:
+        #         f0 = pitch_pred[:, :, 0]
+        #     ret['f0_denorm'] = f0_denorm = denorm_f0(f0, None, hparams, pitch_padding=pitch_padding)
+        #     pitch = f0_to_coarse(f0_denorm)  # start from 0 [B, T_txt]
+        #     pitch = F.pad(pitch, [1, 0])
+        #     pitch = torch.gather(pitch, 1, mel2ph)  # [B, T_mel]
+        #     pitch_embedding = pitch_embed(pitch)
+        #     return pitch_embedding
+        
+        decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+
+        pitch_padding = (mel2ph == 0)
+
+        # if hparams['pitch_type'] == 'cwt':
+        #     # NOTE: this part of script is *isolated* from other scripts, which means
+        #     #       it may not be compatible with the current version.    
+        #     pass
+        #     # pitch_padding = None
+        #     # ret['cwt'] = cwt_out = self.cwt_predictor(decoder_inp)
+        #     # stats_out = self.cwt_stats_layers(encoder_out[:, 0, :])  # [B, 2]
+        #     # mean = ret['f0_mean'] = stats_out[:, 0]
+        #     # std = ret['f0_std'] = stats_out[:, 1]
+        #     # cwt_spec = cwt_out[:, :, :10]
+        #     # if f0 is None:
+        #     #     std = std * hparams['cwt_std_scale']
+        #     #     f0 = self.cwt2f0_norm(cwt_spec, mean, std, mel2ph)
+        #     #     if hparams['use_uv']:
+        #     #         assert cwt_out.shape[-1] == 11
+        #     #         uv = cwt_out[:, :, -1] > 0
+        # elif hparams['pitch_ar']:
+        #     ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp, f0 if is_training else None)
+        #     if f0 is None:
+        #         f0 = pitch_pred[:, :, 0]
+        # else:
+        #ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp)
+        # if f0 is None:
+        #     f0 = pitch_pred[:, :, 0]
+        # if hparams['use_uv'] and uv is None:
+        #     uv = pitch_pred[:, :, 1] > 0
+        ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding)
+        if pitch_padding is not None:
+            f0[pitch_padding] = 0
+        
+        pitch = f0_to_coarse(f0_denorm,hparams)  # start from 0
+        ret['pitch_pred']=pitch.unsqueeze(-1)
+        # print(ret['pitch_pred'].shape)
+        # print(pitch.shape)
+        pitch_embedding = self.pitch_embed(pitch)
+        return pitch_embedding
+
+    def add_energy(self,decoder_inp, energy, ret):
+        decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+        ret['energy_pred'] = energy#energy_pred = self.energy_predictor(decoder_inp)[:, :, 0]
+        # if energy is None:
+        #     energy = energy_pred
+        energy = torch.clamp(energy * 256 // 4, max=255).long() # energy_to_coarse
+        energy_embedding = self.energy_embed(energy)
+        return energy_embedding
+
+    @staticmethod
+    def mel_norm(x):
+        return (x + 5.5) / (6.3 / 2) - 1
+
+    @staticmethod
+    def mel_denorm(x):
+        return (x + 1) * (6.3 / 2) - 5.5
diff --git a/modules/fastspeech/pe.py b/modules/fastspeech/pe.py
new file mode 100644
index 0000000000000000000000000000000000000000..da0d46e3446bbf45d8ee3682edcaf0d8d64dcdfb
--- /dev/null
+++ b/modules/fastspeech/pe.py
@@ -0,0 +1,149 @@
+from modules.commons.common_layers import *
+from utils.hparams import hparams
+from modules.fastspeech.tts_modules import PitchPredictor
+from utils.pitch_utils import denorm_f0
+
+
+class Prenet(nn.Module):
+    def __init__(self, in_dim=80, out_dim=256, kernel=5, n_layers=3, strides=None):
+        super(Prenet, self).__init__()
+        padding = kernel // 2
+        self.layers = []
+        self.strides = strides if strides is not None else [1] * n_layers
+        for l in range(n_layers):
+            self.layers.append(nn.Sequential(
+                nn.Conv1d(in_dim, out_dim, kernel_size=kernel, padding=padding, stride=self.strides[l]),
+                nn.ReLU(),
+                nn.BatchNorm1d(out_dim)
+            ))
+            in_dim = out_dim
+        self.layers = nn.ModuleList(self.layers)
+        self.out_proj = nn.Linear(out_dim, out_dim)
+
+    def forward(self, x):
+        """
+
+        :param x: [B, T, 80]
+        :return: [L, B, T, H], [B, T, H]
+        """
+        # padding_mask = x.abs().sum(-1).eq(0).data  # [B, T]
+        padding_mask = x.abs().sum(-1).eq(0).detach()
+        nonpadding_mask_TB = 1 - padding_mask.float()[:, None, :]  # [B, 1, T]
+        x = x.transpose(1, 2)
+        hiddens = []
+        for i, l in enumerate(self.layers):
+            nonpadding_mask_TB = nonpadding_mask_TB[:, :, ::self.strides[i]]
+            x = l(x) * nonpadding_mask_TB
+        hiddens.append(x)
+        hiddens = torch.stack(hiddens, 0)  # [L, B, H, T]
+        hiddens = hiddens.transpose(2, 3)  # [L, B, T, H]
+        x = self.out_proj(x.transpose(1, 2))  # [B, T, H]
+        x = x * nonpadding_mask_TB.transpose(1, 2)
+        return hiddens, x
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, idim=80, n_chans=256, kernel_size=3, stride=1, norm='gn', dropout=0):
+        super().__init__()
+        self.conv = ConvNorm(idim, n_chans, kernel_size, stride=stride)
+        self.norm = norm
+        if self.norm == 'bn':
+            self.norm = nn.BatchNorm1d(n_chans)
+        elif self.norm == 'in':
+            self.norm = nn.InstanceNorm1d(n_chans, affine=True)
+        elif self.norm == 'gn':
+            self.norm = nn.GroupNorm(n_chans // 16, n_chans)
+        elif self.norm == 'ln':
+            self.norm = LayerNorm(n_chans // 16, n_chans)
+        elif self.norm == 'wn':
+            self.conv = torch.nn.utils.weight_norm(self.conv.conv)
+        self.dropout = nn.Dropout(dropout)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        """
+
+        :param x: [B, C, T]
+        :return: [B, C, T]
+        """
+        x = self.conv(x)
+        if not isinstance(self.norm, str):
+            if self.norm == 'none':
+                pass
+            elif self.norm == 'ln':
+                x = self.norm(x.transpose(1, 2)).transpose(1, 2)
+            else:
+                x = self.norm(x)
+        x = self.relu(x)
+        x = self.dropout(x)
+        return x
+
+
+class ConvStacks(nn.Module):
+    def __init__(self, idim=80, n_layers=5, n_chans=256, odim=32, kernel_size=5, norm='gn',
+                 dropout=0, strides=None, res=True):
+        super().__init__()
+        self.conv = torch.nn.ModuleList()
+        self.kernel_size = kernel_size
+        self.res = res
+        self.in_proj = Linear(idim, n_chans)
+        if strides is None:
+            strides = [1] * n_layers
+        else:
+            assert len(strides) == n_layers
+        for idx in range(n_layers):
+            self.conv.append(ConvBlock(
+                n_chans, n_chans, kernel_size, stride=strides[idx], norm=norm, dropout=dropout))
+        self.out_proj = Linear(n_chans, odim)
+
+    def forward(self, x, return_hiddens=False):
+        """
+
+        :param x: [B, T, H]
+        :return: [B, T, H]
+        """
+        x = self.in_proj(x)
+        x = x.transpose(1, -1)  # (B, idim, Tmax)
+        hiddens = []
+        for f in self.conv:
+            x_ = f(x)
+            x = x + x_ if self.res else x_  # (B, C, Tmax)
+            hiddens.append(x)
+        x = x.transpose(1, -1)
+        x = self.out_proj(x)  # (B, Tmax, H)
+        if return_hiddens:
+            hiddens = torch.stack(hiddens, 1)  # [B, L, C, T]
+            return x, hiddens
+        return x
+
+
+class PitchExtractor(nn.Module):
+    def __init__(self, n_mel_bins=80, conv_layers=2):
+        super().__init__()
+        self.hidden_size = hparams['hidden_size']
+        self.predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
+        self.conv_layers = conv_layers
+
+        self.mel_prenet = Prenet(n_mel_bins, self.hidden_size, strides=[1, 1, 1])
+        if self.conv_layers > 0:
+            self.mel_encoder = ConvStacks(
+                    idim=self.hidden_size, n_chans=self.hidden_size, odim=self.hidden_size, n_layers=self.conv_layers)
+        self.pitch_predictor = PitchPredictor(
+            self.hidden_size, n_chans=self.predictor_hidden,
+            n_layers=5, dropout_rate=0.1, odim=2,
+            padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel'])
+
+    def forward(self, mel_input=None):
+        ret = {}
+        mel_hidden = self.mel_prenet(mel_input)[1]
+        if self.conv_layers > 0:
+            mel_hidden = self.mel_encoder(mel_hidden)
+
+        ret['pitch_pred'] = pitch_pred = self.pitch_predictor(mel_hidden)
+
+        pitch_padding = mel_input.abs().sum(-1) == 0
+        use_uv = hparams['pitch_type'] == 'frame' #and hparams['use_uv']
+        ret['f0_denorm_pred'] = denorm_f0(
+            pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None,
+            hparams, pitch_padding=pitch_padding)
+        return ret
\ No newline at end of file
diff --git a/modules/fastspeech/tts_modules.py b/modules/fastspeech/tts_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fdd417d3cc354a2d65f905326f8fe053dea6d97
--- /dev/null
+++ b/modules/fastspeech/tts_modules.py
@@ -0,0 +1,364 @@
+import logging
+import math
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from modules.commons.espnet_positional_embedding import RelPositionalEncoding
+from modules.commons.common_layers import SinusoidalPositionalEmbedding, Linear, EncSALayer, DecSALayer, BatchNorm1dTBC
+from utils.hparams import hparams
+
+DEFAULT_MAX_SOURCE_POSITIONS = 2000
+DEFAULT_MAX_TARGET_POSITIONS = 2000
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(self, hidden_size, dropout, kernel_size=None, num_heads=2, norm='ln'):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.dropout = dropout
+        self.num_heads = num_heads
+        self.op = EncSALayer(
+            hidden_size, num_heads, dropout=dropout,
+            attention_dropout=0.0, relu_dropout=dropout,
+            kernel_size=kernel_size
+            if kernel_size is not None else hparams['enc_ffn_kernel_size'],
+            padding=hparams['ffn_padding'],
+            norm=norm, act=hparams['ffn_act'])
+
+    def forward(self, x, **kwargs):
+        return self.op(x, **kwargs)
+
+
+######################
+# fastspeech modules
+######################
+class LayerNorm(torch.nn.LayerNorm):
+    """Layer normalization module.
+    :param int nout: output dim size
+    :param int dim: dimension to be normalized
+    """
+
+    def __init__(self, nout, dim=-1):
+        """Construct an LayerNorm object."""
+        super(LayerNorm, self).__init__(nout, eps=1e-12)
+        self.dim = dim
+
+    def forward(self, x):
+        """Apply layer normalization.
+        :param torch.Tensor x: input tensor
+        :return: layer normalized tensor
+        :rtype torch.Tensor
+        """
+        if self.dim == -1:
+            return super(LayerNorm, self).forward(x)
+        return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
+
+
+class DurationPredictor(torch.nn.Module):
+    """Duration predictor module.
+    This is a module of duration predictor described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
+    The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder.
+    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
+        https://arxiv.org/pdf/1905.09263.pdf
+    Note:
+        The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`,
+        the outputs are calculated in log domain but in `inference`, those are calculated in linear domain.
+    """
+
+    def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0, padding='SAME'):
+        """Initilize duration predictor module.
+        Args:
+            idim (int): Input dimension.
+            n_layers (int, optional): Number of convolutional layers.
+            n_chans (int, optional): Number of channels of convolutional layers.
+            kernel_size (int, optional): Kernel size of convolutional layers.
+            dropout_rate (float, optional): Dropout rate.
+            offset (float, optional): Offset value to avoid nan in log domain.
+        """
+        super(DurationPredictor, self).__init__()
+        self.offset = offset
+        self.conv = torch.nn.ModuleList()
+        self.kernel_size = kernel_size
+        self.padding = padding
+        for idx in range(n_layers):
+            in_chans = idim if idx == 0 else n_chans
+            self.conv += [torch.nn.Sequential(
+                torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2)
+                                       if padding == 'SAME'
+                                       else (kernel_size - 1, 0), 0),
+                torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0),
+                torch.nn.ReLU(),
+                LayerNorm(n_chans, dim=1),
+                torch.nn.Dropout(dropout_rate)
+            )]
+        if hparams['dur_loss'] in ['mse', 'huber']:
+            odims = 1
+        elif hparams['dur_loss'] == 'mog':
+            odims = 15
+        elif hparams['dur_loss'] == 'crf':
+            odims = 32
+            from torchcrf import CRF
+            self.crf = CRF(odims, batch_first=True)
+        self.linear = torch.nn.Linear(n_chans, odims)
+
+    def _forward(self, xs, x_masks=None, is_inference=False):
+        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
+        for f in self.conv:
+            xs = f(xs)  # (B, C, Tmax)
+            if x_masks is not None:
+                xs = xs * (1 - x_masks.float())[:, None, :]
+
+        xs = self.linear(xs.transpose(1, -1))  # [B, T, C]
+        xs = xs * (1 - x_masks.float())[:, :, None]  # (B, T, C)
+        if is_inference:
+            return self.out2dur(xs), xs
+        else:
+            if hparams['dur_loss'] in ['mse']:
+                xs = xs.squeeze(-1)  # (B, Tmax)
+        return xs
+
+    def out2dur(self, xs):
+        if hparams['dur_loss'] in ['mse']:
+            # NOTE: calculate in log domain
+            xs = xs.squeeze(-1)  # (B, Tmax)
+            dur = torch.clamp(torch.round(xs.exp() - self.offset), min=0).long()  # avoid negative value
+        elif hparams['dur_loss'] == 'mog':
+            return NotImplementedError
+        elif hparams['dur_loss'] == 'crf':
+            dur = torch.LongTensor(self.crf.decode(xs)).cuda()
+        return dur
+
+    def forward(self, xs, x_masks=None):
+        """Calculate forward propagation.
+        Args:
+            xs (Tensor): Batch of input sequences (B, Tmax, idim).
+            x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax).
+        Returns:
+            Tensor: Batch of predicted durations in log domain (B, Tmax).
+        """
+        return self._forward(xs, x_masks, False)
+
+    def inference(self, xs, x_masks=None):
+        """Inference duration.
+        Args:
+            xs (Tensor): Batch of input sequences (B, Tmax, idim).
+            x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax).
+        Returns:
+            LongTensor: Batch of predicted durations in linear domain (B, Tmax).
+        """
+        return self._forward(xs, x_masks, True)
+
+
+class LengthRegulator(torch.nn.Module):
+    def __init__(self, pad_value=0.0):
+        super(LengthRegulator, self).__init__()
+        self.pad_value = pad_value
+
+    def forward(self, dur, dur_padding=None, alpha=1.0):
+        """
+        Example (no batch dim version):
+            1. dur = [2,2,3]
+            2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4]
+            3. token_mask = [[1,1,0,0,0,0,0],
+                             [0,0,1,1,0,0,0],
+                             [0,0,0,0,1,1,1]]
+            4. token_idx * token_mask = [[1,1,0,0,0,0,0],
+                                         [0,0,2,2,0,0,0],
+                                         [0,0,0,0,3,3,3]]
+            5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3]
+
+        :param dur: Batch of durations of each frame (B, T_txt)
+        :param dur_padding: Batch of padding of each frame (B, T_txt)
+        :param alpha: duration rescale coefficient
+        :return:
+            mel2ph (B, T_speech)
+        """
+        assert alpha > 0
+        dur = torch.round(dur.float() * alpha).long()
+        if dur_padding is not None:
+            dur = dur * (1 - dur_padding.long())
+        token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device)
+        dur_cumsum = torch.cumsum(dur, 1)
+        dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0)
+
+        pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device)
+        token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None])
+        mel2ph = (token_idx * token_mask.long()).sum(1)
+        return mel2ph
+
+
+class PitchPredictor(torch.nn.Module):
+    def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5,
+                 dropout_rate=0.1, padding='SAME'):
+        """Initilize pitch predictor module.
+        Args:
+            idim (int): Input dimension.
+            n_layers (int, optional): Number of convolutional layers.
+            n_chans (int, optional): Number of channels of convolutional layers.
+            kernel_size (int, optional): Kernel size of convolutional layers.
+            dropout_rate (float, optional): Dropout rate.
+        """
+        super(PitchPredictor, self).__init__()
+        self.conv = torch.nn.ModuleList()
+        self.kernel_size = kernel_size
+        self.padding = padding
+        for idx in range(n_layers):
+            in_chans = idim if idx == 0 else n_chans
+            self.conv += [torch.nn.Sequential(
+                torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2)
+                                       if padding == 'SAME'
+                                       else (kernel_size - 1, 0), 0),
+                torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0),
+                torch.nn.ReLU(),
+                LayerNorm(n_chans, dim=1),
+                torch.nn.Dropout(dropout_rate)
+            )]
+        self.linear = torch.nn.Linear(n_chans, odim)
+        self.embed_positions = SinusoidalPositionalEmbedding(idim, 0, init_size=4096)
+        self.pos_embed_alpha = nn.Parameter(torch.Tensor([1]))
+
+    def forward(self, xs):
+        """
+
+        :param xs: [B, T, H]
+        :return: [B, T, H]
+        """
+        positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0])
+        xs = xs + positions
+        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
+        for f in self.conv:
+            xs = f(xs)  # (B, C, Tmax)
+        # NOTE: calculate in log domain
+        xs = self.linear(xs.transpose(1, -1))  # (B, Tmax, H)
+        return xs
+
+
+class EnergyPredictor(PitchPredictor):
+    pass
+
+
+def mel2ph_to_dur(mel2ph, T_txt, max_dur=None):
+    B, _ = mel2ph.shape
+    dur = mel2ph.new_zeros(B, T_txt + 1).scatter_add(1, mel2ph, torch.ones_like(mel2ph))
+    dur = dur[:, 1:]
+    if max_dur is not None:
+        dur = dur.clamp(max=max_dur)
+    return dur
+
+
+class FFTBlocks(nn.Module):
+    def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=None, num_heads=2,
+                 use_pos_embed=True, use_last_norm=True, norm='ln', use_pos_embed_alpha=True):
+        super().__init__()
+        self.num_layers = num_layers
+        embed_dim = self.hidden_size = hidden_size
+        self.dropout = dropout if dropout is not None else hparams['dropout']
+        self.use_pos_embed = use_pos_embed
+        self.use_last_norm = use_last_norm
+        if use_pos_embed:
+            self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS
+            self.padding_idx = 0
+            self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1
+            self.embed_positions = SinusoidalPositionalEmbedding(
+                embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
+            )
+
+        self.layers = nn.ModuleList([])
+        self.layers.extend([
+            TransformerEncoderLayer(self.hidden_size, self.dropout,
+                                    kernel_size=ffn_kernel_size, num_heads=num_heads)
+            for _ in range(self.num_layers)
+        ])
+        if self.use_last_norm:
+            if norm == 'ln':
+                self.layer_norm = nn.LayerNorm(embed_dim)
+            elif norm == 'bn':
+                self.layer_norm = BatchNorm1dTBC(embed_dim)
+        else:
+            self.layer_norm = None
+
+    def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False):
+        """
+        :param x: [B, T, C]
+        :param padding_mask: [B, T]
+        :return: [B, T, C] or [L, B, T, C]
+        """
+        # padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
+        padding_mask = x.abs().sum(-1).eq(0).detach() if padding_mask is None else padding_mask
+        nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None]  # [T, B, 1]
+        if self.use_pos_embed:
+            positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
+            x = x + positions
+            x = F.dropout(x, p=self.dropout, training=self.training)
+        # B x T x C -> T x B x C
+        x = x.transpose(0, 1) * nonpadding_mask_TB
+        hiddens = []
+        for layer in self.layers:
+            x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
+            hiddens.append(x)
+        if self.use_last_norm:
+            x = self.layer_norm(x) * nonpadding_mask_TB
+        if return_hiddens:
+            x = torch.stack(hiddens, 0)  # [L, T, B, C]
+            x = x.transpose(1, 2)  # [L, B, T, C]
+        else:
+            x = x.transpose(0, 1)  # [B, T, C]
+        return x
+
+
+class FastspeechEncoder(FFTBlocks):
+    '''
+        compared to FFTBlocks:
+        - input is [B, T, H], not [B, T, C]
+        - supports "relative" positional encoding
+    '''
+    def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=2):
+        hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size
+        kernel_size = hparams['enc_ffn_kernel_size'] if kernel_size is None else kernel_size
+        num_layers = hparams['dec_layers'] if num_layers is None else num_layers
+        super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads,
+                         use_pos_embed=False)  # use_pos_embed_alpha for compatibility
+        #self.embed_tokens = embed_tokens
+        self.embed_scale = math.sqrt(hidden_size)
+        self.padding_idx = 0
+        if hparams.get('rel_pos') is not None and hparams['rel_pos']:
+            self.embed_positions = RelPositionalEncoding(hidden_size, dropout_rate=0.0)
+        else:
+            self.embed_positions = SinusoidalPositionalEmbedding(
+                hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
+            )
+
+    def forward(self, hubert):
+        """
+
+        :param hubert: [B, T, H ]
+        :return: {
+            'encoder_out': [T x B x C]
+        }
+        """
+        # encoder_padding_mask = txt_tokens.eq(self.padding_idx).data
+        encoder_padding_mask = (hubert==0).all(-1)
+        x = self.forward_embedding(hubert)  # [B, T, H]
+        x = super(FastspeechEncoder, self).forward(x, encoder_padding_mask)
+        return x
+
+    def forward_embedding(self, hubert):
+        # embed tokens and positions
+        x = self.embed_scale * hubert
+        if hparams['use_pos_embed']:
+            positions = self.embed_positions(hubert)
+            x = x + positions
+        x = F.dropout(x, p=self.dropout, training=self.training)
+        return x
+
+
+class FastspeechDecoder(FFTBlocks):
+    def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None):
+        num_heads = hparams['num_heads'] if num_heads is None else num_heads
+        hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size
+        kernel_size = hparams['dec_ffn_kernel_size'] if kernel_size is None else kernel_size
+        num_layers = hparams['dec_layers'] if num_layers is None else num_layers
+        super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads)
+
diff --git a/modules/hifigan/__pycache__/hifigan.cpython-38.pyc b/modules/hifigan/__pycache__/hifigan.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2e8491c1afef1e7e079b27962e8cb06b6efea0b4
Binary files /dev/null and b/modules/hifigan/__pycache__/hifigan.cpython-38.pyc differ
diff --git a/modules/hifigan/hifigan.py b/modules/hifigan/hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae7e61f56b00d60bcc49a18ece3edbe54746f7ea
--- /dev/null
+++ b/modules/hifigan/hifigan.py
@@ -0,0 +1,365 @@
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork
+from modules.parallel_wavegan.models.source import SourceModuleHnNSF
+import numpy as np
+
+LRELU_SLOPE = 0.1
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def apply_weight_norm(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        weight_norm(m)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.h = h
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.h = h
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Conv1d1x1(Conv1d):
+    """1x1 Conv1d with customized initialization."""
+
+    def __init__(self, in_channels, out_channels, bias):
+        """Initialize 1x1 Conv1d module."""
+        super(Conv1d1x1, self).__init__(in_channels, out_channels,
+                                        kernel_size=1, padding=0,
+                                        dilation=1, bias=bias)
+
+
+class HifiGanGenerator(torch.nn.Module):
+    def __init__(self, h, c_out=1):
+        super(HifiGanGenerator, self).__init__()
+        self.h = h
+        self.num_kernels = len(h['resblock_kernel_sizes'])
+        self.num_upsamples = len(h['upsample_rates'])
+
+        if h['use_pitch_embed']:
+            self.harmonic_num = 8
+            self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates']))
+            self.m_source = SourceModuleHnNSF(
+                sampling_rate=h['audio_sample_rate'],
+                harmonic_num=self.harmonic_num)
+            self.noise_convs = nn.ModuleList()
+        self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3))
+        resblock = ResBlock1 if h['resblock'] == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h['upsample_rates'], h['upsample_kernel_sizes'])):
+            c_cur = h['upsample_initial_channel'] // (2 ** (i + 1))
+            self.ups.append(weight_norm(
+                ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2)))
+            if h['use_pitch_embed']:
+                if i + 1 < len(h['upsample_rates']):
+                    stride_f0 = np.prod(h['upsample_rates'][i + 1:])
+                    self.noise_convs.append(Conv1d(
+                        1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
+                else:
+                    self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h['upsample_initial_channel'] // (2 ** (i + 1))
+            for j, (k, d) in enumerate(zip(h['resblock_kernel_sizes'], h['resblock_dilation_sizes'])):
+                self.resblocks.append(resblock(h, ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+
+    def forward(self, x, f0=None):
+        if f0 is not None:
+            # harmonic-source signal, noise-source signal, uv flag
+            f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)
+            har_source, noi_source, uv = self.m_source(f0)
+            har_source = har_source.transpose(1, 2)
+
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            if f0 is not None:
+                x_source = self.noise_convs[i](har_source)
+                x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1):
+        super(DiscriminatorP, self).__init__()
+        self.use_cond = use_cond
+        if use_cond:
+            from utils.hparams import hparams
+            t = hparams['hop_size']
+            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
+            c_in = 2
+
+        self.period = period
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(c_in, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x, mel):
+        fmap = []
+        if self.use_cond:
+            x_mel = self.cond_net(mel)
+            x = torch.cat([x_mel, x], 1)
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_cond=False, c_in=1):
+        super(MultiPeriodDiscriminator, self).__init__()
+        self.discriminators = nn.ModuleList([
+            DiscriminatorP(2, use_cond=use_cond, c_in=c_in),
+            DiscriminatorP(3, use_cond=use_cond, c_in=c_in),
+            DiscriminatorP(5, use_cond=use_cond, c_in=c_in),
+            DiscriminatorP(7, use_cond=use_cond, c_in=c_in),
+            DiscriminatorP(11, use_cond=use_cond, c_in=c_in),
+        ])
+
+    def forward(self, y, y_hat, mel=None):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y, mel)
+            y_d_g, fmap_g = d(y_hat, mel)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False, use_cond=False, upsample_rates=None, c_in=1):
+        super(DiscriminatorS, self).__init__()
+        self.use_cond = use_cond
+        if use_cond:
+            t = np.prod(upsample_rates)
+            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
+            c_in = 2
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(c_in, 128, 15, 1, padding=7)),
+            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
+            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
+            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x, mel):
+        if self.use_cond:
+            x_mel = self.cond_net(mel)
+            x = torch.cat([x_mel, x], 1)
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiScaleDiscriminator(torch.nn.Module):
+    def __init__(self, use_cond=False, c_in=1):
+        super(MultiScaleDiscriminator, self).__init__()
+        from utils.hparams import hparams
+        self.discriminators = nn.ModuleList([
+            DiscriminatorS(use_spectral_norm=True, use_cond=use_cond,
+                           upsample_rates=[4, 4, hparams['hop_size'] // 16],
+                           c_in=c_in),
+            DiscriminatorS(use_cond=use_cond,
+                           upsample_rates=[4, 4, hparams['hop_size'] // 32],
+                           c_in=c_in),
+            DiscriminatorS(use_cond=use_cond,
+                           upsample_rates=[4, 4, hparams['hop_size'] // 64],
+                           c_in=c_in),
+        ])
+        self.meanpools = nn.ModuleList([
+            AvgPool1d(4, 2, padding=1),
+            AvgPool1d(4, 2, padding=1)
+        ])
+
+    def forward(self, y, y_hat, mel=None):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            if i != 0:
+                y = self.meanpools[i - 1](y)
+                y_hat = self.meanpools[i - 1](y_hat)
+            y_d_r, fmap_r = d(y, mel)
+            y_d_g, fmap_g = d(y_hat, mel)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+def feature_loss(fmap_r, fmap_g):
+    loss = 0
+    for dr, dg in zip(fmap_r, fmap_g):
+        for rl, gl in zip(dr, dg):
+            loss += torch.mean(torch.abs(rl - gl))
+
+    return loss * 2
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+    r_losses = 0
+    g_losses = 0
+    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+        r_loss = torch.mean((1 - dr) ** 2)
+        g_loss = torch.mean(dg ** 2)
+        r_losses += r_loss
+        g_losses += g_loss
+    r_losses = r_losses / len(disc_real_outputs)
+    g_losses = g_losses / len(disc_real_outputs)
+    return r_losses, g_losses
+
+
+def cond_discriminator_loss(outputs):
+    loss = 0
+    for dg in outputs:
+        g_loss = torch.mean(dg ** 2)
+        loss += g_loss
+    loss = loss / len(outputs)
+    return loss
+
+
+def generator_loss(disc_outputs):
+    loss = 0
+    for dg in disc_outputs:
+        l = torch.mean((1 - dg) ** 2)
+        loss += l
+    loss = loss / len(disc_outputs)
+    return loss
diff --git a/modules/hifigan/mel_utils.py b/modules/hifigan/mel_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..06e0f7d4d16fa3e4aefc8949347455f5a6e938da
--- /dev/null
+++ b/modules/hifigan/mel_utils.py
@@ -0,0 +1,80 @@
+import numpy as np
+import torch
+import torch.utils.data
+from librosa.filters import mel as librosa_mel_fn
+from scipy.io.wavfile import read
+
+MAX_WAV_VALUE = 32768.0
+
+
+def load_wav(full_path):
+    sampling_rate, data = read(full_path)
+    return data, sampling_rate
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    return np.exp(x) / C
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def mel_spectrogram(y, hparams, center=False, complex=False):
+    # hop_size: 512  # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate)
+    # win_size: 2048  # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate)
+    # fmin: 55  # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
+    # fmax: 10000  # To be increased/reduced depending on data.
+    # fft_size: 2048  # Extra window size is filled with 0 paddings to match this parameter
+    # n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax,
+    n_fft = hparams['fft_size']
+    num_mels = hparams['audio_num_mel_bins']
+    sampling_rate = hparams['audio_sample_rate']
+    hop_size = hparams['hop_size']
+    win_size = hparams['win_size']
+    fmin = hparams['fmin']
+    fmax = hparams['fmax']
+    y = y.clamp(min=-1., max=1.)
+    global mel_basis, hann_window
+    if fmax not in mel_basis:
+        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+        mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
+        hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+                                mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True)
+
+    if not complex:
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+        spec = torch.matmul(mel_basis[str(fmax) + '_' + str(y.device)], spec)
+        spec = spectral_normalize_torch(spec)
+    else:
+        B, C, T, _ = spec.shape
+        spec = spec.transpose(1, 2)  # [B, T, n_fft, 2]
+    return spec
diff --git a/modules/nsf_hifigan/__pycache__/env.cpython-38.pyc b/modules/nsf_hifigan/__pycache__/env.cpython-38.pyc
new file mode 100644
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diff --git a/modules/nsf_hifigan/env.py b/modules/nsf_hifigan/env.py
new file mode 100644
index 0000000000000000000000000000000000000000..02a2739a3eb25764527a14b46347ee386b0241b0
--- /dev/null
+++ b/modules/nsf_hifigan/env.py
@@ -0,0 +1,15 @@
+import os
+import shutil
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
+
+
+def build_env(config, config_name, path):
+    t_path = os.path.join(path, config_name)
+    if config != t_path:
+        os.makedirs(path, exist_ok=True)
+        shutil.copyfile(config, os.path.join(path, config_name))
\ No newline at end of file
diff --git a/modules/nsf_hifigan/models.py b/modules/nsf_hifigan/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe065e2cab257e48a49ed22e53e52035c0d38b80
--- /dev/null
+++ b/modules/nsf_hifigan/models.py
@@ -0,0 +1,549 @@
+import os
+import json
+from .env import AttrDict
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from .utils import init_weights, get_padding
+
+LRELU_SLOPE = 0.1
+
+def load_model(model_path, device='cuda'):
+    config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
+    with open(config_file) as f:
+        data = f.read()
+
+    global h
+    json_config = json.loads(data)
+    h = AttrDict(json_config)
+
+    generator = Generator(h).to(device)
+
+    cp_dict = torch.load(model_path, map_location='cpu')
+    generator.load_state_dict(cp_dict['generator'])
+    generator.eval()
+    generator.remove_weight_norm()
+    del cp_dict
+    return generator, h
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.h = h
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.h = h
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, h):
+        super(Generator, self).__init__()
+        self.h = h
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
+        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+
+    def forward(self, x):
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0,
+                 flag_for_pulse=False):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+
+    def _f02sine(self, f0_values):
+        """ f0_values: (batchsize, length, dim)
+            where dim indicates fundamental tone and overtones
+        """
+        # convert to F0 in rad. The interger part n can be ignored
+        # because 2 * np.pi * n doesn't affect phase
+        rad_values = (f0_values / self.sampling_rate) % 1
+
+        # initial phase noise (no noise for fundamental component)
+        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
+                              device=f0_values.device)
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+        if not self.flag_for_pulse:
+            # for normal case
+
+            # To prevent torch.cumsum numerical overflow,
+            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
+            # Buffer tmp_over_one_idx indicates the time step to add -1.
+            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
+            tmp_over_one = torch.cumsum(rad_values, 1) % 1
+            tmp_over_one_idx = (tmp_over_one[:, 1:, :] -
+                                tmp_over_one[:, :-1, :]) < 0
+            cumsum_shift = torch.zeros_like(rad_values)
+            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
+                              * 2 * np.pi)
+        else:
+            # If necessary, make sure that the first time step of every
+            # voiced segments is sin(pi) or cos(0)
+            # This is used for pulse-train generation
+
+            # identify the last time step in unvoiced segments
+            uv = self._f02uv(f0_values)
+            uv_1 = torch.roll(uv, shifts=-1, dims=1)
+            uv_1[:, -1, :] = 1
+            u_loc = (uv < 1) * (uv_1 > 0)
+
+            # get the instantanouse phase
+            tmp_cumsum = torch.cumsum(rad_values, dim=1)
+            # different batch needs to be processed differently
+            for idx in range(f0_values.shape[0]):
+                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+                # stores the accumulation of i.phase within
+                # each voiced segments
+                tmp_cumsum[idx, :, :] = 0
+                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+            # rad_values - tmp_cumsum: remove the accumulation of i.phase
+            # within the previous voiced segment.
+            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+            # get the sines
+            sines = torch.cos(i_phase * 2 * np.pi)
+        return sines
+
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
+                                 device=f0.device)
+            # fundamental component
+            f0_buf[:, :, 0] = f0[:, :, 0]
+            for idx in np.arange(self.harmonic_num):
+                # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+            # generate sine waveforms
+            sine_waves = self._f02sine(f0_buf) * self.sine_amp
+
+            # generate uv signal
+            # uv = torch.ones(f0.shape)
+            # uv = uv * (f0 > self.voiced_threshold)
+            uv = self._f02uv(f0)
+
+            # noise: for unvoiced should be similar to sine_amp
+            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+            # .       for voiced regions is self.noise_std
+            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+
+            # first: set the unvoiced part to 0 by uv
+            # then: additive noise
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+class SourceModuleHnNSF(torch.nn.Module):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
+                                 sine_amp, add_noise_std, voiced_threshod)
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        # source for harmonic branch
+        sine_wavs, uv, _ = self.l_sin_gen(x)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        # source for noise branch, in the same shape as uv
+        noise = torch.randn_like(uv) * self.sine_amp / 3
+        return sine_merge, noise, uv
+
+class Generator(torch.nn.Module):
+    def __init__(self, h):
+        super(Generator, self).__init__()
+        self.h = h
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h.upsample_rates))
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=h.sampling_rate,
+            harmonic_num=8)
+        self.noise_convs = nn.ModuleList()
+        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
+        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            c_cur = h.upsample_initial_channel // (2 ** (i + 1))
+            self.ups.append(weight_norm(
+                ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+            if i + 1 < len(h.upsample_rates):#
+                stride_f0 = np.prod(h.upsample_rates[i + 1:])
+                self.noise_convs.append(Conv1d(
+                    1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
+            else:
+                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+
+    def forward(self, x,f0):
+        # print(1,x.shape,f0.shape,f0[:, None].shape)
+        f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)#bs,n,t
+        # print(2,f0.shape)
+        har_source, noi_source, uv = self.m_source(f0)
+        har_source = har_source.transpose(1, 2)
+        x = self.conv_pre(x)
+        # print(124,x.shape,har_source.shape)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            # print(3,x.shape)
+            x = self.ups[i](x)
+            x_source = self.noise_convs[i](har_source)
+            # print(4,x_source.shape,har_source.shape,x.shape)
+            x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, periods=None):
+        super(MultiPeriodDiscriminator, self).__init__()
+        self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
+        self.discriminators = nn.ModuleList()
+        for period in self.periods:
+            self.discriminators.append(DiscriminatorP(period))
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
+            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
+            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
+            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiScaleDiscriminator(torch.nn.Module):
+    def __init__(self):
+        super(MultiScaleDiscriminator, self).__init__()
+        self.discriminators = nn.ModuleList([
+            DiscriminatorS(use_spectral_norm=True),
+            DiscriminatorS(),
+            DiscriminatorS(),
+        ])
+        self.meanpools = nn.ModuleList([
+            AvgPool1d(4, 2, padding=2),
+            AvgPool1d(4, 2, padding=2)
+        ])
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            if i != 0:
+                y = self.meanpools[i-1](y)
+                y_hat = self.meanpools[i-1](y_hat)
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+def feature_loss(fmap_r, fmap_g):
+    loss = 0
+    for dr, dg in zip(fmap_r, fmap_g):
+        for rl, gl in zip(dr, dg):
+            loss += torch.mean(torch.abs(rl - gl))
+
+    return loss*2
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+    loss = 0
+    r_losses = []
+    g_losses = []
+    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+        r_loss = torch.mean((1-dr)**2)
+        g_loss = torch.mean(dg**2)
+        loss += (r_loss + g_loss)
+        r_losses.append(r_loss.item())
+        g_losses.append(g_loss.item())
+
+    return loss, r_losses, g_losses
+
+
+def generator_loss(disc_outputs):
+    loss = 0
+    gen_losses = []
+    for dg in disc_outputs:
+        l = torch.mean((1-dg)**2)
+        gen_losses.append(l)
+        loss += l
+
+    return loss, gen_losses
\ No newline at end of file
diff --git a/modules/nsf_hifigan/nvSTFT.py b/modules/nsf_hifigan/nvSTFT.py
new file mode 100644
index 0000000000000000000000000000000000000000..35635c844ea1ae6258112f0ba92e417e81a22642
--- /dev/null
+++ b/modules/nsf_hifigan/nvSTFT.py
@@ -0,0 +1,111 @@
+import math
+import os
+os.environ["LRU_CACHE_CAPACITY"] = "3"
+import random
+import torch
+import torch.utils.data
+import numpy as np
+import librosa
+from librosa.util import normalize
+from librosa.filters import mel as librosa_mel_fn
+from scipy.io.wavfile import read
+import soundfile as sf
+
+def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
+    sampling_rate = None
+    try:
+        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
+    except Exception as ex:
+        print(f"'{full_path}' failed to load.\nException:")
+        print(ex)
+        if return_empty_on_exception:
+            return [], sampling_rate or target_sr or 48000
+        else:
+            raise Exception(ex)
+
+    if len(data.shape) > 1:
+        data = data[:, 0]
+        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
+
+    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
+        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
+    else: # if audio data is type fp32
+        max_mag = max(np.amax(data), -np.amin(data))
+        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
+
+    data = torch.FloatTensor(data.astype(np.float32))/max_mag
+
+    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
+        return [], sampling_rate or target_sr or 48000
+    if target_sr is not None and sampling_rate != target_sr:
+        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
+        sampling_rate = target_sr
+
+    return data, sampling_rate
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+def dynamic_range_decompression(x, C=1):
+    return np.exp(x) / C
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+def dynamic_range_decompression_torch(x, C=1):
+    return torch.exp(x) / C
+
+class STFT():
+    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
+        self.target_sr = sr
+
+        self.n_mels     = n_mels
+        self.n_fft      = n_fft
+        self.win_size   = win_size
+        self.hop_length = hop_length
+        self.fmin     = fmin
+        self.fmax     = fmax
+        self.clip_val = clip_val
+        self.mel_basis = {}
+        self.hann_window = {}
+
+    def get_mel(self, y, center=False):
+        sampling_rate = self.target_sr
+        n_mels     = self.n_mels
+        n_fft      = self.n_fft
+        win_size   = self.win_size
+        hop_length = self.hop_length
+        fmin       = self.fmin
+        fmax       = self.fmax
+        clip_val   = self.clip_val
+
+        if torch.min(y) < -1.:
+            print('min value is ', torch.min(y))
+        if torch.max(y) > 1.:
+            print('max value is ', torch.max(y))
+
+        if fmax not in self.mel_basis:
+            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
+            self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
+            self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
+
+        y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
+        y = y.squeeze(1)
+
+        spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
+                          center=center, pad_mode='reflect', normalized=False, onesided=True)
+        # print(111,spec)
+        spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
+        # print(222,spec)
+        spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
+        # print(333,spec)
+        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
+        # print(444,spec)
+        return spec
+
+    def __call__(self, audiopath):
+        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
+        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
+        return spect
+
+stft = STFT()
\ No newline at end of file
diff --git a/modules/nsf_hifigan/utils.py b/modules/nsf_hifigan/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ad5a37507987bd6e1200cb9241edf97989e3ead
--- /dev/null
+++ b/modules/nsf_hifigan/utils.py
@@ -0,0 +1,67 @@
+import glob
+import os
+import matplotlib
+import torch
+from torch.nn.utils import weight_norm
+matplotlib.use("Agg")
+import matplotlib.pylab as plt
+
+
+def plot_spectrogram(spectrogram):
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                   interpolation='none')
+    plt.colorbar(im, ax=ax)
+
+    fig.canvas.draw()
+    plt.close()
+
+    return fig
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def apply_weight_norm(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        weight_norm(m)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size*dilation - dilation)/2)
+
+
+def load_checkpoint(filepath, device):
+    assert os.path.isfile(filepath)
+    print("Loading '{}'".format(filepath))
+    checkpoint_dict = torch.load(filepath, map_location=device)
+    print("Complete.")
+    return checkpoint_dict
+
+
+def save_checkpoint(filepath, obj):
+    print("Saving checkpoint to {}".format(filepath))
+    torch.save(obj, filepath)
+    print("Complete.")
+
+
+def del_old_checkpoints(cp_dir, prefix, n_models=2):
+    pattern = os.path.join(cp_dir, prefix + '????????')
+    cp_list = glob.glob(pattern) # get checkpoint paths
+    cp_list = sorted(cp_list)# sort by iter
+    if len(cp_list) > n_models: # if more than n_models models are found
+        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
+            open(cp, 'w').close()# empty file contents
+            os.unlink(cp)# delete file (move to trash when using Colab)
+
+
+def scan_checkpoint(cp_dir, prefix):
+    pattern = os.path.join(cp_dir, prefix + '????????')
+    cp_list = glob.glob(pattern)
+    if len(cp_list) == 0:
+        return None
+    return sorted(cp_list)[-1]
\ No newline at end of file
diff --git a/modules/parallel_wavegan/__init__.py b/modules/parallel_wavegan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/parallel_wavegan/__pycache__/__init__.cpython-38.pyc b/modules/parallel_wavegan/__pycache__/__init__.cpython-38.pyc
new file mode 100644
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diff --git a/modules/parallel_wavegan/layers/__init__.py b/modules/parallel_wavegan/layers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e477f51116a3157781b1aefefbaf32fe4d4bd1f0
--- /dev/null
+++ b/modules/parallel_wavegan/layers/__init__.py
@@ -0,0 +1,5 @@
+from .causal_conv import *  # NOQA
+from .pqmf import *  # NOQA
+from .residual_block import *  # NOQA
+from modules.parallel_wavegan.layers.residual_stack import *  # NOQA
+from .upsample import *  # NOQA
diff --git a/modules/parallel_wavegan/layers/__pycache__/__init__.cpython-38.pyc b/modules/parallel_wavegan/layers/__pycache__/__init__.cpython-38.pyc
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diff --git a/modules/parallel_wavegan/layers/__pycache__/upsample.cpython-38.pyc b/modules/parallel_wavegan/layers/__pycache__/upsample.cpython-38.pyc
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diff --git a/modules/parallel_wavegan/layers/causal_conv.py b/modules/parallel_wavegan/layers/causal_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..fca77daf65f234e6fbe355ed148fc8f0ee85038a
--- /dev/null
+++ b/modules/parallel_wavegan/layers/causal_conv.py
@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Causal convolusion layer modules."""
+
+
+import torch
+
+
+class CausalConv1d(torch.nn.Module):
+    """CausalConv1d module with customized initialization."""
+
+    def __init__(self, in_channels, out_channels, kernel_size,
+                 dilation=1, bias=True, pad="ConstantPad1d", pad_params={"value": 0.0}):
+        """Initialize CausalConv1d module."""
+        super(CausalConv1d, self).__init__()
+        self.pad = getattr(torch.nn, pad)((kernel_size - 1) * dilation, **pad_params)
+        self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size,
+                                    dilation=dilation, bias=bias)
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, in_channels, T).
+
+        Returns:
+            Tensor: Output tensor (B, out_channels, T).
+
+        """
+        return self.conv(self.pad(x))[:, :, :x.size(2)]
+
+
+class CausalConvTranspose1d(torch.nn.Module):
+    """CausalConvTranspose1d module with customized initialization."""
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=True):
+        """Initialize CausalConvTranspose1d module."""
+        super(CausalConvTranspose1d, self).__init__()
+        self.deconv = torch.nn.ConvTranspose1d(
+            in_channels, out_channels, kernel_size, stride, bias=bias)
+        self.stride = stride
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, in_channels, T_in).
+
+        Returns:
+            Tensor: Output tensor (B, out_channels, T_out).
+
+        """
+        return self.deconv(x)[:, :, :-self.stride]
diff --git a/modules/parallel_wavegan/layers/pqmf.py b/modules/parallel_wavegan/layers/pqmf.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac21074fd32a370a099fa2facb62cfd3253d7579
--- /dev/null
+++ b/modules/parallel_wavegan/layers/pqmf.py
@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Pseudo QMF modules."""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from scipy.signal import kaiser
+
+
+def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
+    """Design prototype filter for PQMF.
+
+    This method is based on `A Kaiser window approach for the design of prototype
+    filters of cosine modulated filterbanks`_.
+
+    Args:
+        taps (int): The number of filter taps.
+        cutoff_ratio (float): Cut-off frequency ratio.
+        beta (float): Beta coefficient for kaiser window.
+
+    Returns:
+        ndarray: Impluse response of prototype filter (taps + 1,).
+
+    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
+        https://ieeexplore.ieee.org/abstract/document/681427
+
+    """
+    # check the arguments are valid
+    assert taps % 2 == 0, "The number of taps mush be even number."
+    assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
+
+    # make initial filter
+    omega_c = np.pi * cutoff_ratio
+    with np.errstate(invalid='ignore'):
+        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \
+            / (np.pi * (np.arange(taps + 1) - 0.5 * taps))
+    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
+
+    # apply kaiser window
+    w = kaiser(taps + 1, beta)
+    h = h_i * w
+
+    return h
+
+
+class PQMF(torch.nn.Module):
+    """PQMF module.
+
+    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
+
+    .. _`Near-perfect-reconstruction pseudo-QMF banks`:
+        https://ieeexplore.ieee.org/document/258122
+
+    """
+
+    def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0):
+        """Initilize PQMF module.
+
+        Args:
+            subbands (int): The number of subbands.
+            taps (int): The number of filter taps.
+            cutoff_ratio (float): Cut-off frequency ratio.
+            beta (float): Beta coefficient for kaiser window.
+
+        """
+        super(PQMF, self).__init__()
+
+        # define filter coefficient
+        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
+        h_analysis = np.zeros((subbands, len(h_proto)))
+        h_synthesis = np.zeros((subbands, len(h_proto)))
+        for k in range(subbands):
+            h_analysis[k] = 2 * h_proto * np.cos(
+                (2 * k + 1) * (np.pi / (2 * subbands)) *
+                (np.arange(taps + 1) - ((taps - 1) / 2)) +
+                (-1) ** k * np.pi / 4)
+            h_synthesis[k] = 2 * h_proto * np.cos(
+                (2 * k + 1) * (np.pi / (2 * subbands)) *
+                (np.arange(taps + 1) - ((taps - 1) / 2)) -
+                (-1) ** k * np.pi / 4)
+
+        # convert to tensor
+        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1)
+        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0)
+
+        # register coefficients as beffer
+        self.register_buffer("analysis_filter", analysis_filter)
+        self.register_buffer("synthesis_filter", synthesis_filter)
+
+        # filter for downsampling & upsampling
+        updown_filter = torch.zeros((subbands, subbands, subbands)).float()
+        for k in range(subbands):
+            updown_filter[k, k, 0] = 1.0
+        self.register_buffer("updown_filter", updown_filter)
+        self.subbands = subbands
+
+        # keep padding info
+        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
+
+    def analysis(self, x):
+        """Analysis with PQMF.
+
+        Args:
+            x (Tensor): Input tensor (B, 1, T).
+
+        Returns:
+            Tensor: Output tensor (B, subbands, T // subbands).
+
+        """
+        x = F.conv1d(self.pad_fn(x), self.analysis_filter)
+        return F.conv1d(x, self.updown_filter, stride=self.subbands)
+
+    def synthesis(self, x):
+        """Synthesis with PQMF.
+
+        Args:
+            x (Tensor): Input tensor (B, subbands, T // subbands).
+
+        Returns:
+            Tensor: Output tensor (B, 1, T).
+
+        """
+        x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands)
+        return F.conv1d(self.pad_fn(x), self.synthesis_filter)
diff --git a/modules/parallel_wavegan/layers/residual_block.py b/modules/parallel_wavegan/layers/residual_block.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a267a86c1fa521c2824addf9dda304c43f1ff1f
--- /dev/null
+++ b/modules/parallel_wavegan/layers/residual_block.py
@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+
+"""Residual block module in WaveNet.
+
+This code is modified from https://github.com/r9y9/wavenet_vocoder.
+
+"""
+
+import math
+
+import torch
+import torch.nn.functional as F
+
+
+class Conv1d(torch.nn.Conv1d):
+    """Conv1d module with customized initialization."""
+
+    def __init__(self, *args, **kwargs):
+        """Initialize Conv1d module."""
+        super(Conv1d, self).__init__(*args, **kwargs)
+
+    def reset_parameters(self):
+        """Reset parameters."""
+        torch.nn.init.kaiming_normal_(self.weight, nonlinearity="relu")
+        if self.bias is not None:
+            torch.nn.init.constant_(self.bias, 0.0)
+
+
+class Conv1d1x1(Conv1d):
+    """1x1 Conv1d with customized initialization."""
+
+    def __init__(self, in_channels, out_channels, bias):
+        """Initialize 1x1 Conv1d module."""
+        super(Conv1d1x1, self).__init__(in_channels, out_channels,
+                                        kernel_size=1, padding=0,
+                                        dilation=1, bias=bias)
+
+
+class ResidualBlock(torch.nn.Module):
+    """Residual block module in WaveNet."""
+
+    def __init__(self,
+                 kernel_size=3,
+                 residual_channels=64,
+                 gate_channels=128,
+                 skip_channels=64,
+                 aux_channels=80,
+                 dropout=0.0,
+                 dilation=1,
+                 bias=True,
+                 use_causal_conv=False
+                 ):
+        """Initialize ResidualBlock module.
+
+        Args:
+            kernel_size (int): Kernel size of dilation convolution layer.
