import open_clip
import torch
import tempfile
import subprocess
import os
from datetime import datetime
from collections import defaultdict
from datasets import load_dataset
from qdrant_client import QdrantClient
from huggingface_hub import login

tokenizer = open_clip.get_tokenizer("ViT-B-32")

model, _, preprocess = open_clip.create_model_and_transforms("ViT-B-32", pretrained=None)
checkpoint_path = "finetuned_clip.pt"
model.load_state_dict(torch.load(checkpoint_path, map_location="cpu"))
model.eval()

qdrant = QdrantClient(url=os.environ["QDRANT_CLOUD_URL"],
    api_key=os.environ["QDRANT_API_KEY"],
    prefer_grpc=False)
collection_name = "video_chunks"

login(token=os.environ["HF_API_TOKEN"])

def timestamp_to_seconds(ts):
    h, m, s = ts.split(":")
    return int(h) * 3600 + int(m) * 60 + float(s)


def seconds_to_timestamp(seconds):
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = seconds % 60
    return f"{h:02}:{m:02}:{s:06.3f}"


def smart_merge_subtitles(a, b):
    if b in a:
        return a
    if a in b:
        return b

    for i in range(min(len(a), len(b)), 0, -1):
        if a.endswith(b[:i]):
            return a + b[i:]
        if b.endswith(a[:i]):
            return b + a[i:]
    return a + " " + b


def merge_chunks(chunks):
    grouped = defaultdict(list)

    for chunk in chunks:
        payload = chunk.payload
        grouped[payload["video_id"]].append({
            "start": timestamp_to_seconds(payload["start_time"]),
            "end": timestamp_to_seconds(payload["end_time"]),
            "subtitle": payload["subtitle"].strip(),
            "merged": False
        })

    merged_chunks = []

    for video_id, video_chunks in grouped.items():
        for i, chunk in enumerate(video_chunks):
            if chunk["merged"]:
                continue

            merged_chunk = chunk.copy()
            chunk["merged"] = True

            for j, other in enumerate(video_chunks):
                if i == j or other["merged"]:
                    continue

                if not (merged_chunk["end"] < other["start"] or other["end"] < merged_chunk["start"]):
                    merged_chunk["start"] = min(merged_chunk["start"], other["start"])
                    merged_chunk["end"] = max(merged_chunk["end"], other["end"])
                    merged_chunk["subtitle"] = smart_merge_subtitles(merged_chunk["subtitle"], other["subtitle"])
                    other["merged"] = True

            merged_chunks.append({
                "video_id": video_id,
                "start_time": seconds_to_timestamp(merged_chunk["start"]),
                "end_time": seconds_to_timestamp(merged_chunk["end"]),
                "subtitle": merged_chunk["subtitle"]
            })

    return merged_chunks


def get_video_segment(video_id, start_time, end_time):
    dataset = load_dataset("aegean-ai/ai-lectures-spring-24", split="train", streaming=True)
    for sample in dataset:
        if sample["__key__"] == video_id:
            break

    tmp_dir = tempfile.gettempdir()
    timestamp = datetime.now().strftime("%Y%m%d%H%M%S%f")

    full_path = os.path.join(tmp_dir, f"full_{timestamp}.mp4")
    trimmed_path = os.path.join(tmp_dir, f"clip_{timestamp}.mp4")

    with open(full_path, "wb") as f:
        f.write(sample["mp4"])

    cmd = [
        "ffmpeg", "-ss", start_time, "-to", end_time,
        "-i", full_path,
        "-c:v", "copy", "-c:a", "copy", "-y",
        trimmed_path
    ]

    result = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    os.remove(full_path)

    if result.returncode != 0:
        print("FFmpeg failed")
        return None

    return trimmed_path


def retrieval(question):
    text_tokens = tokenizer([question])

    with torch.no_grad():
        query_vec = model.encode_text(text_tokens).squeeze(0).cpu().numpy()

    search_result = qdrant.search(
        collection_name=collection_name,
        query_vector=query_vec.tolist(),
        limit=40
    )

    filtered_results = [
        res for res in search_result
        if len(res.payload.get("subtitle", "")) >= 35
    ]

    return filtered_results[:10]