# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. All rights reserved.
# Copyright (c) 2024 Black Forest Labs and The XLabs-AI Team. All rights reserved.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from typing import Literal

import torch
from einops import rearrange, repeat
from torch import Tensor
from tqdm import tqdm

from .model import Flux
from .modules.conditioner import HFEmbedder


def get_noise(
    num_samples: int,
    height: int,
    width: int,
    device: torch.device,
    dtype: torch.dtype,
    seed: int,
):
    return torch.randn(
        num_samples,
        16,
        # allow for packing
        2 * math.ceil(height / 16),
        2 * math.ceil(width / 16),
        device=device,
        dtype=dtype,
        generator=torch.Generator(device=device).manual_seed(seed),
    )


def prepare(
    t5: HFEmbedder,
    clip: HFEmbedder,
    img: Tensor,
    prompt: str | list[str],
    ref_img: None | Tensor=None,
    pe: Literal['d', 'h', 'w', 'o'] ='d'
) -> dict[str, Tensor]:
    assert pe in ['d', 'h', 'w', 'o']
    bs, c, h, w = img.shape
    if bs == 1 and not isinstance(prompt, str):
        bs = len(prompt)

    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
    if img.shape[0] == 1 and bs > 1:
        img = repeat(img, "1 ... -> bs ...", bs=bs)

    img_ids = torch.zeros(h // 2, w // 2, 3)
    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)

    if ref_img is not None:
        _, _, ref_h, ref_w = ref_img.shape
        ref_img = rearrange(ref_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
        if ref_img.shape[0] == 1 and bs > 1:
            ref_img = repeat(ref_img, "1 ... -> bs ...", bs=bs)
        ref_img_ids = torch.zeros(ref_h // 2, ref_w // 2, 3)
        # img id分别在宽高偏移各自最大值
        h_offset = h // 2 if pe in {'d', 'h'} else 0
        w_offset = w // 2 if pe in {'d', 'w'} else 0
        ref_img_ids[..., 1] = ref_img_ids[..., 1] + torch.arange(ref_h // 2)[:, None] + h_offset
        ref_img_ids[..., 2] = ref_img_ids[..., 2] + torch.arange(ref_w // 2)[None, :] + w_offset
        ref_img_ids = repeat(ref_img_ids, "h w c -> b (h w) c", b=bs)

    if isinstance(prompt, str):
        prompt = [prompt]
    txt = t5(prompt)
    if txt.shape[0] == 1 and bs > 1:
        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
    txt_ids = torch.zeros(bs, txt.shape[1], 3)

    vec = clip(prompt)
    if vec.shape[0] == 1 and bs > 1:
        vec = repeat(vec, "1 ... -> bs ...", bs=bs)

    if ref_img is not None:
        return {
            "img": img,
            "img_ids": img_ids.to(img.device),
            "ref_img": ref_img,
            "ref_img_ids": ref_img_ids.to(img.device),
            "txt": txt.to(img.device),
            "txt_ids": txt_ids.to(img.device),
            "vec": vec.to(img.device),
        }
    else:
        return {
            "img": img,
            "img_ids": img_ids.to(img.device),
            "txt": txt.to(img.device),
            "txt_ids": txt_ids.to(img.device),
            "vec": vec.to(img.device),
        }

def prepare_multi_ip(
    t5: HFEmbedder,
    clip: HFEmbedder,
    img: Tensor,
    prompt: str | list[str],
    ref_imgs: list[Tensor] | None = None,
    pe: Literal['d', 'h', 'w', 'o'] = 'd'
) -> dict[str, Tensor]:
    assert pe in ['d', 'h', 'w', 'o']
    bs, c, h, w = img.shape
    if bs == 1 and not isinstance(prompt, str):
        bs = len(prompt)