+            residual_channels (int): Number of channels for residual connection.
+            skip_channels (int): Number of channels for skip connection.
+            aux_channels (int): Local conditioning channels i.e. auxiliary input dimension.
+            dropout (float): Dropout probability.
+            dilation (int): Dilation factor.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            use_causal_conv (bool): Whether to use use_causal_conv or non-use_causal_conv convolution.
+
+        """
+        super(ResidualBlock, self).__init__()
+        self.dropout = dropout
+        # no future time stamps available
+        if use_causal_conv:
+            padding = (kernel_size - 1) * dilation
+        else:
+            assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+            padding = (kernel_size - 1) // 2 * dilation
+        self.use_causal_conv = use_causal_conv
+
+        # dilation conv
+        self.conv = Conv1d(residual_channels, gate_channels, kernel_size,
+                           padding=padding, dilation=dilation, bias=bias)
+
+        # local conditioning
+        if aux_channels > 0:
+            self.conv1x1_aux = Conv1d1x1(aux_channels, gate_channels, bias=False)
+        else:
+            self.conv1x1_aux = None
+
+        # conv output is split into two groups
+        gate_out_channels = gate_channels // 2
+        self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias)
+        self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_channels, bias=bias)
+
+    def forward(self, x, c):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, residual_channels, T).
+            c (Tensor): Local conditioning auxiliary tensor (B, aux_channels, T).
+
+        Returns:
+            Tensor: Output tensor for residual connection (B, residual_channels, T).
+            Tensor: Output tensor for skip connection (B, skip_channels, T).
+
+        """
+        residual = x
+        x = F.dropout(x, p=self.dropout, training=self.training)
+        x = self.conv(x)
+
+        # remove future time steps if use_causal_conv conv
+        x = x[:, :, :residual.size(-1)] if self.use_causal_conv else x
+
+        # split into two part for gated activation
+        splitdim = 1
+        xa, xb = x.split(x.size(splitdim) // 2, dim=splitdim)
+
+        # local conditioning
+        if c is not None:
+            assert self.conv1x1_aux is not None
+            c = self.conv1x1_aux(c)
+            ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim)
+            xa, xb = xa + ca, xb + cb
+
+        x = torch.tanh(xa) * torch.sigmoid(xb)
+
+        # for skip connection
+        s = self.conv1x1_skip(x)
+
+        # for residual connection
+        x = (self.conv1x1_out(x) + residual) * math.sqrt(0.5)
+
+        return x, s
diff --git a/modules/parallel_wavegan/layers/residual_stack.py b/modules/parallel_wavegan/layers/residual_stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e07c8803ad348dd923f6b7c0f7aff14aab9cf78
--- /dev/null
+++ b/modules/parallel_wavegan/layers/residual_stack.py
@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Residual stack module in MelGAN."""
+
+import torch
+
+from . import CausalConv1d
+
+
+class ResidualStack(torch.nn.Module):
+    """Residual stack module introduced in MelGAN."""
+
+    def __init__(self,
+                 kernel_size=3,
+                 channels=32,
+                 dilation=1,
+                 bias=True,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 pad="ReflectionPad1d",
+                 pad_params={},
+                 use_causal_conv=False,
+                 ):
+        """Initialize ResidualStack module.
+
+        Args:
+            kernel_size (int): Kernel size of dilation convolution layer.
+            channels (int): Number of channels of convolution layers.
+            dilation (int): Dilation factor.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            pad (str): Padding function module name before dilated convolution layer.
+            pad_params (dict): Hyperparameters for padding function.
+            use_causal_conv (bool): Whether to use causal convolution.
+
+        """
+        super(ResidualStack, self).__init__()
+
+        # defile residual stack part
+        if not use_causal_conv:
+            assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+            self.stack = torch.nn.Sequential(
+                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params),
+                torch.nn.Conv1d(channels, channels, kernel_size, dilation=dilation, bias=bias),
+                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                torch.nn.Conv1d(channels, channels, 1, bias=bias),
+            )
+        else:
+            self.stack = torch.nn.Sequential(
+                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                CausalConv1d(channels, channels, kernel_size, dilation=dilation,
+                             bias=bias, pad=pad, pad_params=pad_params),
+                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                torch.nn.Conv1d(channels, channels, 1, bias=bias),
+            )
+
+        # defile extra layer for skip connection
+        self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias)
+
+    def forward(self, c):
+        """Calculate forward propagation.
+
+        Args:
+            c (Tensor): Input tensor (B, channels, T).
+
+        Returns:
+            Tensor: Output tensor (B, chennels, T).
+
+        """
+        return self.stack(c) + self.skip_layer(c)
diff --git a/modules/parallel_wavegan/layers/tf_layers.py b/modules/parallel_wavegan/layers/tf_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0f46bd755c161cda2ac904fe37f3f3c6357a88d
--- /dev/null
+++ b/modules/parallel_wavegan/layers/tf_layers.py
@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 MINH ANH (@dathudeptrai)
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Tensorflow Layer modules complatible with pytorch."""
+
+import tensorflow as tf
+
+
+class TFReflectionPad1d(tf.keras.layers.Layer):
+    """Tensorflow ReflectionPad1d module."""
+
+    def __init__(self, padding_size):
+        """Initialize TFReflectionPad1d module.
+
+        Args:
+            padding_size (int): Padding size.
+
+        """
+        super(TFReflectionPad1d, self).__init__()
+        self.padding_size = padding_size
+
+    @tf.function
+    def call(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, T, 1, C).
+
+        Returns:
+            Tensor: Padded tensor (B, T + 2 * padding_size, 1, C).
+
+        """
+        return tf.pad(x, [[0, 0], [self.padding_size, self.padding_size], [0, 0], [0, 0]], "REFLECT")
+
+
+class TFConvTranspose1d(tf.keras.layers.Layer):
+    """Tensorflow ConvTranspose1d module."""
+
+    def __init__(self, channels, kernel_size, stride, padding):
+        """Initialize TFConvTranspose1d( module.
+
+        Args:
+            channels (int): Number of channels.
+            kernel_size (int): kernel size.
+            strides (int): Stride width.
+            padding (str): Padding type ("same" or "valid").
+
+        """
+        super(TFConvTranspose1d, self).__init__()
+        self.conv1d_transpose = tf.keras.layers.Conv2DTranspose(
+            filters=channels,
+            kernel_size=(kernel_size, 1),
+            strides=(stride, 1),
+            padding=padding,
+        )
+
+    @tf.function
+    def call(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, T, 1, C).
+
+        Returns:
+            Tensors: Output tensor (B, T', 1, C').
+
+        """
+        x = self.conv1d_transpose(x)
+        return x
+
+
+class TFResidualStack(tf.keras.layers.Layer):
+    """Tensorflow ResidualStack module."""
+
+    def __init__(self,
+                 kernel_size,
+                 channels,
+                 dilation,
+                 bias,
+                 nonlinear_activation,
+                 nonlinear_activation_params,
+                 padding,
+                 ):
+        """Initialize TFResidualStack module.
+
+        Args:
+            kernel_size (int): Kernel size.
+            channles (int): Number of channels.
+            dilation (int): Dilation ine.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            padding (str): Padding type ("same" or "valid").
+
+        """
+        super(TFResidualStack, self).__init__()
+        self.block = [
+            getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params),
+            TFReflectionPad1d(dilation),
+            tf.keras.layers.Conv2D(
+                filters=channels,
+                kernel_size=(kernel_size, 1),
+                dilation_rate=(dilation, 1),
+                use_bias=bias,
+                padding="valid",
+            ),
+            getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params),
+            tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias)
+        ]
+        self.shortcut = tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias)
+
+    @tf.function
+    def call(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, T, 1, C).
+
+        Returns:
+            Tensor: Output tensor (B, T, 1, C).
+
+        """
+        _x = tf.identity(x)
+        for i, layer in enumerate(self.block):
+            _x = layer(_x)
+        shortcut = self.shortcut(x)
+        return shortcut + _x
diff --git a/modules/parallel_wavegan/layers/upsample.py b/modules/parallel_wavegan/layers/upsample.py
new file mode 100644
index 0000000000000000000000000000000000000000..18c6397c420a81fadc5320e3a48f3249534decd8
--- /dev/null
+++ b/modules/parallel_wavegan/layers/upsample.py
@@ -0,0 +1,183 @@
+# -*- coding: utf-8 -*-
+
+"""Upsampling module.
+
+This code is modified from https://github.com/r9y9/wavenet_vocoder.
+
+"""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from . import Conv1d
+
+
+class Stretch2d(torch.nn.Module):
+    """Stretch2d module."""
+
+    def __init__(self, x_scale, y_scale, mode="nearest"):
+        """Initialize Stretch2d module.
+
+        Args:
+            x_scale (int): X scaling factor (Time axis in spectrogram).
+            y_scale (int): Y scaling factor (Frequency axis in spectrogram).
+            mode (str): Interpolation mode.
+
+        """
+        super(Stretch2d, self).__init__()
+        self.x_scale = x_scale
+        self.y_scale = y_scale
+        self.mode = mode
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, C, F, T).
+
+        Returns:
+            Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale),
+
+        """
+        return F.interpolate(
+            x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode)
+
+
+class Conv2d(torch.nn.Conv2d):
+    """Conv2d module with customized initialization."""
+
+    def __init__(self, *args, **kwargs):
+        """Initialize Conv2d module."""
+        super(Conv2d, self).__init__(*args, **kwargs)
+
+    def reset_parameters(self):
+        """Reset parameters."""
+        self.weight.data.fill_(1. / np.prod(self.kernel_size))
+        if self.bias is not None:
+            torch.nn.init.constant_(self.bias, 0.0)
+
+
+class UpsampleNetwork(torch.nn.Module):
+    """Upsampling network module."""
+
+    def __init__(self,
+                 upsample_scales,
+                 nonlinear_activation=None,
+                 nonlinear_activation_params={},
+                 interpolate_mode="nearest",
+                 freq_axis_kernel_size=1,
+                 use_causal_conv=False,
+                 ):
+        """Initialize upsampling network module.
+
+        Args:
+            upsample_scales (list): List of upsampling scales.
+            nonlinear_activation (str): Activation function name.
+            nonlinear_activation_params (dict): Arguments for specified activation function.
+            interpolate_mode (str): Interpolation mode.
+            freq_axis_kernel_size (int): Kernel size in the direction of frequency axis.
+
+        """
+        super(UpsampleNetwork, self).__init__()
+        self.use_causal_conv = use_causal_conv
+        self.up_layers = torch.nn.ModuleList()
+        for scale in upsample_scales:
+            # interpolation layer
+            stretch = Stretch2d(scale, 1, interpolate_mode)
+            self.up_layers += [stretch]
+
+            # conv layer
+            assert (freq_axis_kernel_size - 1) % 2 == 0, "Not support even number freq axis kernel size."
+            freq_axis_padding = (freq_axis_kernel_size - 1) // 2
+            kernel_size = (freq_axis_kernel_size, scale * 2 + 1)
+            if use_causal_conv:
+                padding = (freq_axis_padding, scale * 2)
+            else:
+                padding = (freq_axis_padding, scale)
+            conv = Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)
+            self.up_layers += [conv]
+
+            # nonlinear
+            if nonlinear_activation is not None:
+                nonlinear = getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)
+                self.up_layers += [nonlinear]
+
+    def forward(self, c):
+        """Calculate forward propagation.
+
+        Args:
+            c : Input tensor (B, C, T).
+
+        Returns:
+            Tensor: Upsampled tensor (B, C, T'), where T' = T * prod(upsample_scales).
+
+        """
+        c = c.unsqueeze(1)  # (B, 1, C, T)
+        for f in self.up_layers:
+            if self.use_causal_conv and isinstance(f, Conv2d):
+                c = f(c)[..., :c.size(-1)]
+            else:
+                c = f(c)
+        return c.squeeze(1)  # (B, C, T')
+
+
+class ConvInUpsampleNetwork(torch.nn.Module):
+    """Convolution + upsampling network module."""
+
+    def __init__(self,
+                 upsample_scales,
+                 nonlinear_activation=None,
+                 nonlinear_activation_params={},
+                 interpolate_mode="nearest",
+                 freq_axis_kernel_size=1,
+                 aux_channels=80,
+                 aux_context_window=0,
+                 use_causal_conv=False
+                 ):
+        """Initialize convolution + upsampling network module.
+
+        Args:
+            upsample_scales (list): List of upsampling scales.
+            nonlinear_activation (str): Activation function name.
+            nonlinear_activation_params (dict): Arguments for specified activation function.
+            mode (str): Interpolation mode.
+            freq_axis_kernel_size (int): Kernel size in the direction of frequency axis.
+            aux_channels (int): Number of channels of pre-convolutional layer.
+            aux_context_window (int): Context window size of the pre-convolutional layer.
+            use_causal_conv (bool): Whether to use causal structure.
+
+        """
+        super(ConvInUpsampleNetwork, self).__init__()
+        self.aux_context_window = aux_context_window
+        self.use_causal_conv = use_causal_conv and aux_context_window > 0
+        # To capture wide-context information in conditional features
+        kernel_size = aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1
+        # NOTE(kan-bayashi): Here do not use padding because the input is already padded
+        self.conv_in = Conv1d(aux_channels, aux_channels, kernel_size=kernel_size, bias=False)
+        self.upsample = UpsampleNetwork(
+            upsample_scales=upsample_scales,
+            nonlinear_activation=nonlinear_activation,
+            nonlinear_activation_params=nonlinear_activation_params,
+            interpolate_mode=interpolate_mode,
+            freq_axis_kernel_size=freq_axis_kernel_size,
+            use_causal_conv=use_causal_conv,
+        )
+
+    def forward(self, c):
+        """Calculate forward propagation.
+
+        Args:
+            c : Input tensor (B, C, T').
+
+        Returns:
+            Tensor: Upsampled tensor (B, C, T),
+                where T = (T' - aux_context_window * 2) * prod(upsample_scales).
+
+        Note:
+            The length of inputs considers the context window size.
+
+        """
+        c_ = self.conv_in(c)
+        c = c_[:, :, :-self.aux_context_window] if self.use_causal_conv else c_
+        return self.upsample(c)
diff --git a/modules/parallel_wavegan/losses/__init__.py b/modules/parallel_wavegan/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b03080a907cb5cb4b316ceb74866ddbc406b33bf
--- /dev/null
+++ b/modules/parallel_wavegan/losses/__init__.py
@@ -0,0 +1 @@
+from .stft_loss import *  # NOQA
diff --git a/modules/parallel_wavegan/losses/stft_loss.py b/modules/parallel_wavegan/losses/stft_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..74d2aa21ad30ba094c406366e652067462f49cd2
--- /dev/null
+++ b/modules/parallel_wavegan/losses/stft_loss.py
@@ -0,0 +1,153 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2019 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""STFT-based Loss modules."""
+
+import torch
+import torch.nn.functional as F
+
+
+def stft(x, fft_size, hop_size, win_length, window):
+    """Perform STFT and convert to magnitude spectrogram.
+
+    Args:
+        x (Tensor): Input signal tensor (B, T).
+        fft_size (int): FFT size.
+        hop_size (int): Hop size.
+        win_length (int): Window length.
+        window (str): Window function type.
+
+    Returns:
+        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
+
+    """
+    x_stft = torch.stft(x, fft_size, hop_size, win_length, window)
+    real = x_stft[..., 0]
+    imag = x_stft[..., 1]
+
+    # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
+    return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
+
+
+class SpectralConvergengeLoss(torch.nn.Module):
+    """Spectral convergence loss module."""
+
+    def __init__(self):
+        """Initilize spectral convergence loss module."""
+        super(SpectralConvergengeLoss, self).__init__()
+
+    def forward(self, x_mag, y_mag):
+        """Calculate forward propagation.
+
+        Args:
+            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
+            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
+
+        Returns:
+            Tensor: Spectral convergence loss value.
+
+        """
+        return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
+
+
+class LogSTFTMagnitudeLoss(torch.nn.Module):
+    """Log STFT magnitude loss module."""
+
+    def __init__(self):
+        """Initilize los STFT magnitude loss module."""
+        super(LogSTFTMagnitudeLoss, self).__init__()
+
+    def forward(self, x_mag, y_mag):
+        """Calculate forward propagation.
+
+        Args:
+            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
+            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
+
+        Returns:
+            Tensor: Log STFT magnitude loss value.
+
+        """
+        return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
+
+
+class STFTLoss(torch.nn.Module):
+    """STFT loss module."""
+
+    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
+        """Initialize STFT loss module."""
+        super(STFTLoss, self).__init__()
+        self.fft_size = fft_size
+        self.shift_size = shift_size
+        self.win_length = win_length
+        self.window = getattr(torch, window)(win_length)
+        self.spectral_convergenge_loss = SpectralConvergengeLoss()
+        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
+
+    def forward(self, x, y):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Predicted signal (B, T).
+            y (Tensor): Groundtruth signal (B, T).
+
+        Returns:
+            Tensor: Spectral convergence loss value.
+            Tensor: Log STFT magnitude loss value.
+
+        """
+        x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
+        y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
+        sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
+        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
+
+        return sc_loss, mag_loss
+
+
+class MultiResolutionSTFTLoss(torch.nn.Module):
+    """Multi resolution STFT loss module."""
+
+    def __init__(self,
+                 fft_sizes=[1024, 2048, 512],
+                 hop_sizes=[120, 240, 50],
+                 win_lengths=[600, 1200, 240],
+                 window="hann_window"):
+        """Initialize Multi resolution STFT loss module.
+
+        Args:
+            fft_sizes (list): List of FFT sizes.
+            hop_sizes (list): List of hop sizes.
+            win_lengths (list): List of window lengths.
+            window (str): Window function type.
+
+        """
+        super(MultiResolutionSTFTLoss, self).__init__()
+        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
+        self.stft_losses = torch.nn.ModuleList()
+        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
+            self.stft_losses += [STFTLoss(fs, ss, wl, window)]
+
+    def forward(self, x, y):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Predicted signal (B, T).
+            y (Tensor): Groundtruth signal (B, T).
+
+        Returns:
+            Tensor: Multi resolution spectral convergence loss value.
+            Tensor: Multi resolution log STFT magnitude loss value.
+
+        """
+        sc_loss = 0.0
+        mag_loss = 0.0
+        for f in self.stft_losses:
+            sc_l, mag_l = f(x, y)
+            sc_loss += sc_l
+            mag_loss += mag_l
+        sc_loss /= len(self.stft_losses)
+        mag_loss /= len(self.stft_losses)
+
+        return sc_loss, mag_loss
diff --git a/modules/parallel_wavegan/models/__init__.py b/modules/parallel_wavegan/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4803ba6b2a0afc8022e756ae5b3f4c7403c3c1bd
--- /dev/null
+++ b/modules/parallel_wavegan/models/__init__.py
@@ -0,0 +1,2 @@
+from .melgan import *  # NOQA
+from .parallel_wavegan import *  # NOQA
diff --git a/modules/parallel_wavegan/models/__pycache__/__init__.cpython-38.pyc b/modules/parallel_wavegan/models/__pycache__/__init__.cpython-38.pyc
new file mode 100644
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diff --git a/modules/parallel_wavegan/models/melgan.py b/modules/parallel_wavegan/models/melgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..e021ae4817a8c1c97338e61b00b230c881836fd8
--- /dev/null
+++ b/modules/parallel_wavegan/models/melgan.py
@@ -0,0 +1,427 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""MelGAN Modules."""
+
+import logging
+
+import numpy as np
+import torch
+
+from modules.parallel_wavegan.layers import CausalConv1d
+from modules.parallel_wavegan.layers import CausalConvTranspose1d
+from modules.parallel_wavegan.layers import ResidualStack
+
+
+class MelGANGenerator(torch.nn.Module):
+    """MelGAN generator module."""
+
+    def __init__(self,
+                 in_channels=80,
+                 out_channels=1,
+                 kernel_size=7,
+                 channels=512,
+                 bias=True,
+                 upsample_scales=[8, 8, 2, 2],
+                 stack_kernel_size=3,
+                 stacks=3,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 pad="ReflectionPad1d",
+                 pad_params={},
+                 use_final_nonlinear_activation=True,
+                 use_weight_norm=True,
+                 use_causal_conv=False,
+                 ):
+        """Initialize MelGANGenerator module.
+
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            kernel_size (int): Kernel size of initial and final conv layer.
+            channels (int): Initial number of channels for conv layer.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            upsample_scales (list): List of upsampling scales.
+            stack_kernel_size (int): Kernel size of dilated conv layers in residual stack.
+            stacks (int): Number of stacks in a single residual stack.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            pad (str): Padding function module name before dilated convolution layer.
+            pad_params (dict): Hyperparameters for padding function.
+            use_final_nonlinear_activation (torch.nn.Module): Activation function for the final layer.
+            use_weight_norm (bool): Whether to use weight norm.
+                If set to true, it will be applied to all of the conv layers.
+            use_causal_conv (bool): Whether to use causal convolution.
+
+        """
+        super(MelGANGenerator, self).__init__()
+
+        # check hyper parameters is valid
+        assert channels >= np.prod(upsample_scales)
+        assert channels % (2 ** len(upsample_scales)) == 0
+        if not use_causal_conv:
+            assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+
+        # add initial layer
+        layers = []
+        if not use_causal_conv:
+            layers += [
+                getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params),
+                torch.nn.Conv1d(in_channels, channels, kernel_size, bias=bias),
+            ]
+        else:
+            layers += [
+                CausalConv1d(in_channels, channels, kernel_size,
+                             bias=bias, pad=pad, pad_params=pad_params),
+            ]
+
+        for i, upsample_scale in enumerate(upsample_scales):
+            # add upsampling layer
+            layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)]
+            if not use_causal_conv:
+                layers += [
+                    torch.nn.ConvTranspose1d(
+                        channels // (2 ** i),
+                        channels // (2 ** (i + 1)),
+                        upsample_scale * 2,
+                        stride=upsample_scale,
+                        padding=upsample_scale // 2 + upsample_scale % 2,
+                        output_padding=upsample_scale % 2,
+                        bias=bias,
+                    )
+                ]
+            else:
+                layers += [
+                    CausalConvTranspose1d(
+                        channels // (2 ** i),
+                        channels // (2 ** (i + 1)),
+                        upsample_scale * 2,
+                        stride=upsample_scale,
+                        bias=bias,
+                    )
+                ]
+
+            # add residual stack
+            for j in range(stacks):
+                layers += [
+                    ResidualStack(
+                        kernel_size=stack_kernel_size,
+                        channels=channels // (2 ** (i + 1)),
+                        dilation=stack_kernel_size ** j,
+                        bias=bias,
+                        nonlinear_activation=nonlinear_activation,
+                        nonlinear_activation_params=nonlinear_activation_params,
+                        pad=pad,
+                        pad_params=pad_params,
+                        use_causal_conv=use_causal_conv,
+                    )
+                ]
+
+        # add final layer
+        layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)]
+        if not use_causal_conv:
+            layers += [
+                getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params),
+                torch.nn.Conv1d(channels // (2 ** (i + 1)), out_channels, kernel_size, bias=bias),
+            ]
+        else:
+            layers += [
+                CausalConv1d(channels // (2 ** (i + 1)), out_channels, kernel_size,
+                             bias=bias, pad=pad, pad_params=pad_params),
+            ]
+        if use_final_nonlinear_activation:
+            layers += [torch.nn.Tanh()]
+
+        # define the model as a single function
+        self.melgan = torch.nn.Sequential(*layers)
+
+        # apply weight norm
+        if use_weight_norm:
+            self.apply_weight_norm()
+
+        # reset parameters
+        self.reset_parameters()
+
+    def forward(self, c):
+        """Calculate forward propagation.
+
+        Args:
+            c (Tensor): Input tensor (B, channels, T).
+
+        Returns:
+            Tensor: Output tensor (B, 1, T ** prod(upsample_scales)).
+
+        """
+        return self.melgan(c)
+
+    def remove_weight_norm(self):
+        """Remove weight normalization module from all of the layers."""
+        def _remove_weight_norm(m):
+            try:
+                logging.debug(f"Weight norm is removed from {m}.")
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
+
+    def apply_weight_norm(self):
+        """Apply weight normalization module from all of the layers."""
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
+                torch.nn.utils.weight_norm(m)
+                logging.debug(f"Weight norm is applied to {m}.")
+
+        self.apply(_apply_weight_norm)
+
+    def reset_parameters(self):
+        """Reset parameters.
+
+        This initialization follows official implementation manner.
+        https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py
+
+        """
+        def _reset_parameters(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
+                m.weight.data.normal_(0.0, 0.02)
+                logging.debug(f"Reset parameters in {m}.")
+
+        self.apply(_reset_parameters)
+
+
+class MelGANDiscriminator(torch.nn.Module):
+    """MelGAN discriminator module."""
+
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_sizes=[5, 3],
+                 channels=16,
+                 max_downsample_channels=1024,
+                 bias=True,
+                 downsample_scales=[4, 4, 4, 4],
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 pad="ReflectionPad1d",
+                 pad_params={},
+                 ):
+        """Initilize MelGAN discriminator module.
+
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            kernel_sizes (list): List of two kernel sizes. The prod will be used for the first conv layer,
+                and the first and the second kernel sizes will be used for the last two layers.
+                For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15,
+                the last two layers' kernel size will be 5 and 3, respectively.
+            channels (int): Initial number of channels for conv layer.
+            max_downsample_channels (int): Maximum number of channels for downsampling layers.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            downsample_scales (list): List of downsampling scales.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            pad (str): Padding function module name before dilated convolution layer.
+            pad_params (dict): Hyperparameters for padding function.
+
+        """
+        super(MelGANDiscriminator, self).__init__()
+        self.layers = torch.nn.ModuleList()
+
+        # check kernel size is valid
+        assert len(kernel_sizes) == 2
+        assert kernel_sizes[0] % 2 == 1
+        assert kernel_sizes[1] % 2 == 1
+
+        # add first layer
+        self.layers += [
+            torch.nn.Sequential(
+                getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
+                torch.nn.Conv1d(in_channels, channels, np.prod(kernel_sizes), bias=bias),
+                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+            )
+        ]
+
+        # add downsample layers
+        in_chs = channels
+        for downsample_scale in downsample_scales:
+            out_chs = min(in_chs * downsample_scale, max_downsample_channels)
+            self.layers += [
+                torch.nn.Sequential(
+                    torch.nn.Conv1d(
+                        in_chs, out_chs,
+                        kernel_size=downsample_scale * 10 + 1,
+                        stride=downsample_scale,
+                        padding=downsample_scale * 5,
+                        groups=in_chs // 4,
+                        bias=bias,
+                    ),
+                    getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                )
+            ]
+            in_chs = out_chs
+
+        # add final layers
+        out_chs = min(in_chs * 2, max_downsample_channels)
+        self.layers += [
+            torch.nn.Sequential(
+                torch.nn.Conv1d(
+                    in_chs, out_chs, kernel_sizes[0],
+                    padding=(kernel_sizes[0] - 1) // 2,
+                    bias=bias,
+                ),
+                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+            )
+        ]
+        self.layers += [
+            torch.nn.Conv1d(
+                out_chs, out_channels, kernel_sizes[1],
+                padding=(kernel_sizes[1] - 1) // 2,
+                bias=bias,
+            ),
+        ]
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input noise signal (B, 1, T).
+
+        Returns:
+            List: List of output tensors of each layer.
+
+        """
+        outs = []
+        for f in self.layers:
+            x = f(x)
+            outs += [x]
+
+        return outs
+
+
+class MelGANMultiScaleDiscriminator(torch.nn.Module):
+    """MelGAN multi-scale discriminator module."""
+
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 scales=3,
+                 downsample_pooling="AvgPool1d",
+                 # follow the official implementation setting
+                 downsample_pooling_params={
+                     "kernel_size": 4,
+                     "stride": 2,
+                     "padding": 1,
+                     "count_include_pad": False,
+                 },
+                 kernel_sizes=[5, 3],
+                 channels=16,
+                 max_downsample_channels=1024,
+                 bias=True,
+                 downsample_scales=[4, 4, 4, 4],
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 pad="ReflectionPad1d",
+                 pad_params={},
+                 use_weight_norm=True,
+                 ):
+        """Initilize MelGAN multi-scale discriminator module.
+
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            downsample_pooling (str): Pooling module name for downsampling of the inputs.
+            downsample_pooling_params (dict): Parameters for the above pooling module.
+            kernel_sizes (list): List of two kernel sizes. The sum will be used for the first conv layer,
+                and the first and the second kernel sizes will be used for the last two layers.
+            channels (int): Initial number of channels for conv layer.
+            max_downsample_channels (int): Maximum number of channels for downsampling layers.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            downsample_scales (list): List of downsampling scales.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            pad (str): Padding function module name before dilated convolution layer.
+            pad_params (dict): Hyperparameters for padding function.
+            use_causal_conv (bool): Whether to use causal convolution.
+
+        """
+        super(MelGANMultiScaleDiscriminator, self).__init__()
+        self.discriminators = torch.nn.ModuleList()
+
+        # add discriminators
+        for _ in range(scales):
+            self.discriminators += [
+                MelGANDiscriminator(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    kernel_sizes=kernel_sizes,
+                    channels=channels,
+                    max_downsample_channels=max_downsample_channels,
+                    bias=bias,
+                    downsample_scales=downsample_scales,
+                    nonlinear_activation=nonlinear_activation,
+                    nonlinear_activation_params=nonlinear_activation_params,
+                    pad=pad,
+                    pad_params=pad_params,
+                )
+            ]
+        self.pooling = getattr(torch.nn, downsample_pooling)(**downsample_pooling_params)
+
+        # apply weight norm
+        if use_weight_norm:
+            self.apply_weight_norm()
+
+        # reset parameters
+        self.reset_parameters()
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input noise signal (B, 1, T).
+
+        Returns:
+            List: List of list of each discriminator outputs, which consists of each layer output tensors.
+
+        """
+        outs = []
+        for f in self.discriminators:
+            outs += [f(x)]
+            x = self.pooling(x)
+
+        return outs
+
+    def remove_weight_norm(self):
+        """Remove weight normalization module from all of the layers."""
+        def _remove_weight_norm(m):
+            try:
+                logging.debug(f"Weight norm is removed from {m}.")
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
+
+    def apply_weight_norm(self):
+        """Apply weight normalization module from all of the layers."""
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
+                torch.nn.utils.weight_norm(m)
+                logging.debug(f"Weight norm is applied to {m}.")
+
+        self.apply(_apply_weight_norm)
+
+    def reset_parameters(self):
+        """Reset parameters.
+
+        This initialization follows official implementation manner.
+        https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py
+
+        """
+        def _reset_parameters(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
+                m.weight.data.normal_(0.0, 0.02)
+                logging.debug(f"Reset parameters in {m}.")
+
+        self.apply(_reset_parameters)
diff --git a/modules/parallel_wavegan/models/parallel_wavegan.py b/modules/parallel_wavegan/models/parallel_wavegan.py
new file mode 100644
index 0000000000000000000000000000000000000000..c63b59f67aa48342179415c1d1beac68574a5498
--- /dev/null
+++ b/modules/parallel_wavegan/models/parallel_wavegan.py
@@ -0,0 +1,434 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2019 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Parallel WaveGAN Modules."""
+
+import logging
+import math
+
+import torch
+from torch import nn
+
+from modules.parallel_wavegan.layers import Conv1d
+from modules.parallel_wavegan.layers import Conv1d1x1
+from modules.parallel_wavegan.layers import ResidualBlock
+from modules.parallel_wavegan.layers import upsample
+from modules.parallel_wavegan import models
+
+
+class ParallelWaveGANGenerator(torch.nn.Module):
+    """Parallel WaveGAN Generator module."""
+
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_size=3,
+                 layers=30,
+                 stacks=3,
+                 residual_channels=64,
+                 gate_channels=128,
+                 skip_channels=64,
+                 aux_channels=80,
+                 aux_context_window=2,
+                 dropout=0.0,
+                 bias=True,
+                 use_weight_norm=True,
+                 use_causal_conv=False,
+                 upsample_conditional_features=True,
+                 upsample_net="ConvInUpsampleNetwork",
+                 upsample_params={"upsample_scales": [4, 4, 4, 4]},
+                 use_pitch_embed=False,
+                 ):
+        """Initialize Parallel WaveGAN Generator module.
+
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            kernel_size (int): Kernel size of dilated convolution.
+            layers (int): Number of residual block layers.
+            stacks (int): Number of stacks i.e., dilation cycles.
+            residual_channels (int): Number of channels in residual conv.
+            gate_channels (int):  Number of channels in gated conv.
+            skip_channels (int): Number of channels in skip conv.
+            aux_channels (int): Number of channels for auxiliary feature conv.
+            aux_context_window (int): Context window size for auxiliary feature.
+            dropout (float): Dropout rate. 0.0 means no dropout applied.
+            bias (bool): Whether to use bias parameter in conv layer.
+            use_weight_norm (bool): Whether to use weight norm.
+                If set to true, it will be applied to all of the conv layers.
+            use_causal_conv (bool): Whether to use causal structure.
+            upsample_conditional_features (bool): Whether to use upsampling network.
+            upsample_net (str): Upsampling network architecture.
+            upsample_params (dict): Upsampling network parameters.
+
+        """
+        super(ParallelWaveGANGenerator, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.aux_channels = aux_channels
+        self.layers = layers
+        self.stacks = stacks
+        self.kernel_size = kernel_size
+
+        # check the number of layers and stacks
+        assert layers % stacks == 0
+        layers_per_stack = layers // stacks
+
+        # define first convolution
+        self.first_conv = Conv1d1x1(in_channels, residual_channels, bias=True)
+
+        # define conv + upsampling network
+        if upsample_conditional_features:
+            upsample_params.update({
+                "use_causal_conv": use_causal_conv,
+            })
+            if upsample_net == "MelGANGenerator":
+                assert aux_context_window == 0
+                upsample_params.update({
+                    "use_weight_norm": False,  # not to apply twice
+                    "use_final_nonlinear_activation": False,
+                })
+                self.upsample_net = getattr(models, upsample_net)(**upsample_params)
+            else:
+                if upsample_net == "ConvInUpsampleNetwork":
+                    upsample_params.update({
+                        "aux_channels": aux_channels,
+                        "aux_context_window": aux_context_window,
+                    })
+                self.upsample_net = getattr(upsample, upsample_net)(**upsample_params)
+        else:
+            self.upsample_net = None
+
+        # define residual blocks
+        self.conv_layers = torch.nn.ModuleList()
+        for layer in range(layers):
+            dilation = 2 ** (layer % layers_per_stack)
+            conv = ResidualBlock(
+                kernel_size=kernel_size,
+                residual_channels=residual_channels,
+                gate_channels=gate_channels,
+                skip_channels=skip_channels,
+                aux_channels=aux_channels,
+                dilation=dilation,
+                dropout=dropout,
+                bias=bias,
+                use_causal_conv=use_causal_conv,
+            )
+            self.conv_layers += [conv]
+
+        # define output layers
+        self.last_conv_layers = torch.nn.ModuleList([
+            torch.nn.ReLU(inplace=True),
+            Conv1d1x1(skip_channels, skip_channels, bias=True),
+            torch.nn.ReLU(inplace=True),
+            Conv1d1x1(skip_channels, out_channels, bias=True),
+        ])
+
+        self.use_pitch_embed = use_pitch_embed
+        if use_pitch_embed:
+            self.pitch_embed = nn.Embedding(300, aux_channels, 0)
+            self.c_proj = nn.Linear(2 * aux_channels, aux_channels)
+
+        # apply weight norm
+        if use_weight_norm:
+            self.apply_weight_norm()
+
+    def forward(self, x, c=None, pitch=None, **kwargs):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input noise signal (B, C_in, T).
+            c (Tensor): Local conditioning auxiliary features (B, C ,T').
+            pitch (Tensor): Local conditioning pitch (B, T').
+
+        Returns:
+            Tensor: Output tensor (B, C_out, T)
+
+        """
+        # perform upsampling
+        if c is not None and self.upsample_net is not None:
+            if self.use_pitch_embed:
+                p = self.pitch_embed(pitch)
+                c = self.c_proj(torch.cat([c.transpose(1, 2), p], -1)).transpose(1, 2)
+            c = self.upsample_net(c)
+            assert c.size(-1) == x.size(-1), (c.size(-1), x.size(-1))
+
+        # encode to hidden representation
+        x = self.first_conv(x)
+        skips = 0
+        for f in self.conv_layers:
+            x, h = f(x, c)
+            skips += h
+        skips *= math.sqrt(1.0 / len(self.conv_layers))
+
+        # apply final layers
+        x = skips
+        for f in self.last_conv_layers:
+            x = f(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        """Remove weight normalization module from all of the layers."""
+        def _remove_weight_norm(m):
+            try:
+                logging.debug(f"Weight norm is removed from {m}.")
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
+
+    def apply_weight_norm(self):
+        """Apply weight normalization module from all of the layers."""
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
+                torch.nn.utils.weight_norm(m)
+                logging.debug(f"Weight norm is applied to {m}.")
+
+        self.apply(_apply_weight_norm)
+
+    @staticmethod
+    def _get_receptive_field_size(layers, stacks, kernel_size,
+                                  dilation=lambda x: 2 ** x):
+        assert layers % stacks == 0
+        layers_per_cycle = layers // stacks
+        dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
+        return (kernel_size - 1) * sum(dilations) + 1
+
+    @property
+    def receptive_field_size(self):
+        """Return receptive field size."""
+        return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
+
+
+class ParallelWaveGANDiscriminator(torch.nn.Module):
+    """Parallel WaveGAN Discriminator module."""
+
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_size=3,
+                 layers=10,
+                 conv_channels=64,
+                 dilation_factor=1,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 bias=True,
+                 use_weight_norm=True,
+                 ):
+        """Initialize Parallel WaveGAN Discriminator module.
+
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            kernel_size (int): Number of output channels.
+            layers (int): Number of conv layers.
+            conv_channels (int): Number of chnn layers.
+            dilation_factor (int): Dilation factor. For example, if dilation_factor = 2,
+                the dilation will be 2, 4, 8, ..., and so on.
+            nonlinear_activation (str): Nonlinear function after each conv.
+            nonlinear_activation_params (dict): Nonlinear function parameters
+            bias (bool): Whether to use bias parameter in conv.
+            use_weight_norm (bool) Whether to use weight norm.
+                If set to true, it will be applied to all of the conv layers.
+
+        """
+        super(ParallelWaveGANDiscriminator, self).__init__()
+        assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+        assert dilation_factor > 0, "Dilation factor must be > 0."
+        self.conv_layers = torch.nn.ModuleList()
+        conv_in_channels = in_channels
+        for i in range(layers - 1):
+            if i == 0:
+                dilation = 1
+            else:
+                dilation = i if dilation_factor == 1 else dilation_factor ** i
+                conv_in_channels = conv_channels
+            padding = (kernel_size - 1) // 2 * dilation
+            conv_layer = [
+                Conv1d(conv_in_channels, conv_channels,
+                       kernel_size=kernel_size, padding=padding,
+                       dilation=dilation, bias=bias),
+                getattr(torch.nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params)
+            ]
+            self.conv_layers += conv_layer
+        padding = (kernel_size - 1) // 2
+        last_conv_layer = Conv1d(
+            conv_in_channels, out_channels,
+            kernel_size=kernel_size, padding=padding, bias=bias)
+        self.conv_layers += [last_conv_layer]
+
+        # apply weight norm
+        if use_weight_norm:
+            self.apply_weight_norm()
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input noise signal (B, 1, T).
+
+        Returns:
+            Tensor: Output tensor (B, 1, T)
+
+        """
+        for f in self.conv_layers:
+            x = f(x)
+        return x
+
+    def apply_weight_norm(self):
+        """Apply weight normalization module from all of the layers."""
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
+                torch.nn.utils.weight_norm(m)
+                logging.debug(f"Weight norm is applied to {m}.")
+
+        self.apply(_apply_weight_norm)
+
+    def remove_weight_norm(self):
+        """Remove weight normalization module from all of the layers."""
+        def _remove_weight_norm(m):
+            try:
+                logging.debug(f"Weight norm is removed from {m}.")
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
+
+
+class ResidualParallelWaveGANDiscriminator(torch.nn.Module):
+    """Parallel WaveGAN Discriminator module."""
+
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_size=3,
+                 layers=30,
+                 stacks=3,
+                 residual_channels=64,
+                 gate_channels=128,
+                 skip_channels=64,
+                 dropout=0.0,
+                 bias=True,
+                 use_weight_norm=True,
+                 use_causal_conv=False,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 ):
+        """Initialize Parallel WaveGAN Discriminator module.
+
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            kernel_size (int): Kernel size of dilated convolution.
+            layers (int): Number of residual block layers.
+            stacks (int): Number of stacks i.e., dilation cycles.
+            residual_channels (int): Number of channels in residual conv.
+            gate_channels (int):  Number of channels in gated conv.
+            skip_channels (int): Number of channels in skip conv.
+            dropout (float): Dropout rate. 0.0 means no dropout applied.
+            bias (bool): Whether to use bias parameter in conv.
+            use_weight_norm (bool): Whether to use weight norm.
+                If set to true, it will be applied to all of the conv layers.
+            use_causal_conv (bool): Whether to use causal structure.
+            nonlinear_activation_params (dict): Nonlinear function parameters
+
+        """
+        super(ResidualParallelWaveGANDiscriminator, self).__init__()
+        assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.layers = layers
+        self.stacks = stacks
+        self.kernel_size = kernel_size
+
+        # check the number of layers and stacks
+        assert layers % stacks == 0
+        layers_per_stack = layers // stacks
+
+        # define first convolution
+        self.first_conv = torch.nn.Sequential(
+            Conv1d1x1(in_channels, residual_channels, bias=True),
+            getattr(torch.nn, nonlinear_activation)(
+                inplace=True, **nonlinear_activation_params),
+        )
+
+        # define residual blocks
+        self.conv_layers = torch.nn.ModuleList()
+        for layer in range(layers):
+            dilation = 2 ** (layer % layers_per_stack)
+            conv = ResidualBlock(
+                kernel_size=kernel_size,
+                residual_channels=residual_channels,
+                gate_channels=gate_channels,
+                skip_channels=skip_channels,
+                aux_channels=-1,
+                dilation=dilation,
+                dropout=dropout,
+                bias=bias,
+                use_causal_conv=use_causal_conv,
+            )
+            self.conv_layers += [conv]
+
+        # define output layers
+        self.last_conv_layers = torch.nn.ModuleList([
+            getattr(torch.nn, nonlinear_activation)(
+                inplace=True, **nonlinear_activation_params),
+            Conv1d1x1(skip_channels, skip_channels, bias=True),
+            getattr(torch.nn, nonlinear_activation)(
+                inplace=True, **nonlinear_activation_params),
+            Conv1d1x1(skip_channels, out_channels, bias=True),
+        ])
+
+        # apply weight norm
+        if use_weight_norm:
+            self.apply_weight_norm()
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input noise signal (B, 1, T).
+
+        Returns:
+            Tensor: Output tensor (B, 1, T)
+
+        """
+        x = self.first_conv(x)
+
+        skips = 0
+        for f in self.conv_layers:
+            x, h = f(x, None)
+            skips += h
+        skips *= math.sqrt(1.0 / len(self.conv_layers))
+
+        # apply final layers
+        x = skips
+        for f in self.last_conv_layers:
+            x = f(x)
+        return x
+
+    def apply_weight_norm(self):
+        """Apply weight normalization module from all of the layers."""
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
+                torch.nn.utils.weight_norm(m)
+                logging.debug(f"Weight norm is applied to {m}.")
+
+        self.apply(_apply_weight_norm)
+
+    def remove_weight_norm(self):
+        """Remove weight normalization module from all of the layers."""
+        def _remove_weight_norm(m):
+            try:
+                logging.debug(f"Weight norm is removed from {m}.")
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
diff --git a/modules/parallel_wavegan/models/source.py b/modules/parallel_wavegan/models/source.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2a006e53c0e2194036fd08ea9d6ed4d9a10d6cf
--- /dev/null
+++ b/modules/parallel_wavegan/models/source.py
@@ -0,0 +1,538 @@
+import torch
+import numpy as np
+import sys
+import torch.nn.functional as torch_nn_func
+
+
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0,
+                 flag_for_pulse=False):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+
+    def _f02sine(self, f0_values):
+        """ f0_values: (batchsize, length, dim)
+            where dim indicates fundamental tone and overtones
+        """
+        # convert to F0 in rad. The interger part n can be ignored
+        # because 2 * np.pi * n doesn't affect phase
+        rad_values = (f0_values / self.sampling_rate) % 1
+
+        # initial phase noise (no noise for fundamental component)
+        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
+                              device=f0_values.device)
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+        if not self.flag_for_pulse:
+            # for normal case
+
+            # To prevent torch.cumsum numerical overflow,
+            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
+            # Buffer tmp_over_one_idx indicates the time step to add -1.
+            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
+            tmp_over_one = torch.cumsum(rad_values, 1) % 1
+            tmp_over_one_idx = (tmp_over_one[:, 1:, :] -
+                                tmp_over_one[:, :-1, :]) < 0
+            cumsum_shift = torch.zeros_like(rad_values)
+            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
+                              * 2 * np.pi)
+        else:
+            # If necessary, make sure that the first time step of every
+            # voiced segments is sin(pi) or cos(0)
+            # This is used for pulse-train generation
+
+            # identify the last time step in unvoiced segments
+            uv = self._f02uv(f0_values)
+            uv_1 = torch.roll(uv, shifts=-1, dims=1)
+            uv_1[:, -1, :] = 1
+            u_loc = (uv < 1) * (uv_1 > 0)
+
+            # get the instantanouse phase
+            tmp_cumsum = torch.cumsum(rad_values, dim=1)
+            # different batch needs to be processed differently
+            for idx in range(f0_values.shape[0]):
+                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+                # stores the accumulation of i.phase within
+                # each voiced segments
+                tmp_cumsum[idx, :, :] = 0
+                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+            # rad_values - tmp_cumsum: remove the accumulation of i.phase
+            # within the previous voiced segment.
+            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+            # get the sines
+            sines = torch.cos(i_phase * 2 * np.pi)
+        return sines
+
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
+                                 device=f0.device)
+            # fundamental component
+            f0_buf[:, :, 0] = f0[:, :, 0]
+            for idx in np.arange(self.harmonic_num):
+                # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+            # generate sine waveforms
+            sine_waves = self._f02sine(f0_buf) * self.sine_amp
+
+            # generate uv signal
+            # uv = torch.ones(f0.shape)
+            # uv = uv * (f0 > self.voiced_threshold)
+            uv = self._f02uv(f0)
+
+            # noise: for unvoiced should be similar to sine_amp
+            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+            # .       for voiced regions is self.noise_std
+            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+
+            # first: set the unvoiced part to 0 by uv
+            # then: additive noise
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class PulseGen(torch.nn.Module):
+    """ Definition of Pulse train generator
+
+    There are many ways to implement pulse generator.
+    Here, PulseGen is based on SinGen. For a perfect
+    """
+    def __init__(self, samp_rate, pulse_amp = 0.1,
+                 noise_std = 0.003, voiced_threshold = 0):
+        super(PulseGen, self).__init__()
+        self.pulse_amp = pulse_amp
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.noise_std = noise_std
+        self.l_sinegen = SineGen(self.sampling_rate, harmonic_num=0, \
+                                 sine_amp=self.pulse_amp, noise_std=0, \
+                                 voiced_threshold=self.voiced_threshold, \
+                                 flag_for_pulse=True)
+
+    def forward(self, f0):
+        """ Pulse train generator
+        pulse_train, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output pulse_train: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+
+        Note: self.l_sine doesn't make sure that the initial phase of
+        a voiced segment is np.pi, the first pulse in a voiced segment
+        may not be at the first time step within a voiced segment
+        """
+        with torch.no_grad():
+            sine_wav, uv, noise = self.l_sinegen(f0)
+
+            # sine without additive noise
+            pure_sine = sine_wav - noise
+
+            # step t corresponds to a pulse if
+            # sine[t] > sine[t+1] & sine[t] > sine[t-1]
+            # & sine[t-1], sine[t+1], and sine[t] are voiced
+            # or
+            # sine[t] is voiced, sine[t-1] is unvoiced
+            # we use torch.roll to simulate sine[t+1] and sine[t-1]
+            sine_1 = torch.roll(pure_sine, shifts=1, dims=1)
+            uv_1 = torch.roll(uv, shifts=1, dims=1)
+            uv_1[:, 0, :] = 0
+            sine_2 = torch.roll(pure_sine, shifts=-1, dims=1)
+            uv_2 = torch.roll(uv, shifts=-1, dims=1)
+            uv_2[:, -1, :] = 0
+
+            loc = (pure_sine > sine_1) * (pure_sine > sine_2) \
+                  * (uv_1 > 0) * (uv_2 > 0) * (uv > 0) \
+                  + (uv_1 < 1) * (uv > 0)
+
+            # pulse train without noise
+            pulse_train = pure_sine * loc
+
+            # additive noise to pulse train
+            # note that noise from sinegen is zero in voiced regions
+            pulse_noise = torch.randn_like(pure_sine) * self.noise_std
+
+            # with additive noise on pulse, and unvoiced regions
+            pulse_train += pulse_noise * loc + pulse_noise * (1 - uv)
+        return pulse_train, sine_wav, uv, pulse_noise
+
+
+class SignalsConv1d(torch.nn.Module):
+    """ Filtering input signal with time invariant filter
+    Note: FIRFilter conducted filtering given fixed FIR weight
+          SignalsConv1d convolves two signals
+    Note: this is based on torch.nn.functional.conv1d
+
+    """
+
+    def __init__(self):
+        super(SignalsConv1d, self).__init__()
+
+    def forward(self, signal, system_ir):
+        """ output = forward(signal, system_ir)
+
+        signal:    (batchsize, length1, dim)
+        system_ir: (length2, dim)
+
+        output:    (batchsize, length1, dim)
+        """
+        if signal.shape[-1] != system_ir.shape[-1]:
+            print("Error: SignalsConv1d expects shape:")
+            print("signal    (batchsize, length1, dim)")
+            print("system_id (batchsize, length2, dim)")
+            print("But received signal: {:s}".format(str(signal.shape)))
+            print(" system_ir: {:s}".format(str(system_ir.shape)))
+            sys.exit(1)
+        padding_length = system_ir.shape[0] - 1
+        groups = signal.shape[-1]
+
+        # pad signal on the left
+        signal_pad = torch_nn_func.pad(signal.permute(0, 2, 1), \
+                                       (padding_length, 0))
+        # prepare system impulse response as (dim, 1, length2)
+        # also flip the impulse response
+        ir = torch.flip(system_ir.unsqueeze(1).permute(2, 1, 0), \
+                        dims=[2])
+        # convolute
+        output = torch_nn_func.conv1d(signal_pad, ir, groups=groups)
+        return output.permute(0, 2, 1)
+
+
+class CyclicNoiseGen_v1(torch.nn.Module):
+    """ CyclicnoiseGen_v1
+    Cyclic noise with a single parameter of beta.
+    Pytorch v1 implementation assumes f_t is also fixed
+    """
+
+    def __init__(self, samp_rate,
+                 noise_std=0.003, voiced_threshold=0):
+        super(CyclicNoiseGen_v1, self).__init__()
+        self.samp_rate = samp_rate
+        self.noise_std = noise_std
+        self.voiced_threshold = voiced_threshold
+
+        self.l_pulse = PulseGen(samp_rate, pulse_amp=1.0,
+                                noise_std=noise_std,
+                                voiced_threshold=voiced_threshold)
+        self.l_conv = SignalsConv1d()
+
+    def noise_decay(self, beta, f0mean):
+        """ decayed_noise = noise_decay(beta, f0mean)
+        decayed_noise =  n[t]exp(-t * f_mean / beta / samp_rate)
+
+        beta: (dim=1) or (batchsize=1, 1, dim=1)
+        f0mean (batchsize=1, 1, dim=1)
+
+        decayed_noise (batchsize=1, length, dim=1)
+        """
+        with torch.no_grad():
+            # exp(-1.0 n / T) < 0.01 => n > -log(0.01)*T = 4.60*T
+            # truncate the noise when decayed by -40 dB
+            length = 4.6 * self.samp_rate / f0mean
+            length = length.int()
+            time_idx = torch.arange(0, length, device=beta.device)
+            time_idx = time_idx.unsqueeze(0).unsqueeze(2)
+            time_idx = time_idx.repeat(beta.shape[0], 1, beta.shape[2])
+
+        noise = torch.randn(time_idx.shape, device=beta.device)
+
+        # due to Pytorch implementation, use f0_mean as the f0 factor
+        decay = torch.exp(-time_idx * f0mean / beta / self.samp_rate)
+        return noise * self.noise_std * decay
+
+    def forward(self, f0s, beta):
+        """ Producde cyclic-noise
+        """
+        # pulse train
+        pulse_train, sine_wav, uv, noise = self.l_pulse(f0s)
+        pure_pulse = pulse_train - noise
+
+        # decayed_noise (length, dim=1)
+        if (uv < 1).all():
+            # all unvoiced
+            cyc_noise = torch.zeros_like(sine_wav)
+        else:
+            f0mean = f0s[uv > 0].mean()
+
+            decayed_noise = self.noise_decay(beta, f0mean)[0, :, :]
+            # convolute
+            cyc_noise = self.l_conv(pure_pulse, decayed_noise)
+
+        # add noise in invoiced segments
+        cyc_noise = cyc_noise + noise * (1.0 - uv)
+        return cyc_noise, pulse_train, sine_wav, uv, noise
+
+
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0,
+                 flag_for_pulse=False):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+
+    def _f02sine(self, f0_values):
+        """ f0_values: (batchsize, length, dim)
+            where dim indicates fundamental tone and overtones
+        """
+        # convert to F0 in rad. The interger part n can be ignored
+        # because 2 * np.pi * n doesn't affect phase
+        rad_values = (f0_values / self.sampling_rate) % 1
+
+        # initial phase noise (no noise for fundamental component)
+        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
+                              device=f0_values.device)
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+        if not self.flag_for_pulse:
+            # for normal case
+
+            # To prevent torch.cumsum numerical overflow,
+            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
+            # Buffer tmp_over_one_idx indicates the time step to add -1.
+            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
+            tmp_over_one = torch.cumsum(rad_values, 1) % 1
+            tmp_over_one_idx = (tmp_over_one[:, 1:, :] -
+                                tmp_over_one[:, :-1, :]) < 0
+            cumsum_shift = torch.zeros_like(rad_values)
+            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
+                              * 2 * np.pi)
+        else:
+            # If necessary, make sure that the first time step of every
+            # voiced segments is sin(pi) or cos(0)
+            # This is used for pulse-train generation
+
+            # identify the last time step in unvoiced segments
+            uv = self._f02uv(f0_values)
+            uv_1 = torch.roll(uv, shifts=-1, dims=1)
+            uv_1[:, -1, :] = 1
+            u_loc = (uv < 1) * (uv_1 > 0)
+
+            # get the instantanouse phase
+            tmp_cumsum = torch.cumsum(rad_values, dim=1)
+            # different batch needs to be processed differently
+            for idx in range(f0_values.shape[0]):
+                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+                # stores the accumulation of i.phase within
+                # each voiced segments
+                tmp_cumsum[idx, :, :] = 0
+                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+            # rad_values - tmp_cumsum: remove the accumulation of i.phase
+            # within the previous voiced segment.
+            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+            # get the sines
+            sines = torch.cos(i_phase * 2 * np.pi)
+        return sines
+
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, \
+                                 device=f0.device)
+            # fundamental component
+            f0_buf[:, :, 0] = f0[:, :, 0]
+            for idx in np.arange(self.harmonic_num):
+                # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+            # generate sine waveforms
+            sine_waves = self._f02sine(f0_buf) * self.sine_amp
+
+            # generate uv signal
+            # uv = torch.ones(f0.shape)
+            # uv = uv * (f0 > self.voiced_threshold)
+            uv = self._f02uv(f0)
+
+            # noise: for unvoiced should be similar to sine_amp
+            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+            # .       for voiced regions is self.noise_std
+            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+
+            # first: set the unvoiced part to 0 by uv
+            # then: additive noise
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleCycNoise_v1(torch.nn.Module):
+    """ SourceModuleCycNoise_v1
+    SourceModule(sampling_rate, noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+
+    noise_std: std of Gaussian noise (default: 0.003)
+    voiced_threshold: threshold to set U/V given F0 (default: 0)
+
+    cyc, noise, uv = SourceModuleCycNoise_v1(F0_upsampled, beta)
+    F0_upsampled (batchsize, length, 1)
+    beta (1)
+    cyc (batchsize, length, 1)
+    noise (batchsize, length, 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleCycNoise_v1, self).__init__()
+        self.sampling_rate = sampling_rate
+        self.noise_std = noise_std
+        self.l_cyc_gen = CyclicNoiseGen_v1(sampling_rate, noise_std,
+                                           voiced_threshod)
+
+    def forward(self, f0_upsamped, beta):
+        """
+        cyc, noise, uv = SourceModuleCycNoise_v1(F0, beta)
+        F0_upsampled (batchsize, length, 1)
+        beta (1)
+        cyc (batchsize, length, 1)
+        noise (batchsize, length, 1)
+        uv (batchsize, length, 1)
+        """
+        # source for harmonic branch
+        cyc, pulse, sine, uv, add_noi = self.l_cyc_gen(f0_upsamped, beta)
+
+        # source for noise branch, in the same shape as uv
+        noise = torch.randn_like(uv) * self.noise_std / 3
+        return cyc, noise, uv
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
+                                 sine_amp, add_noise_std, voiced_threshod)
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        # source for harmonic branch
+        sine_wavs, uv, _ = self.l_sin_gen(x)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        # source for noise branch, in the same shape as uv
+        noise = torch.randn_like(uv) * self.sine_amp / 3
+        return sine_merge, noise, uv
+
+
+if __name__ == '__main__':
+    source = SourceModuleCycNoise_v1(24000)
+    x = torch.randn(16, 25600, 1)
+
+
diff --git a/modules/parallel_wavegan/optimizers/__init__.py b/modules/parallel_wavegan/optimizers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0e0c5932838281e912079e5784d84d43444a61a
--- /dev/null
+++ b/modules/parallel_wavegan/optimizers/__init__.py
@@ -0,0 +1,2 @@
+from torch.optim import *  # NOQA
+from .radam import *  # NOQA
diff --git a/modules/parallel_wavegan/optimizers/radam.py b/modules/parallel_wavegan/optimizers/radam.py
new file mode 100644
index 0000000000000000000000000000000000000000..e805d7e34921bee436e1e7fd9e1f753c7609186b
--- /dev/null
+++ b/modules/parallel_wavegan/optimizers/radam.py
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+
+"""RAdam optimizer.
+
+This code is drived from https://github.com/LiyuanLucasLiu/RAdam.
+"""
+
+import math
+import torch
+
+from torch.optim.optimizer import Optimizer
+
+
+class RAdam(Optimizer):
+    """Rectified Adam optimizer."""
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
+        """Initilize RAdam optimizer."""
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        self.buffer = [[None, None, None] for ind in range(10)]
+        super(RAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        """Set state."""
+        super(RAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+        """Run one step."""
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError('RAdam does not support sparse gradients')
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                buffered = self.buffer[int(state['step'] % 10)]
+                if state['step'] == buffered[0]:
+                    N_sma, step_size = buffered[1], buffered[2]
+                else:
+                    buffered[0] = state['step']
+                    beta2_t = beta2 ** state['step']
+                    N_sma_max = 2 / (1 - beta2) - 1
+                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+                    buffered[1] = N_sma
+
+                    # more conservative since it's an approximated value
+                    if N_sma >= 5:
+                        step_size = math.sqrt(
+                            (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])  # NOQA
+                    else:
+                        step_size = 1.0 / (1 - beta1 ** state['step'])
+                    buffered[2] = step_size
+
+                if group['weight_decay'] != 0:
+                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
+                else:
+                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
+
+                p.data.copy_(p_data_fp32)
+
+        return loss
diff --git a/modules/parallel_wavegan/stft_loss.py b/modules/parallel_wavegan/stft_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..229e6c777dc9ec7f710842d1e648dba1189ec8b4
--- /dev/null
+++ b/modules/parallel_wavegan/stft_loss.py
@@ -0,0 +1,100 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2019 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""STFT-based Loss modules."""
+import librosa
+import torch
+
+from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft
+
+
+class STFTLoss(torch.nn.Module):
+    """STFT loss module."""
+
+    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window",
+                 use_mel_loss=False):
+        """Initialize STFT loss module."""
+        super(STFTLoss, self).__init__()
+        self.fft_size = fft_size
+        self.shift_size = shift_size
+        self.win_length = win_length
+        self.window = getattr(torch, window)(win_length)
+        self.spectral_convergenge_loss = SpectralConvergengeLoss()
+        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
+        self.use_mel_loss = use_mel_loss
+        self.mel_basis = None
+
+    def forward(self, x, y):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Predicted signal (B, T).
+            y (Tensor): Groundtruth signal (B, T).
+
+        Returns:
+            Tensor: Spectral convergence loss value.
+            Tensor: Log STFT magnitude loss value.
+
+        """
+        x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
+        y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
+        if self.use_mel_loss:
+            if self.mel_basis is None:
+                self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T
+            x_mag = x_mag @ self.mel_basis
+            y_mag = y_mag @ self.mel_basis
+
+        sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
+        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
+
+        return sc_loss, mag_loss
+
+
+class MultiResolutionSTFTLoss(torch.nn.Module):
+    """Multi resolution STFT loss module."""
+
+    def __init__(self,
+                 fft_sizes=[1024, 2048, 512],
+                 hop_sizes=[120, 240, 50],
+                 win_lengths=[600, 1200, 240],
+                 window="hann_window",
+                 use_mel_loss=False):
+        """Initialize Multi resolution STFT loss module.
+
+        Args:
+            fft_sizes (list): List of FFT sizes.
+            hop_sizes (list): List of hop sizes.
+            win_lengths (list): List of window lengths.
+            window (str): Window function type.
+
+        """
+        super(MultiResolutionSTFTLoss, self).__init__()
+        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
+        self.stft_losses = torch.nn.ModuleList()
+        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
+            self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)]
+
+    def forward(self, x, y):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Predicted signal (B, T).
+            y (Tensor): Groundtruth signal (B, T).
+
+        Returns:
+            Tensor: Multi resolution spectral convergence loss value.
+            Tensor: Multi resolution log STFT magnitude loss value.
+
+        """
+        sc_loss = 0.0
+        mag_loss = 0.0
+        for f in self.stft_losses:
+            sc_l, mag_l = f(x, y)
+            sc_loss += sc_l
+            mag_loss += mag_l
+        sc_loss /= len(self.stft_losses)
+        mag_loss /= len(self.stft_losses)
+
+        return sc_loss, mag_loss
diff --git a/modules/parallel_wavegan/utils/__init__.py b/modules/parallel_wavegan/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8fa95a020706b5412c3959fbf6e5980019c0d5f
--- /dev/null
+++ b/modules/parallel_wavegan/utils/__init__.py
@@ -0,0 +1 @@
+from .utils import *  # NOQA
diff --git a/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-38.pyc b/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..856a1203a49a5f131e2e1f110f701ee988cc1b5f
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diff --git a/modules/parallel_wavegan/utils/__pycache__/utils.cpython-38.pyc b/modules/parallel_wavegan/utils/__pycache__/utils.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1f3736354ac834dab8860599354f3a0d2726b613
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diff --git a/modules/parallel_wavegan/utils/utils.py b/modules/parallel_wavegan/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a30e803723d224d9825a753baf4cd91c94c1677
--- /dev/null
+++ b/modules/parallel_wavegan/utils/utils.py
@@ -0,0 +1,169 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2019 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Utility functions."""
+
+import fnmatch
+import logging
+import os
+import sys
+
+import h5py
+import numpy as np
+
+
+def find_files(root_dir, query="*.wav", include_root_dir=True):
+    """Find files recursively.
+
+    Args:
+        root_dir (str): Root root_dir to find.
+        query (str): Query to find.
+        include_root_dir (bool): If False, root_dir name is not included.
+
+    Returns:
+        list: List of found filenames.
+
+    """
+    files = []
+    for root, dirnames, filenames in os.walk(root_dir, followlinks=True):
+        for filename in fnmatch.filter(filenames, query):
+            files.append(os.path.join(root, filename))
+    if not include_root_dir:
+        files = [file_.replace(root_dir + "/", "") for file_ in files]
+
+    return files
+
+
+def read_hdf5(hdf5_name, hdf5_path):
+    """Read hdf5 dataset.
+
+    Args:
+        hdf5_name (str): Filename of hdf5 file.
+        hdf5_path (str): Dataset name in hdf5 file.
+
+    Return:
+        any: Dataset values.
+
+    """
+    if not os.path.exists(hdf5_name):
+        logging.error(f"There is no such a hdf5 file ({hdf5_name}).")
+        sys.exit(1)
+
+    hdf5_file = h5py.File(hdf5_name, "r")
+
+    if hdf5_path not in hdf5_file:
+        logging.error(f"There is no such a data in hdf5 file. ({hdf5_path})")
+        sys.exit(1)
+
+    hdf5_data = hdf5_file[hdf5_path][()]
+    hdf5_file.close()
+
+    return hdf5_data
+
+
+def write_hdf5(hdf5_name, hdf5_path, write_data, is_overwrite=True):
+    """Write dataset to hdf5.
+
+    Args:
+        hdf5_name (str): Hdf5 dataset filename.
+        hdf5_path (str): Dataset path in hdf5.
+        write_data (ndarray): Data to write.
+        is_overwrite (bool): Whether to overwrite dataset.
+
+    """
+    # convert to numpy array
+    write_data = np.array(write_data)
+
+    # check folder existence
+    folder_name, _ = os.path.split(hdf5_name)
+    if not os.path.exists(folder_name) and len(folder_name) != 0:
+        os.makedirs(folder_name)
+
+    # check hdf5 existence
+    if os.path.exists(hdf5_name):
+        # if already exists, open with r+ mode
+        hdf5_file = h5py.File(hdf5_name, "r+")
+        # check dataset existence
+        if hdf5_path in hdf5_file:
+            if is_overwrite:
+                logging.warning("Dataset in hdf5 file already exists. "
+                                "recreate dataset in hdf5.")
+                hdf5_file.__delitem__(hdf5_path)
+            else:
+                logging.error("Dataset in hdf5 file already exists. "
+                              "if you want to overwrite, please set is_overwrite = True.")
+                hdf5_file.close()
+                sys.exit(1)
+    else:
+        # if not exists, open with w mode
+        hdf5_file = h5py.File(hdf5_name, "w")
+
+    # write data to hdf5
+    hdf5_file.create_dataset(hdf5_path, data=write_data)
+    hdf5_file.flush()
+    hdf5_file.close()
+
+
+class HDF5ScpLoader(object):
+    """Loader class for a fests.scp file of hdf5 file.
+
+    Examples:
+        key1 /some/path/a.h5:feats
+        key2 /some/path/b.h5:feats
+        key3 /some/path/c.h5:feats
+        key4 /some/path/d.h5:feats
+        ...
+        >>> loader = HDF5ScpLoader("hdf5.scp")
+        >>> array = loader["key1"]
+
+        key1 /some/path/a.h5
+        key2 /some/path/b.h5
+        key3 /some/path/c.h5
+        key4 /some/path/d.h5
+        ...
+        >>> loader = HDF5ScpLoader("hdf5.scp", "feats")
+        >>> array = loader["key1"]
+
+    """
+
+    def __init__(self, feats_scp, default_hdf5_path="feats"):
+        """Initialize HDF5 scp loader.
+
+        Args:
+            feats_scp (str): Kaldi-style feats.scp file with hdf5 format.
+            default_hdf5_path (str): Path in hdf5 file. If the scp contain the info, not used.
+
+        """
+        self.default_hdf5_path = default_hdf5_path
+        with open(feats_scp, encoding='utf-8') as f:
+            lines = [line.replace("\n", "") for line in f.readlines()]
+        self.data = {}
+        for line in lines:
+            key, value = line.split()
+            self.data[key] = value
+
+    def get_path(self, key):
+        """Get hdf5 file path for a given key."""
+        return self.data[key]
+
+    def __getitem__(self, key):
+        """Get ndarray for a given key."""
+        p = self.data[key]
+        if ":" in p:
+            return read_hdf5(*p.split(":"))
+        else:
+            return read_hdf5(p, self.default_hdf5_path)
+
+    def __len__(self):
+        """Return the length of the scp file."""
+        return len(self.data)
+
+    def __iter__(self):
+        """Return the iterator of the scp file."""
+        return iter(self.data)
+
+    def keys(self):
+        """Return the keys of the scp file."""
+        return self.data.keys()
diff --git a/network/diff/__pycache__/candidate_decoder.cpython-38.pyc b/network/diff/__pycache__/candidate_decoder.cpython-38.pyc
new file mode 100644
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diff --git a/network/diff/candidate_decoder.py b/network/diff/candidate_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..bccb47aad0285cab1e7aaca759294c8a1270849c
--- /dev/null
+++ b/network/diff/candidate_decoder.py
@@ -0,0 +1,98 @@
+from modules.fastspeech.tts_modules import FastspeechDecoder
+# from modules.fastspeech.fast_tacotron import DecoderRNN
+# from modules.fastspeech.speedy_speech.speedy_speech import ConvBlocks
+# from modules.fastspeech.conformer.conformer import ConformerDecoder
+import torch
+from torch.nn import functional as F
+import torch.nn as nn
+import math
+from utils.hparams import hparams
+from modules.commons.common_layers import Mish
+Linear = nn.Linear
+
+class SinusoidalPosEmb(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
+        emb = x[:, None] * emb[None, :]
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+def Conv1d(*args, **kwargs):
+    layer = nn.Conv1d(*args, **kwargs)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+class FFT(FastspeechDecoder): # unused, because DiffSinger only uses FastspeechEncoder
+    # NOTE: this part of script is *isolated* from other scripts, which means
+    #       it may not be compatible with the current version.
+    
+    def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None):
+        super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads)
+        dim = hparams['residual_channels']
+        self.input_projection = Conv1d(hparams['audio_num_mel_bins'], dim, 1)
+        self.diffusion_embedding = SinusoidalPosEmb(dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, dim * 4),
+            Mish(),
+            nn.Linear(dim * 4, dim)
+        )
+        self.get_mel_out = Linear(hparams['hidden_size'], 80, bias=True)
+        self.get_decode_inp = Linear(hparams['hidden_size'] + dim + dim,
+                                     hparams['hidden_size'])  # hs + dim + 80 -> hs
+
+    def forward(self, spec, diffusion_step, cond, padding_mask=None, attn_mask=None, return_hiddens=False):
+        """
+        :param spec: [B, 1, 80, T]
+        :param diffusion_step: [B, 1]
+        :param cond: [B, M, T]
+        :return:
+        """
+        x = spec[:, 0]
+        x = self.input_projection(x).permute([0, 2, 1])  #  [B, T, residual_channel]
+        diffusion_step = self.diffusion_embedding(diffusion_step)
+        diffusion_step = self.mlp(diffusion_step)  # [B, dim]
+        cond = cond.permute([0, 2, 1])  # [B, T, M]
+
+        seq_len = cond.shape[1]  # [T_mel]
+        time_embed = diffusion_step[:, None, :]  # [B, 1, dim]
+        time_embed = time_embed.repeat([1, seq_len, 1])  # # [B, T, dim]
+
+        decoder_inp = torch.cat([x, cond, time_embed], dim=-1)  # [B, T, dim + H + dim]
+        decoder_inp = self.get_decode_inp(decoder_inp)  # [B, T, H]
+        x = decoder_inp
+
+        '''
+        Required x: [B, T, C]
+        :return: [B, T, C] or [L, B, T, C]
+        '''
+        padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
+        nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None]  # [T, B, 1]
+        if self.use_pos_embed:
+            positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
+            x = x + positions
+            x = F.dropout(x, p=self.dropout, training=self.training)
+        # B x T x C -> T x B x C
+        x = x.transpose(0, 1) * nonpadding_mask_TB
+        hiddens = []
+        for layer in self.layers:
+            x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
+            hiddens.append(x)
+        if self.use_last_norm:
+            x = self.layer_norm(x) * nonpadding_mask_TB
+        if return_hiddens:
+            x = torch.stack(hiddens, 0)  # [L, T, B, C]
+            x = x.transpose(1, 2)  # [L, B, T, C]
+        else:
+            x = x.transpose(0, 1)  # [B, T, C]
+
+        x = self.get_mel_out(x).permute([0, 2, 1])  # [B, 80, T]
+        return x[:, None, :, :]
\ No newline at end of file
diff --git a/network/diff/diffusion.py b/network/diff/diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ff05212929a970974a59735735d704be83ecd3c
--- /dev/null
+++ b/network/diff/diffusion.py
@@ -0,0 +1,332 @@
+from collections import deque
+from functools import partial
+from inspect import isfunction
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from tqdm import tqdm
+
+from modules.fastspeech.fs2 import FastSpeech2
+# from modules.diffsinger_midi.fs2 import FastSpeech2MIDI
+from utils.hparams import hparams
+from training.train_pipeline import Batch2Loss
+
+
+def exists(x):
+    return x is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+# gaussian diffusion trainer class
+
+def extract(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
+
+
+def linear_beta_schedule(timesteps, max_beta=hparams.get('max_beta', 0.01)):
+    """
+    linear schedule
+    """
+    betas = np.linspace(1e-4, max_beta, timesteps)
+    return betas
+
+
+def cosine_beta_schedule(timesteps, s=0.008):
+    """
+    cosine schedule
+    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
+    """
+    steps = timesteps + 1
+    x = np.linspace(0, steps, steps)
+    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
+    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
+    return np.clip(betas, a_min=0, a_max=0.999)
+
+
+beta_schedule = {
+    "cosine": cosine_beta_schedule,
+    "linear": linear_beta_schedule,
+}
+
+
+class GaussianDiffusion(nn.Module):
+    def __init__(self, phone_encoder, out_dims, denoise_fn,
+                 timesteps=1000, K_step=1000, loss_type=hparams.get('diff_loss_type', 'l1'), betas=None, spec_min=None,
+                 spec_max=None):
+        super().__init__()
+        self.denoise_fn = denoise_fn
+        # if hparams.get('use_midi') is not None and hparams['use_midi']:
+        #     self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims)
+        # else:
+        self.fs2 = FastSpeech2(phone_encoder, out_dims)
+        self.mel_bins = out_dims
+
+        if exists(betas):
+            betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
+        else:
+            if 'schedule_type' in hparams.keys():
+                betas = beta_schedule[hparams['schedule_type']](timesteps)
+            else:
+                betas = cosine_beta_schedule(timesteps)
+
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.K_step = K_step
+        self.loss_type = loss_type
+
+        self.noise_list = deque(maxlen=4)
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']])
+        self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']])
+
+    def q_mean_variance(self, x_start, t):
+        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, cond, clip_denoised: bool):
+        noise_pred = self.denoise_fn(x, t, cond=cond)
+        x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+    
+    @torch.no_grad()
+    def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False):
+        """
+        Use the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778).
+        """
+
+        def get_x_pred(x, noise_t, t):
+            a_t = extract(self.alphas_cumprod, t, x.shape)
+            a_prev = extract(self.alphas_cumprod, torch.max(t-interval, torch.zeros_like(t)), x.shape)
+            a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
+
+            x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
+            x_pred = x + x_delta
+
+            return x_pred
+
+        noise_list = self.noise_list
+        noise_pred = self.denoise_fn(x, t, cond=cond)
+
+        if len(noise_list) == 0:
+            x_pred = get_x_pred(x, noise_pred, t)
+            noise_pred_prev = self.denoise_fn(x_pred, max(t-interval, 0), cond=cond)
+            noise_pred_prime = (noise_pred + noise_pred_prev) / 2
+        elif len(noise_list) == 1:
+            noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2
+        elif len(noise_list) == 2:
+            noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12
+        elif len(noise_list) >= 3:
+            noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24
+
+        x_prev = get_x_pred(x, noise_pred_prime, t)
+        noise_list.append(noise_pred)
+
+        return x_prev
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (
+                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def p_losses(self, x_start, t, cond, noise=None, nonpadding=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        x_recon = self.denoise_fn(x_noisy, t, cond)
+
+        if self.loss_type == 'l1':
+            if nonpadding is not None:
+                loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean()
+            else:
+                # print('are you sure w/o nonpadding?')
+                loss = (noise - x_recon).abs().mean()
+
+        elif self.loss_type == 'l2':
+            loss = F.mse_loss(noise, x_recon)
+        else:
+            raise NotImplementedError()
+
+        return loss
+
+    def forward(self, hubert, mel2ph=None, spk_embed=None,
+                ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs):
+        '''
+            conditioning diffusion, use fastspeech2 encoder output as the condition
+        '''
+        ret = self.fs2(hubert, mel2ph, spk_embed, None, f0, uv, energy,
+                       skip_decoder=True, infer=infer, **kwargs)
+        cond = ret['decoder_inp'].transpose(1, 2)
+        b, *_, device = *hubert.shape, hubert.device
+
+        if not infer:
+            Batch2Loss.module4(
+                self.p_losses,
+                self.norm_spec(ref_mels), cond, ret, self.K_step, b, device
+            )
+        else:
+            '''
+            ret['fs2_mel'] = ret['mel_out']
+            fs2_mels = ret['mel_out']
+            t = self.K_step
+            fs2_mels = self.norm_spec(fs2_mels)
+            fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :]
+            x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long())
+            if hparams.get('gaussian_start') is not None and hparams['gaussian_start']:
+                print('===> gaussion start.')
+                shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
+                x = torch.randn(shape, device=device)
+            '''
+            if 'use_gt_mel' in kwargs.keys() and kwargs['use_gt_mel']:
+                t =kwargs['add_noise_step']
+                print('===>using ground truth mel as start, please make sure parameter "key==0" !')
+                fs2_mels = ref_mels
+                fs2_mels = self.norm_spec(fs2_mels)
+                fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :]
+                x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long())
+                # for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
+                #     x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
+            else:
+                t = self.K_step
+                #print('===> gaussion start.')
+                shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
+                x = torch.randn(shape, device=device)
+            if hparams.get('pndm_speedup') and hparams['pndm_speedup'] > 1:
+                self.noise_list = deque(maxlen=4)
+                iteration_interval =hparams['pndm_speedup']
+                for i in tqdm(reversed(range(0, t, iteration_interval)), desc='sample time step',
+                              total=t // iteration_interval):
+                    x = self.p_sample_plms(x, torch.full((b,), i, device=device, dtype=torch.long), iteration_interval,
+                                           cond)
+            else:
+                for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
+                    x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
+            x = x[:, 0].transpose(1, 2)
+            if mel2ph is not None:  # for singing
+                ret['mel_out'] = self.denorm_spec(x) * ((mel2ph > 0).float()[:, :, None])
+            else:
+                ret['mel_out'] = self.denorm_spec(x)
+        return ret
+
+    def norm_spec(self, x):
+        return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
+
+    def denorm_spec(self, x):
+        return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
+
+    def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph):
+        return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph)
+
+    def out2mel(self, x):
+        return x
+
+
+class OfflineGaussianDiffusion(GaussianDiffusion):
+    def forward(self, txt_tokens, mel2ph=None, spk_embed=None,
+                ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs):
+        b, *_, device = *txt_tokens.shape, txt_tokens.device
+
+        ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy,
+                       skip_decoder=True, infer=True, **kwargs)
+        cond = ret['decoder_inp'].transpose(1, 2)
+        fs2_mels = ref_mels[1]
+        ref_mels = ref_mels[0]
+
+        if not infer:
+            t = torch.randint(0, self.K_step, (b,), device=device).long()
+            x = ref_mels
+            x = self.norm_spec(x)
+            x = x.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
+            ret['diff_loss'] = self.p_losses(x, t, cond)
+        else:
+            t = self.K_step
+            fs2_mels = self.norm_spec(fs2_mels)
+            fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :]
+
+            x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long())
+
+            if hparams.get('gaussian_start') is not None and hparams['gaussian_start']:
+                print('===> gaussion start.')
+                shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
+                x = torch.randn(shape, device=device)
+            for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
+                x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
+            x = x[:, 0].transpose(1, 2)
+            ret['mel_out'] = self.denorm_spec(x)
+
+        return ret
\ No newline at end of file
diff --git a/network/diff/net.py b/network/diff/net.py
new file mode 100644
index 0000000000000000000000000000000000000000..df46b54cbd545b99e13b7f3802b7fc18f388e0c8
--- /dev/null
+++ b/network/diff/net.py
@@ -0,0 +1,135 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from math import sqrt
+
+from utils.hparams import hparams
+from modules.commons.common_layers import Mish
+
+Linear = nn.Linear
+ConvTranspose2d = nn.ConvTranspose2d
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
+
+    def override(self, attrs):
+        if isinstance(attrs, dict):
+            self.__dict__.update(**attrs)
+        elif isinstance(attrs, (list, tuple, set)):
+            for attr in attrs:
+                self.override(attr)
+        elif attrs is not None:
+            raise NotImplementedError
+        return self
+
+
+class SinusoidalPosEmb(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
+        emb = x[:, None] * emb[None, :]
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+def Conv1d(*args, **kwargs):
+    layer = nn.Conv1d(*args, **kwargs)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+@torch.jit.script
+def silu(x):
+    return x * torch.sigmoid(x)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, encoder_hidden, residual_channels, dilation):
+        super().__init__()
+        self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
+        self.diffusion_projection = Linear(residual_channels, residual_channels)
+        self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1)
+        self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
+
+    def forward(self, x, conditioner, diffusion_step):
+        diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
+        conditioner = self.conditioner_projection(conditioner)
+        y = x + diffusion_step
+
+        y = self.dilated_conv(y) + conditioner
+
+        gate, filter = torch.chunk(y, 2, dim=1)
+        # Using torch.split instead of torch.chunk to avoid using onnx::Slice
+        # gate, filter = torch.split(y, torch.div(y.shape[1], 2), dim=1)
+
+        y = torch.sigmoid(gate) * torch.tanh(filter)
+
+        y = self.output_projection(y)
+        residual, skip = torch.chunk(y, 2, dim=1)
+        # Using torch.split instead of torch.chunk to avoid using onnx::Slice
+        # residual, skip = torch.split(y, torch.div(y.shape[1], 2), dim=1)
+        
+        return (x + residual) / sqrt(2.0), skip
+
+class DiffNet(nn.Module):
+    def __init__(self, in_dims=80):
+        super().__init__()
+        self.params = params = AttrDict(
+            # Model params
+            encoder_hidden=hparams['hidden_size'],
+            residual_layers=hparams['residual_layers'],
+            residual_channels=hparams['residual_channels'],
+            dilation_cycle_length=hparams['dilation_cycle_length'],
+        )
+        self.input_projection = Conv1d(in_dims, params.residual_channels, 1)
+        self.diffusion_embedding = SinusoidalPosEmb(params.residual_channels)
+        dim = params.residual_channels
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, dim * 4),
+            Mish(),
+            nn.Linear(dim * 4, dim)
+        )
+        self.residual_layers = nn.ModuleList([
+            ResidualBlock(params.encoder_hidden, params.residual_channels, 2 ** (i % params.dilation_cycle_length))
+            for i in range(params.residual_layers)
+        ])
+        self.skip_projection = Conv1d(params.residual_channels, params.residual_channels, 1)
+        self.output_projection = Conv1d(params.residual_channels, in_dims, 1)
+        nn.init.zeros_(self.output_projection.weight)
+
+    def forward(self, spec, diffusion_step, cond):
+        """
+
+        :param spec: [B, 1, M, T]
+        :param diffusion_step: [B, 1]
+        :param cond: [B, M, T]
+        :return:
+        """
+        x = spec[:, 0]
+        x = self.input_projection(x)  # x [B, residual_channel, T]
+
+        x = F.relu(x)
+        diffusion_step = self.diffusion_embedding(diffusion_step)
+        diffusion_step = self.mlp(diffusion_step)
+        skip = []
+        for layer_id, layer in enumerate(self.residual_layers):
+            x, skip_connection = layer(x, cond, diffusion_step)
+            skip.append(skip_connection)
+
+        x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers))
+        x = self.skip_projection(x)
+        x = F.relu(x)
+        x = self.output_projection(x)  # [B, 80, T]
+        return x[:, None, :, :]
diff --git a/network/hubert/Hifi.txt b/network/hubert/Hifi.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4fc0b8e986f454fa2a5a9b878f1b89d56dd6a1b8
--- /dev/null
+++ b/network/hubert/Hifi.txt
@@ -0,0 +1 @@
+fuck2023arianitesshitcelebrities
\ No newline at end of file
diff --git a/network/hubert/__pycache__/hubert_model.cpython-38.pyc b/network/hubert/__pycache__/hubert_model.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..877626aac6c044efc0b362a07328a14903be4ae7
Binary files /dev/null and b/network/hubert/__pycache__/hubert_model.cpython-38.pyc differ
diff --git a/network/hubert/__pycache__/vec_model.cpython-38.pyc b/network/hubert/__pycache__/vec_model.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..13cb994a010702d266eb9925006e4971a7092732
Binary files /dev/null and b/network/hubert/__pycache__/vec_model.cpython-38.pyc differ
diff --git a/network/hubert/hubert_model.py b/network/hubert/hubert_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..54379f3d47383810c545c53e042d806734e9f8de
--- /dev/null
+++ b/network/hubert/hubert_model.py
@@ -0,0 +1,276 @@
+import copy
+import os
+import random
+from typing import Optional, Tuple
+
+import librosa
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as t_func
+from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
+
+from utils import hparams
+
+
+class Hubert(nn.Module):
+    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
+        super().__init__()
+        self._mask = mask
+        self.feature_extractor = FeatureExtractor()
+        self.feature_projection = FeatureProjection()
+        self.positional_embedding = PositionalConvEmbedding()
+        self.norm = nn.LayerNorm(768)
+        self.dropout = nn.Dropout(0.1)
+        self.encoder = TransformerEncoder(
+            nn.TransformerEncoderLayer(
+                768, 12, 3072, activation="gelu", batch_first=True
+            ),
+            12,
+        )
+        self.proj = nn.Linear(768, 256)
+
+        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
+        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
+
+    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        mask = None
+        if self.training and self._mask:
+            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
+            x[mask] = self.masked_spec_embed.to(x.dtype)
+        return x, mask
+
+    def encode(
+            self, x: torch.Tensor, layer: Optional[int] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = self.feature_extractor(x)
+        x = self.feature_projection(x.transpose(1, 2))
+        x, mask = self.mask(x)
+        x = x + self.positional_embedding(x)
+        x = self.dropout(self.norm(x))
+        x = self.encoder(x, output_layer=layer)
+        return x, mask
+
+    def logits(self, x: torch.Tensor) -> torch.Tensor:
+        logits = torch.cosine_similarity(
+            x.unsqueeze(2),
+            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
+            dim=-1,
+        )
+        return logits / 0.1
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        x, mask = self.encode(x)
+        x = self.proj(x)
+        logits = self.logits(x)
+        return logits, mask
+
+
+class HubertSoft(Hubert):
+    def __init__(self):
+        super().__init__()
+
+    # @torch.inference_mode()
+    def units(self, wav: torch.Tensor) -> torch.Tensor:
+        wav = torch.nn.functional.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
+        x, _ = self.encode(wav)
+        return self.proj(x)
+
+    def forward(self, wav: torch.Tensor):
+        return self.units(wav)
+
+
+class FeatureExtractor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
+        self.norm0 = nn.GroupNorm(512, 512)
+        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
+        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = t_func.gelu(self.norm0(self.conv0(x)))
+        x = t_func.gelu(self.conv1(x))
+        x = t_func.gelu(self.conv2(x))
+        x = t_func.gelu(self.conv3(x))
+        x = t_func.gelu(self.conv4(x))
+        x = t_func.gelu(self.conv5(x))
+        x = t_func.gelu(self.conv6(x))
+        return x
+
+
+class FeatureProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.norm = nn.LayerNorm(512)
+        self.projection = nn.Linear(512, 768)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm(x)
+        x = self.projection(x)
+        x = self.dropout(x)
+        return x
+
+
+class PositionalConvEmbedding(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            768,
+            768,
+            kernel_size=128,
+            padding=128 // 2,
+            groups=16,
+        )
+        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x.transpose(1, 2))
+        x = t_func.gelu(x[:, :, :-1])
+        return x.transpose(1, 2)
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
+    ) -> None:
+        super(TransformerEncoder, self).__init__()
+        self.layers = nn.ModuleList(
+            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
+        )
+        self.num_layers = num_layers
+
+    def forward(
+            self,
+            src: torch.Tensor,
+            mask: torch.Tensor = None,
+            src_key_padding_mask: torch.Tensor = None,
+            output_layer: Optional[int] = None,
+    ) -> torch.Tensor:
+        output = src
+        for layer in self.layers[:output_layer]:
+            output = layer(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+        return output
+
+
+def _compute_mask(
+        shape: Tuple[int, int],
+        mask_prob: float,
+        mask_length: int,
+        device: torch.device,
+        min_masks: int = 0,
+) -> torch.Tensor:
+    batch_size, sequence_length = shape
+
+    if mask_length < 1:
+        raise ValueError("`mask_length` has to be bigger than 0.")
+
+    if mask_length > sequence_length:
+        raise ValueError(
+            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
+        )
+
+    # compute number of masked spans in batch
+    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
+    num_masked_spans = max(num_masked_spans, min_masks)
+
+    # make sure num masked indices <= sequence_length
+    if num_masked_spans * mask_length > sequence_length:
+        num_masked_spans = sequence_length // mask_length
+
+    # SpecAugment mask to fill
+    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
+
+    # uniform distribution to sample from, make sure that offset samples are < sequence_length
+    uniform_dist = torch.ones(
+        (batch_size, sequence_length - (mask_length - 1)), device=device
+    )
+
+    # get random indices to mask
+    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
+
+    # expand masked indices to masked spans
+    mask_indices = (
+        mask_indices.unsqueeze(dim=-1)
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    offsets = (
+        torch.arange(mask_length, device=device)[None, None, :]
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    mask_idxs = mask_indices + offsets
+
+    # scatter indices to mask
+    mask = mask.scatter(1, mask_idxs, True)
+
+    return mask
+
+
+def hubert_soft(
+        path: str
+) -> HubertSoft:
+    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
+    Args:
+        path (str): path of a pretrained model
+    """
+    dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    hubert = HubertSoft()
+    checkpoint = torch.load(path)
+    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
+    hubert.load_state_dict(checkpoint)
+    hubert.eval().to(dev)
+    return hubert
+
+
+def get_units(hbt_soft, raw_wav_path, dev=torch.device('cuda')):
+    wav, sr = librosa.load(raw_wav_path, sr=None)
+    assert (sr >= 16000)
+    if len(wav.shape) > 1:
+        wav = librosa.to_mono(wav)
+    if sr != 16000:
+        wav16 = librosa.resample(wav, sr, 16000)
+    else:
+        wav16 = wav
+    dev = torch.device("cuda" if (dev == torch.device('cuda') and torch.cuda.is_available()) else "cpu")
+    torch.cuda.is_available() and torch.cuda.empty_cache()
+    with torch.inference_mode():
+        units = hbt_soft.units(torch.FloatTensor(wav16.astype(float)).unsqueeze(0).unsqueeze(0).to(dev))
+        return units
+
+
+def get_end_file(dir_path, end):
+    file_list = []
+    for root, dirs, files in os.walk(dir_path):
+        files = [f for f in files if f[0] != '.']
+        dirs[:] = [d for d in dirs if d[0] != '.']
+        for f_file in files:
+            if f_file.endswith(end):
+                file_list.append(os.path.join(root, f_file).replace("\\", "/"))
+    return file_list
+
+
+if __name__ == '__main__':
+    from pathlib import Path
+
+    dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    # hubert的模型路径
+    hbt_model = hubert_soft(str(list(Path(hparams['hubert_path']).home().rglob('*.pt'))[0]))
+    # 这个不用改，自动在根目录下所有wav的同文件夹生成其对应的npy
+    file_lists = list(Path(hparams['raw_data_dir']).rglob('*.wav'))
+    nums = len(file_lists)
+    count = 0
+    for wav_path in file_lists:
+        npy_path = wav_path.with_suffix(".npy")
+        npy_content = get_units(hbt_model, wav_path).cpu().numpy()[0]
+        np.save(str(npy_path), npy_content)
+        count += 1
+        print(f"hubert process：{round(count * 100 / nums, 2)}%")
diff --git a/network/hubert/vec_model.py b/network/hubert/vec_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee4b7a152ab9f299d43d6f519564caca25d836a5
--- /dev/null
+++ b/network/hubert/vec_model.py
@@ -0,0 +1,60 @@
+from pathlib import Path
+
+import librosa
+import numpy as np
+import torch
+
+
+
+def load_model(vec_path):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("load model(s) from {}".format(vec_path))
+    from fairseq import checkpoint_utils
+    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+        [vec_path],
+        suffix="",
+    )
+    model = models[0]
+    model = model.to(device)
+    model.eval()
+    return model
+
+
+def get_vec_units(con_model, audio_path, dev):
+    audio, sampling_rate = librosa.load(audio_path)
+    if len(audio.shape) > 1:
+        audio = librosa.to_mono(audio.transpose(1, 0))
+    if sampling_rate != 16000:
+        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+
+    feats = torch.from_numpy(audio).float()
+    if feats.dim() == 2:  # double channels
+        feats = feats.mean(-1)
+    assert feats.dim() == 1, feats.dim()
+    feats = feats.view(1, -1)
+    padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+    inputs = {
+        "source": feats.to(dev),
+        "padding_mask": padding_mask.to(dev),
+        "output_layer": 9,  # layer 9
+    }
+    with torch.no_grad():
+        logits = con_model.extract_features(**inputs)
+        feats = con_model.final_proj(logits[0])
+    return feats
+
+
+if __name__ == '__main__':
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model_path = "../../checkpoints/checkpoint_best_legacy_500.pt"  # checkpoint_best_legacy_500.pt
+    vec_model = load_model(model_path)
+    # 这个不用改，自动在根目录下所有wav的同文件夹生成其对应的npy
+    file_lists = list(Path("../../data/vecfox").rglob('*.wav'))
+    nums = len(file_lists)
+    count = 0
+    for wav_path in file_lists:
+        npy_path = wav_path.with_suffix(".npy")
+        npy_content = get_vec_units(vec_model, str(wav_path), device).cpu().numpy()[0]
+        np.save(str(npy_path), npy_content)
+        count += 1
+        print(f"hubert process：{round(count * 100 / nums, 2)}%")
diff --git a/network/vocoders/__init__.py b/network/vocoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6631bafa406a3e3add4903f3e7a11957d416a78f
--- /dev/null
+++ b/network/vocoders/__init__.py
@@ -0,0 +1,2 @@
+from network.vocoders import hifigan
+from network.vocoders import nsf_hifigan
diff --git a/network/vocoders/__pycache__/__init__.cpython-38.pyc b/network/vocoders/__pycache__/__init__.cpython-38.pyc
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diff --git a/network/vocoders/__pycache__/pwg.cpython-38.pyc b/network/vocoders/__pycache__/pwg.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..def508d0e8f28d332075ffb6098bfacf79f612fa
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diff --git a/network/vocoders/__pycache__/vocoder_utils.cpython-38.pyc b/network/vocoders/__pycache__/vocoder_utils.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c5d1324a12266ff142ca0e9a727d5bec5be8ae1f
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diff --git a/network/vocoders/base_vocoder.py b/network/vocoders/base_vocoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe49a9e4f790ecdc5e76d60a23f96602b59fc48d
--- /dev/null
+++ b/network/vocoders/base_vocoder.py
@@ -0,0 +1,39 @@
+import importlib
+VOCODERS = {}
+
+
+def register_vocoder(cls):
+    VOCODERS[cls.__name__.lower()] = cls
+    VOCODERS[cls.__name__] = cls
+    return cls
+
+
+def get_vocoder_cls(hparams):
+    if hparams['vocoder'] in VOCODERS:
+        return VOCODERS[hparams['vocoder']]
+    else:
+        vocoder_cls = hparams['vocoder']
+        pkg = ".".join(vocoder_cls.split(".")[:-1])
+        cls_name = vocoder_cls.split(".")[-1]
+        vocoder_cls = getattr(importlib.import_module(pkg), cls_name)
+        return vocoder_cls
+
+
+class BaseVocoder:
+    def spec2wav(self, mel):
+        """
+
+        :param mel: [T, 80]
+        :return: wav: [T']
+        """
+
+        raise NotImplementedError
+
+    @staticmethod
+    def wav2spec(wav_fn):
+        """
+
+        :param wav_fn: str
+        :return: wav, mel: [T, 80]
+        """
+        raise NotImplementedError
diff --git a/network/vocoders/hifigan.py b/network/vocoders/hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..8838b9316e9c5608dba799381c52276710136d4a
--- /dev/null
+++ b/network/vocoders/hifigan.py
@@ -0,0 +1,83 @@
+import glob
+import json
+import os
+import re
+
+import librosa
+import torch
+
+import utils
+from modules.hifigan.hifigan import HifiGanGenerator
+from utils.hparams import hparams, set_hparams
+from network.vocoders.base_vocoder import register_vocoder
+from network.vocoders.pwg import PWG
+from network.vocoders.vocoder_utils import denoise
+
+
+def load_model(config_path, file_path):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    ext = os.path.splitext(file_path)[-1]
+    if ext == '.pth':
+        if '.yaml' in config_path:
+            config = set_hparams(config_path, global_hparams=False)
+        elif '.json' in config_path:
+            config = json.load(open(config_path, 'r', encoding='utf-8'))
+        model = torch.load(file_path, map_location="cpu")
+    elif ext == '.ckpt':
+        ckpt_dict = torch.load(file_path, map_location="cpu")
+        if '.yaml' in config_path:
+            config = set_hparams(config_path, global_hparams=False)
+            state = ckpt_dict["state_dict"]["model_gen"]
+        elif '.json' in config_path:
+            config = json.load(open(config_path, 'r', encoding='utf-8'))
+            state = ckpt_dict["generator"]
+        model = HifiGanGenerator(config)
+        model.load_state_dict(state, strict=True)
+        model.remove_weight_norm()
+    model = model.eval().to(device)
+    print(f"| Loaded model parameters from {file_path}.")
+    print(f"| HifiGAN device: {device}.")
+    return model, config, device
+
+
+total_time = 0
+
+
+@register_vocoder
+class HifiGAN(PWG):
+    def __init__(self):
+        base_dir = hparams['vocoder_ckpt']
+        config_path = f'{base_dir}/config.yaml'
+        if os.path.exists(config_path):
+            file_path = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.*'), key=
+            lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).*', x.replace('\\','/'))[0]))[-1]
+            print('| load HifiGAN: ', file_path)
+            self.model, self.config, self.device = load_model(config_path=config_path, file_path=file_path)
+        else:
+            config_path = f'{base_dir}/config.json'
+            ckpt = f'{base_dir}/generator_v1'
+            if os.path.exists(config_path):
+                self.model, self.config, self.device = load_model(config_path=config_path, file_path=file_path)
+
+    def spec2wav(self, mel, **kwargs):
+        device = self.device
+        with torch.no_grad():
+            c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(device)
+            with utils.Timer('hifigan', print_time=hparams['profile_infer']):
+                f0 = kwargs.get('f0')
+                if f0 is not None and hparams.get('use_nsf'):
+                    f0 = torch.FloatTensor(f0[None, :]).to(device)
+                    y = self.model(c, f0).view(-1)
+                else:
+                    y = self.model(c).view(-1)
+        wav_out = y.cpu().numpy()
+        if hparams.get('vocoder_denoise_c', 0.0) > 0:
+            wav_out = denoise(wav_out, v=hparams['vocoder_denoise_c'])
+        return wav_out
+
+    # @staticmethod
+    # def wav2spec(wav_fn, **kwargs):
+    #     wav, _ = librosa.core.load(wav_fn, sr=hparams['audio_sample_rate'])
+    #     wav_torch = torch.FloatTensor(wav)[None, :]
+    #     mel = mel_spectrogram(wav_torch, hparams).numpy()[0]
+    #     return wav, mel.T
diff --git a/network/vocoders/nsf_hifigan.py b/network/vocoders/nsf_hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..93975546a7acff64279b3fc84b4edd0a7d292714
--- /dev/null
+++ b/network/vocoders/nsf_hifigan.py
@@ -0,0 +1,92 @@
+import os
+import torch
+from modules.nsf_hifigan.models import load_model, Generator
+from modules.nsf_hifigan.nvSTFT import load_wav_to_torch, STFT
+from utils.hparams import hparams
+from network.vocoders.base_vocoder import BaseVocoder, register_vocoder
+
+@register_vocoder
+class NsfHifiGAN(BaseVocoder):
+    def __init__(self, device=None):
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = device
+        model_path = hparams['vocoder_ckpt']
+        if os.path.exists(model_path):
+            print('| Load HifiGAN: ', model_path)
+            self.model, self.h = load_model(model_path, device=self.device)
+        else:
+            print('Error: HifiGAN model file is not found!')