    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
    if img.shape[0] == 1 and bs > 1:
        img = repeat(img, "1 ... -> bs ...", bs=bs)

    img_ids = torch.zeros(h // 2, w // 2, 3)
    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)

    ref_img_ids = []
    ref_imgs_list = []
    pe_shift_w, pe_shift_h = w // 2, h // 2
    for ref_img in ref_imgs:
        _, _, ref_h1, ref_w1 = ref_img.shape
        ref_img = rearrange(ref_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
        if ref_img.shape[0] == 1 and bs > 1:
            ref_img = repeat(ref_img, "1 ... -> bs ...", bs=bs)
        ref_img_ids1 = torch.zeros(ref_h1 // 2, ref_w1 // 2, 3)
        # img id分别在宽高偏移各自最大值
        h_offset = pe_shift_h if pe in {'d', 'h'} else 0
        w_offset = pe_shift_w if pe in {'d', 'w'} else 0
        ref_img_ids1[..., 1] = ref_img_ids1[..., 1] + torch.arange(ref_h1 // 2)[:, None] + h_offset
        ref_img_ids1[..., 2] = ref_img_ids1[..., 2] + torch.arange(ref_w1 // 2)[None, :] + w_offset
        ref_img_ids1 = repeat(ref_img_ids1, "h w c -> b (h w) c", b=bs)
        ref_img_ids.append(ref_img_ids1)
        ref_imgs_list.append(ref_img)

        # 更新pe shift
        pe_shift_h += ref_h1 // 2
        pe_shift_w += ref_w1 // 2

    if isinstance(prompt, str):
        prompt = [prompt]
    txt = t5(prompt)
    if txt.shape[0] == 1 and bs > 1:
        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
    txt_ids = torch.zeros(bs, txt.shape[1], 3)

    vec = clip(prompt)
    if vec.shape[0] == 1 and bs > 1:
        vec = repeat(vec, "1 ... -> bs ...", bs=bs)

    return {
        "img": img,
        "img_ids": img_ids.to(img.device),
        "ref_img": tuple(ref_imgs_list),
        "ref_img_ids": [ref_img_id.to(img.device) for ref_img_id in ref_img_ids],
        "txt": txt.to(img.device),
        "txt_ids": txt_ids.to(img.device),
        "vec": vec.to(img.device),
    }


def time_shift(mu: float, sigma: float, t: Tensor):
    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)


def get_lin_function(
    x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15
):
    m = (y2 - y1) / (x2 - x1)
    b = y1 - m * x1
    return lambda x: m * x + b


def get_schedule(
    num_steps: int,
    image_seq_len: int,
    base_shift: float = 0.5,
    max_shift: float = 1.15,
    shift: bool = True,
) -> list[float]:
    # extra step for zero
    timesteps = torch.linspace(1, 0, num_steps + 1)

    # shifting the schedule to favor high timesteps for higher signal images
    if shift:
        # eastimate mu based on linear estimation between two points
        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
        timesteps = time_shift(mu, 1.0, timesteps)

    return timesteps.tolist()


def denoise(
    model: Flux,
    # model input
    img: Tensor,
    img_ids: Tensor,
    txt: Tensor,
    txt_ids: Tensor,
    vec: Tensor,
    # sampling parameters
    timesteps: list[float],
    guidance: float = 4.0,
    ref_img: Tensor=None,
    ref_img_ids: Tensor=None,
):
    i = 0
    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
    for t_curr, t_prev in tqdm(zip(timesteps[:-1], timesteps[1:]), total=len(timesteps) - 1):
        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
        pred = model(
            img=img,
            img_ids=img_ids,
            ref_img=ref_img,
            ref_img_ids=ref_img_ids,
            txt=txt,
            txt_ids=txt_ids,
            y=vec,
            timesteps=t_vec,
            guidance=guidance_vec
        )
        img = img + (t_prev - t_curr) * pred
        i += 1
    return img


def unpack(x: Tensor, height: int, width: int) -> Tensor:
    return rearrange(
        x,
        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
        h=math.ceil(height / 16),
        w=math.ceil(width / 16),
        ph=2,
        pw=2,
    )