+
+    def spec2wav_torch(self, mel, **kwargs): # mel: [B, T, bins]
+        if self.h.sampling_rate != hparams['audio_sample_rate']:
+            print('Mismatch parameters: hparams[\'audio_sample_rate\']=',hparams['audio_sample_rate'],'!=',self.h.sampling_rate,'(vocoder)')
+        if self.h.num_mels != hparams['audio_num_mel_bins']:
+            print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=',hparams['audio_num_mel_bins'],'!=',self.h.num_mels,'(vocoder)')
+        if self.h.n_fft != hparams['fft_size']:
+            print('Mismatch parameters: hparams[\'fft_size\']=',hparams['fft_size'],'!=',self.h.n_fft,'(vocoder)')
+        if self.h.win_size != hparams['win_size']:
+            print('Mismatch parameters: hparams[\'win_size\']=',hparams['win_size'],'!=',self.h.win_size,'(vocoder)')
+        if self.h.hop_size != hparams['hop_size']:
+            print('Mismatch parameters: hparams[\'hop_size\']=',hparams['hop_size'],'!=',self.h.hop_size,'(vocoder)')
+        if self.h.fmin != hparams['fmin']:
+            print('Mismatch parameters: hparams[\'fmin\']=',hparams['fmin'],'!=',self.h.fmin,'(vocoder)')
+        if self.h.fmax != hparams['fmax']:
+            print('Mismatch parameters: hparams[\'fmax\']=',hparams['fmax'],'!=',self.h.fmax,'(vocoder)')
+        with torch.no_grad():
+            c = mel.transpose(2, 1) #[B, T, bins]
+            #log10 to log mel
+            c = 2.30259 * c
+            f0 = kwargs.get('f0') #[B, T]
+            if f0 is not None and hparams.get('use_nsf'):
+                y = self.model(c, f0).view(-1)
+            else:
+                y = self.model(c).view(-1)
+        return y
+
+    def spec2wav(self, mel, **kwargs):
+        if self.h.sampling_rate != hparams['audio_sample_rate']:
+            print('Mismatch parameters: hparams[\'audio_sample_rate\']=',hparams['audio_sample_rate'],'!=',self.h.sampling_rate,'(vocoder)')
+        if self.h.num_mels != hparams['audio_num_mel_bins']:
+            print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=',hparams['audio_num_mel_bins'],'!=',self.h.num_mels,'(vocoder)')
+        if self.h.n_fft != hparams['fft_size']:
+            print('Mismatch parameters: hparams[\'fft_size\']=',hparams['fft_size'],'!=',self.h.n_fft,'(vocoder)')
+        if self.h.win_size != hparams['win_size']:
+            print('Mismatch parameters: hparams[\'win_size\']=',hparams['win_size'],'!=',self.h.win_size,'(vocoder)')
+        if self.h.hop_size != hparams['hop_size']:
+            print('Mismatch parameters: hparams[\'hop_size\']=',hparams['hop_size'],'!=',self.h.hop_size,'(vocoder)')
+        if self.h.fmin != hparams['fmin']:
+            print('Mismatch parameters: hparams[\'fmin\']=',hparams['fmin'],'!=',self.h.fmin,'(vocoder)')
+        if self.h.fmax != hparams['fmax']:
+            print('Mismatch parameters: hparams[\'fmax\']=',hparams['fmax'],'!=',self.h.fmax,'(vocoder)')
+        with torch.no_grad():
+            c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(self.device)
+            #log10 to log mel
+            c = 2.30259 * c
+            f0 = kwargs.get('f0')
+            if f0 is not None and hparams.get('use_nsf'):
+                f0 = torch.FloatTensor(f0[None, :]).to(self.device)
+                y = self.model(c, f0).view(-1)
+            else:
+                y = self.model(c).view(-1)
+        wav_out = y.cpu().numpy()
+        return wav_out
+
+    @staticmethod
+    def wav2spec(inp_path, device=None):
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        sampling_rate = hparams['audio_sample_rate']
+        num_mels = hparams['audio_num_mel_bins']
+        n_fft = hparams['fft_size']
+        win_size =hparams['win_size']
+        hop_size = hparams['hop_size']
+        fmin = hparams['fmin']
+        fmax = hparams['fmax']
+        stft = STFT(sampling_rate, num_mels, n_fft, win_size, hop_size, fmin, fmax)
+        with torch.no_grad():
+            wav_torch, _ = load_wav_to_torch(inp_path, target_sr=stft.target_sr)
+            mel_torch = stft.get_mel(wav_torch.unsqueeze(0).to(device)).squeeze(0).T
+            #log mel to log10 mel
+            mel_torch = 0.434294 * mel_torch
+            return wav_torch.cpu().numpy(), mel_torch.cpu().numpy()
\ No newline at end of file
diff --git a/network/vocoders/pwg.py b/network/vocoders/pwg.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf2de16f271b66c308c604e52e9ab89242d5663e
--- /dev/null
+++ b/network/vocoders/pwg.py
@@ -0,0 +1,137 @@
+import glob
+import re
+import librosa
+import torch
+import yaml
+from sklearn.preprocessing import StandardScaler
+from torch import nn
+from modules.parallel_wavegan.models import ParallelWaveGANGenerator
+from modules.parallel_wavegan.utils import read_hdf5
+from utils.hparams import hparams
+from utils.pitch_utils import f0_to_coarse
+from network.vocoders.base_vocoder import BaseVocoder, register_vocoder
+import numpy as np
+
+
+def load_pwg_model(config_path, checkpoint_path, stats_path):
+    # load config
+    with open(config_path, encoding='utf-8') as f:
+        config = yaml.load(f, Loader=yaml.Loader)
+
+    # setup
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+    model = ParallelWaveGANGenerator(**config["generator_params"])
+
+    ckpt_dict = torch.load(checkpoint_path, map_location="cpu")
+    if 'state_dict' not in ckpt_dict:  # official vocoder
+        model.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["model"]["generator"])
+        scaler = StandardScaler()
+        if config["format"] == "hdf5":
+            scaler.mean_ = read_hdf5(stats_path, "mean")
+            scaler.scale_ = read_hdf5(stats_path, "scale")
+        elif config["format"] == "npy":
+            scaler.mean_ = np.load(stats_path)[0]
+            scaler.scale_ = np.load(stats_path)[1]
+        else:
+            raise ValueError("support only hdf5 or npy format.")
+    else:  # custom PWG vocoder
+        fake_task = nn.Module()
+        fake_task.model_gen = model
+        fake_task.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["state_dict"], strict=False)
+        scaler = None
+
+    model.remove_weight_norm()
+    model = model.eval().to(device)
+    print(f"| Loaded model parameters from {checkpoint_path}.")
+    print(f"| PWG device: {device}.")
+    return model, scaler, config, device
+
+
+@register_vocoder
+class PWG(BaseVocoder):
+    def __init__(self):
+        if hparams['vocoder_ckpt'] == '':  # load LJSpeech PWG pretrained model
+            base_dir = 'wavegan_pretrained'
+            ckpts = glob.glob(f'{base_dir}/checkpoint-*steps.pkl')
+            ckpt = sorted(ckpts, key=
+            lambda x: int(re.findall(f'{base_dir}/checkpoint-(\d+)steps.pkl', x)[0]))[-1]
+            config_path = f'{base_dir}/config.yaml'
+            print('| load PWG: ', ckpt)
+            self.model, self.scaler, self.config, self.device = load_pwg_model(
+                config_path=config_path,
+                checkpoint_path=ckpt,
+                stats_path=f'{base_dir}/stats.h5',
+            )
+        else:
+            base_dir = hparams['vocoder_ckpt']
+            print(base_dir)
+            config_path = f'{base_dir}/config.yaml'
+            ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
+            lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1]
+            print('| load PWG: ', ckpt)
+            self.scaler = None
+            self.model, _, self.config, self.device = load_pwg_model(
+                config_path=config_path,
+                checkpoint_path=ckpt,
+                stats_path=f'{base_dir}/stats.h5',
+            )
+
+    def spec2wav(self, mel, **kwargs):
+        # start generation
+        config = self.config
+        device = self.device
+        pad_size = (config["generator_params"]["aux_context_window"],
+                    config["generator_params"]["aux_context_window"])
+        c = mel
+        if self.scaler is not None:
+            c = self.scaler.transform(c)
+
+        with torch.no_grad():
+            z = torch.randn(1, 1, c.shape[0] * config["hop_size"]).to(device)
+            c = np.pad(c, (pad_size, (0, 0)), "edge")
+            c = torch.FloatTensor(c).unsqueeze(0).transpose(2, 1).to(device)
+            p = kwargs.get('f0')
+            if p is not None:
+                p = f0_to_coarse(p)
+                p = np.pad(p, (pad_size,), "edge")
+                p = torch.LongTensor(p[None, :]).to(device)
+            y = self.model(z, c, p).view(-1)
+        wav_out = y.cpu().numpy()
+        return wav_out
+
+    @staticmethod
+    def wav2spec(wav_fn, return_linear=False):
+        from preprocessing.data_gen_utils import process_utterance
+        res = process_utterance(
+            wav_fn, fft_size=hparams['fft_size'],
+            hop_size=hparams['hop_size'],
+            win_length=hparams['win_size'],
+            num_mels=hparams['audio_num_mel_bins'],
+            fmin=hparams['fmin'],
+            fmax=hparams['fmax'],
+            sample_rate=hparams['audio_sample_rate'],
+            loud_norm=hparams['loud_norm'],
+            min_level_db=hparams['min_level_db'],
+            return_linear=return_linear, vocoder='pwg', eps=float(hparams.get('wav2spec_eps', 1e-10)))
+        if return_linear:
+            return res[0], res[1].T, res[2].T  # [T, 80], [T, n_fft]
+        else:
+            return res[0], res[1].T
+
+    @staticmethod
+    def wav2mfcc(wav_fn):
+        fft_size = hparams['fft_size']
+        hop_size = hparams['hop_size']
+        win_length = hparams['win_size']
+        sample_rate = hparams['audio_sample_rate']
+        wav, _ = librosa.core.load(wav_fn, sr=sample_rate)
+        mfcc = librosa.feature.mfcc(y=wav, sr=sample_rate, n_mfcc=13,
+                                    n_fft=fft_size, hop_length=hop_size,
+                                    win_length=win_length, pad_mode="constant", power=1.0)
+        mfcc_delta = librosa.feature.delta(mfcc, order=1)
+        mfcc_delta_delta = librosa.feature.delta(mfcc, order=2)
+        mfcc = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta]).T
+        return mfcc
diff --git a/network/vocoders/vocoder_utils.py b/network/vocoders/vocoder_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..db5d5ca1765928e4b047db04435a8a39b52592ca
--- /dev/null
+++ b/network/vocoders/vocoder_utils.py
@@ -0,0 +1,15 @@
+import librosa
+
+from utils.hparams import hparams
+import numpy as np
+
+
+def denoise(wav, v=0.1):
+    spec = librosa.stft(y=wav, n_fft=hparams['fft_size'], hop_length=hparams['hop_size'],
+                        win_length=hparams['win_size'], pad_mode='constant')
+    spec_m = np.abs(spec)
+    spec_m = np.clip(spec_m - v, a_min=0, a_max=None)
+    spec_a = np.angle(spec)
+
+    return librosa.istft(spec_m * np.exp(1j * spec_a), hop_length=hparams['hop_size'],
+                         win_length=hparams['win_size'])
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f9e57bd15883069dcd71837fe76a4171b50ad77
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1 @@
+libsndfile1-dev
\ No newline at end of file
diff --git a/preprocessing/SVCpre.py b/preprocessing/SVCpre.py
new file mode 100644
index 0000000000000000000000000000000000000000..2faa0737fb5d61f6bdb4ac1fb959711c50311d0e
--- /dev/null
+++ b/preprocessing/SVCpre.py
@@ -0,0 +1,63 @@
+'''
+
+    item: one piece of data
+    item_name: data id
+    wavfn: wave file path
+    txt: lyrics
+    ph: phoneme
+    tgfn: text grid file path (unused)
+    spk: dataset name
+    wdb: word boundary
+    ph_durs: phoneme durations
+    midi: pitch as midi notes
+    midi_dur: midi duration
+    is_slur: keep singing upon note changes
+'''
+
+
+from copy import deepcopy
+
+import logging
+
+from preprocessing.process_pipeline import File2Batch
+from utils.hparams import hparams
+from preprocessing.base_binarizer import BaseBinarizer
+
+SVCSINGING_ITEM_ATTRIBUTES = ['wav_fn', 'spk_id']
+class SVCBinarizer(BaseBinarizer):
+    def __init__(self, item_attributes=SVCSINGING_ITEM_ATTRIBUTES):
+        super().__init__(item_attributes)
+        print('spkers: ', set(item['spk_id'] for item in self.items.values()))
+        self.item_names = sorted(list(self.items.keys()))
+        self._train_item_names, self._test_item_names = self.split_train_test_set(self.item_names)
+        # self._valid_item_names=[]
+
+    def split_train_test_set(self, item_names):
+        item_names = deepcopy(item_names)
+        if hparams['choose_test_manually']:
+            test_item_names = [x for x in item_names if any([x.startswith(ts) for ts in hparams['test_prefixes']])]
+        else:
+            test_item_names = item_names[-5:]
+        train_item_names = [x for x in item_names if x not in set(test_item_names)]
+        logging.info("train {}".format(len(train_item_names)))
+        logging.info("test {}".format(len(test_item_names)))
+        return train_item_names, test_item_names
+    
+    @property
+    def train_item_names(self):
+        return self._train_item_names
+
+    @property
+    def valid_item_names(self):
+        return self._test_item_names
+
+    @property
+    def test_item_names(self):
+        return self._test_item_names
+
+    def load_meta_data(self):
+        self.items = File2Batch.file2temporary_dict()
+    
+    def _phone_encoder(self):
+        from preprocessing.hubertinfer import Hubertencoder
+        return Hubertencoder(hparams['hubert_path'])
\ No newline at end of file
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new file mode 100644
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new file mode 100644
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diff --git a/preprocessing/__pycache__/process_pipeline.cpython-38.pyc b/preprocessing/__pycache__/process_pipeline.cpython-38.pyc
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diff --git a/preprocessing/base_binarizer.py b/preprocessing/base_binarizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f070584c60c091c3fa4bc1da377733698b0165b6
--- /dev/null
+++ b/preprocessing/base_binarizer.py
@@ -0,0 +1,237 @@
+import os
+from webbrowser import get
+os.environ["OMP_NUM_THREADS"] = "1"
+import yaml
+from utils.multiprocess_utils import chunked_multiprocess_run
+import random
+import json
+# from resemblyzer import VoiceEncoder
+from tqdm import tqdm
+from preprocessing.data_gen_utils import get_mel2ph, get_pitch_parselmouth, build_phone_encoder,get_pitch_crepe
+from utils.hparams import set_hparams, hparams
+import numpy as np
+from utils.indexed_datasets import IndexedDatasetBuilder
+
+
+class BinarizationError(Exception):
+    pass
+
+BASE_ITEM_ATTRIBUTES = ['txt', 'ph', 'wav_fn', 'tg_fn', 'spk_id']
+
+class BaseBinarizer:
+    '''
+        Base class for data processing.
+        1. *process* and *process_data_split*:
+            process entire data, generate the train-test split (support parallel processing);
+        2. *process_item*:
+            process singe piece of data;
+        3. *get_pitch*:
+            infer the pitch using some algorithm;
+        4. *get_align*:
+            get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263).
+        5. phoneme encoder, voice encoder, etc.
+
+        Subclasses should define:
+        1. *load_metadata*:
+            how to read multiple datasets from files;
+        2. *train_item_names*, *valid_item_names*, *test_item_names*:
+            how to split the dataset;
+        3. load_ph_set:
+            the phoneme set.
+    '''
+    def __init__(self, item_attributes=BASE_ITEM_ATTRIBUTES):
+        self.binarization_args = hparams['binarization_args']
+        #self.pre_align_args = hparams['pre_align_args']
+        
+        self.items = {}
+        # every item in self.items has some attributes
+        self.item_attributes = item_attributes
+
+        self.load_meta_data()
+        # check program correctness 检查itemdict的key只能在给定的列表中取值
+        assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
+        self.item_names = sorted(list(self.items.keys()))
+        
+        if self.binarization_args['shuffle']:
+            random.seed(1234)
+            random.shuffle(self.item_names)
+        
+        # set default get_pitch algorithm
+        if hparams['use_crepe']:
+            self.get_pitch_algorithm = get_pitch_crepe
+        else:
+            self.get_pitch_algorithm = get_pitch_parselmouth
+
+    def load_meta_data(self):
+        raise NotImplementedError
+
+    @property
+    def train_item_names(self):
+        raise NotImplementedError
+        
+    @property
+    def valid_item_names(self):
+        raise NotImplementedError
+
+    @property
+    def test_item_names(self):
+        raise NotImplementedError
+
+    def build_spk_map(self):
+        spk_map = set()
+        for item_name in self.item_names:
+            spk_name = self.items[item_name]['spk_id']
+            spk_map.add(spk_name)
+        spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))}
+        assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
+        return spk_map
+
+    def item_name2spk_id(self, item_name):
+        return self.spk_map[self.items[item_name]['spk_id']]
+
+    def _phone_encoder(self):
+        '''
+        use hubert encoder
+        '''
+        raise NotImplementedError
+        '''
+            create 'phone_set.json' file if it doesn't exist
+        '''
+        ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json"
+        ph_set = []
+        if hparams['reset_phone_dict'] or not os.path.exists(ph_set_fn):
+            self.load_ph_set(ph_set)
+            ph_set = sorted(set(ph_set))
+            json.dump(ph_set, open(ph_set_fn, 'w', encoding='utf-8'))
+            print("| Build phone set: ", ph_set)
+        else:
+            ph_set = json.load(open(ph_set_fn, 'r', encoding='utf-8'))
+            print("| Load phone set: ", ph_set)
+        return build_phone_encoder(hparams['binary_data_dir'])
+    
+
+    def load_ph_set(self, ph_set):
+        raise NotImplementedError
+
+    def meta_data_iterator(self, prefix):
+        if prefix == 'valid':
+            item_names = self.valid_item_names
+        elif prefix == 'test':
+            item_names = self.test_item_names
+        else:
+            item_names = self.train_item_names
+        for item_name in item_names:
+            meta_data = self.items[item_name]
+            yield item_name, meta_data
+
+    def process(self):
+        os.makedirs(hparams['binary_data_dir'], exist_ok=True)
+        self.spk_map = self.build_spk_map()
+        print("| spk_map: ", self.spk_map)
+        spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json"
+        json.dump(self.spk_map, open(spk_map_fn, 'w', encoding='utf-8'))
+
+        self.phone_encoder =self._phone_encoder()
+        self.process_data_split('valid')
+        self.process_data_split('test')
+        self.process_data_split('train')
+
+    def process_data_split(self, prefix):
+        data_dir = hparams['binary_data_dir']
+        args = []
+        builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
+        lengths = []
+        f0s = []
+        total_sec = 0
+        # if self.binarization_args['with_spk_embed']:
+        #     voice_encoder = VoiceEncoder().cuda()
+
+        for item_name, meta_data in self.meta_data_iterator(prefix):
+            args.append([item_name, meta_data, self.binarization_args])
+        spec_min=[]
+        spec_max=[]
+        # code for single cpu processing
+        for i in tqdm(reversed(range(len(args))), total=len(args)):
+            a = args[i]
+            item = self.process_item(*a)
+            if item is None:
+                continue
+            spec_min.append(item['spec_min'])
+            spec_max.append(item['spec_max'])
+            # item['spk_embe'] = voice_encoder.embed_utterance(item['wav']) \
+            #     if self.binardization_args['with_spk_embed'] else None
+            if not self.binarization_args['with_wav'] and 'wav' in item:
+                if hparams['debug']:
+                    print("del wav")
+                del item['wav']
+            if(hparams['debug']):
+                print(item)
+            builder.add_item(item)
+            lengths.append(item['len'])
+            total_sec += item['sec']
+            # if item.get('f0') is not None:
+            #     f0s.append(item['f0'])
+        if prefix=='train':
+            spec_max=np.max(spec_max,0)
+            spec_min=np.min(spec_min,0)
+            print(spec_max.shape)
+            with open(hparams['config_path'], encoding='utf-8') as f:
+                _hparams=yaml.safe_load(f)
+                _hparams['spec_max']=spec_max.tolist()
+                _hparams['spec_min']=spec_min.tolist()
+            with open(hparams['config_path'], 'w', encoding='utf-8') as f:
+                yaml.safe_dump(_hparams,f)
+        builder.finalize()
+        np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
+        if len(f0s) > 0:
+            f0s = np.concatenate(f0s, 0)
+            f0s = f0s[f0s != 0]
+            np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()])
+        print(f"| {prefix} total duration: {total_sec:.3f}s")
+
+    def process_item(self, item_name, meta_data, binarization_args):
+        from preprocessing.process_pipeline import File2Batch
+        return File2Batch.temporary_dict2processed_input(item_name, meta_data, self.phone_encoder, binarization_args)
+
+    def get_align(self, meta_data, mel, phone_encoded, res):
+        raise NotImplementedError
+
+    def get_align_from_textgrid(self, meta_data, mel, phone_encoded, res):
+        '''
+            NOTE: this part of script is *isolated* from other scripts, which means
+                  it may not be compatible with the current version.
+        '''
+        return
+        tg_fn, ph = meta_data['tg_fn'], meta_data['ph']
+        if tg_fn is not None and os.path.exists(tg_fn):
+            mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams)
+        else:
+            raise BinarizationError(f"Align not found")
+        if mel2ph.max() - 1 >= len(phone_encoded):
+            raise BinarizationError(
+                f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}")
+        res['mel2ph'] = mel2ph
+        res['dur'] = dur
+
+    def get_f0cwt(self, f0, res):
+        '''
+            NOTE: this part of script is *isolated* from other scripts, which means
+                  it may not be compatible with the current version.
+        '''
+        return
+        from utils.cwt import get_cont_lf0, get_lf0_cwt
+        uv, cont_lf0_lpf = get_cont_lf0(f0)
+        logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf)
+        cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org
+        Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)
+        if np.any(np.isnan(Wavelet_lf0)):
+            raise BinarizationError("NaN CWT")
+        res['cwt_spec'] = Wavelet_lf0
+        res['cwt_scales'] = scales
+        res['f0_mean'] = logf0s_mean_org
+        res['f0_std'] = logf0s_std_org
+
+
+if __name__ == "__main__":
+    set_hparams()
+    BaseBinarizer().process()
diff --git a/preprocessing/binarize.py b/preprocessing/binarize.py
new file mode 100644
index 0000000000000000000000000000000000000000..df3bff078132d5c1e031af449855fe9c2ba998a1
--- /dev/null
+++ b/preprocessing/binarize.py
@@ -0,0 +1,20 @@
+import os
+
+os.environ["OMP_NUM_THREADS"] = "1"
+
+import importlib
+from utils.hparams import set_hparams, hparams
+
+
+def binarize():
+    binarizer_cls = hparams.get("binarizer_cls", 'basics.base_binarizer.BaseBinarizer')
+    pkg = ".".join(binarizer_cls.split(".")[:-1])
+    cls_name = binarizer_cls.split(".")[-1]
+    binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
+    print("| Binarizer: ", binarizer_cls)
+    binarizer_cls().process()
+
+
+if __name__ == '__main__':
+    set_hparams()
+    binarize()
diff --git a/preprocessing/data_gen_utils.py b/preprocessing/data_gen_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..68b9f981144329698da77a463222a73618e6007b
--- /dev/null
+++ b/preprocessing/data_gen_utils.py
@@ -0,0 +1,426 @@
+from io import BytesIO
+import json
+import os
+import re
+import struct
+import warnings
+from collections import OrderedDict
+
+import librosa
+import numpy as np
+import parselmouth
+import pyloudnorm as pyln
+import resampy
+import torch
+import torchcrepe
+import webrtcvad
+from scipy.ndimage.morphology import binary_dilation
+from skimage.transform import resize
+import pyworld as world
+
+from utils import audio
+from utils.pitch_utils import f0_to_coarse
+from utils.text_encoder import TokenTextEncoder
+
+warnings.filterwarnings("ignore")
+PUNCS = '!,.?;:'
+
+int16_max = (2 ** 15) - 1
+
+
+def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12):
+    """
+    Ensures that segments without voice in the waveform remain no longer than a
+    threshold determined by the VAD parameters in params.py.
+    :param wav: the raw waveform as a numpy array of floats
+    :param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have.
+    :return: the same waveform with silences trimmed away (length <= original wav length)
+    """
+
+    ## Voice Activation Detection
+    # Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
+    # This sets the granularity of the VAD. Should not need to be changed.
+    sampling_rate = 16000
+    wav_raw, sr = librosa.core.load(path, sr=sr)
+
+    if norm:
+        meter = pyln.Meter(sr)  # create BS.1770 meter
+        loudness = meter.integrated_loudness(wav_raw)
+        wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0)
+        if np.abs(wav_raw).max() > 1.0:
+            wav_raw = wav_raw / np.abs(wav_raw).max()
+
+    wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best')
+
+    vad_window_length = 30  # In milliseconds
+    # Number of frames to average together when performing the moving average smoothing.
+    # The larger this value, the larger the VAD variations must be to not get smoothed out.
+    vad_moving_average_width = 8
+
+    # Compute the voice detection window size
+    samples_per_window = (vad_window_length * sampling_rate) // 1000
+
+    # Trim the end of the audio to have a multiple of the window size
+    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
+
+    # Convert the float waveform to 16-bit mono PCM
+    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
+
+    # Perform voice activation detection
+    voice_flags = []
+    vad = webrtcvad.Vad(mode=3)
+    for window_start in range(0, len(wav), samples_per_window):
+        window_end = window_start + samples_per_window
+        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
+                                         sample_rate=sampling_rate))
+    voice_flags = np.array(voice_flags)
+
+    # Smooth the voice detection with a moving average
+    def moving_average(array, width):
+        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
+        ret = np.cumsum(array_padded, dtype=float)
+        ret[width:] = ret[width:] - ret[:-width]
+        return ret[width - 1:] / width
+
+    audio_mask = moving_average(voice_flags, vad_moving_average_width)
+    audio_mask = np.round(audio_mask).astype(np.bool)
+
+    # Dilate the voiced regions
+    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
+    audio_mask = np.repeat(audio_mask, samples_per_window)
+    audio_mask = resize(audio_mask, (len(wav_raw),)) > 0
+    if return_raw_wav:
+        return wav_raw, audio_mask, sr
+    return wav_raw[audio_mask], audio_mask, sr
+
+
+def process_utterance(wav_path,
+                      fft_size=1024,
+                      hop_size=256,
+                      win_length=1024,
+                      window="hann",
+                      num_mels=80,
+                      fmin=80,
+                      fmax=7600,
+                      eps=1e-6,
+                      sample_rate=22050,
+                      loud_norm=False,
+                      min_level_db=-100,
+                      return_linear=False,
+                      trim_long_sil=False, vocoder='pwg'):
+    if isinstance(wav_path, str) or isinstance(wav_path, BytesIO):
+        if trim_long_sil:
+            wav, _, _ = trim_long_silences(wav_path, sample_rate)
+        else:
+            wav, _ = librosa.core.load(wav_path, sr=sample_rate)
+    else:
+        wav = wav_path
+    if loud_norm:
+        meter = pyln.Meter(sample_rate)  # create BS.1770 meter
+        loudness = meter.integrated_loudness(wav)
+        wav = pyln.normalize.loudness(wav, loudness, -22.0)
+        if np.abs(wav).max() > 1:
+            wav = wav / np.abs(wav).max()
+
+    # get amplitude spectrogram
+    x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size,
+                          win_length=win_length, window=window, pad_mode="constant")
+    spc = np.abs(x_stft)  # (n_bins, T)
+
+    # get mel basis
+    fmin = 0 if fmin == -1 else fmin
+    fmax = sample_rate / 2 if fmax == -1 else fmax
+    mel_basis = librosa.filters.mel(sample_rate, fft_size, num_mels, fmin, fmax)
+    mel = mel_basis @ spc
+
+    if vocoder == 'pwg':
+        mel = np.log10(np.maximum(eps, mel))  # (n_mel_bins, T)
+    else:
+        assert False, f'"{vocoder}" is not in ["pwg"].'
+
+    l_pad, r_pad = audio.librosa_pad_lr(wav, fft_size, hop_size, 1)
+    wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0)
+    wav = wav[:mel.shape[1] * hop_size]
+
+    if not return_linear:
+        return wav, mel
+    else:
+        spc = audio.amp_to_db(spc)
+        spc = audio.normalize(spc, {'min_level_db': min_level_db})
+        return wav, mel, spc
+
+
+def get_pitch_parselmouth(wav_data, mel, hparams):
+    """
+
+    :param wav_data: [T]
+    :param mel: [T, 80]
+    :param hparams:
+    :return:
+    """
+    # time_step = hparams['hop_size'] / hparams['audio_sample_rate']
+    # f0_min = hparams['f0_min']
+    # f0_max = hparams['f0_max']
+
+    # if hparams['hop_size'] == 128:
+    #     pad_size = 4
+    # elif hparams['hop_size'] == 256:
+    #     pad_size = 2
+    # else:
+    #     assert False
+
+    # f0 = parselmouth.Sound(wav_data, hparams['audio_sample_rate']).to_pitch_ac(
+    #     time_step=time_step, voicing_threshold=0.6,
+    #     pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
+    # lpad = pad_size * 2
+    # rpad = len(mel) - len(f0) - lpad
+    # f0 = np.pad(f0, [[lpad, rpad]], mode='constant')
+    # # mel and f0 are extracted by 2 different libraries. we should force them to have the same length.
+    # # Attention: we find that new version of some libraries could cause ``rpad'' to be a negetive value...
+    # # Just to be sure, we recommend users to set up the same environments as them in requirements_auto.txt (by Anaconda)
+    # delta_l = len(mel) - len(f0)
+    # assert np.abs(delta_l) <= 8
+    # if delta_l > 0:
+    #     f0 = np.concatenate([f0, [f0[-1]] * delta_l], 0)
+    # f0 = f0[:len(mel)]
+    # pad_size=(int(len(wav_data) // hparams['hop_size']) - len(f0) + 1) // 2
+    # f0 = np.pad(f0,[[pad_size,len(mel) - len(f0) - pad_size]], mode='constant')
+    # pitch_coarse = f0_to_coarse(f0, hparams)
+    # return f0, pitch_coarse
+
+    # Bye bye Parselmouth !
+    return get_pitch_world(wav_data, mel, hparams)
+
+def get_pitch_world(wav_data, mel, hparams):
+    """
+
+    :param wav_data: [T]
+    :param mel: [T, 80]
+    :param hparams:
+    :return:
+    """
+    time_step = 1000 * hparams['hop_size'] / hparams['audio_sample_rate']
+    f0_min = hparams['f0_min']
+    f0_max = hparams['f0_max']
+
+    # Here's to hoping it uses numpy stuff !
+    f0, _ = world.harvest(wav_data.astype(np.double), hparams['audio_sample_rate'], f0_min, f0_max, time_step)
+
+    # Change padding
+    len_diff = len(mel) - len(f0)
+    if len_diff > 0:
+        pad_len = (len_diff + 1) // 2
+        f0 = np.pad(f0, [[pad_len, len_diff - pad_len]])
+    else:
+        pad_len = (1 - len_diff) // 2
+        rpad = pad_len + len_diff
+        if rpad != 0:
+            f0 = f0[pad_len:rpad]
+        f0 = f0[pad_len:]
+    pitch_coarse = f0_to_coarse(f0, hparams)
+    return f0, pitch_coarse
+
+
+def get_pitch_crepe(wav_data, mel, hparams, threshold=0.05):
+    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda")
+    # crepe只支持16khz采样率，需要重采样
+    wav16k = resampy.resample(wav_data, hparams['audio_sample_rate'], 16000)
+    wav16k_torch = torch.FloatTensor(wav16k).unsqueeze(0).to(device)
+
+    # 频率范围
+    f0_min = hparams['f0_min']
+    f0_max = hparams['f0_max']
+
+    # 重采样后按照hopsize=80,也就是5ms一帧分析f0
+    f0, pd = torchcrepe.predict(wav16k_torch, 16000, 80, f0_min, f0_max, pad=True, model='full', batch_size=1024,
+                                device=device, return_periodicity=True)
+
+    # 滤波，去掉静音，设置uv阈值，参考原仓库readme
+    pd = torchcrepe.filter.median(pd, 3)
+    pd = torchcrepe.threshold.Silence(-60.)(pd, wav16k_torch, 16000, 80)
+    f0 = torchcrepe.threshold.At(threshold)(f0, pd)
+    f0 = torchcrepe.filter.mean(f0, 3)
+
+    # 将nan频率（uv部分）转换为0频率
+    f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)
+
+    '''
+    np.savetxt('问棋-crepe.csv',np.array([0.005*np.arange(len(f0[0])),f0[0].cpu().numpy()]).transpose(),delimiter=',')
+    '''
+
+    # 去掉0频率，并线性插值
+    nzindex = torch.nonzero(f0[0]).squeeze()
+    f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy()
+    time_org = 0.005 * nzindex.cpu().numpy()
+    time_frame = np.arange(len(mel)) * hparams['hop_size'] / hparams['audio_sample_rate']
+    if f0.shape[0] == 0:
+        f0 = torch.FloatTensor(time_frame.shape[0]).fill_(0)
+        print('f0 all zero!')
+    else:
+        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
+    pitch_coarse = f0_to_coarse(f0, hparams)
+    return f0, pitch_coarse
+
+
+def remove_empty_lines(text):
+    """remove empty lines"""
+    assert (len(text) > 0)
+    assert (isinstance(text, list))
+    text = [t.strip() for t in text]
+    if "" in text:
+        text.remove("")
+    return text
+
+
+class TextGrid(object):
+    def __init__(self, text):
+        text = remove_empty_lines(text)
+        self.text = text
+        self.line_count = 0
+        self._get_type()
+        self._get_time_intval()
+        self._get_size()
+        self.tier_list = []
+        self._get_item_list()
+
+    def _extract_pattern(self, pattern, inc):
+        """
+        Parameters
+        ----------
+        pattern : regex to extract pattern
+        inc : increment of line count after extraction
+        Returns
+        -------
+        group : extracted info
+        """
+        try:
+            group = re.match(pattern, self.text[self.line_count]).group(1)
+            self.line_count += inc
+        except AttributeError:
+            raise ValueError("File format error at line %d:%s" % (self.line_count, self.text[self.line_count]))
+        return group
+
+    def _get_type(self):
+        self.file_type = self._extract_pattern(r"File type = \"(.*)\"", 2)
+
+    def _get_time_intval(self):
+        self.xmin = self._extract_pattern(r"xmin = (.*)", 1)
+        self.xmax = self._extract_pattern(r"xmax = (.*)", 2)
+
+    def _get_size(self):
+        self.size = int(self._extract_pattern(r"size = (.*)", 2))
+
+    def _get_item_list(self):
+        """Only supports IntervalTier currently"""
+        for itemIdx in range(1, self.size + 1):
+            tier = OrderedDict()
+            item_list = []
+            tier_idx = self._extract_pattern(r"item \[(.*)\]:", 1)
+            tier_class = self._extract_pattern(r"class = \"(.*)\"", 1)
+            if tier_class != "IntervalTier":
+                raise NotImplementedError("Only IntervalTier class is supported currently")
+            tier_name = self._extract_pattern(r"name = \"(.*)\"", 1)
+            tier_xmin = self._extract_pattern(r"xmin = (.*)", 1)
+            tier_xmax = self._extract_pattern(r"xmax = (.*)", 1)
+            tier_size = self._extract_pattern(r"intervals: size = (.*)", 1)
+            for i in range(int(tier_size)):
+                item = OrderedDict()
+                item["idx"] = self._extract_pattern(r"intervals \[(.*)\]", 1)
+                item["xmin"] = self._extract_pattern(r"xmin = (.*)", 1)
+                item["xmax"] = self._extract_pattern(r"xmax = (.*)", 1)
+                item["text"] = self._extract_pattern(r"text = \"(.*)\"", 1)
+                item_list.append(item)
+            tier["idx"] = tier_idx
+            tier["class"] = tier_class
+            tier["name"] = tier_name
+            tier["xmin"] = tier_xmin
+            tier["xmax"] = tier_xmax
+            tier["size"] = tier_size
+            tier["items"] = item_list
+            self.tier_list.append(tier)
+
+    def toJson(self):
+        _json = OrderedDict()
+        _json["file_type"] = self.file_type
+        _json["xmin"] = self.xmin
+        _json["xmax"] = self.xmax
+        _json["size"] = self.size
+        _json["tiers"] = self.tier_list
+        return json.dumps(_json, ensure_ascii=False, indent=2)
+
+
+def get_mel2ph(tg_fn, ph, mel, hparams):
+    ph_list = ph.split(" ")
+    with open(tg_fn, "r", encoding='utf-8') as f:
+        tg = f.readlines()
+    tg = remove_empty_lines(tg)
+    tg = TextGrid(tg)
+    tg = json.loads(tg.toJson())
+    split = np.ones(len(ph_list) + 1, np.float) * -1
+    tg_idx = 0
+    ph_idx = 0
+    tg_align = [x for x in tg['tiers'][-1]['items']]
+    tg_align_ = []
+    for x in tg_align:
+        x['xmin'] = float(x['xmin'])
+        x['xmax'] = float(x['xmax'])
+        if x['text'] in ['sil', 'sp', '', 'SIL', 'PUNC']:
+            x['text'] = ''
+            if len(tg_align_) > 0 and tg_align_[-1]['text'] == '':
+                tg_align_[-1]['xmax'] = x['xmax']
+                continue
+        tg_align_.append(x)
+    tg_align = tg_align_
+    tg_len = len([x for x in tg_align if x['text'] != ''])
+    ph_len = len([x for x in ph_list if not is_sil_phoneme(x)])
+    assert tg_len == ph_len, (tg_len, ph_len, tg_align, ph_list, tg_fn)
+    while tg_idx < len(tg_align) or ph_idx < len(ph_list):
+        if tg_idx == len(tg_align) and is_sil_phoneme(ph_list[ph_idx]):
+            split[ph_idx] = 1e8
+            ph_idx += 1
+            continue
+        x = tg_align[tg_idx]
+        if x['text'] == '' and ph_idx == len(ph_list):
+            tg_idx += 1
+            continue
+        assert ph_idx < len(ph_list), (tg_len, ph_len, tg_align, ph_list, tg_fn)
+        ph = ph_list[ph_idx]
+        if x['text'] == '' and not is_sil_phoneme(ph):
+            assert False, (ph_list, tg_align)
+        if x['text'] != '' and is_sil_phoneme(ph):
+            ph_idx += 1
+        else:
+            assert (x['text'] == '' and is_sil_phoneme(ph)) \
+                   or x['text'].lower() == ph.lower() \
+                   or x['text'].lower() == 'sil', (x['text'], ph)
+            split[ph_idx] = x['xmin']
+            if ph_idx > 0 and split[ph_idx - 1] == -1 and is_sil_phoneme(ph_list[ph_idx - 1]):
+                split[ph_idx - 1] = split[ph_idx]
+            ph_idx += 1
+            tg_idx += 1
+    assert tg_idx == len(tg_align), (tg_idx, [x['text'] for x in tg_align])
+    assert ph_idx >= len(ph_list) - 1, (ph_idx, ph_list, len(ph_list), [x['text'] for x in tg_align], tg_fn)
+    mel2ph = np.zeros([mel.shape[0]], np.int)
+    split[0] = 0
+    split[-1] = 1e8
+    for i in range(len(split) - 1):
+        assert split[i] != -1 and split[i] <= split[i + 1], (split[:-1],)
+    split = [int(s * hparams['audio_sample_rate'] / hparams['hop_size'] + 0.5) for s in split]
+    for ph_idx in range(len(ph_list)):
+        mel2ph[split[ph_idx]:split[ph_idx + 1]] = ph_idx + 1
+    mel2ph_torch = torch.from_numpy(mel2ph)
+    T_t = len(ph_list)
+    dur = mel2ph_torch.new_zeros([T_t + 1]).scatter_add(0, mel2ph_torch, torch.ones_like(mel2ph_torch))
+    dur = dur[1:].numpy()
+    return mel2ph, dur
+
+
+def build_phone_encoder(data_dir):
+    phone_list_file = os.path.join(data_dir, 'phone_set.json')
+    phone_list = json.load(open(phone_list_file, encoding='utf-8'))
+    return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',')
+
+
+def is_sil_phoneme(p):
+    return not p[0].isalpha()
diff --git a/preprocessing/hubertinfer.py b/preprocessing/hubertinfer.py
new file mode 100644
index 0000000000000000000000000000000000000000..15077cb66c31cde45e19ea39f1e6eef2f51b60f9
--- /dev/null
+++ b/preprocessing/hubertinfer.py
@@ -0,0 +1,42 @@
+import os.path
+from io import BytesIO
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from network.hubert.hubert_model import hubert_soft, get_units
+from network.hubert.vec_model import load_model, get_vec_units
+from utils.hparams import hparams
+
+
+class Hubertencoder():
+    def __init__(self, pt_path=f'.checkpoints/hubert/hubert_soft.pt'):
+        if not 'use_vec' in hparams.keys():
+            hparams['use_vec'] = False
+        if hparams['use_vec']:
+            pt_path = f".checkpoints/vec/checkpoint_best_legacy_500.pt"
+            self.dev = torch.device("cuda")
+            self.hbt_model = load_model(pt_path)
+        else:
+            pt_path = list(Path(pt_path).parent.rglob('*.pt'))[0]
+            if 'hubert_gpu' in hparams.keys():
+                self.use_gpu = hparams['hubert_gpu']
+            else:
+                self.use_gpu = True
+            self.dev = torch.device("cuda" if self.use_gpu and torch.cuda.is_available() else "cpu")
+            self.hbt_model = hubert_soft(str(pt_path)).to(self.dev)
+
+    def encode(self, wav_path):
+        if isinstance(wav_path, BytesIO):
+            npy_path = ""
+            wav_path.seek(0)
+        else:
+            npy_path = Path(wav_path).with_suffix('.npy')
+        if os.path.exists(npy_path):
+            units = np.load(str(npy_path))
+        elif hparams['use_vec']:
+            units = get_vec_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0]
+        else:
+            units = get_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0]
+        return units  # [T,256]
diff --git a/preprocessing/process_pipeline.py b/preprocessing/process_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..12dd11b1621bd321c46443cf4de277974db390bd
--- /dev/null
+++ b/preprocessing/process_pipeline.py
@@ -0,0 +1,199 @@
+'''
+    file -> temporary_dict -> processed_input -> batch
+'''
+from utils.hparams import hparams
+from network.vocoders.base_vocoder import VOCODERS
+import numpy as np
+import traceback
+from pathlib import Path
+from .data_gen_utils import get_pitch_parselmouth,get_pitch_crepe
+from .base_binarizer import BinarizationError
+import torch
+import utils
+
+class File2Batch:
+    '''
+        pipeline: file -> temporary_dict -> processed_input -> batch
+    '''
+
+    @staticmethod
+    def file2temporary_dict():
+        '''
+            read from file, store data in temporary dicts
+        '''
+        raw_data_dir = Path(hparams['raw_data_dir'])
+        # meta_midi = json.load(open(os.path.join(raw_data_dir, 'meta.json')))   # [list of dict]
+        
+        # if hparams['perform_enhance'] and not hparams['infer']:
+        #     vocoder=get_vocoder_cls(hparams)()
+        #     raw_files = list(raw_data_dir.rglob(f"*.wav"))
+        #     dic=[]
+        #     time_step = hparams['hop_size'] / hparams['audio_sample_rate']
+        #     f0_min = hparams['f0_min']
+        #     f0_max = hparams['f0_max']
+        #     for file in raw_files:
+        #         y, sr = librosa.load(file, sr=hparams['audio_sample_rate'])
+        #         f0 = parselmouth.Sound(y, hparams['audio_sample_rate']).to_pitch_ac(
+        #         time_step=time_step , voicing_threshold=0.6,
+        #         pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
+        #         f0_mean=np.mean(f0[f0>0])
+        #         dic.append(f0_mean)
+        #     for idx in np.where(dic>np.percentile(dic, 80))[0]:
+        #         file=raw_files[idx]
+        #         wav,mel=vocoder.wav2spec(str(file))
+        #         f0,_=get_pitch_parselmouth(wav,mel,hparams)
+        #         f0[f0>0]=f0[f0>0]*(2**(2/12))
+        #         wav_pred=vocoder.spec2wav(torch.FloatTensor(mel),f0=torch.FloatTensor(f0))
+        #         sf.write(file.with_name(file.name[:-4]+'_high.wav'), wav_pred, 24000, 'PCM_16')
+        utterance_labels =[]
+        utterance_labels.extend(list(raw_data_dir.rglob(f"*.wav")))
+        utterance_labels.extend(list(raw_data_dir.rglob(f"*.ogg")))
+        #open(os.path.join(raw_data_dir, 'transcriptions.txt'), encoding='utf-8').readlines()
+
+        all_temp_dict = {}
+        for utterance_label in utterance_labels:
+            #song_info = utterance_label.split('|')
+            item_name =str(utterance_label)#raw_item_name = song_info[0]
+            # print(item_name)
+            temp_dict = {}
+            temp_dict['wav_fn'] =str(utterance_label)#f'{raw_data_dir}/wavs/{item_name}.wav'
+            # temp_dict['txt'] = song_info[1]
+
+            # temp_dict['ph'] = song_info[2]
+            # # self.item2wdb[item_name] = list(np.nonzero([1 if x in ALL_YUNMU + ['AP', 'SP'] else 0 for x in song_info[2].split()])[0])
+            # temp_dict['word_boundary'] = np.array([1 if x in ALL_YUNMU + ['AP', 'SP'] else 0 for x in song_info[2].split()])
+            # temp_dict['ph_durs'] = [float(x) for x in song_info[5].split(" ")]
+
+            # temp_dict['pitch_midi'] = np.array([note_to_midi(x.split("/")[0]) if x != 'rest' else 0
+            #                        for x in song_info[3].split(" ")])
+            # temp_dict['midi_dur'] = np.array([float(x) for x in song_info[4].split(" ")])
+            # temp_dict['is_slur'] = np.array([int(x) for x in song_info[6].split(" ")])
+            temp_dict['spk_id'] = hparams['speaker_id']
+            # assert temp_dict['pitch_midi'].shape == temp_dict['midi_dur'].shape == temp_dict['is_slur'].shape, \
+                # (temp_dict['pitch_midi'].shape, temp_dict['midi_dur'].shape, temp_dict['is_slur'].shape)
+
+            all_temp_dict[item_name] = temp_dict
+
+        return all_temp_dict
+    
+    @staticmethod
+    def temporary_dict2processed_input(item_name, temp_dict, encoder, binarization_args):
+        '''
+            process data in temporary_dicts
+        '''
+        def get_pitch(wav, mel):
+            # get ground truth f0 by self.get_pitch_algorithm
+            if hparams['use_crepe']:
+                gt_f0, gt_pitch_coarse = get_pitch_crepe(wav, mel, hparams)
+            else:
+                gt_f0, gt_pitch_coarse = get_pitch_parselmouth(wav, mel, hparams)
+            if sum(gt_f0) == 0:
+                raise BinarizationError("Empty **gt** f0")
+            processed_input['f0'] = gt_f0
+            processed_input['pitch'] = gt_pitch_coarse
+    
+        def get_align(meta_data, mel, phone_encoded, hop_size=hparams['hop_size'], audio_sample_rate=hparams['audio_sample_rate']):
+            mel2ph = np.zeros([mel.shape[0]], int)
+            start_frame=0
+            ph_durs = mel.shape[0]/phone_encoded.shape[0]
+            if hparams['debug']:
+                print(mel.shape,phone_encoded.shape,mel.shape[0]/phone_encoded.shape[0])
+            for i_ph in range(phone_encoded.shape[0]):
+                
+                end_frame = int(i_ph*ph_durs +ph_durs+ 0.5)
+                mel2ph[start_frame:end_frame+1] = i_ph + 1
+                start_frame = end_frame+1
+
+            processed_input['mel2ph'] = mel2ph
+
+        if hparams['vocoder'] in VOCODERS:
+            wav, mel = VOCODERS[hparams['vocoder']].wav2spec(temp_dict['wav_fn'])
+        else:
+            wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(temp_dict['wav_fn'])
+        processed_input = {
+            'item_name': item_name, 'mel': mel, 'wav': wav,
+            'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]
+        }
+        processed_input = {**temp_dict, **processed_input} # merge two dicts
+        processed_input['spec_min']=np.min(mel,axis=0)
+        processed_input['spec_max']=np.max(mel,axis=0)
+        #(processed_input['spec_min'].shape)
+        try:
+            if binarization_args['with_f0']:
+                get_pitch(wav, mel)
+            if binarization_args['with_hubert']:
+                try:
+                    hubert_encoded = processed_input['hubert'] = encoder.encode(temp_dict['wav_fn'])
+                except:
+                    traceback.print_exc()
+                    raise Exception(f"hubert encode error")
+                if binarization_args['with_align']:
+                    get_align(temp_dict, mel, hubert_encoded)
+        except Exception as e:
+            print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {temp_dict['wav_fn']}")
+            return None
+        return processed_input
+
+    @staticmethod
+    def processed_input2batch(samples):
+        '''
+            Args:
+                samples: one batch of processed_input
+            NOTE:
+                the batch size is controlled by hparams['max_sentences']
+        '''
+        if len(samples) == 0:
+            return {}
+        id = torch.LongTensor([s['id'] for s in samples])
+        item_names = [s['item_name'] for s in samples]
+        #text = [s['text'] for s in samples]
+        #txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0)
+        hubert = utils.collate_2d([s['hubert'] for s in samples], 0.0)
+        f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
+        pitch = utils.collate_1d([s['pitch'] for s in samples])
+        uv = utils.collate_1d([s['uv'] for s in samples])
+        energy = utils.collate_1d([s['energy'] for s in samples], 0.0)
+        mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
+            if samples[0]['mel2ph'] is not None else None
+        mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
+        #txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples])
+        hubert_lengths = torch.LongTensor([s['hubert'].shape[0] for s in samples])
+        mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
+
+        batch = {
+            'id': id,
+            'item_name': item_names,
+            'nsamples': len(samples),
+            # 'text': text,
+            # 'txt_tokens': txt_tokens,
+            # 'txt_lengths': txt_lengths,
+            'hubert':hubert,
+            'mels': mels,
+            'mel_lengths': mel_lengths,
+            'mel2ph': mel2ph,
+            'energy': energy,
+            'pitch': pitch,
+            'f0': f0,
+            'uv': uv,
+        }
+        #========not used=================
+        # if hparams['use_spk_embed']:
+        #     spk_embed = torch.stack([s['spk_embed'] for s in samples])
+        #     batch['spk_embed'] = spk_embed
+        # if hparams['use_spk_id']:
+        #     spk_ids = torch.LongTensor([s['spk_id'] for s in samples])
+        #     batch['spk_ids'] = spk_ids
+        # if hparams['pitch_type'] == 'cwt':
+        #     cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples])
+        #     f0_mean = torch.Tensor([s['f0_mean'] for s in samples])
+        #     f0_std = torch.Tensor([s['f0_std'] for s in samples])
+        #     batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std})
+        # elif hparams['pitch_type'] == 'ph':
+        #     batch['f0'] = utils.collate_1d([s['f0_ph'] for s in samples])
+
+        # batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0)
+        # batch['midi_dur'] = utils.collate_1d([s['midi_dur'] for s in samples], 0)
+        # batch['is_slur'] = utils.collate_1d([s['is_slur'] for s in samples], 0)
+        # batch['word_boundary'] = utils.collate_1d([s['word_boundary'] for s in samples], 0)
+
+        return batch
\ No newline at end of file
diff --git a/raw/test_input.wav b/raw/test_input.wav
new file mode 100644
index 0000000000000000000000000000000000000000..b04148a941b60c6b9ff8db693c83b1ff8d07ffa1
Binary files /dev/null and b/raw/test_input.wav differ
diff --git a/requirements.png b/requirements.png
new file mode 100644
index 0000000000000000000000000000000000000000..7c4e35bfb664f6a997c8cf69c0e536c01653d655
Binary files /dev/null and b/requirements.png differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..528770ad9209c839bbe213ac1eb092d417096ffc
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,27 @@
+gradio
+torchcrepe==0.0.17
+tornado==6.2
+tqdm==4.64.1
+traitlets==5.5.0
+praat-parselmouth==0.4.1
+scikit-image
+ipython
+ipykernel
+pyloudnorm
+webrtcvad-wheels
+h5py
+einops
+pycwt
+librosa
+scikit-image
+ipython
+ipykernel
+pyloudnorm
+webrtcvad-wheels
+h5py
+einops
+pycwt
+torchmetrics==0.5
+pytorch_lightning==1.3.3
+cython
+pyworld
\ No newline at end of file
diff --git a/requirements.txtback b/requirements.txtback
new file mode 100644
index 0000000000000000000000000000000000000000..3be46ffd2a92427062099a82cfd091b81688f3a4
--- /dev/null
+++ b/requirements.txtback
@@ -0,0 +1,25 @@
+torchcrepe==0.0.17
+tornado==6.2
+tqdm==4.64.1
+traitlets==5.5.0
+praat-parselmouth==0.4.1
+scikit-image
+ipython
+ipykernel
+pyloudnorm
+webrtcvad-wheels
+h5py
+einops
+pycwt
+librosa
+scikit-image
+ipython
+ipykernel
+pyloudnorm
+webrtcvad-wheels
+h5py
+einops
+pycwt
+torchmetrics==0.5
+pytorch_lightning==1.3.3
+streamlit
\ No newline at end of file
diff --git a/requirements.txtback2 b/requirements.txtback2
new file mode 100644
index 0000000000000000000000000000000000000000..365d82e5261124803985856114b94984eca540e1
--- /dev/null
+++ b/requirements.txtback2
@@ -0,0 +1,99 @@
+absl-py==1.3.0
+aiohttp==3.8.3
+aiosignal==1.2.0
+appdirs==1.4.4
+asttokens==2.0.8
+async-timeout==4.0.2
+attrs==22.1.0
+audioread==3.0.0
+backcall==0.2.0
+cachetools==5.2.0
+cffi==1.15.1
+charset-normalizer==2.1.1
+colorama==0.4.6
+contourpy==1.0.5
+cycler==0.11.0
+debugpy==1.6.3
+decorator==5.1.1
+einops==0.5.0
+entrypoints==0.4
+executing==1.1.1
+fonttools==4.38.0
+frozenlist==1.3.1
+fsspec==2022.10.0
+future==0.18.2
+google-auth==2.13.0
+google-auth-oauthlib==0.4.6
+grpcio==1.50.0
+h5py==3.7.0
+idna==3.4
+imageio==2.22.2
+importlib-metadata==5.0.0
+ipykernel==6.16.2
+ipython==8.5.0
+jedi==0.18.1
+joblib==1.2.0
+jupyter_client==7.4.4
+jupyter_core==4.11.2
+kiwisolver==1.4.4
+librosa==0.9.1
+llvmlite==0.39.1
+Markdown==3.4.1
+MarkupSafe==2.1.1
+matplotlib==3.6.1
+matplotlib-inline==0.1.6
+multidict==6.0.2
+nest-asyncio==1.5.6
+networkx==2.8.7
+numba==0.56.3
+numpy==1.23.4
+oauthlib==3.2.2
+packaging==21.3
+parso==0.8.3
+pickleshare==0.7.5
+Pillow==9.2.0
+pooch==1.6.0
+praat-parselmouth==0.4.1
+prompt-toolkit==3.0.31
+protobuf==3.19.6
+psutil==5.9.3
+pure-eval==0.2.2
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.21
+pycwt==0.3.0a22
+pyDeprecate==0.3.0
+Pygments==2.13.0
+pyloudnorm==0.1.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+pytorch-lightning==1.3.3
+PyWavelets==1.4.1
+PyYAML==5.4.1
+pyzmq==24.0.1
+requests==2.28.1
+requests-oauthlib==1.3.1
+resampy==0.4.2
+rsa==4.9
+scikit-image==0.19.3
+scikit-learn==1.1.3
+scipy==1.9.3
+six==1.16.0
+soundfile==0.11.0
+stack-data==0.5.1
+threadpoolctl==3.1.0
+tifffile==2022.10.10
+tornado==6.2
+tqdm==4.64.1
+traitlets==5.5.0
+typing_extensions==4.4.0
+urllib3==1.26.12
+wcwidth==0.2.5
+webrtcvad-wheels
+Werkzeug==2.2.2
+wincertstore==0.2
+yarl==1.8.1
+zipp==3.10.0
+streamlit
+torchcrepe==0.0.17
+torchmetrics==0.5.0
\ No newline at end of file
diff --git a/requirements_short.txt b/requirements_short.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fa3f3a016f4bd5b76f2c2c3971607ad6234f6da0
--- /dev/null
+++ b/requirements_short.txt
@@ -0,0 +1,14 @@
+torchcrepe
+praat-parselmouth==0.4.1
+scikit-image
+ipython
+ipykernel
+pyloudnorm
+webrtcvad
+h5py
+einops
+pycwt
+torchmetrics==0.5
+pytorch_lightning==1.3.3
+pyworld
+librosa
\ No newline at end of file
diff --git a/run.py b/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..40d119f4be35d5631e283dca414bb6f407ea71b5
--- /dev/null
+++ b/run.py
@@ -0,0 +1,16 @@
+import importlib
+import os
+from utils.hparams import set_hparams, hparams
+set_hparams(print_hparams=False)
+
+def run_task():
+    assert hparams['task_cls'] != ''
+    pkg = ".".join(hparams["task_cls"].split(".")[:-1])
+    cls_name = hparams["task_cls"].split(".")[-1]
+    task_cls = getattr(importlib.import_module(pkg), cls_name)
+    task_cls.start()
+
+
+if __name__ == '__main__':
+    run_task()
+
diff --git a/simplify.py b/simplify.py
new file mode 100644
index 0000000000000000000000000000000000000000..75c187cc77e41b26a4bfd6bbfe3dad85e434ea59
--- /dev/null
+++ b/simplify.py
@@ -0,0 +1,28 @@
+from argparse import ArgumentParser
+
+import torch
+
+
+def simplify_pth(pth_name, project_name):
+    model_path = f'./checkpoints/{project_name}'
+    checkpoint_dict = torch.load(f'{model_path}/{pth_name}')
+    torch.save({'epoch': checkpoint_dict['epoch'],
+                'state_dict': checkpoint_dict['state_dict'],
+                'global_step': None,
+                'checkpoint_callback_best': None,
+                'optimizer_states': None,
+                'lr_schedulers': None
+                }, f'./clean_{pth_name}')
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('--proj', type=str)
+    parser.add_argument('--steps', type=str)
+    args = parser.parse_args()
+    model_name = f"model_ckpt_steps_{args.steps}.ckpt"
+    simplify_pth(model_name, args.proj)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/training/__pycache__/train_pipeline.cpython-38.pyc b/training/__pycache__/train_pipeline.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3d3bb1542d0d0d46272bf572feabe0ac6b796e59
Binary files /dev/null and b/training/__pycache__/train_pipeline.cpython-38.pyc differ
diff --git a/training/config.yaml b/training/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d49f0ecb2e118cc255d97e6077eb8e62046f4a05
--- /dev/null
+++ b/training/config.yaml
@@ -0,0 +1,349 @@
+K_step: 1000
+accumulate_grad_batches: 1
+audio_num_mel_bins: 80
+audio_sample_rate: 24000
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_hubert: true
+  with_spk_embed: false
+  with_wav: false
+binarizer_cls: preprocessing.SVCpre.SVCBinarizer
+binary_data_dir: data/binary/atri
+check_val_every_n_epoch: 10
+choose_test_manually: false
+clip_grad_norm: 1
+config_path: training/config.yaml
+content_cond_steps: []
+cwt_add_f0_loss: false
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_std_scale: 0.8
+datasets:
+- opencpop
+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+decay_steps: 30000
+decoder_type: fft
+dict_dir: ''
+diff_decoder_type: wavenet
+diff_loss_type: l2
+dilation_cycle_length: 4
+dropout: 0.1
+ds_workers: 4
+dur_enc_hidden_stride_kernel:
+- 0,2,3
+- 0,2,3
+- 0,1,3
+dur_loss: mse
+dur_predictor_kernel: 3
+dur_predictor_layers: 5
+enc_ffn_kernel_size: 9
+enc_layers: 4
+encoder_K: 8
+encoder_type: fft
+endless_ds: False
+f0_bin: 256
+f0_max: 1100.0
+f0_min: 50.0
+ffn_act: gelu
+ffn_padding: SAME
+fft_size: 512
+fmax: 12000
+fmin: 30
+fs2_ckpt: ''
+gaussian_start: true
+gen_dir_name: ''
+gen_tgt_spk_id: -1
+hidden_size: 256
+hop_size: 128
+hubert_gpu: true
+hubert_path: checkpoints/hubert/hubert_soft.pt
+infer: false
+keep_bins: 80
+lambda_commit: 0.25
+lambda_energy: 0.0
+lambda_f0: 1.0
+lambda_ph_dur: 0.3
+lambda_sent_dur: 1.0
+lambda_uv: 1.0
+lambda_word_dur: 1.0
+load_ckpt: ''
+log_interval: 100
+loud_norm: false
+lr: 5.0e-05
+max_beta: 0.02
+max_epochs: 3000
+max_eval_sentences: 1
+max_eval_tokens: 60000
+max_frames: 42000
+max_input_tokens: 60000
+max_sentences: 24
+max_tokens: 128000
+max_updates: 1000000
+mel_loss: ssim:0.5|l1:0.5
+mel_vmax: 1.5
+mel_vmin: -6.0
+min_level_db: -120
+norm_type: gn
+num_ckpt_keep: 10
+num_heads: 2
+num_sanity_val_steps: 1
+num_spk: 1
+num_test_samples: 0
+num_valid_plots: 10
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+out_wav_norm: false
+pe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
+pe_enable: false
+perform_enhance: true
+pitch_ar: false
+pitch_enc_hidden_stride_kernel:
+- 0,2,5
+- 0,2,5
+- 0,2,5
+pitch_extractor: parselmouth
+pitch_loss: l2
+pitch_norm: log
+pitch_type: frame
+pndm_speedup: 10
+pre_align_args:
+  allow_no_txt: false
+  denoise: false
+  forced_align: mfa
+  txt_processor: zh_g2pM
+  use_sox: true
+  use_tone: false
+pre_align_cls: data_gen.singing.pre_align.SingingPreAlign
+predictor_dropout: 0.5
+predictor_grad: 0.1
+predictor_hidden: -1
+predictor_kernel: 5
+predictor_layers: 5
+prenet_dropout: 0.5
+prenet_hidden_size: 256
+pretrain_fs_ckpt: pretrain/nyaru/model_ckpt_steps_60000.ckpt
+processed_data_dir: xxx
+profile_infer: false
+raw_data_dir: data/raw/atri
+ref_norm_layer: bn
+rel_pos: true
+reset_phone_dict: true
+residual_channels: 256
+residual_layers: 20
+save_best: false
+save_ckpt: true
+save_codes:
+- configs
+- modules
+- src
+- utils
+save_f0: true
+save_gt: false
+schedule_type: linear
+seed: 1234
+sort_by_len: true
+speaker_id: atri
+spec_max:
+- 0.2987259328365326
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+- -0.9320166110992432
+- -1.2862414121627808
+- -2.8936190605163574
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+spec_min:
+- -6.0
+- -6.0
+- -6.0
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+- -6.0
+spk_cond_steps: []
+stop_token_weight: 5.0
+task_cls: training.task.SVC_task.SVCTask
+test_ids: []
+test_input_dir: ''
+test_num: 0
+test_prefixes:
+- test
+test_set_name: test
+timesteps: 1000
+train_set_name: train
+use_crepe: true
+use_denoise: false
+use_energy_embed: false
+use_gt_dur: false
+use_gt_f0: false
+use_midi: false
+use_nsf: true
+use_pitch_embed: true
+use_pos_embed: true
+use_spk_embed: false
+use_spk_id: false
+use_split_spk_id: false
+use_uv: false
+use_var_enc: false
+use_vec: false
+val_check_interval: 2000
+valid_num: 0
+valid_set_name: valid
+vocoder: network.vocoders.hifigan.HifiGAN
+vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128
+warmup_updates: 2000
+wav2spec_eps: 1e-6
+weight_decay: 0
+win_size: 512
+work_dir: checkpoints/atri
+no_fs2: false
\ No newline at end of file
diff --git a/training/config_nsf.yaml b/training/config_nsf.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..37beccc14e0b8978e065a79bc7319c2c057bf1e5
--- /dev/null
+++ b/training/config_nsf.yaml
@@ -0,0 +1,445 @@
+K_step: 1000
+accumulate_grad_batches: 1
+audio_num_mel_bins: 128
+audio_sample_rate: 44100
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_hubert: true
+  with_spk_embed: false
+  with_wav: false
+binarizer_cls: preprocessing.SVCpre.SVCBinarizer
+binary_data_dir: data/binary/Meiko
+check_val_every_n_epoch: 10
+choose_test_manually: false
+clip_grad_norm: 1
+config_path: training/config_nsf.yaml
+content_cond_steps: []
+cwt_add_f0_loss: false
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_std_scale: 0.8
+datasets:
+- opencpop
+debug: false
+dec_ffn_kernel_size: 9
+dec_layers: 4
+decay_steps: 50000
+decoder_type: fft
+dict_dir: ''
+diff_decoder_type: wavenet
+diff_loss_type: l2
+dilation_cycle_length: 4
+dropout: 0.1
+ds_workers: 4
+dur_enc_hidden_stride_kernel:
+- 0,2,3
+- 0,2,3
+- 0,1,3
+dur_loss: mse
+dur_predictor_kernel: 3
+dur_predictor_layers: 5
+enc_ffn_kernel_size: 9
+enc_layers: 4
+encoder_K: 8
+encoder_type: fft
+endless_ds: false
+f0_bin: 256
+f0_max: 1100.0
+f0_min: 40.0
+ffn_act: gelu
+ffn_padding: SAME
+fft_size: 2048
+fmax: 16000
+fmin: 40
+fs2_ckpt: ''
+gaussian_start: true
+gen_dir_name: ''
+gen_tgt_spk_id: -1
+hidden_size: 256
+hop_size: 512
+hubert_gpu: true
+hubert_path: checkpoints/hubert/hubert_soft.pt
+infer: false
+keep_bins: 128
+lambda_commit: 0.25
+lambda_energy: 0.0
+lambda_f0: 1.0
+lambda_ph_dur: 0.3
+lambda_sent_dur: 1.0
+lambda_uv: 1.0
+lambda_word_dur: 1.0
+load_ckpt: ''
+log_interval: 100
+loud_norm: false
+lr: 0.0006
+max_beta: 0.02
+max_epochs: 3000
+max_eval_sentences: 1
+max_eval_tokens: 60000
+max_frames: 42000
+max_input_tokens: 60000
+max_sentences: 11
+max_tokens: 128000
+max_updates: 1000000
+mel_loss: ssim:0.5|l1:0.5
+mel_vmax: 1.5
+mel_vmin: -6.0
+min_level_db: -120
+no_fs2: true
+norm_type: gn
+num_ckpt_keep: 10
+num_heads: 2
+num_sanity_val_steps: 1
+num_spk: 1
+num_test_samples: 0
+num_valid_plots: 10
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+out_wav_norm: false
+pe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
+pe_enable: false
+perform_enhance: true
+pitch_ar: false
+pitch_enc_hidden_stride_kernel:
+- 0,2,5
+- 0,2,5
+- 0,2,5
+pitch_extractor: parselmouth
+pitch_loss: l2
+pitch_norm: log
+pitch_type: frame
+pndm_speedup: 10
+pre_align_args:
+  allow_no_txt: false
+  denoise: false
+  forced_align: mfa
+  txt_processor: zh_g2pM
+  use_sox: true
+  use_tone: false
+pre_align_cls: data_gen.singing.pre_align.SingingPreAlign
+predictor_dropout: 0.5
+predictor_grad: 0.1
+predictor_hidden: -1
+predictor_kernel: 5
+predictor_layers: 5
+prenet_dropout: 0.5
+prenet_hidden_size: 256
+pretrain_fs_ckpt: ''
+processed_data_dir: xxx
+profile_infer: false
+raw_data_dir: data/raw/Meiko
+ref_norm_layer: bn
+rel_pos: true
+reset_phone_dict: true
+residual_channels: 384
+residual_layers: 20
+save_best: true
+save_ckpt: true
+save_codes:
+- configs
+- modules
+- src
+- utils
+save_f0: true
+save_gt: false
+schedule_type: linear
+seed: 1234
+sort_by_len: true
+speaker_id: Meiko
+spec_max:
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+- -4.999994277954102
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+- -4.999994277954102
+- -4.942144870758057
+- -4.772783279418945
+- -4.7206244468688965
+- -4.5759992599487305
+- -4.509932518005371
+spk_cond_steps: []
+stop_token_weight: 5.0
+task_cls: training.task.SVC_task.SVCTask
+test_ids: []
+test_input_dir: ''
+test_num: 0
+test_prefixes:
+- test
+test_set_name: test
+timesteps: 1000
+train_set_name: train
+use_crepe: false
+use_denoise: false
+use_energy_embed: false
+use_gt_dur: false
+use_gt_f0: false
+use_midi: false
+use_nsf: true
+use_pitch_embed: true
+use_pos_embed: true
+use_spk_embed: false
+use_spk_id: false
+use_split_spk_id: false
+use_uv: false
+use_var_enc: false
+use_vec: false
+val_check_interval: 1000
+valid_num: 0
+valid_set_name: valid
+vocoder: network.vocoders.nsf_hifigan.NsfHifiGAN
+vocoder_ckpt: checkpoints/nsf_hifigan/model
+warmup_updates: 2000
+wav2spec_eps: 1e-6
+weight_decay: 0
+win_size: 2048
+work_dir: checkpoints/Meiko
diff --git a/training/dataset/__pycache__/base_dataset.cpython-38.pyc b/training/dataset/__pycache__/base_dataset.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..eb751fa101b07c244ec4327c8daa71c767c84889
Binary files /dev/null and b/training/dataset/__pycache__/base_dataset.cpython-38.pyc differ
diff --git a/training/dataset/__pycache__/fs2_utils.cpython-38.pyc b/training/dataset/__pycache__/fs2_utils.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..95fde02cfa553720d97934e331342e0a8d2c8442
Binary files /dev/null and b/training/dataset/__pycache__/fs2_utils.cpython-38.pyc differ
diff --git a/training/dataset/base_dataset.py b/training/dataset/base_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7d96e0fab43788757bd67c1737b9eab77b98c4d
--- /dev/null
+++ b/training/dataset/base_dataset.py
@@ -0,0 +1,66 @@
+import torch
+from utils.hparams import hparams
+import numpy as np
+import os
+
+class BaseDataset(torch.utils.data.Dataset):
+    '''
+        Base class for datasets.
+        1. *ordered_indices*:
+            if self.shuffle == True, shuffle the indices;
+            if self.sort_by_len == True, sort data by length;
+        2. *sizes*:
+            clipped length if "max_frames" is set;
+        3. *num_tokens*:
+            unclipped length.
+
+        Subclasses should define:
+        1. *collate*:
+            take the longest data, pad other data to the same length;
+        2. *__getitem__*:
+            the index function.
+    '''
+    def __init__(self, shuffle):
+        super().__init__()
+        self.hparams = hparams
+        self.shuffle = shuffle
+        self.sort_by_len = hparams['sort_by_len']
+        self.sizes = None
+
+    @property
+    def _sizes(self):
+        return self.sizes
+
+    def __getitem__(self, index):
+        raise NotImplementedError
+
+    def collater(self, samples):
+        raise NotImplementedError
+
+    def __len__(self):
+        return len(self._sizes)
+
+    def num_tokens(self, index):
+        return self.size(index)
+
+    def size(self, index):
+        """Return an example's size as a float or tuple. This value is used when
+        filtering a dataset with ``--max-positions``."""
+        size = min(self._sizes[index], hparams['max_frames'])
+        return size
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        if self.shuffle:
+            indices = np.random.permutation(len(self))
+            if self.sort_by_len:
+                indices = indices[np.argsort(np.array(self._sizes)[indices], kind='mergesort')]
+                # 先random, 然后稳定排序, 保证排序后同长度的数据顺序是依照random permutation的 (被其随机打乱).
+        else:
+            indices = np.arange(len(self))
+        return indices
+
+    @property
+    def num_workers(self):
+        return int(os.getenv('NUM_WORKERS', hparams['ds_workers']))
diff --git a/training/dataset/fs2_utils.py b/training/dataset/fs2_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..23833faa9b2858f86d31dadc77930cb78b456509
--- /dev/null
+++ b/training/dataset/fs2_utils.py
@@ -0,0 +1,178 @@
+import matplotlib
+
+matplotlib.use('Agg')
+
+import glob
+import importlib
+from utils.cwt import get_lf0_cwt
+import os
+import torch.optim
+import torch.utils.data
+from utils.indexed_datasets import IndexedDataset
+from utils.pitch_utils import norm_interp_f0
+import numpy as np
+from training.dataset.base_dataset import BaseDataset
+import torch
+import torch.optim
+import torch.utils.data
+import utils
+import torch.distributions
+from utils.hparams import hparams
+
+
+class FastSpeechDataset(BaseDataset):
+    def __init__(self, prefix, shuffle=False):
+        super().__init__(shuffle)
+        self.data_dir = hparams['binary_data_dir']
+        self.prefix = prefix
+        self.hparams = hparams
+        self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
+        self.indexed_ds = None
+        # self.name2spk_id={}
+
+        # pitch stats
+        f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy'
+        if os.path.exists(f0_stats_fn):
+            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn)
+            hparams['f0_mean'] = float(hparams['f0_mean'])
+            hparams['f0_std'] = float(hparams['f0_std'])
+        else:
+            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None
+
+        if prefix == 'test':
+            if hparams['test_input_dir'] != '':
+                self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir'])
+            else:
+                if hparams['num_test_samples'] > 0:
+                    self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids']
+                    self.sizes = [self.sizes[i] for i in self.avail_idxs]
+
+        if hparams['pitch_type'] == 'cwt':
+            _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10))
+
+    def _get_item(self, index):
+        if hasattr(self, 'avail_idxs') and self.avail_idxs is not None:
+            index = self.avail_idxs[index]
+        if self.indexed_ds is None:
+            self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
+        return self.indexed_ds[index]
+
+    def __getitem__(self, index):
+        hparams = self.hparams
+        item = self._get_item(index)
+        max_frames = hparams['max_frames']
+        spec = torch.Tensor(item['mel'])[:max_frames]
+        energy = (spec.exp() ** 2).sum(-1).sqrt()
+        mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
+        f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
+        #phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']])
+        hubert=torch.Tensor(item['hubert'][:hparams['max_input_tokens']])
+        pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
+        # print(item.keys(), item['mel'].shape, spec.shape)
+        sample = {
+            "id": index,
+            "item_name": item['item_name'],
+           # "text": item['txt'],
+           # "txt_token": phone,
+            "hubert":hubert,
+            "mel": spec,
+            "pitch": pitch,
+            "energy": energy,
+            "f0": f0,
+            "uv": uv,
+            "mel2ph": mel2ph,
+            "mel_nonpadding": spec.abs().sum(-1) > 0,
+        }
+        if self.hparams['use_spk_embed']:
+            sample["spk_embed"] = torch.Tensor(item['spk_embed'])
+        if self.hparams['use_spk_id']:
+            sample["spk_id"] = item['spk_id']
+            # sample['spk_id'] = 0
+            # for key in self.name2spk_id.keys():
+            #     if key in item['item_name']:
+            #         sample['spk_id'] = self.name2spk_id[key]
+            #         break
+        #======not used==========
+        # if self.hparams['pitch_type'] == 'cwt':
+        #     cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames]
+        #     f0_mean = item.get('f0_mean', item.get('cwt_mean'))
+        #     f0_std = item.get('f0_std', item.get('cwt_std'))
+        #     sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std})
+        # elif self.hparams['pitch_type'] == 'ph':
+        #     f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0)
+        #     f0_phlevel_num = torch.zeros_like(phone).float().scatter_add(
+        #         0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1)
+        #     sample["f0_ph"] = f0_phlevel_sum / f0_phlevel_num
+        return sample
+
+    def collater(self, samples):
+        if len(samples) == 0:
+            return {}
+        id = torch.LongTensor([s['id'] for s in samples])
+        item_names = [s['item_name'] for s in samples]
+        text = [s['text'] for s in samples]
+        txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0)
+        f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
+        pitch = utils.collate_1d([s['pitch'] for s in samples],1)
+        uv = utils.collate_1d([s['uv'] for s in samples])
+        energy = utils.collate_1d([s['energy'] for s in samples], 0.0)
+        mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
+            if samples[0]['mel2ph'] is not None else None
+        mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
+        txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples])
+        mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
+
+        batch = {
+            'id': id,
+            'item_name': item_names,
+            'nsamples': len(samples),
+            'text': text,
+            'txt_tokens': txt_tokens,
+            'txt_lengths': txt_lengths,
+            'mels': mels,
+            'mel_lengths': mel_lengths,
+            'mel2ph': mel2ph,
+            'energy': energy,
+            'pitch': pitch,
+            'f0': f0,
+            'uv': uv,
+        }
+
+        if self.hparams['use_spk_embed']:
+            spk_embed = torch.stack([s['spk_embed'] for s in samples])
+            batch['spk_embed'] = spk_embed
+        if self.hparams['use_spk_id']:
+            spk_ids = torch.LongTensor([s['spk_id'] for s in samples])
+            batch['spk_ids'] = spk_ids
+        if self.hparams['pitch_type'] == 'cwt':
+            cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples])
+            f0_mean = torch.Tensor([s['f0_mean'] for s in samples])
+            f0_std = torch.Tensor([s['f0_std'] for s in samples])
+            batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std})
+        elif self.hparams['pitch_type'] == 'ph':
+            batch['f0'] = utils.collate_1d([s['f0_ph'] for s in samples])
+
+        return batch
+
+    def load_test_inputs(self, test_input_dir, spk_id=0):
+        inp_wav_paths = glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3')
+        sizes = []
+        items = []
+
+        binarizer_cls = hparams.get("binarizer_cls", 'basics.base_binarizer.BaseBinarizer')
+        pkg = ".".join(binarizer_cls.split(".")[:-1])
+        cls_name = binarizer_cls.split(".")[-1]
+        binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
+        binarization_args = hparams['binarization_args']
+        from preprocessing.hubertinfer import Hubertencoder
+        for wav_fn in inp_wav_paths:
+            item_name = os.path.basename(wav_fn)
+            ph = txt = tg_fn = ''
+            wav_fn = wav_fn
+            encoder = Hubertencoder(hparams['hubert_path'])
+
+            item = binarizer_cls.process_item(item_name, {'wav_fn':wav_fn}, encoder, binarization_args)
+            print(item)
+            items.append(item)
+            sizes.append(item['len'])
+        return items, sizes
diff --git a/training/from huggingface_hub import Repository.py b/training/from huggingface_hub import Repository.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c3c8ded414d2af662eb49404e608a8a15462e9a
--- /dev/null
+++ b/training/from huggingface_hub import Repository.py	
@@ -0,0 +1,2 @@
+from huggingface_hub import Repository
+repo = Repository(local_dir="w2v2", clone_from="facebook/wav2vec2-large-960h-lv60")
\ No newline at end of file
diff --git a/training/pe.py b/training/pe.py
new file mode 100644
index 0000000000000000000000000000000000000000..584a518649ba8465ad0c7690b51cce9762592da5
--- /dev/null
+++ b/training/pe.py
@@ -0,0 +1,155 @@
+import matplotlib
+matplotlib.use('Agg')
+
+import torch
+import numpy as np
+import os
+
+from training.dataset.base_dataset import BaseDataset
+from training.task.fs2 import FastSpeech2Task
+from modules.fastspeech.pe import PitchExtractor
+import utils
+from utils.indexed_datasets import IndexedDataset
+from utils.hparams import hparams
+from utils.plot import f0_to_figure
+from utils.pitch_utils import norm_interp_f0, denorm_f0
+
+
+class PeDataset(BaseDataset):
+    def __init__(self, prefix, shuffle=False):
+        super().__init__(shuffle)
+        self.data_dir = hparams['binary_data_dir']
+        self.prefix = prefix
+        self.hparams = hparams
+        self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
+        self.indexed_ds = None
+
+        # pitch stats
+        f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy'
+        if os.path.exists(f0_stats_fn):
+            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn)
+            hparams['f0_mean'] = float(hparams['f0_mean'])
+            hparams['f0_std'] = float(hparams['f0_std'])
+        else:
+            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None
+
+        if prefix == 'test':
+            if hparams['num_test_samples'] > 0:
+                self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids']
+                self.sizes = [self.sizes[i] for i in self.avail_idxs]
+
+    def _get_item(self, index):
+        if hasattr(self, 'avail_idxs') and self.avail_idxs is not None:
+            index = self.avail_idxs[index]
+        if self.indexed_ds is None:
+            self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
+        return self.indexed_ds[index]
+
+    def __getitem__(self, index):
+        hparams = self.hparams
+        item = self._get_item(index)
+        max_frames = hparams['max_frames']
+        spec = torch.Tensor(item['mel'])[:max_frames]
+        # mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
+        f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
+        pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
+        # print(item.keys(), item['mel'].shape, spec.shape)
+        sample = {
+            "id": index,
+            "item_name": item['item_name'],
+            "text": item['txt'],
+            "mel": spec,
+            "pitch": pitch,
+            "f0": f0,
+            "uv": uv,
+            # "mel2ph": mel2ph,
+            # "mel_nonpadding": spec.abs().sum(-1) > 0,
+        }
+        return sample
+
+    def collater(self, samples):
+        if len(samples) == 0:
+            return {}
+        id = torch.LongTensor([s['id'] for s in samples])
+        item_names = [s['item_name'] for s in samples]
+        text = [s['text'] for s in samples]
+        f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
+        pitch = utils.collate_1d([s['pitch'] for s in samples])
+        uv = utils.collate_1d([s['uv'] for s in samples])
+        mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
+        mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
+        # mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
+        #     if samples[0]['mel2ph'] is not None else None
+        # mel_nonpaddings = utils.collate_1d([s['mel_nonpadding'].float() for s in samples], 0.0)
+
+        batch = {
+            'id': id,
+            'item_name': item_names,
+            'nsamples': len(samples),
+            'text': text,
+            'mels': mels,
+            'mel_lengths': mel_lengths,
+            'pitch': pitch,
+            # 'mel2ph': mel2ph,
+            # 'mel_nonpaddings': mel_nonpaddings,
+            'f0': f0,
+            'uv': uv,
+        }
+        return batch
+
+
+class PitchExtractionTask(FastSpeech2Task):
+    def __init__(self):
+        super().__init__()
+        self.dataset_cls = PeDataset
+
+    def build_tts_model(self):
+        self.model = PitchExtractor(conv_layers=hparams['pitch_extractor_conv_layers'])
+
+    # def build_scheduler(self, optimizer):
+    #     return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5)
+    def _training_step(self, sample, batch_idx, _):
+        loss_output = self.run_model(self.model, sample)
+        total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad])
+        loss_output['batch_size'] = sample['mels'].size()[0]
+        return total_loss, loss_output
+
+    def validation_step(self, sample, batch_idx):
+        outputs = {}
+        outputs['losses'] = {}
+        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=True)
+        outputs['total_loss'] = sum(outputs['losses'].values())
+        outputs['nsamples'] = sample['nsamples']
+        outputs = utils.tensors_to_scalars(outputs)
+        if batch_idx < hparams['num_valid_plots']:
+            self.plot_pitch(batch_idx, model_out, sample)
+        return outputs
+
+    def run_model(self, model, sample, return_output=False, infer=False):
+        f0 = sample['f0']
+        uv = sample['uv']
+        output = model(sample['mels'])
+        losses = {}
+        self.add_pitch_loss(output, sample, losses)
+        if not return_output:
+            return losses
+        else:
+            return losses, output
+
+    def plot_pitch(self, batch_idx, model_out, sample):
+        gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
+        self.logger.experiment.add_figure(
+            f'f0_{batch_idx}',
+            f0_to_figure(gt_f0[0], None, model_out['f0_denorm_pred'][0]),
+            self.global_step)
+
+    def add_pitch_loss(self, output, sample, losses):
+        # mel2ph = sample['mel2ph']  # [B, T_s]
+        mel = sample['mels']
+        f0 = sample['f0']
+        uv = sample['uv']
+        # nonpadding = (mel2ph != 0).float() if hparams['pitch_type'] == 'frame' \
+        #     else (sample['txt_tokens'] != 0).float()
+        nonpadding = (mel.abs().sum(-1) > 0).float()  # sample['mel_nonpaddings']
+        # print(nonpadding[0][-8:], nonpadding.shape)
+        self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding)
\ No newline at end of file
diff --git a/training/rgsdgsd.py b/training/rgsdgsd.py
new file mode 100644
index 0000000000000000000000000000000000000000..527fd3fc836518096a691c37a2d9a9a578441413
--- /dev/null
+++ b/training/rgsdgsd.py
@@ -0,0 +1,107 @@
+import pygame
+import mido
+from gtts import gTTS
+from pygame.locals import MOUSEBUTTONDOWN, KEYDOWN, K_RETURN
+import time
+
+# Initialize the click count and the time of the last click
+click_count = 0
+last_click_time = 0
+
+# Initialize Pygame and create a window for the MIDI editor
+pygame.init()
+screen = pygame.display.set_mode((640, 480))
+
+class MidiNote:
+    def __init__(self, note, velocity, time, x, y, width, height):
+        self.note = note
+        self.velocity = velocity
+        self.time = time
+        self.x = x
+        self.y = y
+        self.width = width
+        self.height = height
+
+
+# Create a new MIDI file
+mid = mido.MidiFile()
+
+# Create a new MIDI track
+track = mido.MidiTrack()
+
+# Add the MIDI track to the file
+mid.tracks.append(track)
+
+# Create a function to add lyrics to a specific MIDI note
+def add_lyrics(note, lyrics):
+    # Add the lyrics to the MIDI note
+    note.text = lyrics
+    # Update the MIDI file with the new lyrics
+    mid.save("song.mid")
+
+# Create a function to get the MIDI note that was clicked on
+def get_clicked_note(pos):
+    # Iterate through the MIDI notes in the file
+    for track in mid.tracks:
+        for note in track:
+            if isinstance(note, mido.Message):
+                # Check if the mouse position is within the bounds of the MIDI note
+                if pos[0] > note.x and pos[0] < note.x + note.width:
+                    if pos[1] > note.y and pos[1] < note.y + note.height:
+                        return note
+    return None
+
+# Create a function to convert the lyrics from Japanese to speech
+def speak_lyrics(lyrics):
+    tts = gTTS(lyrics, lang='ja')
+    tts.save('lyrics.mp3')
+    pygame.mixer.music.load('lyrics.mp3')
+    pygame.mixer.music.play()
+
+
+# Main loop
+while True:
+    for event in pygame.event.get():
+        if event.type == MOUSEBUTTONDOWN:
+            # Increment the click count
+            click_count += 1
+            # Check if the user double-clicked on a MIDI note
+            if time.time() - last_click_time < 0.5:
+                # Get the MIDI note that was clicked on
+                note = get_clicked_note(event.pos)
+                # Add the lyrics to the MIDI note
+                add_lyrics(note, lyrics)
+                # Reset the click count
+                click_count = 0
+            # Update the time of the last click
+            last_click_time = time.time()
+        if event.type == KEYDOWN:
+            if event.key == K_RETURN:
+                # Get the lyrics from the input field
+                lyrics = input_field.get_text()
+                # Convert the lyrics to speech and play them
+                speak_lyrics(lyrics)
+    # If the click count is not reset, it means that the user has single-clicked
+    if click_count == 1:
+        # Get the position of the single click
+        pos = pygame.mouse.get_pos()
+        # Create a new MIDI note with the specified position and length
+        note = MidiNote(60, 64, 0, event.pos[0], event.pos[1], 100, 100)
+        note.x = pos[0]
+        note.y = pos[1]
+        note.width = 100
+        note.height = 100
+        # Add the MIDI note to the track
+        track.append(note)
+        mid.save("song.mid")
+        # Reset the click count
+        click_count = 0
+    lyrics = ""
+
+    input_field = pygame.font.Font(None, 32).render(lyrics, True, (0, 0, 0))
+
+    # Display the input field on the window
+    screen.blit(input_field, (10, 10))
+    pygame.display.flip()
+
+       
diff --git a/training/song.mid b/training/song.mid
new file mode 100644
index 0000000000000000000000000000000000000000..2e11450ce1dede01b45d22381102e424236fc679
Binary files /dev/null and b/training/song.mid differ
diff --git a/training/task/SVC_task.py b/training/task/SVC_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..56c66751af4a279adeb76089b45d0ab93b29e6ad
--- /dev/null
+++ b/training/task/SVC_task.py
@@ -0,0 +1,223 @@
+import torch
+
+import utils
+from utils.hparams import hparams
+from network.diff.net import DiffNet
+from network.diff.diffusion import GaussianDiffusion, OfflineGaussianDiffusion
+from training.task.fs2 import FastSpeech2Task
+from network.vocoders.base_vocoder import get_vocoder_cls, BaseVocoder
+from modules.fastspeech.tts_modules import mel2ph_to_dur
+
+from network.diff.candidate_decoder import FFT
+from utils.pitch_utils import denorm_f0
+from training.dataset.fs2_utils import FastSpeechDataset
+
+import numpy as np
+import os
+import torch.nn.functional as F
+
+DIFF_DECODERS = {
+    'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
+    'fft': lambda hp: FFT(
+        hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
+}
+
+
+class SVCDataset(FastSpeechDataset):
+    def collater(self, samples):
+        from preprocessing.process_pipeline import File2Batch
+        return File2Batch.processed_input2batch(samples)
+
+
+class SVCTask(FastSpeech2Task):
+    def __init__(self):
+        super(SVCTask, self).__init__()
+        self.dataset_cls = SVCDataset
+        self.vocoder: BaseVocoder = get_vocoder_cls(hparams)()
+    
+    def build_tts_model(self):
+        # import torch
+        # from tqdm import tqdm
+        # v_min = torch.ones([80]) * 100
+        # v_max = torch.ones([80]) * -100
+        # for i, ds in enumerate(tqdm(self.dataset_cls('train'))):
+        #     v_max = torch.max(torch.max(ds['mel'].reshape(-1, 80), 0)[0], v_max)
+        #     v_min = torch.min(torch.min(ds['mel'].reshape(-1, 80), 0)[0], v_min)
+        #     if i % 100 == 0:
+        #         print(i, v_min, v_max)
+        # print('final', v_min, v_max)
+        mel_bins = hparams['audio_num_mel_bins']
+        self.model = GaussianDiffusion(
+            phone_encoder=self.phone_encoder,
+            out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
+            timesteps=hparams['timesteps'],
+            K_step=hparams['K_step'],
+            loss_type=hparams['diff_loss_type'],
+            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
+        )
+
+    
+    def build_optimizer(self, model):
+        self.optimizer = optimizer = torch.optim.AdamW(
+            filter(lambda p: p.requires_grad, model.parameters()),
+            lr=hparams['lr'],
+            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
+            weight_decay=hparams['weight_decay'])
+        return optimizer
+
+    def run_model(self, model, sample, return_output=False, infer=False):
+        '''
+            steps:
+            1. run the full model, calc the main loss
+            2. calculate loss for dur_predictor, pitch_predictor, energy_predictor
+        '''
+        hubert = sample['hubert']  # [B, T_t,H]
+        target = sample['mels']  # [B, T_s, 80]
+        mel2ph = sample['mel2ph'] # [B, T_s]
+        f0 = sample['f0']
+        uv = sample['uv']
+        energy = sample['energy']
+
+        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
+        if hparams['pitch_type'] == 'cwt':
+            # NOTE: this part of script is *isolated* from other scripts, which means
+            #       it may not be compatible with the current version.    
+            pass
+            # cwt_spec = sample[f'cwt_spec']
+            # f0_mean = sample['f0_mean']
+            # f0_std = sample['f0_std']
+            # sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)
+
+        # output == ret
+        # model == src.diff.diffusion.GaussianDiffusion
+        output = model(hubert, mel2ph=mel2ph, spk_embed=spk_embed,
+                       ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer)
+
+        losses = {}
+        if 'diff_loss' in output:
+            losses['mel'] = output['diff_loss']
+        #self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses)
+        # if hparams['use_pitch_embed']:
+        #     self.add_pitch_loss(output, sample, losses)
+        # if hparams['use_energy_embed']:
+        #     self.add_energy_loss(output['energy_pred'], energy, losses)
+        if not return_output:
+            return losses
+        else:
+            return losses, output
+    
+    def _training_step(self, sample, batch_idx, _):
+        log_outputs = self.run_model(self.model, sample)
+        total_loss = sum([v for v in log_outputs.values() if isinstance(v, torch.Tensor) and v.requires_grad])
+        log_outputs['batch_size'] = sample['hubert'].size()[0]
+        log_outputs['lr'] = self.scheduler.get_lr()[0]
+        return total_loss, log_outputs
+
+    def build_scheduler(self, optimizer):
+        return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5)
+
+    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx):
+        if optimizer is None:
+            return
+        optimizer.step()
+        optimizer.zero_grad()
+        if self.scheduler is not None:
+            self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
+
+    def validation_step(self, sample, batch_idx):
+        outputs = {}
+        hubert = sample['hubert']  # [B, T_t]
+
+        target = sample['mels']  # [B, T_s, 80]
+        energy = sample['energy']
+        # fs2_mel = sample['fs2_mels']
+        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
+        mel2ph = sample['mel2ph']
+
+        outputs['losses'] = {}
+
+        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
+
+        outputs['total_loss'] = sum(outputs['losses'].values())
+        outputs['nsamples'] = sample['nsamples']
+        outputs = utils.tensors_to_scalars(outputs)
+        if batch_idx < hparams['num_valid_plots']:
+            model_out = self.model(
+                hubert, spk_embed=spk_embed, mel2ph=mel2ph, f0=sample['f0'], uv=sample['uv'], energy=energy, ref_mels=None, infer=True
+                )
+
+            if hparams.get('pe_enable') is not None and hparams['pe_enable']:
+                gt_f0 = self.pe(sample['mels'])['f0_denorm_pred']  # pe predict from GT mel
+                pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred']  # pe predict from Pred mel
+            else:
+                gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
+                pred_f0 = model_out.get('f0_denorm')
+            self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
+            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
+            #self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}')
+            if hparams['use_pitch_embed']:
+                self.plot_pitch(batch_idx, sample, model_out)
+        return outputs
+
+    def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None):
+        """
+            the effect of each loss component:
+                hparams['dur_loss'] : align each phoneme
+                hparams['lambda_word_dur']: align each word
+                hparams['lambda_sent_dur']: align each sentence
+            
+            :param dur_pred: [B, T], float, log scale
+            :param mel2ph: [B, T]
+            :param txt_tokens: [B, T]
+            :param losses:
+            :return:
+        """
+        B, T = txt_tokens.shape
+        nonpadding = (txt_tokens != 0).float()
+        dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding
+        is_sil = torch.zeros_like(txt_tokens).bool()
+        for p in self.sil_ph:
+            is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0])
+        is_sil = is_sil.float()  # [B, T_txt]
+
+        # phone duration loss
+        if hparams['dur_loss'] == 'mse':
+            losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none')
+            losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum()
+            losses['pdur'] = losses['pdur'] * hparams['lambda_ph_dur']
+            dur_pred = (dur_pred.exp() - 1).clamp(min=0)
+        else:
+            raise NotImplementedError
+
+        # use linear scale for sent and word duration
+        if hparams['lambda_word_dur'] > 0:
+            #idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1]
+            idx = wdb.cumsum(axis=1)
+            # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur)  # midi_dur can be implied by add gt-ph_dur
+            word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred)
+            word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt)
+            wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none')
+            word_nonpadding = (word_dur_g > 0).float()
+            wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum()
+            losses['wdur'] = wdur_loss * hparams['lambda_word_dur']
+        if hparams['lambda_sent_dur'] > 0:
+            sent_dur_p = dur_pred.sum(-1)
+            sent_dur_g = dur_gt.sum(-1)
+            sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean')
+            losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']
+    
+    ############
+    # validation plots
+    ############
+    def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None):
+        gt_wav = gt_wav[0].cpu().numpy()
+        wav_out = wav_out[0].cpu().numpy()
+        gt_f0 = gt_f0[0].cpu().numpy()
+        f0 = f0[0].cpu().numpy()
+        if is_mel:
+            gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0)
+            wav_out = self.vocoder.spec2wav(wav_out, f0=f0)
+        self.logger.experiment.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step)
+        self.logger.experiment.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step)
+
+
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diff --git a/training/task/__pycache__/base_task.cpython-38.pyc b/training/task/__pycache__/base_task.cpython-38.pyc
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diff --git a/training/task/__pycache__/fs2.cpython-38.pyc b/training/task/__pycache__/fs2.cpython-38.pyc
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diff --git a/training/task/__pycache__/tts.cpython-38.pyc b/training/task/__pycache__/tts.cpython-38.pyc
new file mode 100644
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diff --git a/training/task/base_task.py b/training/task/base_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..369bd18da4aeceaf45da0f913be6bc3a1948aa4b
--- /dev/null
+++ b/training/task/base_task.py
@@ -0,0 +1,337 @@
+from datetime import datetime
+import shutil
+
+import matplotlib
+
+matplotlib.use('Agg')
+
+from utils.hparams import hparams, set_hparams
+import random
+import sys
+import numpy as np
+import torch.distributed as dist
+from pytorch_lightning.loggers import TensorBoardLogger
+from utils.pl_utils import LatestModelCheckpoint, BaseTrainer, data_loader, DDP
+from torch import nn
+import torch.utils.data
+import utils
+import logging
+import os
+
+torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system'))
+
+log_format = '%(asctime)s %(message)s'
+logging.basicConfig(stream=sys.stdout, level=logging.INFO,
+                    format=log_format, datefmt='%m/%d %I:%M:%S %p')
+
+class BaseTask(nn.Module):
+    '''
+        Base class for training tasks.
+        1. *load_ckpt*:
+            load checkpoint;
+        2. *training_step*:
+            record and log the loss;
+        3. *optimizer_step*:
+            run backwards step;
+        4. *start*:
+            load training configs, backup code, log to tensorboard, start training;
+        5. *configure_ddp* and *init_ddp_connection*:
+            start parallel training.
+
+        Subclasses should define:
+        1. *build_model*, *build_optimizer*, *build_scheduler*:
+            how to build the model, the optimizer and the training scheduler;
+        2. *_training_step*:
+            one training step of the model;
+        3. *validation_end* and *_validation_end*:
+            postprocess the validation output.
+    '''
+    def __init__(self, *args, **kwargs):
+        # dataset configs
+        super(BaseTask, self).__init__(*args, **kwargs)
+        self.current_epoch = 0
+        self.global_step = 0
+        self.loaded_optimizer_states_dict = {}
+        self.trainer = None
+        self.logger = None
+        self.on_gpu = False
+        self.use_dp = False
+        self.use_ddp = False
+        self.example_input_array = None
+
+        self.max_tokens = hparams['max_tokens']
+        self.max_sentences = hparams['max_sentences']
+        self.max_eval_tokens = hparams['max_eval_tokens']
+        if self.max_eval_tokens == -1:
+            hparams['max_eval_tokens'] = self.max_eval_tokens = self.max_tokens
+        self.max_eval_sentences = hparams['max_eval_sentences']
+        if self.max_eval_sentences == -1:
+            hparams['max_eval_sentences'] = self.max_eval_sentences = self.max_sentences
+
+        self.model = None
+        self.training_losses_meter = None
+
+    ###########
+    # Training, validation and testing
+    ###########
+    def build_model(self):
+        raise NotImplementedError
+
+    def load_ckpt(self, ckpt_base_dir, current_model_name=None, model_name='model', force=True, strict=True):
+        # This function is updated on 2021.12.13
+        if current_model_name is None:
+            current_model_name = model_name
+        utils.load_ckpt(self.__getattr__(current_model_name), ckpt_base_dir, current_model_name, force, strict)
+
+    def on_epoch_start(self):
+        self.training_losses_meter = {'total_loss': utils.AvgrageMeter()}
+
+    def _training_step(self, sample, batch_idx, optimizer_idx):
+        """
+
+        :param sample:
+        :param batch_idx:
+        :return: total loss: torch.Tensor, loss_log: dict
+        """
+        raise NotImplementedError
+
+    def training_step(self, sample, batch_idx, optimizer_idx=-1):
+        loss_ret = self._training_step(sample, batch_idx, optimizer_idx)
+        self.opt_idx = optimizer_idx
+        if loss_ret is None:
+            return {'loss': None}
+        total_loss, log_outputs = loss_ret
+        log_outputs = utils.tensors_to_scalars(log_outputs)
+        for k, v in log_outputs.items():
+            if k not in self.training_losses_meter:
+                self.training_losses_meter[k] = utils.AvgrageMeter()
+            if not np.isnan(v):
+                self.training_losses_meter[k].update(v)
+        self.training_losses_meter['total_loss'].update(total_loss.item())
+
+        try:
+            log_outputs['lr'] = self.scheduler.get_lr()
+            if isinstance(log_outputs['lr'], list):
+                log_outputs['lr'] = log_outputs['lr'][0]
+        except:
+            pass
+
+        # log_outputs['all_loss'] = total_loss.item()
+        progress_bar_log = log_outputs
+        tb_log = {f'tr/{k}': v for k, v in log_outputs.items()}
+        return {
+            'loss': total_loss,
+            'progress_bar': progress_bar_log,
+            'log': tb_log
+        }
+
+    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx):
+        optimizer.step()
+        optimizer.zero_grad()
+        if self.scheduler is not None:
+            self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
+
+    def on_epoch_end(self):
+        loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()}
+        print(f"\n==============\n "
+              f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}"
+              f"\n==============\n")
+
+    def validation_step(self, sample, batch_idx):
+        """
+
+        :param sample:
+        :param batch_idx:
+        :return: output: dict
+        """
+        raise NotImplementedError
+
+    def _validation_end(self, outputs):
+        """
+
+        :param outputs:
+        :return: loss_output: dict
+        """
+        raise NotImplementedError
+
+    def validation_end(self, outputs):
+        loss_output = self._validation_end(outputs)
+        print(f"\n==============\n "
+              f"valid results: {loss_output}"
+              f"\n==============\n")
+        return {
+            'log': {f'val/{k}': v for k, v in loss_output.items()},
+            'val_loss': loss_output['total_loss']
+        }
+
+    def build_scheduler(self, optimizer):
+        raise NotImplementedError
+
+    def build_optimizer(self, model):
+        raise NotImplementedError
+
+    def configure_optimizers(self):
+        optm = self.build_optimizer(self.model)
+        self.scheduler = self.build_scheduler(optm)
+        return [optm]
+
+    def test_start(self):
+        pass
+
+    def test_step(self, sample, batch_idx):
+        return self.validation_step(sample, batch_idx)
+
+    def test_end(self, outputs):
+        return self.validation_end(outputs)
+
+    ###########
+    # Running configuration
+    ###########
+
+    @classmethod
+    def start(cls):
+        set_hparams()
+        os.environ['MASTER_PORT'] = str(random.randint(15000, 30000))
+        random.seed(hparams['seed'])
+        np.random.seed(hparams['seed'])
+        task = cls()
+        work_dir = hparams['work_dir']
+        trainer = BaseTrainer(checkpoint_callback=LatestModelCheckpoint(
+                                  filepath=work_dir,
+                                  verbose=True,
+                                  monitor='val_loss',
+                                  mode='min',
+                                  num_ckpt_keep=hparams['num_ckpt_keep'],
+                                  save_best=hparams['save_best'],
+                                  period=1 if hparams['save_ckpt'] else 100000
+                              ),
+                              logger=TensorBoardLogger(
+                                  save_dir=work_dir,
+                                  name='lightning_logs',
+                                  version='lastest'
+                              ),
+                              gradient_clip_val=hparams['clip_grad_norm'],
+                              val_check_interval=hparams['val_check_interval'],
+                              row_log_interval=hparams['log_interval'],
+                              max_updates=hparams['max_updates'],
+                              num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams[
+                                  'validate'] else 10000,
+                              accumulate_grad_batches=hparams['accumulate_grad_batches'])
+        if not hparams['infer']:  # train
+            # copy_code = input(f'{hparams["save_codes"]} code backup? y/n: ') == 'y'
+            # copy_code = True # backup code every time
+            # if copy_code:
+            #     t = datetime.now().strftime('%Y%m%d%H%M%S')
+            #     code_dir = f'{work_dir}/codes/{t}'
+            #     # TODO: test filesystem calls
+            #     os.makedirs(code_dir, exist_ok=True)
+            #     # subprocess.check_call(f'mkdir "{code_dir}"', shell=True)
+            #     for c in hparams['save_codes']:
+            #         shutil.copytree(c, code_dir, dirs_exist_ok=True)
+            #         # subprocess.check_call(f'xcopy "{c}" "{code_dir}/" /s /e /y', shell=True)
+            #     print(f"| Copied codes to {code_dir}.")
+            trainer.checkpoint_callback.task = task
+            trainer.fit(task)
+        else:
+            trainer.test(task)
+
+    def configure_ddp(self, model, device_ids):
+        model = DDP(
+            model,
+            device_ids=device_ids,
+            find_unused_parameters=True
+        )
+        if dist.get_rank() != 0 and not hparams['debug']:
+            sys.stdout = open(os.devnull, "w")
+            sys.stderr = open(os.devnull, "w")
+        random.seed(hparams['seed'])
+        np.random.seed(hparams['seed'])
+        return model
+
+    def training_end(self, *args, **kwargs):
+        return None
+
+    def init_ddp_connection(self, proc_rank, world_size):
+        set_hparams(print_hparams=False)
+        # guarantees unique ports across jobs from same grid search
+        default_port = 12910
+        # if user gave a port number, use that one instead
+        try:
+            default_port = os.environ['MASTER_PORT']
+        except Exception:
+            os.environ['MASTER_PORT'] = str(default_port)
+
+        # figure out the root node addr
+        root_node = '127.0.0.2'
+        root_node = self.trainer.resolve_root_node_address(root_node)
+        os.environ['MASTER_ADDR'] = root_node
+        dist.init_process_group('nccl', rank=proc_rank, world_size=world_size)
+
+    @data_loader
+    def train_dataloader(self):
+        return None
+
+    @data_loader
+    def test_dataloader(self):
+        return None
+
+    @data_loader
+    def val_dataloader(self):
+        return None
+
+    def on_load_checkpoint(self, checkpoint):
+        pass
+
+    def on_save_checkpoint(self, checkpoint):
+        pass
+
+    def on_sanity_check_start(self):
+        pass
+
+    def on_train_start(self):
+        pass
+
+    def on_train_end(self):
+        pass
+
+    def on_batch_start(self, batch):
+        pass
+
+    def on_batch_end(self):
+        pass
+
+    def on_pre_performance_check(self):
+        pass
+
+    def on_post_performance_check(self):
+        pass
+
+    def on_before_zero_grad(self, optimizer):
+        pass
+
+    def on_after_backward(self):
+        pass
+
+    def backward(self, loss, optimizer):
+        loss.backward()
+
+    def grad_norm(self, norm_type):
+        results = {}
+        total_norm = 0
+        for name, p in self.named_parameters():
+            if p.requires_grad:
+                try:
+                    param_norm = p.grad.data.norm(norm_type)
+                    total_norm += param_norm ** norm_type
+                    norm = param_norm ** (1 / norm_type)
+
+                    grad = round(norm.data.cpu().numpy().flatten()[0], 3)
+                    results['grad_{}_norm_{}'.format(norm_type, name)] = grad
+                except Exception:
+                    # this param had no grad
+                    pass
+
+        total_norm = total_norm ** (1. / norm_type)
+        grad = round(total_norm.data.cpu().numpy().flatten()[0], 3)
+        results['grad_{}_norm_total'.format(norm_type)] = grad
+        return results
diff --git a/training/task/fs2.py b/training/task/fs2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e1618297cf869721c283baffa2fd7bda1c89020
--- /dev/null
+++ b/training/task/fs2.py
@@ -0,0 +1,539 @@
+import matplotlib
+
+matplotlib.use('Agg')
+
+from utils import audio
+import matplotlib.pyplot as plt
+from preprocessing.data_gen_utils import get_pitch_parselmouth
+from training.dataset.fs2_utils import FastSpeechDataset
+from utils.cwt import cwt2f0
+from utils.pl_utils import data_loader
+import os
+from multiprocessing.pool import Pool
+from tqdm import tqdm
+from modules.fastspeech.tts_modules import mel2ph_to_dur
+from utils.hparams import hparams
+from utils.plot import spec_to_figure, dur_to_figure, f0_to_figure
+from utils.pitch_utils import denorm_f0
+from modules.fastspeech.fs2 import FastSpeech2
+from training.task.tts import TtsTask
+import torch
+import torch.optim
+import torch.utils.data
+import torch.nn.functional as F
+import utils
+import torch.distributions
+import numpy as np
+from modules.commons.ssim import ssim
+
+class FastSpeech2Task(TtsTask):
+    def __init__(self):
+        super(FastSpeech2Task, self).__init__()
+        self.dataset_cls = FastSpeechDataset
+        self.mse_loss_fn = torch.nn.MSELoss()
+        mel_losses = hparams['mel_loss'].split("|")
+        self.loss_and_lambda = {}
+        for i, l in enumerate(mel_losses):
+            if l == '':
+                continue
+            if ':' in l:
+                l, lbd = l.split(":")
+                lbd = float(lbd)
+            else:
+                lbd = 1.0
+            self.loss_and_lambda[l] = lbd
+        print("| Mel losses:", self.loss_and_lambda)
+        #self.sil_ph = self.phone_encoder.sil_phonemes()
+
+    @data_loader
+    def train_dataloader(self):
+        train_dataset = self.dataset_cls(hparams['train_set_name'], shuffle=True)
+        return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences,
+                                     endless=hparams['endless_ds'])
+
+    @data_loader
+    def val_dataloader(self):
+        valid_dataset = self.dataset_cls(hparams['valid_set_name'], shuffle=False)
+        return self.build_dataloader(valid_dataset, False, self.max_eval_tokens, self.max_eval_sentences)
+
+    @data_loader
+    def test_dataloader(self):
+        test_dataset = self.dataset_cls(hparams['test_set_name'], shuffle=False)
+        return self.build_dataloader(test_dataset, False, self.max_eval_tokens,
+                                     self.max_eval_sentences, batch_by_size=False)
+
+    def build_tts_model(self):
+        '''
+        rewrite
+        '''
+        return
+        # self.model = FastSpeech2(self.phone_encoder)
+
+    def build_model(self):
+        self.build_tts_model()
+        if hparams['load_ckpt'] != '':
+            self.load_ckpt(hparams['load_ckpt'], strict=True)
+        utils.print_arch(self.model)
+        return self.model
+
+    def _training_step(self, sample, batch_idx, _):
+        '''
+        rewrite
+        '''
+        return
+        # loss_output = self.run_model(self.model, sample)
+        # total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad])
+        # loss_output['batch_size'] = sample['txt_tokens'].size()[0]
+        # return total_loss, loss_output
+
+    def validation_step(self, sample, batch_idx):
+        '''
+        rewrite
+        '''
+        return
+        # outputs = {}
+        # outputs['losses'] = {}
+        # outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True)
+        # outputs['total_loss'] = sum(outputs['losses'].values())
+        # outputs['nsamples'] = sample['nsamples']
+        # mel_out = self.model.out2mel(model_out['mel_out'])
+        # outputs = utils.tensors_to_scalars(outputs)
+        # if batch_idx < hparams['num_valid_plots']:
+        #     self.plot_mel(batch_idx, sample['mels'], mel_out)
+        #     self.plot_dur(batch_idx, sample, model_out)
+        #     if hparams['use_pitch_embed']:
+        #         self.plot_pitch(batch_idx, sample, model_out)
+        # return outputs
+
+    def _validation_end(self, outputs):
+        all_losses_meter = {
+            'total_loss': utils.AvgrageMeter(),
+        }
+        for output in outputs:
+            n = output['nsamples']
+            for k, v in output['losses'].items():
+                if k not in all_losses_meter:
+                    all_losses_meter[k] = utils.AvgrageMeter()
+                all_losses_meter[k].update(v, n)
+            all_losses_meter['total_loss'].update(output['total_loss'], n)
+        return {k: round(v.avg, 4) for k, v in all_losses_meter.items()}
+
+    def run_model(self, model, sample, return_output=False):
+        '''
+        rewrite
+        '''
+        return
+        txt_tokens = sample['txt_tokens']  # [B, T_t]
+        target = sample['mels']  # [B, T_s, 80]
+        mel2ph = sample['mel2ph']  # [B, T_s]
+        f0 = sample['f0']
+        uv = sample['uv']
+        energy = sample['energy']
+        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
+        if hparams['pitch_type'] == 'cwt':
+            cwt_spec = sample[f'cwt_spec']
+            f0_mean = sample['f0_mean']
+            f0_std = sample['f0_std']
+            sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)
+
+        output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
+                       ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False)
+
+        losses = {}
+        self.add_mel_loss(output['mel_out'], target, losses)
+        self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
+        if hparams['use_pitch_embed']:
+            self.add_pitch_loss(output, sample, losses)
+        if hparams['use_energy_embed']:
+            self.add_energy_loss(output['energy_pred'], energy, losses)
+        if not return_output:
+            return losses
+        else:
+            return losses, output
+
+    ############
+    # losses
+    ############
+    def add_mel_loss(self, mel_out, target, losses, postfix='', mel_mix_loss=None):
+        if mel_mix_loss is None:
+            for loss_name, lbd in self.loss_and_lambda.items():
+                if 'l1' == loss_name:
+                    l = self.l1_loss(mel_out, target)
+                elif 'mse' == loss_name:
+                    raise NotImplementedError
+                elif 'ssim' == loss_name:
+                    l = self.ssim_loss(mel_out, target)
+                elif 'gdl' == loss_name:
+                    raise NotImplementedError
+                losses[f'{loss_name}{postfix}'] = l * lbd
+        else:
+            raise NotImplementedError
+
+    def l1_loss(self, decoder_output, target):
+        # decoder_output : B x T x n_mel
+        # target : B x T x n_mel
+        l1_loss = F.l1_loss(decoder_output, target, reduction='none')
+        weights = self.weights_nonzero_speech(target)
+        l1_loss = (l1_loss * weights).sum() / weights.sum()
+        return l1_loss
+
+    def ssim_loss(self, decoder_output, target, bias=6.0):
+        # decoder_output : B x T x n_mel
+        # target : B x T x n_mel
+        assert decoder_output.shape == target.shape
+        weights = self.weights_nonzero_speech(target)
+        decoder_output = decoder_output[:, None] + bias
+        target = target[:, None] + bias
+        ssim_loss = 1 - ssim(decoder_output, target, size_average=False)
+        ssim_loss = (ssim_loss * weights).sum() / weights.sum()
+        return ssim_loss
+
+    def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, losses=None):
+        """
+
+        :param dur_pred: [B, T], float, log scale
+        :param mel2ph: [B, T]
+        :param txt_tokens: [B, T]
+        :param losses:
+        :return:
+        """
+        B, T = txt_tokens.shape
+        nonpadding = (txt_tokens != 0).float()
+        dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding
+        is_sil = torch.zeros_like(txt_tokens).bool()
+        for p in self.sil_ph:
+            is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0])
+        is_sil = is_sil.float()  # [B, T_txt]
+
+        # phone duration loss
+        if hparams['dur_loss'] == 'mse':
+            losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none')
+            losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum()
+            dur_pred = (dur_pred.exp() - 1).clamp(min=0)
+        elif hparams['dur_loss'] == 'mog':
+            return NotImplementedError
+        elif hparams['dur_loss'] == 'crf':
+            losses['pdur'] = -self.model.dur_predictor.crf(
+                dur_pred, dur_gt.long().clamp(min=0, max=31), mask=nonpadding > 0, reduction='mean')
+        losses['pdur'] = losses['pdur'] * hparams['lambda_ph_dur']
+
+        # use linear scale for sent and word duration
+        if hparams['lambda_word_dur'] > 0:
+            word_id = (is_sil.cumsum(-1) * (1 - is_sil)).long()
+            word_dur_p = dur_pred.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_pred)[:, 1:]
+            word_dur_g = dur_gt.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_gt)[:, 1:]
+            wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none')
+            word_nonpadding = (word_dur_g > 0).float()
+            wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum()
+            losses['wdur'] = wdur_loss * hparams['lambda_word_dur']
+        if hparams['lambda_sent_dur'] > 0:
+            sent_dur_p = dur_pred.sum(-1)
+            sent_dur_g = dur_gt.sum(-1)
+            sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean')
+            losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']
+
+    def add_pitch_loss(self, output, sample, losses):
+        if hparams['pitch_type'] == 'ph':
+            nonpadding = (sample['txt_tokens'] != 0).float()
+            pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss
+            losses['f0'] = (pitch_loss_fn(output['pitch_pred'][:, :, 0], sample['f0'],
+                                          reduction='none') * nonpadding).sum() \
+                           / nonpadding.sum() * hparams['lambda_f0']
+            return
+        mel2ph = sample['mel2ph']  # [B, T_s]
+        f0 = sample['f0']
+        uv = sample['uv']
+        nonpadding = (mel2ph != 0).float()
+        if hparams['pitch_type'] == 'cwt':
+            cwt_spec = sample[f'cwt_spec']
+            f0_mean = sample['f0_mean']
+            f0_std = sample['f0_std']
+            cwt_pred = output['cwt'][:, :, :10]
+            f0_mean_pred = output['f0_mean']
+            f0_std_pred = output['f0_std']
+            losses['C'] = self.cwt_loss(cwt_pred, cwt_spec) * hparams['lambda_f0']
+            if hparams['use_uv']:
+                assert output['cwt'].shape[-1] == 11
+                uv_pred = output['cwt'][:, :, -1]
+                losses['uv'] = (F.binary_cross_entropy_with_logits(uv_pred, uv, reduction='none') * nonpadding) \
+                                   .sum() / nonpadding.sum() * hparams['lambda_uv']
+            losses['f0_mean'] = F.l1_loss(f0_mean_pred, f0_mean) * hparams['lambda_f0']
+            losses['f0_std'] = F.l1_loss(f0_std_pred, f0_std) * hparams['lambda_f0']
+            if hparams['cwt_add_f0_loss']:
+                f0_cwt_ = self.model.cwt2f0_norm(cwt_pred, f0_mean_pred, f0_std_pred, mel2ph)
+                self.add_f0_loss(f0_cwt_[:, :, None], f0, uv, losses, nonpadding=nonpadding)
+        elif hparams['pitch_type'] == 'frame':
+            self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding)
+
+    def add_f0_loss(self, p_pred, f0, uv, losses, nonpadding):
+        assert p_pred[..., 0].shape == f0.shape
+        if hparams['use_uv']:
+            assert p_pred[..., 1].shape == uv.shape
+            losses['uv'] = (F.binary_cross_entropy_with_logits(
+                p_pred[:, :, 1], uv, reduction='none') * nonpadding).sum() \
+                           / nonpadding.sum() * hparams['lambda_uv']
+            nonpadding = nonpadding * (uv == 0).float()
+
+        f0_pred = p_pred[:, :, 0]
+        if hparams['pitch_loss'] in ['l1', 'l2']:
+            pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss
+            losses['f0'] = (pitch_loss_fn(f0_pred, f0, reduction='none') * nonpadding).sum() \
+                           / nonpadding.sum() * hparams['lambda_f0']
+        elif hparams['pitch_loss'] == 'ssim':
+            return NotImplementedError
+
+    def cwt_loss(self, cwt_p, cwt_g):
+        if hparams['cwt_loss'] == 'l1':
+            return F.l1_loss(cwt_p, cwt_g)
+        if hparams['cwt_loss'] == 'l2':
+            return F.mse_loss(cwt_p, cwt_g)
+        if hparams['cwt_loss'] == 'ssim':
+            return self.ssim_loss(cwt_p, cwt_g, 20)
+
+    def add_energy_loss(self, energy_pred, energy, losses):
+        nonpadding = (energy != 0).float()
+        loss = (F.mse_loss(energy_pred, energy, reduction='none') * nonpadding).sum() / nonpadding.sum()
+        loss = loss * hparams['lambda_energy']
+        losses['e'] = loss
+
+
+    ############
+    # validation plots
+    ############
+    def plot_mel(self, batch_idx, spec, spec_out, name=None):
+        spec_cat = torch.cat([spec, spec_out], -1)
+        name = f'mel_{batch_idx}' if name is None else name
+        vmin = hparams['mel_vmin']
+        vmax = hparams['mel_vmax']
+        self.logger.experiment.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step)
+
+    def plot_dur(self, batch_idx, sample, model_out):
+        T_txt = sample['txt_tokens'].shape[1]
+        dur_gt = mel2ph_to_dur(sample['mel2ph'], T_txt)[0]
+        dur_pred = self.model.dur_predictor.out2dur(model_out['dur']).float()
+        txt = self.phone_encoder.decode(sample['txt_tokens'][0].cpu().numpy())
+        txt = txt.split(" ")
+        self.logger.experiment.add_figure(
+            f'dur_{batch_idx}', dur_to_figure(dur_gt, dur_pred, txt), self.global_step)
+
+    def plot_pitch(self, batch_idx, sample, model_out):
+        f0 = sample['f0']
+        if hparams['pitch_type'] == 'ph':
+            mel2ph = sample['mel2ph']
+            f0 = self.expand_f0_ph(f0, mel2ph)
+            f0_pred = self.expand_f0_ph(model_out['pitch_pred'][:, :, 0], mel2ph)
+            self.logger.experiment.add_figure(
+                f'f0_{batch_idx}', f0_to_figure(f0[0], None, f0_pred[0]), self.global_step)
+            return
+        f0 = denorm_f0(f0, sample['uv'], hparams)
+        if hparams['pitch_type'] == 'cwt':
+            # cwt
+            cwt_out = model_out['cwt']
+            cwt_spec = cwt_out[:, :, :10]
+            cwt = torch.cat([cwt_spec, sample['cwt_spec']], -1)
+            self.logger.experiment.add_figure(f'cwt_{batch_idx}', spec_to_figure(cwt[0]), self.global_step)
+            # f0
+            f0_pred = cwt2f0(cwt_spec, model_out['f0_mean'], model_out['f0_std'], hparams['cwt_scales'])
+            if hparams['use_uv']:
+                assert cwt_out.shape[-1] == 11
+                uv_pred = cwt_out[:, :, -1] > 0
+                f0_pred[uv_pred > 0] = 0
+            f0_cwt = denorm_f0(sample['f0_cwt'], sample['uv'], hparams)
+            self.logger.experiment.add_figure(
+                f'f0_{batch_idx}', f0_to_figure(f0[0], f0_cwt[0], f0_pred[0]), self.global_step)
+        elif hparams['pitch_type'] == 'frame':
+            # f0
+            #uv_pred = model_out['pitch_pred'][:, :, 0] > 0
+            pitch_pred = denorm_f0(model_out['pitch_pred'][:, :, 0], sample['uv'], hparams)
+            self.logger.experiment.add_figure(
+                f'f0_{batch_idx}', f0_to_figure(f0[0], None, pitch_pred[0]), self.global_step)
+
+    ############
+    # infer
+    ############
+    def test_step(self, sample, batch_idx):
+        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
+        hubert = sample['hubert']
+        mel2ph, uv, f0 = None, None, None
+        ref_mels = None
+        if hparams['profile_infer']:
+            pass
+        else:
+            # if hparams['use_gt_dur']:
+            mel2ph = sample['mel2ph']
+            #if hparams['use_gt_f0']:
+            f0 = sample['f0']
+            uv = sample['uv']
+                #print('Here using gt f0!!')
+            if hparams.get('use_midi') is not None and hparams['use_midi']:
+                outputs = self.model(
+                    hubert, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True)
+            else:
+                outputs = self.model(
+                    hubert, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True)
+            sample['outputs'] = self.model.out2mel(outputs['mel_out'])
+            sample['mel2ph_pred'] = outputs['mel2ph']
+            if hparams.get('pe_enable') is not None and hparams['pe_enable']:
+                sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred']  # pe predict from GT mel
+                sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred']  # pe predict from Pred mel
+            else:
+                sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams)
+                sample['f0_pred'] = outputs.get('f0_denorm')
+            return self.after_infer(sample)
+
+    def after_infer(self, predictions):
+        if self.saving_result_pool is None and not hparams['profile_infer']:
+            self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16))
+            self.saving_results_futures = []
+        predictions = utils.unpack_dict_to_list(predictions)
+        t = tqdm(predictions)
+        for num_predictions, prediction in enumerate(t):
+            for k, v in prediction.items():
+                if type(v) is torch.Tensor:
+                    prediction[k] = v.cpu().numpy()
+
+            item_name = prediction.get('item_name')
+            #text = prediction.get('text').replace(":", "%3A")[:80]
+
+            # remove paddings
+            mel_gt = prediction["mels"]
+            mel_gt_mask = np.abs(mel_gt).sum(-1) > 0
+            mel_gt = mel_gt[mel_gt_mask]
+            mel2ph_gt = prediction.get("mel2ph")
+            mel2ph_gt = mel2ph_gt[mel_gt_mask] if mel2ph_gt is not None else None
+            mel_pred = prediction["outputs"]
+            mel_pred_mask = np.abs(mel_pred).sum(-1) > 0
+            mel_pred = mel_pred[mel_pred_mask]
+            mel_gt = np.clip(mel_gt, hparams['mel_vmin'], hparams['mel_vmax'])
+            mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax'])
+
+            mel2ph_pred = prediction.get("mel2ph_pred")
+            if mel2ph_pred is not None:
+                if len(mel2ph_pred) > len(mel_pred_mask):
+                    mel2ph_pred = mel2ph_pred[:len(mel_pred_mask)]
+                mel2ph_pred = mel2ph_pred[mel_pred_mask]
+
+            f0_gt = prediction.get("f0")
+            f0_pred = f0_gt#prediction.get("f0_pred")
+            if f0_pred is not None:
+                f0_gt = f0_gt[mel_gt_mask]
+                if len(f0_pred) > len(mel_pred_mask):
+                    f0_pred = f0_pred[:len(mel_pred_mask)]
+                f0_pred = f0_pred[mel_pred_mask]
+            text=None
+            str_phs = None
+            # if self.phone_encoder is not None and 'txt_tokens' in prediction:
+            #     str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True)
+            # def resize2d(source, target_len):
+            #     source[source<0.001] = np.nan
+            #     target = np.interp(np.linspace(0, len(source)-1, num=target_len,endpoint=True), np.arange(0, len(source)), source)
+            #     return np.nan_to_num(target)
+            # def resize3d(source, target_len):
+            #     newsource=[]
+            #     for i in range(source.shape[1]):
+            #         newsource.append(resize2d(source[:,i],target_len))
+            #     return np.array(newsource).transpose()
+            # print(mel_pred.shape)
+            # print(f0_pred.shape)
+            # mel_pred=resize3d(mel_pred,int(mel_pred.shape[0]/44100*24000))
+            # f0_pred=resize2d(f0_pred,int(f0_pred.shape[0]/44100*24000))
+            # print(mel_pred.shape)
+            # print(f0_pred.shape)
+            gen_dir = os.path.join(hparams['work_dir'],
+                                   f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}')
+            wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred)
+            if not hparams['profile_infer']:
+                os.makedirs(gen_dir, exist_ok=True)
+                os.makedirs(f'{gen_dir}/wavs', exist_ok=True)
+                os.makedirs(f'{gen_dir}/plot', exist_ok=True)
+                os.makedirs(os.path.join(hparams['work_dir'], 'P_mels_npy'), exist_ok=True)
+                os.makedirs(os.path.join(hparams['work_dir'], 'G_mels_npy'), exist_ok=True)
+                self.saving_results_futures.append(
+                    self.saving_result_pool.apply_async(self.save_result, args=[
+                        wav_pred, mel_pred, 'P', item_name, text, gen_dir, str_phs, mel2ph_pred, f0_gt, f0_pred]))
+
+                if mel_gt is not None and hparams['save_gt']:
+                    wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt)
+                    self.saving_results_futures.append(
+                        self.saving_result_pool.apply_async(self.save_result, args=[
+                            wav_gt, mel_gt, 'G', item_name, text, gen_dir, str_phs, mel2ph_gt, f0_gt, f0_pred]))
+                    if hparams['save_f0']:
+                        import matplotlib.pyplot as plt
+                        # f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
+                        f0_pred_ = f0_pred
+                        f0_gt_, _ = get_pitch_parselmouth(wav_gt, mel_gt, hparams)
+                        fig = plt.figure()
+                        plt.plot(f0_pred_, label=r'$f0_P$')
+                        plt.plot(f0_gt_, label=r'$f0_G$')
+                        if hparams.get('pe_enable') is not None and hparams['pe_enable']:
+                            # f0_midi = prediction.get("f0_midi")
+                            # f0_midi = f0_midi[mel_gt_mask]
+                            # plt.plot(f0_midi, label=r'$f0_M$')
+                            pass
+                        plt.legend()
+                        plt.tight_layout()
+                        plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png')
+                        plt.close(fig)
+
+                t.set_description(
+                    f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
+            else:
+                if 'gen_wav_time' not in self.stats:
+                    self.stats['gen_wav_time'] = 0
+                self.stats['gen_wav_time'] += len(wav_pred) / hparams['audio_sample_rate']
+                print('gen_wav_time: ', self.stats['gen_wav_time'])
+
+        return {}
+
+    @staticmethod
+    def save_result(wav_out, mel, prefix, item_name, text, gen_dir, str_phs=None, mel2ph=None, gt_f0=None, pred_f0=None):
+        item_name = item_name.replace('/', '-')
+        base_fn = f'[{item_name}][{prefix}]'
+
+        if text is not None:
+            base_fn += text
+        base_fn += ('-' + hparams['exp_name'])
+        np.save(os.path.join(hparams['work_dir'], f'{prefix}_mels_npy', item_name), mel)
+        audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', 24000,#hparams['audio_sample_rate'],
+                       norm=hparams['out_wav_norm'])
+        fig = plt.figure(figsize=(14, 10))
+        spec_vmin = hparams['mel_vmin']
+        spec_vmax = hparams['mel_vmax']
+        heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax)
+        fig.colorbar(heatmap)
+        if hparams.get('pe_enable') is not None and hparams['pe_enable']:
+            gt_f0 = (gt_f0 - 100) / (800 - 100) * 80 * (gt_f0 > 0)
+            pred_f0 = (pred_f0 - 100) / (800 - 100) * 80 * (pred_f0 > 0)
+            plt.plot(pred_f0, c='white', linewidth=1, alpha=0.6)
+            plt.plot(gt_f0, c='red', linewidth=1, alpha=0.6)
+        else:
+            f0, _ = get_pitch_parselmouth(wav_out, mel, hparams)
+            f0 = (f0 - 100) / (800 - 100) * 80 * (f0 > 0)
+            plt.plot(f0, c='white', linewidth=1, alpha=0.6)
+        if mel2ph is not None and str_phs is not None:
+            decoded_txt = str_phs.split(" ")
+            dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy()
+            dur = [0] + list(np.cumsum(dur))
+            for i in range(len(dur) - 1):
+                shift = (i % 20) + 1
+                plt.text(dur[i], shift, decoded_txt[i])
+                plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black')
+                plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black',
+                           alpha=1, linewidth=1)
+        plt.tight_layout()
+        plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png', dpi=1000)
+        plt.close(fig)
+
+    ##############
+    # utils
+    ##############
+    @staticmethod
+    def expand_f0_ph(f0, mel2ph):
+        f0 = denorm_f0(f0, None, hparams)
+        f0 = F.pad(f0, [1, 0])
+        f0 = torch.gather(f0, 1, mel2ph)  # [B, T_mel]
+        return f0
+
+
+if __name__ == '__main__':
+    FastSpeech2Task.start()
diff --git a/training/task/tts.py b/training/task/tts.py
new file mode 100644
index 0000000000000000000000000000000000000000..acf360a510a8cfc290da8f08269eda051cc77d0f
--- /dev/null
+++ b/training/task/tts.py
@@ -0,0 +1,131 @@
+from multiprocessing.pool import Pool
+
+import matplotlib
+
+from utils.pl_utils import data_loader
+from utils.training_utils import RSQRTSchedule
+from network.vocoders.base_vocoder import get_vocoder_cls, BaseVocoder
+from modules.fastspeech.pe import PitchExtractor
+
+matplotlib.use('Agg')
+import os
+import numpy as np
+from tqdm import tqdm
+import torch.distributed as dist
+
+from training.task.base_task import BaseTask
+from utils.hparams import hparams
+from utils.text_encoder import TokenTextEncoder
+import json
+from preprocessing.hubertinfer import Hubertencoder
+import torch
+import torch.optim
+import torch.utils.data
+import utils
+
+
+
+class TtsTask(BaseTask):
+    def __init__(self, *args, **kwargs):
+        self.vocoder = None
+        self.phone_encoder = Hubertencoder(hparams['hubert_path'])
+        # self.padding_idx = self.phone_encoder.pad()
+        # self.eos_idx = self.phone_encoder.eos()
+        # self.seg_idx = self.phone_encoder.seg()
+        self.saving_result_pool = None
+        self.saving_results_futures = None
+        self.stats = {}
+        super().__init__(*args, **kwargs)
+
+    def build_scheduler(self, optimizer):
+        return RSQRTSchedule(optimizer)
+
+    def build_optimizer(self, model):
+        self.optimizer = optimizer = torch.optim.AdamW(
+            model.parameters(),
+            lr=hparams['lr'])
+        return optimizer
+
+    def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None,
+                         required_batch_size_multiple=-1, endless=False, batch_by_size=True):
+        devices_cnt = torch.cuda.device_count()
+        if devices_cnt == 0:
+            devices_cnt = 1
+        if required_batch_size_multiple == -1:
+            required_batch_size_multiple = devices_cnt
+
+        def shuffle_batches(batches):
+            np.random.shuffle(batches)
+            return batches
+
+        if max_tokens is not None:
+            max_tokens *= devices_cnt
+        if max_sentences is not None:
+            max_sentences *= devices_cnt
+        indices = dataset.ordered_indices()
+        if batch_by_size:
+            batch_sampler = utils.batch_by_size(
+                indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
+                required_batch_size_multiple=required_batch_size_multiple,
+            )
+        else:
+            batch_sampler = []
+            for i in range(0, len(indices), max_sentences):
+                batch_sampler.append(indices[i:i + max_sentences])
+
+        if shuffle:
+            batches = shuffle_batches(list(batch_sampler))
+            if endless:
+                batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))]
+        else:
+            batches = batch_sampler
+            if endless:
+                batches = [b for _ in range(1000) for b in batches]
+        num_workers = dataset.num_workers
+        if self.trainer.use_ddp:
+            num_replicas = dist.get_world_size()
+            rank = dist.get_rank()
+            batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0]
+        return torch.utils.data.DataLoader(dataset,
+                                           collate_fn=dataset.collater,
+                                           batch_sampler=batches,
+                                           num_workers=num_workers,
+                                           pin_memory=False)
+
+    # def build_phone_encoder(self, data_dir):
+    #     phone_list_file = os.path.join(data_dir, 'phone_set.json')
+
+    #     phone_list = json.load(open(phone_list_file, encoding='utf-8'))
+    #     return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',')
+
+    def build_optimizer(self, model):
+        self.optimizer = optimizer = torch.optim.AdamW(
+            model.parameters(),
+            lr=hparams['lr'])
+        return optimizer
+
+    def test_start(self):
+        self.saving_result_pool = Pool(8)
+        self.saving_results_futures = []
+        self.vocoder: BaseVocoder = get_vocoder_cls(hparams)()
+        if hparams.get('pe_enable') is not None and hparams['pe_enable']:
+            self.pe = PitchExtractor().cuda()
+            utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
+            self.pe.eval()
+    def test_end(self, outputs):
+        self.saving_result_pool.close()
+        [f.get() for f in tqdm(self.saving_results_futures)]
+        self.saving_result_pool.join()
+        return {}
+
+    ##########
+    # utils
+    ##########
+    def weights_nonzero_speech(self, target):
+        # target : B x T x mel
+        # Assign weight 1.0 to all labels except for padding (id=0).
+        dim = target.size(-1)
+        return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim)
+
+if __name__ == '__main__':
+    TtsTask.start()
diff --git a/training/train_pipeline.py b/training/train_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f9f99f64ed0dcff0001034a6932a2c623ce706
--- /dev/null
+++ b/training/train_pipeline.py
@@ -0,0 +1,238 @@
+from utils.hparams import hparams
+import torch
+from torch.nn import functional as F
+from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0
+
+class Batch2Loss:
+    '''
+        pipeline: batch -> insert1 -> module1 -> insert2 -> module2 -> insert3 -> module3 -> insert4 -> module4 -> loss
+    '''
+
+    @staticmethod
+    def insert1(pitch_midi, midi_dur, is_slur, # variables
+                midi_embed, midi_dur_layer, is_slur_embed): # modules
+        '''
+            add embeddings for midi, midi_dur, slur
+        '''
+        midi_embedding = midi_embed(pitch_midi)
+        midi_dur_embedding, slur_embedding = 0, 0
+        if midi_dur is not None:
+            midi_dur_embedding = midi_dur_layer(midi_dur[:, :, None])  # [B, T, 1] -> [B, T, H]
+        if is_slur is not None:
+            slur_embedding = is_slur_embed(is_slur)
+        return midi_embedding, midi_dur_embedding, slur_embedding
+
+    @staticmethod
+    def module1(fs2_encoder, # modules
+                txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding): # variables
+        '''
+            get *encoder_out* == fs2_encoder(*txt_tokens*, some embeddings)
+        '''
+        encoder_out = fs2_encoder(txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding)
+        return encoder_out
+
+    @staticmethod
+    def insert2(encoder_out, spk_embed_id, spk_embed_dur_id, spk_embed_f0_id, src_nonpadding, # variables
+                spk_embed_proj): # modules
+        '''
+            1. add embeddings for pspk, spk_dur, sk_f0
+            2. get *dur_inp* ~= *encoder_out* + *spk_embed_dur*
+        '''
+        # add ref style embed
+        # Not implemented
+        # variance encoder
+        var_embed = 0
+
+        # encoder_out_dur denotes encoder outputs for duration predictor
+        # in speech adaptation, duration predictor use old speaker embedding
+        if hparams['use_spk_embed']:
+            spk_embed_dur = spk_embed_f0 = spk_embed = spk_embed_proj(spk_embed_id)[:, None, :]
+        elif hparams['use_spk_id']:
+            if spk_embed_dur_id is None:
+                spk_embed_dur_id = spk_embed_id
+            if spk_embed_f0_id is None:
+                spk_embed_f0_id = spk_embed_id
+            spk_embed = spk_embed_proj(spk_embed_id)[:, None, :]
+            spk_embed_dur = spk_embed_f0 = spk_embed
+            if hparams['use_split_spk_id']:
+                spk_embed_dur = spk_embed_dur(spk_embed_dur_id)[:, None, :]
+                spk_embed_f0 = spk_embed_f0(spk_embed_f0_id)[:, None, :]
+        else:
+            spk_embed_dur = spk_embed_f0 = spk_embed = 0
+
+        # add dur
+        dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding
+        return var_embed, spk_embed, spk_embed_dur, spk_embed_f0, dur_inp
+
+    @staticmethod
+    def module2(dur_predictor, length_regulator, # modules
+                dur_input, mel2ph, txt_tokens, all_vowel_tokens, ret, midi_dur=None): # variables
+        '''
+            1. get *dur* ~= dur_predictor(*dur_inp*)
+            2. (mel2ph is None): get *mel2ph* ~= length_regulater(*dur*)
+        ''' 
+        src_padding = (txt_tokens == 0)
+        dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
+        
+        if mel2ph is None:
+            dur, xs = dur_predictor.inference(dur_input, src_padding)
+            ret['dur'] = xs
+            dur = xs.squeeze(-1).exp() - 1.0
+            for i in range(len(dur)):
+                for j in range(len(dur[i])):
+                    if txt_tokens[i,j] in all_vowel_tokens:
+                        if j < len(dur[i])-1 and txt_tokens[i,j+1] not in all_vowel_tokens:
+                            dur[i,j] = midi_dur[i,j] - dur[i,j+1]
+                            if dur[i,j] < 0:
+                                dur[i,j] = 0
+                                dur[i,j+1] = midi_dur[i,j]
+                        else:
+                            dur[i,j]=midi_dur[i,j]      
+            dur[:,0] = dur[:,0] + 0.5
+            dur_acc = F.pad(torch.round(torch.cumsum(dur, axis=1)), (1,0))
+            dur = torch.clamp(dur_acc[:,1:]-dur_acc[:,:-1], min=0).long()
+            ret['dur_choice'] = dur
+            mel2ph = length_regulator(dur, src_padding).detach()
+        else:
+            ret['dur'] = dur_predictor(dur_input, src_padding)
+        ret['mel2ph'] = mel2ph
+
+        return mel2ph
+    
+    @staticmethod
+    def insert3(encoder_out, mel2ph, var_embed, spk_embed_f0, src_nonpadding, tgt_nonpadding): # variables
+        '''
+            1. get *decoder_inp* ~= gather *encoder_out* according to *mel2ph*
+            2. get *pitch_inp* ~= *decoder_inp* + *spk_embed_f0*
+            3. get *pitch_inp_ph* ~= *encoder_out* + *spk_embed_f0*
+        '''
+        decoder_inp = F.pad(encoder_out, [0, 0, 1, 0])
+        mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]])
+        decoder_inp = decoder_inp_origin = torch.gather(decoder_inp, 1, mel2ph_)  # [B, T, H]
+
+        pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding
+        pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding
+        return decoder_inp, pitch_inp, pitch_inp_ph
+
+    @staticmethod
+    def module3(pitch_predictor, pitch_embed, energy_predictor, energy_embed, # modules
+                pitch_inp, pitch_inp_ph, f0, uv, energy, mel2ph, is_training, ret): # variables
+        '''
+            1. get *ret['pitch_pred']*, *ret['energy_pred']* ~= pitch_predictor(*pitch_inp*), energy_predictor(*pitch_inp*)
+            2. get *pitch_embedding* ~= pitch_embed(f0_to_coarse(denorm_f0(*f0* or *pitch_pred*))
+            3. get *energy_embedding* ~= energy_embed(energy_to_coarse(*energy* or *energy_pred*))
+        '''
+        def add_pitch(decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
+            if hparams['pitch_type'] == 'ph':
+                pitch_pred_inp = encoder_out.detach() + hparams['predictor_grad'] * (encoder_out - encoder_out.detach())
+                pitch_padding = (encoder_out.sum().abs() == 0)
+                ret['pitch_pred'] = pitch_pred = pitch_predictor(pitch_pred_inp)
+                if f0 is None:
+                    f0 = pitch_pred[:, :, 0]
+                ret['f0_denorm'] = f0_denorm = denorm_f0(f0, None, hparams, pitch_padding=pitch_padding)
+                pitch = f0_to_coarse(f0_denorm)  # start from 0 [B, T_txt]
+                pitch = F.pad(pitch, [1, 0])
+                pitch = torch.gather(pitch, 1, mel2ph)  # [B, T_mel]
+                pitch_embedding = pitch_embed(pitch)
+                return pitch_embedding
+            
+            decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+
+            pitch_padding = (mel2ph == 0)
+
+            if hparams['pitch_type'] == 'cwt':
+                # NOTE: this part of script is *isolated* from other scripts, which means
+                #       it may not be compatible with the current version.    
+                pass
+                # pitch_padding = None
+                # ret['cwt'] = cwt_out = self.cwt_predictor(decoder_inp)
+                # stats_out = self.cwt_stats_layers(encoder_out[:, 0, :])  # [B, 2]
+                # mean = ret['f0_mean'] = stats_out[:, 0]
+                # std = ret['f0_std'] = stats_out[:, 1]
+                # cwt_spec = cwt_out[:, :, :10]
+                # if f0 is None:
+                #     std = std * hparams['cwt_std_scale']
+                #     f0 = self.cwt2f0_norm(cwt_spec, mean, std, mel2ph)
+                #     if hparams['use_uv']:
+                #         assert cwt_out.shape[-1] == 11
+                #         uv = cwt_out[:, :, -1] > 0
+            elif hparams['pitch_ar']:
+                ret['pitch_pred'] = pitch_pred = pitch_predictor(decoder_inp, f0 if is_training else None)
+                if f0 is None:
+                    f0 = pitch_pred[:, :, 0]
+            else:
+                ret['pitch_pred'] = pitch_pred = pitch_predictor(decoder_inp)
+                if f0 is None:
+                    f0 = pitch_pred[:, :, 0]
+                if hparams['use_uv'] and uv is None:
+                    uv = pitch_pred[:, :, 1] > 0
+            ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding)
+            if pitch_padding is not None:
+                f0[pitch_padding] = 0
+
+            pitch = f0_to_coarse(f0_denorm)  # start from 0
+            pitch_embedding = pitch_embed(pitch)
+            return pitch_embedding
+
+        def add_energy(decoder_inp, energy, ret):
+            decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+            ret['energy_pred'] = energy_pred = energy_predictor(decoder_inp)[:, :, 0]
+            if energy is None:
+                energy = energy_pred
+            energy = torch.clamp(energy * 256 // 4, max=255).long() # energy_to_coarse
+            energy_embedding = energy_embed(energy)
+            return energy_embedding
+
+        # add pitch and energy embed
+        nframes = mel2ph.size(1)
+
+        pitch_embedding = 0
+        if hparams['use_pitch_embed']:
+            if f0 is not None:
+                delta_l = nframes - f0.size(1)
+                if delta_l > 0:
+                    f0 = torch.cat((f0,torch.FloatTensor([[x[-1]] * delta_l for x in f0]).to(f0.device)),1)
+                f0 = f0[:,:nframes]
+            if uv is not None:
+                delta_l = nframes - uv.size(1)
+                if delta_l > 0:
+                    uv = torch.cat((uv,torch.FloatTensor([[x[-1]] * delta_l for x in uv]).to(uv.device)),1)
+                uv = uv[:,:nframes]
+            pitch_embedding = add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph)
+           
+        energy_embedding = 0
+        if hparams['use_energy_embed']:
+            if energy is not None:
+                delta_l = nframes - energy.size(1)
+                if delta_l > 0:
+                    energy = torch.cat((energy,torch.FloatTensor([[x[-1]] * delta_l for x in energy]).to(energy.device)),1)
+                energy = energy[:,:nframes]
+            energy_embedding = add_energy(pitch_inp, energy, ret)
+        
+        return pitch_embedding, energy_embedding
+    
+    @staticmethod
+    def insert4(decoder_inp, pitch_embedding, energy_embedding, spk_embed, ret, tgt_nonpadding):
+        '''
+            *decoder_inp* ~= *decoder_inp* + embeddings for spk, pitch, energy
+        '''
+        ret['decoder_inp'] = decoder_inp = (decoder_inp + pitch_embedding + energy_embedding + spk_embed) * tgt_nonpadding
+        return decoder_inp
+
+    @staticmethod
+    def module4(diff_main_loss, # modules
+                norm_spec, decoder_inp_t, ret, K_step, batch_size, device): # variables
+        '''
+            training diffusion using spec as input and decoder_inp as condition.
+            
+            Args:
+                norm_spec: (normalized) spec
+                decoder_inp_t: (transposed) decoder_inp
+            Returns:
+                ret['diff_loss']
+        '''
+        t = torch.randint(0, K_step, (batch_size,), device=device).long()
+        norm_spec = norm_spec.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
+        ret['diff_loss'] = diff_main_loss(norm_spec, t, cond=decoder_inp_t)
+        # nonpadding = (mel2ph != 0).float()
+        # ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding)
diff --git a/trans_key.py b/trans_key.py
new file mode 100644
index 0000000000000000000000000000000000000000..c803a6acdbaa065cb75ce0a935b023780ab37026
--- /dev/null
+++ b/trans_key.py
@@ -0,0 +1,61 @@
+head_list = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
+
+
+def trans_f0_seq(feature_pit, transform):
+    feature_pit = feature_pit * 2 ** (transform / 12)
+    return round(feature_pit, 1)
+
+
+def move_key(raw_data, mv_key):
+    head = raw_data[:-1]
+    body = int(raw_data[-1])
+    new_head_index = head_list.index(head) + mv_key
+    while new_head_index < 0:
+        body -= 1
+        new_head_index += 12
+    while new_head_index > 11:
+        body += 1
+        new_head_index -= 12
+    result_data = head_list[new_head_index] + str(body)
+    return result_data
+
+
+def trans_key(raw_data, key):
+    for i in raw_data:
+        note_seq_list = i["note_seq"].split(" ")
+        new_note_seq_list = []
+        for note_seq in note_seq_list:
+            if note_seq != "rest":
+                new_note_seq = move_key(note_seq, key)
+                new_note_seq_list.append(new_note_seq)
+            else:
+                new_note_seq_list.append(note_seq)
+        i["note_seq"] = " ".join(new_note_seq_list)
+
+        f0_seq_list = i["f0_seq"].split(" ")
+        f0_seq_list = [float(x) for x in f0_seq_list]
+        new_f0_seq_list = []
+        for f0_seq in f0_seq_list:
+            new_f0_seq = trans_f0_seq(f0_seq, key)
+            new_f0_seq_list.append(str(new_f0_seq))
+        i["f0_seq"] = " ".join(new_f0_seq_list)
+    return raw_data
+
+
+key = -6
+f_w = open("raw.txt", "w", encoding='utf-8')
+with open("result.txt", "r", encoding='utf-8') as f:
+    raw_data = f.readlines()
+    for raw in raw_data:
+        raw_list = raw.split("|")
+        new_note_seq_list = []
+        for note_seq in raw_list[3].split(" "):
+            if note_seq != "rest":
+                note_seq = note_seq.split("/")[0] if "/" in note_seq else note_seq
+                new_note_seq = move_key(note_seq, key)
+                new_note_seq_list.append(new_note_seq)
+            else:
+                new_note_seq_list.append(note_seq)
+        raw_list[3] = " ".join(new_note_seq_list)
+        f_w.write("|".join(raw_list))
+f_w.close()
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..edd05b1cbcf86d489ce395ab90e50587c7bef4c6
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1,250 @@
+import glob
+import logging
+import re
+import time
+from collections import defaultdict
+import os
+import sys
+import shutil
+import types
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.distributed as dist
+from torch import nn
+
+
+def tensors_to_scalars(metrics):
+    new_metrics = {}
+    for k, v in metrics.items():
+        if isinstance(v, torch.Tensor):
+            v = v.item()
+        if type(v) is dict:
+            v = tensors_to_scalars(v)
+        new_metrics[k] = v
+    return new_metrics
+
+
+class AvgrageMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.avg = 0
+        self.sum = 0
+        self.cnt = 0
+
+    def update(self, val, n=1):
+        self.sum += val * n
+        self.cnt += n
+        self.avg = self.sum / self.cnt
+
+
+def collate_1d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1):
+    """Convert a list of 1d tensors into a padded 2d tensor."""
+    size = max(v.size(0) for v in values) if max_len is None else max_len
+    res = values[0].new(len(values), size).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        if shift_right:
+            dst[1:] = src[:-1]
+            dst[0] = shift_id
+        else:
+            dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
+    return res
+
+
+def collate_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None):
+    """Convert a list of 2d tensors into a padded 3d tensor."""
+    size = max(v.size(0) for v in values) if max_len is None else max_len
+    res = values[0].new(len(values), size, values[0].shape[1]).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        if shift_right:
+            dst[1:] = src[:-1]
+        else:
+            dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
+    return res
+
+
+def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+    if len(batch) == 0:
+        return 0
+    if len(batch) == max_sentences:
+        return 1
+    if num_tokens > max_tokens:
+        return 1
+    return 0
+
+
+def batch_by_size(
+        indices, num_tokens_fn, max_tokens=None, max_sentences=None,
+        required_batch_size_multiple=1, distributed=False
+):
+    """
+    Yield mini-batches of indices bucketed by size. Batches may contain
+    sequences of different lengths.
+
+    Args:
+        indices (List[int]): ordered list of dataset indices
+        num_tokens_fn (callable): function that returns the number of tokens at
+            a given index
+        max_tokens (int, optional): max number of tokens in each batch
+            (default: None).
+        max_sentences (int, optional): max number of sentences in each
+            batch (default: None).
+        required_batch_size_multiple (int, optional): require batch size to
+            be a multiple of N (default: 1).
+    """
+    max_tokens = max_tokens if max_tokens is not None else sys.maxsize
+    max_sentences = max_sentences if max_sentences is not None else sys.maxsize
+    bsz_mult = required_batch_size_multiple
+
+    if isinstance(indices, types.GeneratorType):
+        indices = np.fromiter(indices, dtype=np.int64, count=-1)
+
+    sample_len = 0
+    sample_lens = []
+    batch = []
+    batches = []
+    for i in range(len(indices)):
+        idx = indices[i]
+        num_tokens = num_tokens_fn(idx)
+        sample_lens.append(num_tokens)
+        sample_len = max(sample_len, num_tokens)
+        assert sample_len <= max_tokens, (
+            "sentence at index {} of size {} exceeds max_tokens "
+            "limit of {}!".format(idx, sample_len, max_tokens)
+        )
+        num_tokens = (len(batch) + 1) * sample_len
+
+        if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+            mod_len = max(
+                bsz_mult * (len(batch) // bsz_mult),
+                len(batch) % bsz_mult,
+            )
+            batches.append(batch[:mod_len])
+            batch = batch[mod_len:]
+            sample_lens = sample_lens[mod_len:]
+            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
+        batch.append(idx)
+    if len(batch) > 0:
+        batches.append(batch)
+    return batches
+
+
+def make_positions(tensor, padding_idx):
+    """Replace non-padding symbols with their position numbers.
+
+    Position numbers begin at padding_idx+1. Padding symbols are ignored.
+    """
+    # The series of casts and type-conversions here are carefully
+    # balanced to both work with ONNX export and XLA. In particular XLA
+    # prefers ints, cumsum defaults to output longs, and ONNX doesn't know
+    # how to handle the dtype kwarg in cumsum.
+    mask = tensor.ne(padding_idx).int()
+    return (
+                   torch.cumsum(mask, dim=1).type_as(mask) * mask
+           ).long() + padding_idx
+
+
+def softmax(x, dim):
+    return F.softmax(x, dim=dim, dtype=torch.float32)
+
+
+def unpack_dict_to_list(samples):
+    samples_ = []
+    bsz = samples.get('outputs').size(0)
+    for i in range(bsz):
+        res = {}
+        for k, v in samples.items():
+            try:
+                res[k] = v[i]
+            except:
+                pass
+        samples_.append(res)
+    return samples_
+
+
+def load_ckpt(cur_model, ckpt_base_dir, prefix_in_ckpt='model', force=True, strict=True):
+    if os.path.isfile(ckpt_base_dir):
+        base_dir = os.path.dirname(ckpt_base_dir)
+        checkpoint_path = [ckpt_base_dir]
+    else:
+        base_dir = ckpt_base_dir
+        checkpoint_path = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
+        lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x.replace('\\','/'))[0]))
+    if len(checkpoint_path) > 0:
+        checkpoint_path = checkpoint_path[-1]
+        state_dict = torch.load(checkpoint_path, map_location="cpu")["state_dict"]
+        state_dict = {k[len(prefix_in_ckpt) + 1:]: v for k, v in state_dict.items()
+                      if k.startswith(f'{prefix_in_ckpt}.')}
+        if not strict:
+            cur_model_state_dict = cur_model.state_dict()
+            unmatched_keys = []
+            for key, param in state_dict.items():
+                if key in cur_model_state_dict:
+                    new_param = cur_model_state_dict[key]
+                    if new_param.shape != param.shape:
+                        unmatched_keys.append(key)
+                        print("| Unmatched keys: ", key, new_param.shape, param.shape)
+            for key in unmatched_keys:
+                del state_dict[key]
+        cur_model.load_state_dict(state_dict, strict=strict)
+        print(f"| load '{prefix_in_ckpt}' from '{checkpoint_path}'.")
+    else:
+        e_msg = f"| ckpt not found in {base_dir}."
+        if force:
+            assert False, e_msg
+        else:
+            print(e_msg)
+
+
+def remove_padding(x, padding_idx=0):
+    if x is None:
+        return None
+    assert len(x.shape) in [1, 2]
+    if len(x.shape) == 2:  # [T, H]
+        return x[np.abs(x).sum(-1) != padding_idx]
+    elif len(x.shape) == 1:  # [T]
+        return x[x != padding_idx]
+
+
+class Timer:
+    timer_map = {}
+
+    def __init__(self, name, print_time=False):
+        if name not in Timer.timer_map:
+            Timer.timer_map[name] = 0
+        self.name = name
+        self.print_time = print_time
+
+    def __enter__(self):
+        self.t = time.time()
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        Timer.timer_map[self.name] += time.time() - self.t
+        if self.print_time:
+            print(self.name, Timer.timer_map[self.name])
+
+
+def print_arch(model, model_name='model'):
+    #print(f"| {model_name} Arch: ", model)
+    num_params(model, model_name=model_name)
+
+
+def num_params(model, print_out=True, model_name="model"):
+    parameters = filter(lambda p: p.requires_grad, model.parameters())
+    parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
+    if print_out:
+        print(f'| {model_name} Trainable Parameters: %.3fM' % parameters)
+    return parameters
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diff --git a/utils/audio.py b/utils/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..aba7ab926cf793d085bbdc70c97f376001183fe1
--- /dev/null
+++ b/utils/audio.py
@@ -0,0 +1,56 @@
+import subprocess
+import matplotlib
+
+matplotlib.use('Agg')
+import librosa
+import librosa.filters
+import numpy as np
+from scipy import signal
+from scipy.io import wavfile
+
+
+def save_wav(wav, path, sr, norm=False):
+    if norm:
+        wav = wav / np.abs(wav).max()
+    wav *= 32767
+    # proposed by @dsmiller
+    wavfile.write(path, sr, wav.astype(np.int16))
+
+
+def get_hop_size(hparams):
+    hop_size = hparams['hop_size']
+    if hop_size is None:
+        assert hparams['frame_shift_ms'] is not None
+        hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate'])
+    return hop_size
+
+
+###########################################################################################
+def _stft(y, hparams):
+    return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams),
+                        win_length=hparams['win_size'], pad_mode='constant')
+
+
+def _istft(y, hparams):
+    return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size'])
+
+
+def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
+    '''compute right padding (final frame) or both sides padding (first and final frames)
+    '''
+    assert pad_sides in (1, 2)
+    # return int(fsize // 2)
+    pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
+    if pad_sides == 1:
+        return 0, pad
+    else:
+        return pad // 2, pad // 2 + pad % 2
+
+
+# Conversions
+def amp_to_db(x):
+    return 20 * np.log10(np.maximum(1e-5, x))
+
+
+def normalize(S, hparams):
+    return (S - hparams['min_level_db']) / -hparams['min_level_db']
diff --git a/utils/cwt.py b/utils/cwt.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a08461b9e422aac614438e6240b7355b8e4bb2c
--- /dev/null
+++ b/utils/cwt.py
@@ -0,0 +1,146 @@
+import librosa
+import numpy as np
+from pycwt import wavelet
+from scipy.interpolate import interp1d
+
+
+def load_wav(wav_file, sr):
+    wav, _ = librosa.load(wav_file, sr=sr, mono=True)
+    return wav
+
+
+def convert_continuos_f0(f0):
+    '''CONVERT F0 TO CONTINUOUS F0
+    Args:
+        f0 (ndarray): original f0 sequence with the shape (T)
+    Return:
+        (ndarray): continuous f0 with the shape (T)
+    '''
+    # get uv information as binary
+    f0 = np.copy(f0)
+    uv = np.float32(f0 != 0)
+
+    # get start and end of f0
+    if (f0 == 0).all():
+        print("| all of the f0 values are 0.")
+        return uv, f0
+    start_f0 = f0[f0 != 0][0]
+    end_f0 = f0[f0 != 0][-1]
+
+    # padding start and end of f0 sequence
+    start_idx = np.where(f0 == start_f0)[0][0]
+    end_idx = np.where(f0 == end_f0)[0][-1]
+    f0[:start_idx] = start_f0
+    f0[end_idx:] = end_f0
+
+    # get non-zero frame index
+    nz_frames = np.where(f0 != 0)[0]
+
+    # perform linear interpolation
+    f = interp1d(nz_frames, f0[nz_frames])
+    cont_f0 = f(np.arange(0, f0.shape[0]))
+
+    return uv, cont_f0
+
+
+def get_cont_lf0(f0, frame_period=5.0):
+    uv, cont_f0_lpf = convert_continuos_f0(f0)
+    # cont_f0_lpf = low_pass_filter(cont_f0_lpf, int(1.0 / (frame_period * 0.001)), cutoff=20)
+    cont_lf0_lpf = np.log(cont_f0_lpf)
+    return uv, cont_lf0_lpf
+
+
+def get_lf0_cwt(lf0):
+    '''
+    input:
+        signal of shape (N)
+    output:
+        Wavelet_lf0 of shape(10, N), scales of shape(10)
+    '''
+    mother = wavelet.MexicanHat()
+    dt = 0.005
+    dj = 1
+    s0 = dt * 2
+    J = 9
+
+    Wavelet_lf0, scales, _, _, _, _ = wavelet.cwt(np.squeeze(lf0), dt, dj, s0, J, mother)
+    # Wavelet.shape => (J + 1, len(lf0))
+    Wavelet_lf0 = np.real(Wavelet_lf0).T
+    return Wavelet_lf0, scales
+
+
+def norm_scale(Wavelet_lf0):
+    Wavelet_lf0_norm = np.zeros((Wavelet_lf0.shape[0], Wavelet_lf0.shape[1]))
+    mean = Wavelet_lf0.mean(0)[None, :]
+    std = Wavelet_lf0.std(0)[None, :]
+    Wavelet_lf0_norm = (Wavelet_lf0 - mean) / std
+    return Wavelet_lf0_norm, mean, std
+
+
+def normalize_cwt_lf0(f0, mean, std):
+    uv, cont_lf0_lpf = get_cont_lf0(f0)
+    cont_lf0_norm = (cont_lf0_lpf - mean) / std
+    Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_norm)
+    Wavelet_lf0_norm, _, _ = norm_scale(Wavelet_lf0)
+
+    return Wavelet_lf0_norm
+
+
+def get_lf0_cwt_norm(f0s, mean, std):
+    uvs = list()
+    cont_lf0_lpfs = list()
+    cont_lf0_lpf_norms = list()
+    Wavelet_lf0s = list()
+    Wavelet_lf0s_norm = list()
+    scaless = list()
+
+    means = list()
+    stds = list()
+    for f0 in f0s:
+        uv, cont_lf0_lpf = get_cont_lf0(f0)
+        cont_lf0_lpf_norm = (cont_lf0_lpf - mean) / std
+
+        Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)  # [560,10]
+        Wavelet_lf0_norm, mean_scale, std_scale = norm_scale(Wavelet_lf0)  # [560,10],[1,10],[1,10]
+
+        Wavelet_lf0s_norm.append(Wavelet_lf0_norm)
+        uvs.append(uv)
+        cont_lf0_lpfs.append(cont_lf0_lpf)
+        cont_lf0_lpf_norms.append(cont_lf0_lpf_norm)
+        Wavelet_lf0s.append(Wavelet_lf0)
+        scaless.append(scales)
+        means.append(mean_scale)
+        stds.append(std_scale)
+
+    return Wavelet_lf0s_norm, scaless, means, stds
+
+
+def inverse_cwt_torch(Wavelet_lf0, scales):
+    import torch
+    b = ((torch.arange(0, len(scales)).float().to(Wavelet_lf0.device)[None, None, :] + 1 + 2.5) ** (-2.5))
+    lf0_rec = Wavelet_lf0 * b
+    lf0_rec_sum = lf0_rec.sum(-1)
+    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdim=True)) / lf0_rec_sum.std(-1, keepdim=True)
+    return lf0_rec_sum
+
+
+def inverse_cwt(Wavelet_lf0, scales):
+    b = ((np.arange(0, len(scales))[None, None, :] + 1 + 2.5) ** (-2.5))
+    lf0_rec = Wavelet_lf0 * b
+    lf0_rec_sum = lf0_rec.sum(-1)
+    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdims=True)) / lf0_rec_sum.std(-1, keepdims=True)
+    return lf0_rec_sum
+
+
+def cwt2f0(cwt_spec, mean, std, cwt_scales):
+    assert len(mean.shape) == 1 and len(std.shape) == 1 and len(cwt_spec.shape) == 3
+    import torch
+    if isinstance(cwt_spec, torch.Tensor):
+        f0 = inverse_cwt_torch(cwt_spec, cwt_scales)
+        f0 = f0 * std[:, None] + mean[:, None]
+        f0 = f0.exp()  # [B, T]
+    else:
+        f0 = inverse_cwt(cwt_spec, cwt_scales)
+        f0 = f0 * std[:, None] + mean[:, None]
+        f0 = np.exp(f0)  # [B, T]
+    return f0
diff --git a/utils/hparams.py b/utils/hparams.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d5e6552d88c4609343f968239bfce1a1c177c8b
--- /dev/null
+++ b/utils/hparams.py
@@ -0,0 +1,131 @@
+import argparse
+import os
+import yaml
+
+global_print_hparams = True
+hparams = {}
+
+
+class Args:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            self.__setattr__(k, v)
+
+
+def override_config(old_config: dict, new_config: dict):
+    for k, v in new_config.items():
+        if isinstance(v, dict) and k in old_config:
+            override_config(old_config[k], new_config[k])
+        else:
+            old_config[k] = v
+
+
+def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True,reset=True,infer=True):
+    '''
+        Load hparams from multiple sources:
+        1. config chain (i.e. first load base_config, then load config);
+        2. if reset == True, load from the (auto-saved) complete config file ('config.yaml')
+           which contains all settings and do not rely on base_config;
+        3. load from argument --hparams or hparams_str, as temporary modification.
+    '''
+    if config == '':
+        parser = argparse.ArgumentParser(description='neural music')
+        parser.add_argument('--config', type=str, default='',
+                            help='location of the data corpus')
+        parser.add_argument('--exp_name', type=str, default='', help='exp_name')
+        parser.add_argument('--hparams', type=str, default='',
+                            help='location of the data corpus')
+        parser.add_argument('--infer', action='store_true', help='infer')
+        parser.add_argument('--validate', action='store_true', help='validate')
+        parser.add_argument('--reset', action='store_true', help='reset hparams')
+        parser.add_argument('--debug', action='store_true', help='debug')
+        args, unknown = parser.parse_known_args()
+    else:
+        args = Args(config=config, exp_name=exp_name, hparams=hparams_str,
+                    infer=infer, validate=False, reset=reset, debug=False)
+    args_work_dir = ''
+    if args.exp_name != '':
+        args.work_dir = args.exp_name
+        args_work_dir = f'checkpoints/{args.work_dir}'
+
+    config_chains = []
+    loaded_config = set()
+
+    def load_config(config_fn):  # deep first
+        with open(config_fn, encoding='utf-8') as f:
+            hparams_ = yaml.safe_load(f)
+        loaded_config.add(config_fn)
+        if 'base_config' in hparams_:
+            ret_hparams = {}
+            if not isinstance(hparams_['base_config'], list):
+                hparams_['base_config'] = [hparams_['base_config']]
+            for c in hparams_['base_config']:
+                if c not in loaded_config:
+                    if c.startswith('.'):
+                        c = f'{os.path.dirname(config_fn)}/{c}'
+                        c = os.path.normpath(c)
+                    override_config(ret_hparams, load_config(c))
+            override_config(ret_hparams, hparams_)
+        else:
+            ret_hparams = hparams_
+        config_chains.append(config_fn)
+        return ret_hparams
+
+    global hparams
+    assert args.config != '' or args_work_dir != ''
+    saved_hparams = {}
+    if args_work_dir != 'checkpoints/':
+        ckpt_config_path = f'{args_work_dir}/config.yaml'
+        if os.path.exists(ckpt_config_path):
+            try:
+                with open(ckpt_config_path, encoding='utf-8') as f:
+                    saved_hparams.update(yaml.safe_load(f))
+            except:
+                pass
+        if args.config == '':
+            args.config = ckpt_config_path
+
+    hparams_ = {}
+
+    hparams_.update(load_config(args.config))
+    
+    if not args.reset:
+        hparams_.update(saved_hparams)
+    hparams_['work_dir'] = args_work_dir
+
+    if args.hparams != "":
+        for new_hparam in args.hparams.split(","):
+            k, v = new_hparam.split("=")
+            if k not in hparams_:
+                hparams_[k] = eval(v)
+            if v in ['True', 'False'] or type(hparams_[k]) == bool:
+                hparams_[k] = eval(v)
+            else:
+                hparams_[k] = type(hparams_[k])(v)
+
+    if args_work_dir != '' and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer:
+        os.makedirs(hparams_['work_dir'], exist_ok=True)
+        with open(ckpt_config_path, 'w', encoding='utf-8') as f:
+            yaml.safe_dump(hparams_, f)
+
+    hparams_['infer'] = args.infer
+    hparams_['debug'] = args.debug
+    hparams_['validate'] = args.validate
+    global global_print_hparams
+    if global_hparams:
+        hparams.clear()
+        hparams.update(hparams_)
+
+    if print_hparams and global_print_hparams and global_hparams:
+        print('| Hparams chains: ', config_chains)
+        print('| Hparams: ')
+        for i, (k, v) in enumerate(sorted(hparams_.items())):
+            print(f"\033[;33;m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "")
+        print("")
+        global_print_hparams = False
+    # print(hparams_.keys())
+    if hparams.get('exp_name') is None:
+        hparams['exp_name'] = args.exp_name
+    if hparams_.get('exp_name') is None:
+        hparams_['exp_name'] = args.exp_name
+    return hparams_
diff --git a/utils/indexed_datasets.py b/utils/indexed_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..e15632be30d6296a3c9aa80a1f351058003698b3
--- /dev/null
+++ b/utils/indexed_datasets.py
@@ -0,0 +1,71 @@
+import pickle
+from copy import deepcopy
+
+import numpy as np
+
+
+class IndexedDataset:
+    def __init__(self, path, num_cache=1):
+        super().__init__()
+        self.path = path
+        self.data_file = None
+        self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets']
+        self.data_file = open(f"{path}.data", 'rb', buffering=-1)
+        self.cache = []
+        self.num_cache = num_cache
+
+    def check_index(self, i):
+        if i < 0 or i >= len(self.data_offsets) - 1:
+            raise IndexError('index out of range')
+
+    def __del__(self):
+        if self.data_file:
+            self.data_file.close()
+
+    def __getitem__(self, i):
+        self.check_index(i)
+        if self.num_cache > 0:
+            for c in self.cache:
+                if c[0] == i:
+                    return c[1]
+        self.data_file.seek(self.data_offsets[i])
+        b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i])
+        item = pickle.loads(b)
+        if self.num_cache > 0:
+            self.cache = [(i, deepcopy(item))] + self.cache[:-1]
+        return item
+
+    def __len__(self):
+        return len(self.data_offsets) - 1
+
+class IndexedDatasetBuilder:
+    def __init__(self, path):
+        self.path = path
+        self.out_file = open(f"{path}.data", 'wb')
+        self.byte_offsets = [0]
+
+    def add_item(self, item):
+        s = pickle.dumps(item)
+        bytes = self.out_file.write(s)
+        self.byte_offsets.append(self.byte_offsets[-1] + bytes)
+
+    def finalize(self):
+        self.out_file.close()
+        np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets})
+
+
+if __name__ == "__main__":
+    import random
+    from tqdm import tqdm
+    ds_path = '/tmp/indexed_ds_example'
+    size = 100
+    items = [{"a": np.random.normal(size=[10000, 10]),
+              "b": np.random.normal(size=[10000, 10])} for i in range(size)]
+    builder = IndexedDatasetBuilder(ds_path)
+    for i in tqdm(range(size)):
+        builder.add_item(items[i])
+    builder.finalize()
+    ds = IndexedDataset(ds_path)
+    for i in tqdm(range(10000)):
+        idx = random.randint(0, size - 1)
+        assert (ds[idx]['a'] == items[idx]['a']).all()
diff --git a/utils/multiprocess_utils.py b/utils/multiprocess_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..24876c4ca777f09d1c1e1b75674cd7aaf37a75a6
--- /dev/null
+++ b/utils/multiprocess_utils.py
@@ -0,0 +1,47 @@
+import os
+import traceback
+from multiprocessing import Queue, Process
+
+
+def chunked_worker(worker_id, map_func, args, results_queue=None, init_ctx_func=None):
+    ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None
+    for job_idx, arg in args:
+        try:
+            if ctx is not None:
+                res = map_func(*arg, ctx=ctx)
+            else:
+                res = map_func(*arg)
+            results_queue.put((job_idx, res))
+        except:
+            traceback.print_exc()
+            results_queue.put((job_idx, None))
+
+def chunked_multiprocess_run(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, q_max_size=1000):
+    args = zip(range(len(args)), args)
+    args = list(args)
+    n_jobs = len(args)
+    if num_workers is None:
+        num_workers = int(os.getenv('N_PROC', os.cpu_count()))
+    results_queues = []
+    if ordered:
+        for i in range(num_workers):
+            results_queues.append(Queue(maxsize=q_max_size // num_workers))
+    else:
+        results_queue = Queue(maxsize=q_max_size)
+        for i in range(num_workers):
+            results_queues.append(results_queue)
+    workers = []
+    for i in range(num_workers):
+        args_worker = args[i::num_workers]
+        p = Process(target=chunked_worker, args=(
+            i, map_func, args_worker, results_queues[i], init_ctx_func), daemon=True)
+        workers.append(p)
+        p.start()
+    for n_finished in range(n_jobs):
+        results_queue = results_queues[n_finished % num_workers]
+        job_idx, res = results_queue.get()
+        assert job_idx == n_finished or not ordered, (job_idx, n_finished)
+        yield res
+    for w in workers:
+        w.join()
+        w.close()
diff --git a/utils/pitch_utils.py b/utils/pitch_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1767810e600c8f82e821ff4fc0a164daddaf7af4
--- /dev/null
+++ b/utils/pitch_utils.py
@@ -0,0 +1,76 @@
+#########
+# world
+##########
+import librosa
+import numpy as np
+import torch
+
+# gamma = 0
+# mcepInput = 3  # 0 for dB, 3 for magnitude
+# alpha = 0.45
+# en_floor = 10 ** (-80 / 20)
+# FFT_SIZE = 2048
+
+
+
+
+def f0_to_coarse(f0,hparams):
+    f0_bin = hparams['f0_bin']
+    f0_max = hparams['f0_max']
+    f0_min = hparams['f0_min']
+    is_torch = isinstance(f0, torch.Tensor)
+    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+    f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
+    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
+
+    f0_mel[f0_mel <= 1] = 1
+    f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
+    f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(int)
+    assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
+    return f0_coarse
+
+
+def norm_f0(f0, uv, hparams):
+    is_torch = isinstance(f0, torch.Tensor)
+    if hparams['pitch_norm'] == 'standard':
+        f0 = (f0 - hparams['f0_mean']) / hparams['f0_std']
+    if hparams['pitch_norm'] == 'log':
+        f0 = torch.log2(f0) if is_torch else np.log2(f0)
+    if uv is not None and hparams['use_uv']:
+        f0[uv > 0] = 0
+    return f0
+
+
+def norm_interp_f0(f0, hparams):
+    is_torch = isinstance(f0, torch.Tensor)
+    if is_torch:
+        device = f0.device
+        f0 = f0.data.cpu().numpy()
+    uv = f0 == 0
+    f0 = norm_f0(f0, uv, hparams)
+    if sum(uv) == len(f0):
+        f0[uv] = 0
+    elif sum(uv) > 0:
+        f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
+    uv = torch.FloatTensor(uv)
+    f0 = torch.FloatTensor(f0)
+    if is_torch:
+        f0 = f0.to(device)
+    return f0, uv
+
+
+def denorm_f0(f0, uv, hparams, pitch_padding=None, min=None, max=None):
+    if hparams['pitch_norm'] == 'standard':
+        f0 = f0 * hparams['f0_std'] + hparams['f0_mean']
+    if hparams['pitch_norm'] == 'log':
+        f0 = 2 ** f0
+    if min is not None:
+        f0 = f0.clamp(min=min)
+    if max is not None:
+        f0 = f0.clamp(max=max)
+    if uv is not None and hparams['use_uv']:
+        f0[uv > 0] = 0
+    if pitch_padding is not None:
+        f0[pitch_padding] = 0
+    return f0
diff --git a/utils/pl_utils.py b/utils/pl_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f375637e83f07b3406e788026da2971d510540e7
--- /dev/null
+++ b/utils/pl_utils.py
@@ -0,0 +1,1625 @@
+import matplotlib
+from torch.nn import DataParallel
+from torch.nn.parallel import DistributedDataParallel
+
+matplotlib.use('Agg')
+import glob
+import itertools
+import subprocess
+import threading
+import traceback
+
+from pytorch_lightning.callbacks import GradientAccumulationScheduler
+from pytorch_lightning.callbacks import ModelCheckpoint
+
+from functools import wraps
+from torch.cuda._utils import _get_device_index
+import numpy as np
+import torch.optim
+import torch.utils.data
+import copy
+import logging
+import os
+import re
+import sys
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import tqdm
+from torch.optim.optimizer import Optimizer
+
+
+def get_a_var(obj):  # pragma: no cover
+    if isinstance(obj, torch.Tensor):
+        return obj
+
+    if isinstance(obj, list) or isinstance(obj, tuple):
+        for result in map(get_a_var, obj):
+            if isinstance(result, torch.Tensor):
+                return result
+    if isinstance(obj, dict):
+        for result in map(get_a_var, obj.items()):
+            if isinstance(result, torch.Tensor):
+                return result
+    return None
+
+
+def data_loader(fn):
+    """
+    Decorator to make any fx with this use the lazy property
+    :param fn:
+    :return:
+    """
+
+    wraps(fn)
+    attr_name = '_lazy_' + fn.__name__
+
+    def _get_data_loader(self):
+        try:
+            value = getattr(self, attr_name)
+        except AttributeError:
+            try:
+                value = fn(self)  # Lazy evaluation, done only once.
+                if (
+                        value is not None and
+                        not isinstance(value, list) and
+                        fn.__name__ in ['test_dataloader', 'val_dataloader']
+                ):
+                    value = [value]
+            except AttributeError as e:
+                # Guard against AttributeError suppression. (Issue #142)
+                traceback.print_exc()
+                error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e)
+                raise RuntimeError(error) from e
+            setattr(self, attr_name, value)  # Memoize evaluation.
+        return value
+
+    return _get_data_loader
+
+
+def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):  # pragma: no cover
+    r"""Applies each `module` in :attr:`modules` in parallel on arguments
+    contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
+    on each of :attr:`devices`.
+
+    Args:
+        modules (Module): modules to be parallelized
+        inputs (tensor): inputs to the modules
+        devices (list of int or torch.device): CUDA devices
+
+    :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and
+    :attr:`devices` (if given) should all have same length. Moreover, each
+    element of :attr:`inputs` can either be a single object as the only argument
+    to a module, or a collection of positional arguments.
+    """
+    assert len(modules) == len(inputs)
+    if kwargs_tup is not None:
+        assert len(modules) == len(kwargs_tup)
+    else:
+        kwargs_tup = ({},) * len(modules)
+    if devices is not None:
+        assert len(modules) == len(devices)
+    else:
+        devices = [None] * len(modules)
+    devices = list(map(lambda x: _get_device_index(x, True), devices))
+    lock = threading.Lock()
+    results = {}
+    grad_enabled = torch.is_grad_enabled()
+
+    def _worker(i, module, input, kwargs, device=None):
+        torch.set_grad_enabled(grad_enabled)
+        if device is None:
+            device = get_a_var(input).get_device()
+        try:
+            with torch.cuda.device(device):
+                # this also avoids accidental slicing of `input` if it is a Tensor
+                if not isinstance(input, (list, tuple)):
+                    input = (input,)
+
+                # ---------------
+                # CHANGE
+                if module.training:
+                    output = module.training_step(*input, **kwargs)
+
+                elif module.testing:
+                    output = module.test_step(*input, **kwargs)
+
+                else:
+                    output = module.validation_step(*input, **kwargs)
+                # ---------------
+
+            with lock:
+                results[i] = output
+        except Exception as e:
+            with lock:
+                results[i] = e
+
+    # make sure each module knows what training state it's in...
+    # fixes weird bug where copies are out of sync
+    root_m = modules[0]
+    for m in modules[1:]:
+        m.training = root_m.training
+        m.testing = root_m.testing
+
+    if len(modules) > 1:
+        threads = [threading.Thread(target=_worker,
+                                    args=(i, module, input, kwargs, device))
+                   for i, (module, input, kwargs, device) in
+                   enumerate(zip(modules, inputs, kwargs_tup, devices))]
+
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+    else:
+        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])
+
+    outputs = []
+    for i in range(len(inputs)):
+        output = results[i]
+        if isinstance(output, Exception):
+            raise output
+        outputs.append(output)
+    return outputs
+
+
+def _find_tensors(obj):  # pragma: no cover
+    r"""
+    Recursively find all tensors contained in the specified object.
+    """
+    if isinstance(obj, torch.Tensor):
+        return [obj]
+    if isinstance(obj, (list, tuple)):
+        return itertools.chain(*map(_find_tensors, obj))
+    if isinstance(obj, dict):
+        return itertools.chain(*map(_find_tensors, obj.values()))
+    return []
+
+
+class DDP(DistributedDataParallel):
+    """
+    Override the forward call in lightning so it goes to training and validation step respectively
+    """
+
+    def parallel_apply(self, replicas, inputs, kwargs):
+        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
+
+    def forward(self, *inputs, **kwargs):  # pragma: no cover
+        self._sync_params()
+        if self.device_ids:
+            inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+            if len(self.device_ids) == 1:
+                # --------------
+                # LIGHTNING MOD
+                # --------------
+                # normal
+                # output = self.module(*inputs[0], **kwargs[0])
+                # lightning
+                if self.module.training:
+                    output = self.module.training_step(*inputs[0], **kwargs[0])
+                elif self.module.testing:
+                    output = self.module.test_step(*inputs[0], **kwargs[0])
+                else:
+                    output = self.module.validation_step(*inputs[0], **kwargs[0])
+            else:
+                outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)
+                output = self.gather(outputs, self.output_device)
+        else:
+            # normal
+            output = self.module(*inputs, **kwargs)
+
+        if torch.is_grad_enabled():
+            # We'll return the output object verbatim since it is a freeform
+            # object. We need to find any tensors in this object, though,
+            # because we need to figure out which parameters were used during
+            # this forward pass, to ensure we short circuit reduction for any
+            # unused parameters. Only if `find_unused_parameters` is set.
+            if self.find_unused_parameters:
+                self.reducer.prepare_for_backward(list(_find_tensors(output)))
+            else:
+                self.reducer.prepare_for_backward([])
+        return output
+
+
+class DP(DataParallel):
+    """
+    Override the forward call in lightning so it goes to training and validation step respectively
+    """
+
+    def forward(self, *inputs, **kwargs):
+        if not self.device_ids:
+            return self.module(*inputs, **kwargs)
+
+        for t in itertools.chain(self.module.parameters(), self.module.buffers()):
+            if t.device != self.src_device_obj:
+                raise RuntimeError("module must have its parameters and buffers "
+                                   "on device {} (device_ids[0]) but found one of "
+                                   "them on device: {}".format(self.src_device_obj, t.device))
+
+        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+        if len(self.device_ids) == 1:
+            # lightning
+            if self.module.training:
+                return self.module.training_step(*inputs[0], **kwargs[0])
+            elif self.module.testing:
+                return self.module.test_step(*inputs[0], **kwargs[0])
+            else:
+                return self.module.validation_step(*inputs[0], **kwargs[0])
+
+        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
+        outputs = self.parallel_apply(replicas, inputs, kwargs)
+        return self.gather(outputs, self.output_device)
+
+    def parallel_apply(self, replicas, inputs, kwargs):
+        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
+
+
+class GradientAccumulationScheduler:
+    def __init__(self, scheduling: dict):
+        if scheduling == {}:  # empty dict error
+            raise TypeError("Empty dict cannot be interpreted correct")
+
+        for key in scheduling.keys():
+            if not isinstance(key, int) or not isinstance(scheduling[key], int):
+                raise TypeError("All epoches and accumulation factor must be integers")
+
+        minimal_epoch = min(scheduling.keys())
+        if minimal_epoch < 1:
+            msg = f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct"
+            raise IndexError(msg)
+        elif minimal_epoch != 1:  # if user didnt define first epoch accumulation factor
+            scheduling.update({1: 1})
+
+        self.scheduling = scheduling
+        self.epochs = sorted(scheduling.keys())
+
+    def on_epoch_begin(self, epoch, trainer):
+        epoch += 1  # indexing epochs from 1
+        for i in reversed(range(len(self.epochs))):
+            if epoch >= self.epochs[i]:
+                trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])
+                break
+
+
+class LatestModelCheckpoint(ModelCheckpoint):
+    def __init__(self, filepath, monitor='val_loss', verbose=0, num_ckpt_keep=5,
+                 save_weights_only=False, mode='auto', period=1, prefix='model', save_best=True):
+        super(ModelCheckpoint, self).__init__()
+        self.monitor = monitor
+        self.verbose = verbose
+        self.filepath = filepath
+        os.makedirs(filepath, exist_ok=True)
+        self.num_ckpt_keep = num_ckpt_keep
+        self.save_best = save_best
+        self.save_weights_only = save_weights_only
+        self.period = period
+        self.epochs_since_last_check = 0
+        self.prefix = prefix
+        self.best_k_models = {}
+        # {filename: monitor}
+        self.kth_best_model = ''
+        self.save_top_k = 1
+        self.task = None
+        if mode == 'min':
+            self.monitor_op = np.less
+            self.best = np.Inf
+            self.mode = 'min'
+        elif mode == 'max':
+            self.monitor_op = np.greater
+            self.best = -np.Inf
+            self.mode = 'max'
+        else:
+            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
+                self.monitor_op = np.greater
+                self.best = -np.Inf
+                self.mode = 'max'
+            else:
+                self.monitor_op = np.less
+                self.best = np.Inf
+                self.mode = 'min'
+        if os.path.exists(f'{self.filepath}/best_valid.npy'):
+            self.best = np.load(f'{self.filepath}/best_valid.npy')[0]
+
+    def get_all_ckpts(self):
+        return sorted(glob.glob(f'{self.filepath}/{self.prefix}_ckpt_steps_*.ckpt'),
+                      key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0]))
+
+    def on_epoch_end(self, epoch, logs=None):
+        logs = logs or {}
+        self.epochs_since_last_check += 1
+        best_filepath = f'{self.filepath}/{self.prefix}_ckpt_best.pt'
+        if self.epochs_since_last_check >= self.period:
+            self.epochs_since_last_check = 0
+            filepath = f'{self.filepath}/{self.prefix}_ckpt_steps_{self.task.global_step}.ckpt'
+            if self.verbose > 0:
+                logging.info(f'Epoch {epoch:05d}@{self.task.global_step}: saving model to {filepath}')
+            self._save_model(filepath)
+            for old_ckpt in self.get_all_ckpts()[self.num_ckpt_keep:]:
+                # TODO: test filesystem calls
+                os.remove(old_ckpt)
+                # subprocess.check_call(f'del "{old_ckpt}"', shell=True)
+                if self.verbose > 0:
+                    logging.info(f'Delete ckpt: {os.path.basename(old_ckpt)}')
+            current = logs.get(self.monitor)
+            if current is not None and self.save_best:
+                if self.monitor_op(current, self.best):
+                    self.best = current
+                    if self.verbose > 0:
+                        logging.info(
+                            f'Epoch {epoch:05d}@{self.task.global_step}: {self.monitor} reached'
+                            f' {current:0.5f} (best {self.best:0.5f}), saving model to'
+                            f' {best_filepath} as top 1')
+                    self._save_model(best_filepath)
+                    np.save(f'{self.filepath}/best_valid.npy', [self.best])
+    
+    def _save_model(self,path):
+        return self.save_function(path)
+
+
+
+class BaseTrainer:
+    def __init__(
+            self,
+            logger=True,
+            checkpoint_callback=True,
+            default_save_path=None,
+            gradient_clip_val=0,
+            process_position=0,
+            gpus=-1,
+            log_gpu_memory=None,
+            show_progress_bar=True,
+            track_grad_norm=-1,
+            check_val_every_n_epoch=1,
+            accumulate_grad_batches=1,
+            max_updates=1000,
+            min_epochs=1,
+            val_check_interval=1.0,
+            log_save_interval=100,
+            row_log_interval=10,
+            print_nan_grads=False,
+            weights_summary='full',
+            num_sanity_val_steps=5,
+            resume_from_checkpoint=None,
+    ):
+        self.log_gpu_memory = log_gpu_memory
+        self.gradient_clip_val = gradient_clip_val
+        self.check_val_every_n_epoch = check_val_every_n_epoch
+        self.track_grad_norm = track_grad_norm
+        self.on_gpu = True if (gpus and torch.cuda.is_available()) else False
+        self.process_position = process_position
+        self.weights_summary = weights_summary
+        self.max_updates = max_updates
+        self.min_epochs = min_epochs
+        self.num_sanity_val_steps = num_sanity_val_steps
+        self.print_nan_grads = print_nan_grads
+        self.resume_from_checkpoint = resume_from_checkpoint
+        self.default_save_path = default_save_path
+
+        # training bookeeping
+        self.total_batch_idx = 0
+        self.running_loss = []
+        self.avg_loss = 0
+        self.batch_idx = 0
+        self.tqdm_metrics = {}
+        self.callback_metrics = {}
+        self.num_val_batches = 0
+        self.num_training_batches = 0
+        self.num_test_batches = 0
+        self.get_train_dataloader = None
+        self.get_test_dataloaders = None
+        self.get_val_dataloaders = None
+        self.is_iterable_train_dataloader = False
+
+        # training state
+        self.model = None
+        self.testing = False
+        self.disable_validation = False
+        self.lr_schedulers = []
+        self.optimizers = None
+        self.global_step = 0
+        self.current_epoch = 0
+        self.total_batches = 0
+
+        # configure checkpoint callback
+        self.checkpoint_callback = checkpoint_callback
+        self.checkpoint_callback.save_function = self.save_checkpoint
+        self.weights_save_path = self.checkpoint_callback.filepath
+
+        # accumulated grads
+        self.configure_accumulated_gradients(accumulate_grad_batches)
+
+        # allow int, string and gpu list
+        self.data_parallel_device_ids = [
+            int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != '']
+        if len(self.data_parallel_device_ids) == 0:
+            self.root_gpu = None
+            self.on_gpu = False
+        else:
+            self.root_gpu = self.data_parallel_device_ids[0]
+            self.on_gpu = True
+
+        # distributed backend choice
+        self.use_ddp = False
+        self.use_dp = False
+        self.single_gpu = False
+        self.distributed_backend = 'ddp' if self.num_gpus > 0 else 'dp'
+        self.set_distributed_mode(self.distributed_backend)
+
+        self.proc_rank = 0
+        self.world_size = 1
+        self.node_rank = 0
+
+        # can't init progress bar here because starting a new process
+        # means the progress_bar won't survive pickling
+        self.show_progress_bar = show_progress_bar
+
+        # logging
+        self.log_save_interval = log_save_interval
+        self.val_check_interval = val_check_interval
+        self.logger = logger
+        self.logger.rank = 0
+        self.row_log_interval = row_log_interval
+
+    @property
+    def num_gpus(self):
+        gpus = self.data_parallel_device_ids
+        if gpus is None:
+            return 0
+        else:
+            return len(gpus)
+
+    @property
+    def data_parallel(self):
+        return self.use_dp or self.use_ddp
+
+    def get_model(self):
+        is_dp_module = isinstance(self.model, (DDP, DP))
+        model = self.model.module if is_dp_module else self.model
+        return model
+
+    # -----------------------------
+    # MODEL TRAINING
+    # -----------------------------
+    def fit(self, model):
+        if self.use_ddp:
+            mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,))
+        else:
+            model.model = model.build_model()
+            if not self.testing:
+                self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())
+            if self.use_dp:
+                model.cuda(self.root_gpu)
+                model = DP(model, device_ids=self.data_parallel_device_ids)
+            elif self.single_gpu:
+                model.cuda(self.root_gpu)
+            self.run_pretrain_routine(model)
+        return 1
+
+    def init_optimizers(self, optimizers):
+
+        # single optimizer
+        if isinstance(optimizers, Optimizer):
+            return [optimizers], []
+
+        # two lists
+        elif len(optimizers) == 2 and isinstance(optimizers[0], list):
+            optimizers, lr_schedulers = optimizers
+            return optimizers, lr_schedulers
+
+        # single list or tuple
+        elif isinstance(optimizers, list) or isinstance(optimizers, tuple):
+            return optimizers, []
+
+    def run_pretrain_routine(self, model):
+        """Sanity check a few things before starting actual training.
+
+        :param model:
+        """
+        ref_model = model
+        if self.data_parallel:
+            ref_model = model.module
+
+        # give model convenience properties
+        ref_model.trainer = self
+
+        # set local properties on the model
+        self.copy_trainer_model_properties(ref_model)
+
+        # link up experiment object
+        if self.logger is not None:
+            ref_model.logger = self.logger
+            self.logger.save()
+
+        if self.use_ddp:
+            dist.barrier()
+
+        # set up checkpoint callback
+        # self.configure_checkpoint_callback()
+
+        # transfer data loaders from model
+        self.get_dataloaders(ref_model)
+
+        # track model now.
+        # if cluster resets state, the model will update with the saved weights
+        self.model = model
+
+        # restore training and model before hpc call
+        self.restore_weights(model)
+
+        # when testing requested only run test and return
+        if self.testing:
+            self.run_evaluation(test=True)
+            return
+
+        # check if we should run validation during training
+        self.disable_validation = self.num_val_batches == 0
+
+        # run tiny validation (if validation defined)
+        # to make sure program won't crash during val
+        ref_model.on_sanity_check_start()
+        ref_model.on_train_start()
+        if not self.disable_validation and self.num_sanity_val_steps > 0:
+            # init progress bars for validation sanity check
+            pbar = tqdm.tqdm(desc='Validation sanity check',
+                             total=self.num_sanity_val_steps * len(self.get_val_dataloaders()),
+                             leave=False, position=2 * self.process_position,
+                             disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch')
+            self.main_progress_bar = pbar
+            # dummy validation progress bar
+            self.val_progress_bar = tqdm.tqdm(disable=True)
+
+            self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing)
+
+            # close progress bars
+            self.main_progress_bar.close()
+            self.val_progress_bar.close()
+
+        # init progress bar
+        pbar = tqdm.tqdm(leave=True, position=2 * self.process_position,
+                         disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch',
+                         file=sys.stdout)
+        self.main_progress_bar = pbar
+
+        # clear cache before training
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+
+        # CORE TRAINING LOOP
+        self.train()
+
+    def test(self, model):
+        self.testing = True
+        self.fit(model)
+
+    @property
+    def training_tqdm_dict(self):
+        tqdm_dict = {
+            'step': '{}'.format(self.global_step),
+        }
+        tqdm_dict.update(self.tqdm_metrics)
+        return tqdm_dict
+
+    # --------------------
+    # restore ckpt
+    # --------------------
+    def restore_weights(self, model):
+        """
+        To restore weights we have two cases.
+        First, attempt to restore hpc weights. If successful, don't restore
+        other weights.
+
+        Otherwise, try to restore actual weights
+        :param model:
+        :return:
+        """
+        # clear cache before restore
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+
+        if self.resume_from_checkpoint is not None:
+            self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu)
+        else:
+            # restore weights if same exp version
+            self.restore_state_if_checkpoint_exists(model)
+
+        # wait for all models to restore weights
+        if self.use_ddp:
+            # wait for all processes to catch up
+            dist.barrier()
+
+        # clear cache after restore
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+
+    def restore_state_if_checkpoint_exists(self, model):
+        did_restore = False
+
+        # do nothing if there's not dir or callback
+        no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback)
+        if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):
+            return did_restore
+
+        # restore trainer state and model if there is a weight for this experiment
+        last_steps = -1
+        last_ckpt_name = None
+
+        # find last epoch
+        checkpoints = os.listdir(self.checkpoint_callback.filepath)
+        for name in checkpoints:
+            if '.ckpt' in name and not name.endswith('part'):
+                if 'steps_' in name:
+                    steps = name.split('steps_')[1]
+                    steps = int(re.sub('[^0-9]', '', steps))
+
+                    if steps > last_steps:
+                        last_steps = steps
+                        last_ckpt_name = name
+
+        # restore last checkpoint
+        if last_ckpt_name is not None:
+            last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)
+            self.restore(last_ckpt_path, self.on_gpu)
+            logging.info(f'model and trainer restored from checkpoint: {last_ckpt_path}')
+            did_restore = True
+
+        return did_restore
+
+    def restore(self, checkpoint_path, on_gpu):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+
+        # load model state
+        model = self.get_model()
+
+        # load the state_dict on the model automatically
+        model.load_state_dict(checkpoint['state_dict'], strict=False)
+        if on_gpu:
+            model.cuda(self.root_gpu)
+        # load training state (affects trainer only)
+        self.restore_training_state(checkpoint)
+        model.global_step = self.global_step
+        del checkpoint
+
+        try:
+            if dist.is_initialized() and dist.get_rank() > 0:
+                return
+        except Exception as e:
+            print(e)
+            return
+
+    def restore_training_state(self, checkpoint):
+        """
+        Restore trainer state.
+        Model will get its change to update
+        :param checkpoint:
+        :return:
+        """
+        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
+            # return allowing checkpoints with meta information (global_step, etc)
+            self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']
+
+        self.global_step = checkpoint['global_step']
+        self.current_epoch = checkpoint['epoch']
+
+        if self.testing:
+            return
+
+        # restore the optimizers
+        optimizer_states = checkpoint['optimizer_states']
+        for optimizer, opt_state in zip(self.optimizers, optimizer_states):
+            if optimizer is None:
+                return
+            optimizer.load_state_dict(opt_state)
+
+            # move optimizer to GPU 1 weight at a time
+            # avoids OOM
+            if self.root_gpu is not None:
+                for state in optimizer.state.values():
+                    for k, v in state.items():
+                        if isinstance(v, torch.Tensor):
+                            state[k] = v.cuda(self.root_gpu)
+
+        # restore the lr schedulers
+        lr_schedulers = checkpoint['lr_schedulers']
+        for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
+            scheduler.load_state_dict(lrs_state)
+
+    # --------------------
+    # MODEL SAVE CHECKPOINT
+    # --------------------
+    def _atomic_save(self, checkpoint, filepath):
+        """Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.
+
+        This will create a temporary checkpoint with a suffix of ``.part``, then copy it to the final location once
+        saving is finished.
+
+        Args:
+            checkpoint (object): The object to save.
+                Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save``
+                accepts.
+            filepath (str|pathlib.Path): The path to which the checkpoint will be saved.
+                This points to the file that the checkpoint will be stored in.
+        """
+        tmp_path = str(filepath) + ".part"
+        torch.save(checkpoint, tmp_path)
+        os.replace(tmp_path, filepath)
+
+    def save_checkpoint(self, filepath):
+        checkpoint = self.dump_checkpoint()
+        self._atomic_save(checkpoint, filepath)
+
+    def dump_checkpoint(self):
+
+        checkpoint = {
+            'epoch': self.current_epoch,
+            'global_step': self.global_step
+        }
+
+        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
+            checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best
+
+        # save optimizers
+        optimizer_states = []
+        for i, optimizer in enumerate(self.optimizers):
+            if optimizer is not None:
+                optimizer_states.append(optimizer.state_dict())
+
+        checkpoint['optimizer_states'] = optimizer_states
+
+        # save lr schedulers
+        lr_schedulers = []
+        for i, scheduler in enumerate(self.lr_schedulers):
+            lr_schedulers.append(scheduler.state_dict())
+
+        checkpoint['lr_schedulers'] = lr_schedulers
+
+        # add the hparams and state_dict from the model
+        model = self.get_model()
+        checkpoint['state_dict'] = model.state_dict()
+        # give the model a chance to add a few things
+        model.on_save_checkpoint(checkpoint)
+
+        return checkpoint
+
+    def copy_trainer_model_properties(self, model):
+        if isinstance(model, DP):
+            ref_model = model.module
+        elif isinstance(model, DDP):
+            ref_model = model.module
+        else:
+            ref_model = model
+
+        for m in [model, ref_model]:
+            m.trainer = self
+            m.on_gpu = self.on_gpu
+            m.use_dp = self.use_dp
+            m.use_ddp = self.use_ddp
+            m.testing = self.testing
+            m.single_gpu = self.single_gpu
+
+    def transfer_batch_to_gpu(self, batch, gpu_id):
+        # base case: object can be directly moved using `cuda` or `to`
+        if callable(getattr(batch, 'cuda', None)):
+            return batch.cuda(gpu_id, non_blocking=True)
+
+        elif callable(getattr(batch, 'to', None)):
+            return batch.to(torch.device('cuda', gpu_id), non_blocking=True)
+
+        # when list
+        elif isinstance(batch, list):
+            for i, x in enumerate(batch):
+                batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
+            return batch
+
+        # when tuple
+        elif isinstance(batch, tuple):
+            batch = list(batch)
+            for i, x in enumerate(batch):
+                batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
+            return tuple(batch)
+
+        # when dict
+        elif isinstance(batch, dict):
+            for k, v in batch.items():
+                batch[k] = self.transfer_batch_to_gpu(v, gpu_id)
+
+            return batch
+
+        # nothing matches, return the value as is without transform
+        return batch
+
+    def set_distributed_mode(self, distributed_backend):
+        # skip for CPU
+        if self.num_gpus == 0:
+            return
+
+        # single GPU case
+        # in single gpu case we allow ddp so we can train on multiple
+        # nodes, 1 gpu per node
+        elif self.num_gpus == 1:
+            self.single_gpu = True
+            self.use_dp = False
+            self.use_ddp = False
+            self.root_gpu = 0
+            self.data_parallel_device_ids = [0]
+        else:
+            if distributed_backend is not None:
+                self.use_dp = distributed_backend == 'dp'
+                self.use_ddp = distributed_backend == 'ddp'
+            elif distributed_backend is None:
+                self.use_dp = True
+                self.use_ddp = False
+
+        logging.info(f'gpu available: {torch.cuda.is_available()}, used: {self.on_gpu}')
+
+    def ddp_train(self, gpu_idx, model):
+        """
+        Entry point into a DP thread
+        :param gpu_idx:
+        :param model:
+        :param cluster_obj:
+        :return:
+        """
+        # otherwise default to node rank 0
+        self.node_rank = 0
+
+        # show progressbar only on progress_rank 0
+        self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_idx == 0
+
+        # determine which process we are and world size
+        if self.use_ddp:
+            self.proc_rank = self.node_rank * self.num_gpus + gpu_idx
+            self.world_size = self.num_gpus
+
+        # let the exp know the rank to avoid overwriting logs
+        if self.logger is not None:
+            self.logger.rank = self.proc_rank
+
+        # set up server using proc 0's ip address
+        # try to init for 20 times at max in case ports are taken
+        # where to store ip_table
+        model.trainer = self
+        model.init_ddp_connection(self.proc_rank, self.world_size)
+
+        # CHOOSE OPTIMIZER
+        # allow for lr schedulers as well
+        model.model = model.build_model()
+        if not self.testing:
+            self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())
+
+        # MODEL
+        # copy model to each gpu
+        if self.distributed_backend == 'ddp':
+            torch.cuda.set_device(gpu_idx)
+        model.cuda(gpu_idx)
+
+        # set model properties before going into wrapper
+        self.copy_trainer_model_properties(model)
+
+        # override root GPU
+        self.root_gpu = gpu_idx
+
+        if self.distributed_backend == 'ddp':
+            device_ids = [gpu_idx]
+        else:
+            device_ids = None
+
+        # allow user to configure ddp
+        model = model.configure_ddp(model, device_ids)
+
+        # continue training routine
+        self.run_pretrain_routine(model)
+
+    def resolve_root_node_address(self, root_node):
+        if '[' in root_node:
+            name = root_node.split('[')[0]
+            number = root_node.split(',')[0]
+            if '-' in number:
+                number = number.split('-')[0]
+
+            number = re.sub('[^0-9]', '', number)
+            root_node = name + number
+
+        return root_node
+
+    def log_metrics(self, metrics, grad_norm_dic, step=None):
+        """Logs the metric dict passed in.
+
+        :param metrics:
+        :param grad_norm_dic:
+        """
+        # added metrics by Lightning for convenience
+        metrics['epoch'] = self.current_epoch
+
+        # add norms
+        metrics.update(grad_norm_dic)
+
+        # turn all tensors to scalars
+        scalar_metrics = self.metrics_to_scalars(metrics)
+
+        step = step if step is not None else self.global_step
+        # log actual metrics
+        if self.proc_rank == 0 and self.logger is not None:
+            self.logger.log_metrics(scalar_metrics, step=step)
+            self.logger.save()
+
+    def add_tqdm_metrics(self, metrics):
+        for k, v in metrics.items():
+            if type(v) is torch.Tensor:
+                v = v.item()
+
+            self.tqdm_metrics[k] = v
+
+    def metrics_to_scalars(self, metrics):
+        new_metrics = {}
+        for k, v in metrics.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+
+            if type(v) is dict:
+                v = self.metrics_to_scalars(v)
+
+            new_metrics[k] = v
+
+        return new_metrics
+
+    def process_output(self, output, train=False):
+        """Reduces output according to the training mode.
+
+        Separates loss from logging and tqdm metrics
+        :param output:
+        :return:
+        """
+        # ---------------
+        # EXTRACT CALLBACK KEYS
+        # ---------------
+        # all keys not progress_bar or log are candidates for callbacks
+        callback_metrics = {}
+        for k, v in output.items():
+            if k not in ['progress_bar', 'log', 'hiddens']:
+                callback_metrics[k] = v
+
+        if train and self.use_dp:
+            num_gpus = self.num_gpus
+            callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus)
+
+        for k, v in callback_metrics.items():
+            if isinstance(v, torch.Tensor):
+                callback_metrics[k] = v.item()
+
+        # ---------------
+        # EXTRACT PROGRESS BAR KEYS
+        # ---------------
+        try:
+            progress_output = output['progress_bar']
+
+            # reduce progress metrics for tqdm when using dp
+            if train and self.use_dp:
+                num_gpus = self.num_gpus
+                progress_output = self.reduce_distributed_output(progress_output, num_gpus)
+
+            progress_bar_metrics = progress_output
+        except Exception:
+            progress_bar_metrics = {}
+
+        # ---------------
+        # EXTRACT LOGGING KEYS
+        # ---------------
+        # extract metrics to log to experiment
+        try:
+            log_output = output['log']
+
+            # reduce progress metrics for tqdm when using dp
+            if train and self.use_dp:
+                num_gpus = self.num_gpus
+                log_output = self.reduce_distributed_output(log_output, num_gpus)
+
+            log_metrics = log_output
+        except Exception:
+            log_metrics = {}
+
+        # ---------------
+        # EXTRACT LOSS
+        # ---------------
+        # if output dict doesn't have the keyword loss
+        # then assume the output=loss if scalar
+        loss = None
+        if train:
+            try:
+                loss = output['loss']
+            except Exception:
+                if type(output) is torch.Tensor:
+                    loss = output
+                else:
+                    raise RuntimeError(
+                        'No `loss` value in the dictionary returned from `model.training_step()`.'
+                    )
+
+            # when using dp need to reduce the loss
+            if self.use_dp:
+                loss = self.reduce_distributed_output(loss, self.num_gpus)
+
+        # ---------------
+        # EXTRACT HIDDEN
+        # ---------------
+        hiddens = output.get('hiddens')
+
+        # use every metric passed in as a candidate for callback
+        callback_metrics.update(progress_bar_metrics)
+        callback_metrics.update(log_metrics)
+
+        # convert tensors to numpy
+        for k, v in callback_metrics.items():
+            if isinstance(v, torch.Tensor):
+                callback_metrics[k] = v.item()
+
+        return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens
+
+    def reduce_distributed_output(self, output, num_gpus):
+        if num_gpus <= 1:
+            return output
+
+        # when using DP, we get one output per gpu
+        # average outputs and return
+        if type(output) is torch.Tensor:
+            return output.mean()
+
+        for k, v in output.items():
+            # recurse on nested dics
+            if isinstance(output[k], dict):
+                output[k] = self.reduce_distributed_output(output[k], num_gpus)
+
+            # do nothing when there's a scalar
+            elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:
+                pass
+
+            # reduce only metrics that have the same number of gpus
+            elif output[k].size(0) == num_gpus:
+                reduced = torch.mean(output[k])
+                output[k] = reduced
+        return output
+
+    def clip_gradients(self):
+        if self.gradient_clip_val > 0:
+            model = self.get_model()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), self.gradient_clip_val)
+
+    def print_nan_gradients(self):
+        model = self.get_model()
+        for param in model.parameters():
+            if (param.grad is not None) and torch.isnan(param.grad.float()).any():
+                logging.info(param, param.grad)
+
+    def configure_accumulated_gradients(self, accumulate_grad_batches):
+        self.accumulate_grad_batches = None
+
+        if isinstance(accumulate_grad_batches, dict):
+            self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)
+        elif isinstance(accumulate_grad_batches, int):
+            schedule = {1: accumulate_grad_batches}
+            self.accumulation_scheduler = GradientAccumulationScheduler(schedule)
+        else:
+            raise TypeError("Gradient accumulation supports only int and dict types")
+
+    def get_dataloaders(self, model):
+        if not self.testing:
+            self.init_train_dataloader(model)
+            self.init_val_dataloader(model)
+        else:
+            self.init_test_dataloader(model)
+
+        if self.use_ddp:
+            dist.barrier()
+            if not self.testing:
+                self.get_train_dataloader()
+                self.get_val_dataloaders()
+            else:
+                self.get_test_dataloaders()
+
+    def init_train_dataloader(self, model):
+        self.fisrt_epoch = True
+        self.get_train_dataloader = model.train_dataloader
+        if isinstance(self.get_train_dataloader(), torch.utils.data.DataLoader):
+            self.num_training_batches = len(self.get_train_dataloader())
+            self.num_training_batches = int(self.num_training_batches)
+        else:
+            self.num_training_batches = float('inf')
+            self.is_iterable_train_dataloader = True
+        if isinstance(self.val_check_interval, int):
+            self.val_check_batch = self.val_check_interval
+        else:
+            self._percent_range_check('val_check_interval')
+            self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
+            self.val_check_batch = max(1, self.val_check_batch)
+
+    def init_val_dataloader(self, model):
+        self.get_val_dataloaders = model.val_dataloader
+        self.num_val_batches = 0
+        if self.get_val_dataloaders() is not None:
+            if isinstance(self.get_val_dataloaders()[0], torch.utils.data.DataLoader):
+                self.num_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders())
+                self.num_val_batches = int(self.num_val_batches)
+            else:
+                self.num_val_batches = float('inf')
+
+    def init_test_dataloader(self, model):
+        self.get_test_dataloaders = model.test_dataloader
+        if self.get_test_dataloaders() is not None:
+            if isinstance(self.get_test_dataloaders()[0], torch.utils.data.DataLoader):
+                self.num_test_batches = sum(len(dataloader) for dataloader in self.get_test_dataloaders())
+                self.num_test_batches = int(self.num_test_batches)
+            else:
+                self.num_test_batches = float('inf')
+
+    def evaluate(self, model, dataloaders, max_batches, test=False):
+        """Run evaluation code.
+
+        :param model: PT model
+        :param dataloaders: list of PT dataloaders
+        :param max_batches: Scalar
+        :param test: boolean
+        :return:
+        """
+        # enable eval mode
+        model.zero_grad()
+        model.eval()
+
+        # copy properties for forward overrides
+        self.copy_trainer_model_properties(model)
+
+        # disable gradients to save memory
+        torch.set_grad_enabled(False)
+
+        if test:
+            self.get_model().test_start()
+        # bookkeeping
+        outputs = []
+
+        # run training
+        for dataloader_idx, dataloader in enumerate(dataloaders):
+            dl_outputs = []
+            for batch_idx, batch in enumerate(dataloader):
+
+                if batch is None:  # pragma: no cover
+                    continue
+
+                # stop short when on fast_dev_run (sets max_batch=1)
+                if batch_idx >= max_batches:
+                    break
+
+                # -----------------
+                # RUN EVALUATION STEP
+                # -----------------
+                output = self.evaluation_forward(model,
+                                                 batch,
+                                                 batch_idx,
+                                                 dataloader_idx,
+                                                 test)
+
+                # track outputs for collation
+                dl_outputs.append(output)
+
+                # batch done
+                if test:
+                    self.test_progress_bar.update(1)
+                else:
+                    self.val_progress_bar.update(1)
+            outputs.append(dl_outputs)
+
+        # with a single dataloader don't pass an array
+        if len(dataloaders) == 1:
+            outputs = outputs[0]
+
+        # give model a chance to do something with the outputs (and method defined)
+        model = self.get_model()
+        if test:
+            eval_results_ = model.test_end(outputs)
+        else:
+            eval_results_ = model.validation_end(outputs)
+        eval_results = eval_results_
+
+        # enable train mode again
+        model.train()
+
+        # enable gradients to save memory
+        torch.set_grad_enabled(True)
+
+        return eval_results
+
+    def run_evaluation(self, test=False):
+        # when testing make sure user defined a test step
+        model = self.get_model()
+        model.on_pre_performance_check()
+
+        # select dataloaders
+        if test:
+            dataloaders = self.get_test_dataloaders()
+            max_batches = self.num_test_batches
+        else:
+            # val
+            dataloaders = self.get_val_dataloaders()
+            max_batches = self.num_val_batches
+
+        # init validation or test progress bar
+        # main progress bar will already be closed when testing so initial position is free
+        position = 2 * self.process_position + (not test)
+        desc = 'Testing' if test else 'Validating'
+        pbar = tqdm.tqdm(desc=desc, total=max_batches, leave=test, position=position,
+                         disable=not self.show_progress_bar, dynamic_ncols=True,
+                         unit='batch', file=sys.stdout)
+        setattr(self, f'{"test" if test else "val"}_progress_bar', pbar)
+
+        # run evaluation
+        eval_results = self.evaluate(self.model,
+                                     dataloaders,
+                                     max_batches,
+                                     test)
+        if eval_results is not None:
+            _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(
+                eval_results)
+
+            # add metrics to prog bar
+            self.add_tqdm_metrics(prog_bar_metrics)
+
+            # log metrics
+            self.log_metrics(log_metrics, {})
+
+            # track metrics for callbacks
+            self.callback_metrics.update(callback_metrics)
+
+        # hook
+        model.on_post_performance_check()
+
+        # add model specific metrics
+        tqdm_metrics = self.training_tqdm_dict
+        if not test:
+            self.main_progress_bar.set_postfix(**tqdm_metrics)
+
+        # close progress bar
+        if test:
+            self.test_progress_bar.close()
+        else:
+            self.val_progress_bar.close()
+
+        # model checkpointing
+        if self.proc_rank == 0 and self.checkpoint_callback is not None and not test:
+            self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch,
+                                                  logs=self.callback_metrics)
+
+    def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):
+        # make dataloader_idx arg in validation_step optional
+        args = [batch, batch_idx]
+        # print(batch)
+        if test and len(self.get_test_dataloaders()) > 1:
+            args.append(dataloader_idx)
+
+        elif not test and len(self.get_val_dataloaders()) > 1:
+            args.append(dataloader_idx)
+
+        # handle DP, DDP forward
+        if self.use_ddp or self.use_dp:
+            output = model(*args)
+            return output
+
+        # single GPU
+        if self.single_gpu:
+            # for single GPU put inputs on gpu manually
+            root_gpu = 0
+            if isinstance(self.data_parallel_device_ids, list):
+                root_gpu = self.data_parallel_device_ids[0]
+            batch = self.transfer_batch_to_gpu(batch, root_gpu)
+            args[0] = batch
+
+        # CPU
+        if test:
+            output = model.test_step(*args)
+        else:
+            output = model.validation_step(*args)
+
+        return output
+
+    def train(self):
+        model = self.get_model()
+        # run all epochs
+        for epoch in range(self.current_epoch, 1000000):
+            # set seed for distributed sampler (enables shuffling for each epoch)
+            if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):
+                self.get_train_dataloader().sampler.set_epoch(epoch)
+
+            # get model
+            model = self.get_model()
+
+            # update training progress in trainer and model
+            model.current_epoch = epoch
+            self.current_epoch = epoch
+
+            total_val_batches = 0
+            if not self.disable_validation:
+                # val can be checked multiple times in epoch
+                is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
+                val_checks_per_epoch = self.num_training_batches // self.val_check_batch
+                val_checks_per_epoch = val_checks_per_epoch if is_val_epoch else 0
+                total_val_batches = self.num_val_batches * val_checks_per_epoch
+
+            # total batches includes multiple val checks
+            self.total_batches = self.num_training_batches + total_val_batches
+            self.batch_loss_value = 0  # accumulated grads
+
+            if self.is_iterable_train_dataloader:
+                # for iterable train loader, the progress bar never ends
+                num_iterations = None
+            else:
+                num_iterations = self.total_batches
+
+            # reset progress bar
+            # .reset() doesn't work on disabled progress bar so we should check
+            desc = f'Epoch {epoch + 1}' if not self.is_iterable_train_dataloader else ''
+            self.main_progress_bar.set_description(desc)
+
+            # changing gradient according accumulation_scheduler
+            self.accumulation_scheduler.on_epoch_begin(epoch, self)
+
+            # -----------------
+            # RUN TNG EPOCH
+            # -----------------
+            self.run_training_epoch()
+
+            # update LR schedulers
+            if self.lr_schedulers is not None:
+                for lr_scheduler in self.lr_schedulers:
+                    lr_scheduler.step(epoch=self.current_epoch)
+
+        self.main_progress_bar.close()
+
+        model.on_train_end()
+
+        if self.logger is not None:
+            self.logger.finalize("success")
+
+    def run_training_epoch(self):
+        # before epoch hook
+        if self.is_function_implemented('on_epoch_start'):
+            model = self.get_model()
+            model.on_epoch_start()
+
+        # run epoch
+        for batch_idx, batch in enumerate(self.get_train_dataloader()):
+            # stop epoch if we limited the number of training batches
+            if batch_idx >= self.num_training_batches:
+                break
+
+            self.batch_idx = batch_idx
+
+            model = self.get_model()
+            model.global_step = self.global_step
+
+            # ---------------
+            # RUN TRAIN STEP
+            # ---------------
+            output = self.run_training_batch(batch, batch_idx)
+            batch_result, grad_norm_dic, batch_step_metrics = output
+
+            # when returning -1 from train_step, we end epoch early
+            early_stop_epoch = batch_result == -1
+
+            # ---------------
+            # RUN VAL STEP
+            # ---------------
+            should_check_val = (
+                    not self.disable_validation and self.global_step % self.val_check_batch == 0 and not self.fisrt_epoch)
+            self.fisrt_epoch = False
+
+            if should_check_val:
+                self.run_evaluation(test=self.testing)
+
+            # when logs should be saved
+            should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or early_stop_epoch
+            if should_save_log:
+                if self.proc_rank == 0 and self.logger is not None:
+                    self.logger.save()
+
+            # when metrics should be logged
+            should_log_metrics = batch_idx % self.row_log_interval == 0 or early_stop_epoch
+            if should_log_metrics:
+                # logs user requested information to logger
+                self.log_metrics(batch_step_metrics, grad_norm_dic)
+
+            self.global_step += 1
+            self.total_batch_idx += 1
+
+            # end epoch early
+            # stop when the flag is changed or we've gone past the amount
+            # requested in the batches
+            if early_stop_epoch:
+                break
+            if self.global_step > self.max_updates:
+                print("| Training end..")
+                exit()
+
+        # epoch end hook
+        if self.is_function_implemented('on_epoch_end'):
+            model = self.get_model()
+            model.on_epoch_end()
+
+    def run_training_batch(self, batch, batch_idx):
+        # track grad norms
+        grad_norm_dic = {}
+
+        # track all metrics for callbacks
+        all_callback_metrics = []
+
+        # track metrics to log
+        all_log_metrics = []
+
+        if batch is None:
+            return 0, grad_norm_dic, {}
+
+        # hook
+        if self.is_function_implemented('on_batch_start'):
+            model_ref = self.get_model()
+            response = model_ref.on_batch_start(batch)
+
+            if response == -1:
+                return -1, grad_norm_dic, {}
+
+        splits = [batch]
+        self.hiddens = None
+        for split_idx, split_batch in enumerate(splits):
+            self.split_idx = split_idx
+
+            # call training_step once per optimizer
+            for opt_idx, optimizer in enumerate(self.optimizers):
+                if optimizer is None:
+                    continue
+                # make sure only the gradients of the current optimizer's paramaters are calculated
+                # in the training step to prevent dangling gradients in multiple-optimizer setup.
+                if len(self.optimizers) > 1:
+                    for param in self.get_model().parameters():
+                        param.requires_grad = False
+                    for group in optimizer.param_groups:
+                        for param in group['params']:
+                            param.requires_grad = True
+
+                # wrap the forward step in a closure so second order methods work
+                def optimizer_closure():
+                    # forward pass
+                    output = self.training_forward(
+                        split_batch, batch_idx, opt_idx, self.hiddens)
+
+                    closure_loss = output[0]
+                    progress_bar_metrics = output[1]
+                    log_metrics = output[2]
+                    callback_metrics = output[3]
+                    self.hiddens = output[4]
+                    if closure_loss is None:
+                        return None
+
+                    # accumulate loss
+                    # (if accumulate_grad_batches = 1 no effect)
+                    closure_loss = closure_loss / self.accumulate_grad_batches
+
+                    # backward pass
+                    model_ref = self.get_model()
+                    if closure_loss.requires_grad:
+                        model_ref.backward(closure_loss, optimizer)
+
+                    # track metrics for callbacks
+                    all_callback_metrics.append(callback_metrics)
+
+                    # track progress bar metrics
+                    self.add_tqdm_metrics(progress_bar_metrics)
+                    all_log_metrics.append(log_metrics)
+
+                    # insert after step hook
+                    if self.is_function_implemented('on_after_backward'):
+                        model_ref = self.get_model()
+                        model_ref.on_after_backward()
+
+                    return closure_loss
+
+                # calculate loss
+                loss = optimizer_closure()
+                if loss is None:
+                    continue
+
+                # nan grads
+                if self.print_nan_grads:
+                    self.print_nan_gradients()
+
+                # track total loss for logging (avoid mem leaks)
+                self.batch_loss_value += loss.item()
+
+                # gradient update with accumulated gradients
+                if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:
+
+                    # track gradient norms when requested
+                    if batch_idx % self.row_log_interval == 0:
+                        if self.track_grad_norm > 0:
+                            model = self.get_model()
+                            grad_norm_dic = model.grad_norm(
+                                self.track_grad_norm)
+
+                    # clip gradients
+                    self.clip_gradients()
+
+                    # calls .step(), .zero_grad()
+                    # override function to modify this behavior
+                    model = self.get_model()
+                    model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx)
+
+                    # calculate running loss for display
+                    self.running_loss.append(self.batch_loss_value)
+                    self.batch_loss_value = 0
+                    self.avg_loss = np.mean(self.running_loss[-100:])
+
+        # activate batch end hook
+        if self.is_function_implemented('on_batch_end'):
+            model = self.get_model()
+            model.on_batch_end()
+
+        # update progress bar
+        self.main_progress_bar.update(1)
+        self.main_progress_bar.set_postfix(**self.training_tqdm_dict)
+
+        # collapse all metrics into one dict
+        all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()}
+
+        # track all metrics for callbacks
+        self.callback_metrics.update({k: v for d in all_callback_metrics for k, v in d.items()})
+
+        return 0, grad_norm_dic, all_log_metrics
+
+    def training_forward(self, batch, batch_idx, opt_idx, hiddens):
+        """
+        Handle forward for each training case (distributed, single gpu, etc...)
+        :param batch:
+        :param batch_idx:
+        :return:
+        """
+        # ---------------
+        # FORWARD
+        # ---------------
+        # enable not needing to add opt_idx to training_step
+        args = [batch, batch_idx, opt_idx]
+
+        # distributed forward
+        if self.use_ddp or self.use_dp:
+            output = self.model(*args)
+        # single GPU forward
+        elif self.single_gpu:
+            gpu_id = 0
+            if isinstance(self.data_parallel_device_ids, list):
+                gpu_id = self.data_parallel_device_ids[0]
+            batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id)
+            args[0] = batch
+            output = self.model.training_step(*args)
+        # CPU forward
+        else:
+            output = self.model.training_step(*args)
+
+        # allow any mode to define training_end
+        model_ref = self.get_model()
+        output_ = model_ref.training_end(output)
+        if output_ is not None:
+            output = output_
+
+        # format and reduce outputs accordingly
+        output = self.process_output(output, train=True)
+
+        return output
+
+    # ---------------
+    # Utils
+    # ---------------
+    def is_function_implemented(self, f_name):
+        model = self.get_model()
+        f_op = getattr(model, f_name, None)
+        return callable(f_op)
+
+    def _percent_range_check(self, name):
+        value = getattr(self, name)
+        msg = f"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}."
+        if name == "val_check_interval":
+            msg += " If you want to disable validation set `val_percent_check` to 0.0 instead."
+
+        if not 0. <= value <= 1.:
+            raise ValueError(msg)
diff --git a/utils/plot.py b/utils/plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdca62a8cd80869c707890cd9febd39966cd3658
--- /dev/null
+++ b/utils/plot.py
@@ -0,0 +1,56 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+LINE_COLORS = ['w', 'r', 'y', 'cyan', 'm', 'b', 'lime']
+
+
+def spec_to_figure(spec, vmin=None, vmax=None):
+    if isinstance(spec, torch.Tensor):
+        spec = spec.cpu().numpy()
+    fig = plt.figure(figsize=(12, 6))
+    plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
+    return fig
+
+
+def spec_f0_to_figure(spec, f0s, figsize=None):
+    max_y = spec.shape[1]
+    if isinstance(spec, torch.Tensor):
+        spec = spec.detach().cpu().numpy()
+        f0s = {k: f0.detach().cpu().numpy() for k, f0 in f0s.items()}
+    f0s = {k: f0 / 10 for k, f0 in f0s.items()}
+    fig = plt.figure(figsize=(12, 6) if figsize is None else figsize)
+    plt.pcolor(spec.T)
+    for i, (k, f0) in enumerate(f0s.items()):
+        plt.plot(f0.clip(0, max_y), label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.8)
+    plt.legend()
+    return fig
+
+
+def dur_to_figure(dur_gt, dur_pred, txt):
+    dur_gt = dur_gt.long().cpu().numpy()
+    dur_pred = dur_pred.long().cpu().numpy()
+    dur_gt = np.cumsum(dur_gt)
+    dur_pred = np.cumsum(dur_pred)
+    fig = plt.figure(figsize=(12, 6))
+    for i in range(len(dur_gt)):
+        shift = (i % 8) + 1
+        plt.text(dur_gt[i], shift, txt[i])
+        plt.text(dur_pred[i], 10 + shift, txt[i])
+        plt.vlines(dur_gt[i], 0, 10, colors='b')  # blue is gt
+        plt.vlines(dur_pred[i], 10, 20, colors='r')  # red is pred
+    return fig
+
+
+def f0_to_figure(f0_gt, f0_cwt=None, f0_pred=None):
+    fig = plt.figure()
+    f0_gt = f0_gt.cpu().numpy()
+    plt.plot(f0_gt, color='r', label='gt')
+    if f0_cwt is not None:
+        f0_cwt = f0_cwt.cpu().numpy()
+        plt.plot(f0_cwt, color='b', label='cwt')
+    if f0_pred is not None:
+        f0_pred = f0_pred.cpu().numpy()
+        plt.plot(f0_pred, color='green', label='pred')
+    plt.legend()
+    return fig
diff --git a/utils/text_encoder.py b/utils/text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e0758abc7b4e1f452481cba9715df08ceab543
--- /dev/null
+++ b/utils/text_encoder.py
@@ -0,0 +1,304 @@
+import re
+import six
+from six.moves import range  # pylint: disable=redefined-builtin
+
+PAD = "<pad>"
+EOS = "<EOS>"
+UNK = "<UNK>"
+SEG = "|"
+RESERVED_TOKENS = [PAD, EOS, UNK]
+NUM_RESERVED_TOKENS = len(RESERVED_TOKENS)
+PAD_ID = RESERVED_TOKENS.index(PAD)  # Normally 0
+EOS_ID = RESERVED_TOKENS.index(EOS)  # Normally 1
+UNK_ID = RESERVED_TOKENS.index(UNK)  # Normally 2
+
+if six.PY2:
+    RESERVED_TOKENS_BYTES = RESERVED_TOKENS
+else:
+    RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")]
+
+# Regular expression for unescaping token strings.
+# '\u' is converted to '_'
+# '\\' is converted to '\'
+# '\213;' is converted to unichr(213)
+_UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);")
+_ESCAPE_CHARS = set(u"\\_u;0123456789")
+
+
+def strip_ids(ids, ids_to_strip):
+    """Strip ids_to_strip from the end ids."""
+    ids = list(ids)
+    while ids and ids[-1] in ids_to_strip:
+        ids.pop()
+    return ids
+
+
+class TextEncoder(object):
+    """Base class for converting from ints to/from human readable strings."""
+
+    def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS):
+        self._num_reserved_ids = num_reserved_ids
+
+    @property
+    def num_reserved_ids(self):
+        return self._num_reserved_ids
+
+    def encode(self, s):
+        """Transform a human-readable string into a sequence of int ids.
+
+        The ids should be in the range [num_reserved_ids, vocab_size). Ids [0,
+        num_reserved_ids) are reserved.
+
+        EOS is not appended.
+
+        Args:
+        s: human-readable string to be converted.
+
+        Returns:
+        ids: list of integers
+        """
+        return [int(w) + self._num_reserved_ids for w in s.split()]
+
+    def decode(self, ids, strip_extraneous=False):
+        """Transform a sequence of int ids into a human-readable string.
+
+        EOS is not expected in ids.
+
+        Args:
+        ids: list of integers to be converted.
+        strip_extraneous: bool, whether to strip off extraneous tokens
+            (EOS and PAD).
+
+        Returns:
+        s: human-readable string.
+        """
+        if strip_extraneous:
+            ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
+        return " ".join(self.decode_list(ids))
+
+    def decode_list(self, ids):
+        """Transform a sequence of int ids into a their string versions.
+
+        This method supports transforming individual input/output ids to their
+        string versions so that sequence to/from text conversions can be visualized
+        in a human readable format.
+
+        Args:
+        ids: list of integers to be converted.
+
+        Returns:
+        strs: list of human-readable string.
+        """
+        decoded_ids = []
+        for id_ in ids:
+            if 0 <= id_ < self._num_reserved_ids:
+                decoded_ids.append(RESERVED_TOKENS[int(id_)])
+            else:
+                decoded_ids.append(id_ - self._num_reserved_ids)
+        return [str(d) for d in decoded_ids]
+
+    @property
+    def vocab_size(self):
+        raise NotImplementedError()
+
+
+class ByteTextEncoder(TextEncoder):
+    """Encodes each byte to an id. For 8-bit strings only."""
+
+    def encode(self, s):
+        numres = self._num_reserved_ids
+        if six.PY2:
+            if isinstance(s, unicode):
+                s = s.encode("utf-8")
+            return [ord(c) + numres for c in s]
+        # Python3: explicitly convert to UTF-8
+        return [c + numres for c in s.encode("utf-8")]
+
+    def decode(self, ids, strip_extraneous=False):
+        if strip_extraneous:
+            ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
+        numres = self._num_reserved_ids
+        decoded_ids = []
+        int2byte = six.int2byte
+        for id_ in ids:
+            if 0 <= id_ < numres:
+                decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)])
+            else:
+                decoded_ids.append(int2byte(id_ - numres))
+        if six.PY2:
+            return "".join(decoded_ids)
+        # Python3: join byte arrays and then decode string
+        return b"".join(decoded_ids).decode("utf-8", "replace")
+
+    def decode_list(self, ids):
+        numres = self._num_reserved_ids
+        decoded_ids = []
+        int2byte = six.int2byte
+        for id_ in ids:
+            if 0 <= id_ < numres:
+                decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)])
+            else:
+                decoded_ids.append(int2byte(id_ - numres))
+        # Python3: join byte arrays and then decode string
+        return decoded_ids
+
+    @property
+    def vocab_size(self):
+        return 2**8 + self._num_reserved_ids
+
+
+class ByteTextEncoderWithEos(ByteTextEncoder):
+  """Encodes each byte to an id and appends the EOS token."""
+
+  def encode(self, s):
+        return super(ByteTextEncoderWithEos, self).encode(s) + [EOS_ID]
+
+
+class TokenTextEncoder(TextEncoder):
+    """Encoder based on a user-supplied vocabulary (file or list)."""
+
+    def __init__(self,
+               vocab_filename,
+               reverse=False,
+               vocab_list=None,
+               replace_oov=None,
+               num_reserved_ids=NUM_RESERVED_TOKENS):
+        """Initialize from a file or list, one token per line.
+
+        Handling of reserved tokens works as follows:
+        - When initializing from a list, we add reserved tokens to the vocab.
+        - When initializing from a file, we do not add reserved tokens to the vocab.
+        - When saving vocab files, we save reserved tokens to the file.
+
+        Args:
+            vocab_filename: If not None, the full filename to read vocab from. If this
+                is not None, then vocab_list should be None.
+            reverse: Boolean indicating if tokens should be reversed during encoding
+                and decoding.
+            vocab_list: If not None, a list of elements of the vocabulary. If this is
+                not None, then vocab_filename should be None.
+            replace_oov: If not None, every out-of-vocabulary token seen when
+                encoding will be replaced by this string (which must be in vocab).
+            num_reserved_ids: Number of IDs to save for reserved tokens like <EOS>.
+        """
+        super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids)
+        self._reverse = reverse
+        self._replace_oov = replace_oov
+        if vocab_filename:
+            self._init_vocab_from_file(vocab_filename)
+        else:
+            assert vocab_list is not None
+            self._init_vocab_from_list(vocab_list)
+        self.pad_index = self._token_to_id[PAD]
+        self.eos_index = self._token_to_id[EOS]
+        self.unk_index = self._token_to_id[UNK]
+        self.seg_index = self._token_to_id[SEG] if SEG in self._token_to_id else self.eos_index
+
+    def encode(self, s):
+        """Converts a space-separated string of tokens to a list of ids."""
+        sentence = s
+        tokens = sentence.strip().split()
+        if self._replace_oov is not None:
+            tokens = [t if t in self._token_to_id else self._replace_oov
+                        for t in tokens]
+        ret = [self._token_to_id[tok] for tok in tokens]
+        return ret[::-1] if self._reverse else ret
+
+    def decode(self, ids, strip_eos=False, strip_padding=False):
+        if strip_padding and self.pad() in list(ids):
+            pad_pos = list(ids).index(self.pad())
+            ids = ids[:pad_pos]
+        if strip_eos and self.eos() in list(ids):
+            eos_pos = list(ids).index(self.eos())
+            ids = ids[:eos_pos]
+        return " ".join(self.decode_list(ids))
+
+    def decode_list(self, ids):
+        seq = reversed(ids) if self._reverse else ids
+        return [self._safe_id_to_token(i) for i in seq]
+
+    @property
+    def vocab_size(self):
+        return len(self._id_to_token)
+
+    def __len__(self):
+        return self.vocab_size
+
+    def _safe_id_to_token(self, idx):
+        return self._id_to_token.get(idx, "ID_%d" % idx)
+
+    def _init_vocab_from_file(self, filename):
+        """Load vocab from a file.
+
+        Args:
+        filename: The file to load vocabulary from.
+        """
+        with open(filename) as f:
+            tokens = [token.strip() for token in f.readlines()]
+
+        def token_gen():
+            for token in tokens:
+                yield token
+
+        self._init_vocab(token_gen(), add_reserved_tokens=False)
+
+    def _init_vocab_from_list(self, vocab_list):
+        """Initialize tokens from a list of tokens.
+
+        It is ok if reserved tokens appear in the vocab list. They will be
+        removed. The set of tokens in vocab_list should be unique.
+
+        Args:
+        vocab_list: A list of tokens.
+        """
+        def token_gen():
+            for token in vocab_list:
+                if token not in RESERVED_TOKENS:
+                    yield token
+
+        self._init_vocab(token_gen())
+
+    def _init_vocab(self, token_generator, add_reserved_tokens=True):
+        """Initialize vocabulary with tokens from token_generator."""
+
+        self._id_to_token = {}
+        non_reserved_start_index = 0
+
+        if add_reserved_tokens:
+            self._id_to_token.update(enumerate(RESERVED_TOKENS))
+            non_reserved_start_index = len(RESERVED_TOKENS)
+
+        self._id_to_token.update(
+            enumerate(token_generator, start=non_reserved_start_index))
+
+        # _token_to_id is the reverse of _id_to_token
+        self._token_to_id = dict((v, k)
+                                for k, v in six.iteritems(self._id_to_token))
+
+    def pad(self):
+        return self.pad_index
+
+    def eos(self):
+        return self.eos_index
+
+    def unk(self):
+        return self.unk_index
+
+    def seg(self):
+        return self.seg_index
+
+    def store_to_file(self, filename):
+        """Write vocab file to disk.
+
+        Vocab files have one token per line. The file ends in a newline. Reserved
+        tokens are written to the vocab file as well.
+
+        Args:
+        filename: Full path of the file to store the vocab to.
+        """
+        with open(filename, "w") as f:
+            for i in range(len(self._id_to_token)):
+                f.write(self._id_to_token[i] + "\n")
+
+    def sil_phonemes(self):
+        return [p for p in self._id_to_token.values() if not p[0].isalpha()]
diff --git a/utils/text_norm.py b/utils/text_norm.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0973cebc91e0525aeb6657e70012a1d37b5e6ff
--- /dev/null
+++ b/utils/text_norm.py
@@ -0,0 +1,790 @@
+# coding=utf-8
+# Authors:
+#   2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git)
+#   2019.9 Jiayu DU
+#
+# requirements:
+#   - python 3.X
+# notes: python 2.X WILL fail or produce misleading results
+
+import sys, os, argparse, codecs, string, re
+
+# ================================================================================ #
+#                                    basic constant
+# ================================================================================ #
+CHINESE_DIGIS = u'零一二三四五六七八九'
+BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖'
+BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖'
+SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万'
+SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬'
+LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载'
+LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載'
+SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万'
+SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬'
+
+ZERO_ALT = u'〇'
+ONE_ALT = u'幺'
+TWO_ALTS = [u'两', u'兩']
+
+POSITIVE = [u'正', u'正']
+NEGATIVE = [u'负', u'負']
+POINT = [u'点', u'點']
+# PLUS = [u'加', u'加']
+# SIL = [u'杠', u'槓']
+
+# 中文数字系统类型
+NUMBERING_TYPES = ['low', 'mid', 'high']
+
+CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \
+                 '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)'
+CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)'
+COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \
+                  '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \
+                  '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \
+                  '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \
+                  '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \
+                  '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)'
+
+# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git)
+CHINESE_PUNC_STOP = '！？｡。'
+CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏'
+CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP
+
+
+# ================================================================================ #
+#                                    basic class
+# ================================================================================ #
+class ChineseChar(object):
+    """
+    中文字符
+    每个字符对应简体和繁体,
+    e.g. 简体 = '负', 繁体 = '負'
+    转换时可转换为简体或繁体
+    """
+
+    def __init__(self, simplified, traditional):
+        self.simplified = simplified
+        self.traditional = traditional
+        # self.__repr__ = self.__str__
+
+    def __str__(self):
+        return self.simplified or self.traditional or None
+
+    def __repr__(self):
+        return self.__str__()
+
+
+class ChineseNumberUnit(ChineseChar):
+    """
+    中文数字/数位字符
+    每个字符除繁简体外还有一个额外的大写字符
+    e.g. '陆' 和 '陸'
+    """
+
+    def __init__(self, power, simplified, traditional, big_s, big_t):
+        super(ChineseNumberUnit, self).__init__(simplified, traditional)
+        self.power = power
+        self.big_s = big_s
+        self.big_t = big_t
+
+    def __str__(self):
+        return '10^{}'.format(self.power)
+
+    @classmethod
+    def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False):
+
+        if small_unit:
+            return ChineseNumberUnit(power=index + 1,
+                                     simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1])
+        elif numbering_type == NUMBERING_TYPES[0]:
+            return ChineseNumberUnit(power=index + 8,
+                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
+        elif numbering_type == NUMBERING_TYPES[1]:
+            return ChineseNumberUnit(power=(index + 2) * 4,
+                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
+        elif numbering_type == NUMBERING_TYPES[2]:
+            return ChineseNumberUnit(power=pow(2, index + 3),
+                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
+        else:
+            raise ValueError(
+                'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type))
+
+
+class ChineseNumberDigit(ChineseChar):
+    """
+    中文数字字符
+    """
+
+    def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None):
+        super(ChineseNumberDigit, self).__init__(simplified, traditional)
+        self.value = value
+        self.big_s = big_s
+        self.big_t = big_t
+        self.alt_s = alt_s
+        self.alt_t = alt_t
+
+    def __str__(self):
+        return str(self.value)
+
+    @classmethod
+    def create(cls, i, v):
+        return ChineseNumberDigit(i, v[0], v[1], v[2], v[3])
+
+
+class ChineseMath(ChineseChar):
+    """
+    中文数位字符
+    """
+
+    def __init__(self, simplified, traditional, symbol, expression=None):
+        super(ChineseMath, self).__init__(simplified, traditional)
+        self.symbol = symbol
+        self.expression = expression
+        self.big_s = simplified
+        self.big_t = traditional
+
+
+CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath
+
+
+class NumberSystem(object):
+    """
+    中文数字系统
+    """
+    pass
+
+
+class MathSymbol(object):
+    """
+    用于中文数字系统的数学符号 (繁/简体), e.g.
+    positive = ['正', '正']
+    negative = ['负', '負']
+    point = ['点', '點']
+    """
+
+    def __init__(self, positive, negative, point):
+        self.positive = positive
+        self.negative = negative
+        self.point = point
+
+    def __iter__(self):
+        for v in self.__dict__.values():
+            yield v
+
+
+# class OtherSymbol(object):
+#     """
+#     其他符号
+#     """
+#
+#     def __init__(self, sil):
+#         self.sil = sil
+#
+#     def __iter__(self):
+#         for v in self.__dict__.values():
+#             yield v
+
+
+# ================================================================================ #
+#                                    basic utils
+# ================================================================================ #
+def create_system(numbering_type=NUMBERING_TYPES[1]):
+    """
+    根据数字系统类型返回创建相应的数字系统，默认为 mid
+    NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型
+        low:  '兆' = '亿' * '十' = $10^{9}$,  '京' = '兆' * '十', etc.
+        mid:  '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc.
+        high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc.
+    返回对应的数字系统
+    """
+
+    # chinese number units of '亿' and larger
+    all_larger_units = zip(
+        LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL)
+    larger_units = [CNU.create(i, v, numbering_type, False)
+                    for i, v in enumerate(all_larger_units)]
+    # chinese number units of '十, 百, 千, 万'
+    all_smaller_units = zip(
+        SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL)
+    smaller_units = [CNU.create(i, v, small_unit=True)
+                     for i, v in enumerate(all_smaller_units)]
+    # digis
+    chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS,
+                        BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL)
+    digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)]
+    digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT
+    digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT
+    digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1]
+
+    # symbols
+    positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x)
+    negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x)
+    point_cn = CM(POINT[0], POINT[1], '.', lambda x,
+                                                  y: float(str(x) + '.' + str(y)))
+    # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y)))
+    system = NumberSystem()
+    system.units = smaller_units + larger_units
+    system.digits = digits
+    system.math = MathSymbol(positive_cn, negative_cn, point_cn)
+    # system.symbols = OtherSymbol(sil_cn)
+    return system
+
+
+def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]):
+    def get_symbol(char, system):
+        for u in system.units:
+            if char in [u.traditional, u.simplified, u.big_s, u.big_t]:
+                return u
+        for d in system.digits:
+            if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]:
+                return d
+        for m in system.math:
+            if char in [m.traditional, m.simplified]:
+                return m
+
+    def string2symbols(chinese_string, system):
+        int_string, dec_string = chinese_string, ''
+        for p in [system.math.point.simplified, system.math.point.traditional]:
+            if p in chinese_string:
+                int_string, dec_string = chinese_string.split(p)
+                break
+        return [get_symbol(c, system) for c in int_string], \
+               [get_symbol(c, system) for c in dec_string]
+
+    def correct_symbols(integer_symbols, system):
+        """
+        一百八 to 一百八十
+        一亿一千三百万 to 一亿 一千万 三百万
+        """
+
+        if integer_symbols and isinstance(integer_symbols[0], CNU):
+            if integer_symbols[0].power == 1:
+                integer_symbols = [system.digits[1]] + integer_symbols
+
+        if len(integer_symbols) > 1:
+            if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU):
+                integer_symbols.append(
+                    CNU(integer_symbols[-2].power - 1, None, None, None, None))
+
+        result = []
+        unit_count = 0
+        for s in integer_symbols:
+            if isinstance(s, CND):
+                result.append(s)
+                unit_count = 0
+            elif isinstance(s, CNU):
+                current_unit = CNU(s.power, None, None, None, None)
+                unit_count += 1
+
+            if unit_count == 1:
+                result.append(current_unit)
+            elif unit_count > 1:
+                for i in range(len(result)):
+                    if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power:
+                        result[-i - 1] = CNU(result[-i - 1].power +
+                                             current_unit.power, None, None, None, None)
+        return result
+
+    def compute_value(integer_symbols):
+        """
+        Compute the value.
+        When current unit is larger than previous unit, current unit * all previous units will be used as all previous units.
+        e.g. '两千万' = 2000 * 10000 not 2000 + 10000
+        """
+        value = [0]
+        last_power = 0
+        for s in integer_symbols:
+            if isinstance(s, CND):
+                value[-1] = s.value
+            elif isinstance(s, CNU):
+                value[-1] *= pow(10, s.power)
+                if s.power > last_power:
+                    value[:-1] = list(map(lambda v: v *
+                                                    pow(10, s.power), value[:-1]))
+                    last_power = s.power
+                value.append(0)
+        return sum(value)
+
+    system = create_system(numbering_type)
+    int_part, dec_part = string2symbols(chinese_string, system)
+    int_part = correct_symbols(int_part, system)
+    int_str = str(compute_value(int_part))
+    dec_str = ''.join([str(d.value) for d in dec_part])
+    if dec_part:
+        return '{0}.{1}'.format(int_str, dec_str)
+    else:
+        return int_str
+
+
+def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False,
+            traditional=False, alt_zero=False, alt_one=False, alt_two=True,
+            use_zeros=True, use_units=True):
+    def get_value(value_string, use_zeros=True):
+
+        striped_string = value_string.lstrip('0')
+
+        # record nothing if all zeros
+        if not striped_string:
+            return []
+
+        # record one digits
+        elif len(striped_string) == 1:
+            if use_zeros and len(value_string) != len(striped_string):
+                return [system.digits[0], system.digits[int(striped_string)]]
+            else:
+                return [system.digits[int(striped_string)]]
+
+        # recursively record multiple digits
+        else:
+            result_unit = next(u for u in reversed(
+                system.units) if u.power < len(striped_string))
+            result_string = value_string[:-result_unit.power]
+            return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:])
+
+    system = create_system(numbering_type)
+
+    int_dec = number_string.split('.')
+    if len(int_dec) == 1:
+        int_string = int_dec[0]
+        dec_string = ""
+    elif len(int_dec) == 2:
+        int_string = int_dec[0]
+        dec_string = int_dec[1]
+    else:
+        raise ValueError(
+            "invalid input num string with more than one dot: {}".format(number_string))
+
+    if use_units and len(int_string) > 1:
+        result_symbols = get_value(int_string)
+    else:
+        result_symbols = [system.digits[int(c)] for c in int_string]
+    dec_symbols = [system.digits[int(c)] for c in dec_string]
+    if dec_string:
+        result_symbols += [system.math.point] + dec_symbols
+
+    if alt_two:
+        liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t,
+                    system.digits[2].big_s, system.digits[2].big_t)
+        for i, v in enumerate(result_symbols):
+            if isinstance(v, CND) and v.value == 2:
+                next_symbol = result_symbols[i +
+                                             1] if i < len(result_symbols) - 1 else None
+                previous_symbol = result_symbols[i - 1] if i > 0 else None
+                if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))):
+                    if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)):
+                        result_symbols[i] = liang
+
+    # if big is True, '两' will not be used and `alt_two` has no impact on output
+    if big:
+        attr_name = 'big_'
+        if traditional:
+            attr_name += 't'
+        else:
+            attr_name += 's'
+    else:
+        if traditional:
+            attr_name = 'traditional'
+        else:
+            attr_name = 'simplified'
+
+    result = ''.join([getattr(s, attr_name) for s in result_symbols])
+
+    # if not use_zeros:
+    #     result = result.strip(getattr(system.digits[0], attr_name))
+
+    if alt_zero:
+        result = result.replace(
+            getattr(system.digits[0], attr_name), system.digits[0].alt_s)
+
+    if alt_one:
+        result = result.replace(
+            getattr(system.digits[1], attr_name), system.digits[1].alt_s)
+
+    for i, p in enumerate(POINT):
+        if result.startswith(p):
+            return CHINESE_DIGIS[0] + result
+
+    # ^10, 11, .., 19
+    if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0],
+                                          SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \
+            result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]:
+        result = result[1:]
+
+    return result
+
+
+# ================================================================================ #
+#                          different types of rewriters
+# ================================================================================ #
+class Cardinal:
+    """
+    CARDINAL类
+    """
+
+    def __init__(self, cardinal=None, chntext=None):
+        self.cardinal = cardinal
+        self.chntext = chntext
+
+    def chntext2cardinal(self):
+        return chn2num(self.chntext)
+
+    def cardinal2chntext(self):
+        return num2chn(self.cardinal)
+
+
+class Digit:
+    """
+    DIGIT类
+    """
+
+    def __init__(self, digit=None, chntext=None):
+        self.digit = digit
+        self.chntext = chntext
+
+    # def chntext2digit(self):
+    #     return chn2num(self.chntext)
+
+    def digit2chntext(self):
+        return num2chn(self.digit, alt_two=False, use_units=False)
+
+
+class TelePhone:
+    """
+    TELEPHONE类
+    """
+
+    def __init__(self, telephone=None, raw_chntext=None, chntext=None):
+        self.telephone = telephone
+        self.raw_chntext = raw_chntext
+        self.chntext = chntext
+
+    # def chntext2telephone(self):
+    #     sil_parts = self.raw_chntext.split('<SIL>')
+    #     self.telephone = '-'.join([
+    #         str(chn2num(p)) for p in sil_parts
+    #     ])
+    #     return self.telephone
+
+    def telephone2chntext(self, fixed=False):
+
+        if fixed:
+            sil_parts = self.telephone.split('-')
+            self.raw_chntext = '<SIL>'.join([
+                num2chn(part, alt_two=False, use_units=False) for part in sil_parts
+            ])
+            self.chntext = self.raw_chntext.replace('<SIL>', '')
+        else:
+            sp_parts = self.telephone.strip('+').split()
+            self.raw_chntext = '<SP>'.join([
+                num2chn(part, alt_two=False, use_units=False) for part in sp_parts
+            ])
+            self.chntext = self.raw_chntext.replace('<SP>', '')
+        return self.chntext
+
+
+class Fraction:
+    """
+    FRACTION类
+    """
+
+    def __init__(self, fraction=None, chntext=None):
+        self.fraction = fraction
+        self.chntext = chntext
+
+    def chntext2fraction(self):
+        denominator, numerator = self.chntext.split('分之')
+        return chn2num(numerator) + '/' + chn2num(denominator)
+
+    def fraction2chntext(self):
+        numerator, denominator = self.fraction.split('/')
+        return num2chn(denominator) + '分之' + num2chn(numerator)
+
+
+class Date:
+    """
+    DATE类
+    """
+
+    def __init__(self, date=None, chntext=None):
+        self.date = date
+        self.chntext = chntext
+
+    # def chntext2date(self):
+    #     chntext = self.chntext
+    #     try:
+    #         year, other = chntext.strip().split('年', maxsplit=1)
+    #         year = Digit(chntext=year).digit2chntext() + '年'
+    #     except ValueError:
+    #         other = chntext
+    #         year = ''
+    #     if other:
+    #         try:
+    #             month, day = other.strip().split('月', maxsplit=1)
+    #             month = Cardinal(chntext=month).chntext2cardinal() + '月'
+    #         except ValueError:
+    #             day = chntext
+    #             month = ''
+    #         if day:
+    #             day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1]
+    #     else:
+    #         month = ''
+    #         day = ''
+    #     date = year + month + day
+    #     self.date = date
+    #     return self.date
+
+    def date2chntext(self):
+        date = self.date
+        try:
+            year, other = date.strip().split('年', 1)
+            year = Digit(digit=year).digit2chntext() + '年'
+        except ValueError:
+            other = date
+            year = ''
+        if other:
+            try:
+                month, day = other.strip().split('月', 1)
+                month = Cardinal(cardinal=month).cardinal2chntext() + '月'
+            except ValueError:
+                day = date
+                month = ''
+            if day:
+                day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1]
+        else:
+            month = ''
+            day = ''
+        chntext = year + month + day
+        self.chntext = chntext
+        return self.chntext
+
+
+class Money:
+    """
+    MONEY类
+    """
+
+    def __init__(self, money=None, chntext=None):
+        self.money = money
+        self.chntext = chntext
+
+    # def chntext2money(self):
+    #     return self.money
+
+    def money2chntext(self):
+        money = self.money
+        pattern = re.compile(r'(\d+(\.\d+)?)')
+        matchers = pattern.findall(money)
+        if matchers:
+            for matcher in matchers:
+                money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext())
+        self.chntext = money
+        return self.chntext
+
+
+class Percentage:
+    """
+    PERCENTAGE类
+    """
+
+    def __init__(self, percentage=None, chntext=None):
+        self.percentage = percentage
+        self.chntext = chntext
+
+    def chntext2percentage(self):
+        return chn2num(self.chntext.strip().strip('百分之')) + '%'
+
+    def percentage2chntext(self):
+        return '百分之' + num2chn(self.percentage.strip().strip('%'))
+
+
+# ================================================================================ #
+#                            NSW Normalizer
+# ================================================================================ #
+class NSWNormalizer:
+    def __init__(self, raw_text):
+        self.raw_text = '^' + raw_text + '$'
+        self.norm_text = ''
+
+    def _particular(self):
+        text = self.norm_text
+        pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('particular')
+            for matcher in matchers:
+                text = text.replace(matcher[0], matcher[1] + '2' + matcher[2], 1)
+        self.norm_text = text
+        return self.norm_text
+
+    def normalize(self, remove_punc=True):
+        text = self.raw_text
+
+        # 规范化日期
+        pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('date')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1)
+
+        # 规范化金钱
+        pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('money')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1)
+
+        # 规范化固话/手机号码
+        # 手机
+        # http://www.jihaoba.com/news/show/13680
+        # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
+        # 联通：130、131、132、156、155、186、185、176
+        # 电信：133、153、189、180、181、177
+        pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('telephone')
+            for matcher in matchers:
+                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1)
+        # 固话
+        pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('fixed telephone')
+            for matcher in matchers:
+                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1)
+
+        # 规范化分数
+        pattern = re.compile(r"(\d+/\d+)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('fraction')
+            for matcher in matchers:
+                text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1)
+
+        # 规范化百分数
+        text = text.replace('％', '%')
+        pattern = re.compile(r"(\d+(\.\d+)?%)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('percentage')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1)
+
+        # 规范化纯数+量词
+        pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS)
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('cardinal+quantifier')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
+
+        # 规范化数字编号
+        pattern = re.compile(r"(\d{4,32})")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('digit')
+            for matcher in matchers:
+                text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1)
+
+        # 规范化纯数
+        pattern = re.compile(r"(\d+(\.\d+)?)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('cardinal')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
+
+        self.norm_text = text
+        self._particular()
+
+        text = self.norm_text.lstrip('^').rstrip('$')
+        if remove_punc:
+            # Punctuations removal
+            old_chars = CHINESE_PUNC_LIST + string.punctuation  # includes all CN and EN punctuations
+            new_chars = ' ' * len(old_chars)
+            del_chars = ''
+            text = text.translate(str.maketrans(old_chars, new_chars, del_chars))
+        return text
+
+
+def nsw_test_case(raw_text):
+    print('I:' + raw_text)
+    print('O:' + NSWNormalizer(raw_text).normalize())
+    print('')
+
+
+def nsw_test():
+    nsw_test_case('固话：0595-23865596或23880880。')
+    nsw_test_case('固话：0595-23865596或23880880。')
+    nsw_test_case('手机：+86 19859213959或15659451527。')
+    nsw_test_case('分数：32477/76391。')
+    nsw_test_case('百分数：80.03%。')
+    nsw_test_case('编号：31520181154418。')
+    nsw_test_case('纯数：2983.07克或12345.60米。')
+    nsw_test_case('日期：1999年2月20日或09年3月15号。')
+    nsw_test_case('金钱：12块5，34.5元，20.1万')
+    nsw_test_case('特殊：O2O或B2C。')
+    nsw_test_case('3456万吨')
+    nsw_test_case('2938个')
+    nsw_test_case('938')
+    nsw_test_case('今天吃了115个小笼包231个馒头')
+    nsw_test_case('有62％的概率')
+
+
+if __name__ == '__main__':
+    # nsw_test()
+
+    p = argparse.ArgumentParser()
+    p.add_argument('ifile', help='input filename, assume utf-8 encoding')
+    p.add_argument('ofile', help='output filename')
+    p.add_argument('--to_upper', action='store_true', help='convert to upper case')
+    p.add_argument('--to_lower', action='store_true', help='convert to lower case')
+    p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.")
+    p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines')
+    args = p.parse_args()
+
+    ifile = codecs.open(args.ifile, 'r', 'utf8')
+    ofile = codecs.open(args.ofile, 'w+', 'utf8')
+
+    n = 0
+    for l in ifile:
+        key = ''
+        text = ''
+        if args.has_key:
+            cols = l.split(maxsplit=1)
+            key = cols[0]
+            if len(cols) == 2:
+                text = cols[1]
+            else:
+                text = ''
+        else:
+            text = l
+
+        # cases
+        if args.to_upper and args.to_lower:
+            sys.stderr.write('text norm: to_upper OR to_lower?')
+            exit(1)
+        if args.to_upper:
+            text = text.upper()
+        if args.to_lower:
+            text = text.lower()
+
+        # NSW(Non-Standard-Word) normalization
+        text = NSWNormalizer(text).normalize()
+
+        #
+        if args.has_key:
+            ofile.write(key + '\t' + text)
+        else:
+            ofile.write(text)
+
+        n += 1
+        if n % args.log_interval == 0:
+            sys.stderr.write("text norm: {} lines done.\n".format(n))
+
+    sys.stderr.write("text norm: {} lines done in total.\n".format(n))
+
+    ifile.close()
+    ofile.close()
diff --git a/utils/training_utils.py b/utils/training_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..409b15388790b1aadb24632313bdd1f41b4b06ac
--- /dev/null
+++ b/utils/training_utils.py
@@ -0,0 +1,27 @@
+from utils.hparams import hparams
+
+
+class RSQRTSchedule(object):
+    def __init__(self, optimizer):
+        super().__init__()
+        self.optimizer = optimizer
+        self.constant_lr = hparams['lr']
+        self.warmup_updates = hparams['warmup_updates']
+        self.hidden_size = hparams['hidden_size']
+        self.lr = hparams['lr']
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = self.lr
+        self.step(0)
+
+    def step(self, num_updates):
+        constant_lr = self.constant_lr
+        warmup = min(num_updates / self.warmup_updates, 1.0)
+        rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5
+        rsqrt_hidden = self.hidden_size ** -0.5
+        self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-7)
+        for param_group in self.optimizer.param_groups:
+            param_group['lr'] = self.lr
+        return self.lr
+
+    def get_lr(self):
+        return self.optimizer.param_groups[0]['lr']
diff --git "a/\351\242\204\345\244\204\347\220\206.bat" "b/\351\242\204\345\244\204\347\220\206.bat"
new file mode 100644
index 0000000000000000000000000000000000000000..5ba8f329852f75bab00abb07fa41d9d9662b75a1
--- /dev/null
+++ "b/\351\242\204\345\244\204\347\220\206.bat"
@@ -0,0 +1,4 @@
+set PYTHONPATH=.
+set CUDA_VISIBLE_DEVICES=0 
+python preprocessing/binarize.py --config training/config.yaml
+pause
\ No newline at end of file