
import os
import torch
import lightning as pl
from PIL import Image
from diffsynth import WanVideoAstraPipeline, ModelManager
import json
import imageio
from torchvision.transforms import v2
from einops import rearrange
import argparse
import numpy as np
import pdb
from tqdm import tqdm

os.environ["TOKENIZERS_PARALLELISM"] = "false"

class VideoEncoder(pl.LightningModule):
    def __init__(self, text_encoder_path, vae_path, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
        super().__init__()
        model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
        model_manager.load_models([text_encoder_path, vae_path])
        self.pipe = WanVideoAstraPipeline.from_model_manager(model_manager)
        self.tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
        
        self.frame_process = v2.Compose([
            # v2.CenterCrop(size=(900, 1600)),
            # v2.Resize(size=(900, 1600), antialias=True),
            v2.ToTensor(),
            v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
        ])
    
    def crop_and_resize(self, image):
        width, height = image.size
        # print(width,height)
        width_ori, height_ori_ = 832 , 480
        image = v2.functional.resize(
            image,
            (round(height_ori_), round(width_ori)),
            interpolation=v2.InterpolationMode.BILINEAR
        )
        return image

    def load_video_frames(self, video_path):
        """加载完整视频"""
        reader = imageio.get_reader(video_path)
        frames = []
        
        for frame_data in reader:
            frame = Image.fromarray(frame_data)
            frame = self.crop_and_resize(frame)
            frame = self.frame_process(frame)
            frames.append(frame)
        
        reader.close()
        
        if len(frames) == 0:
            return None
            
        frames = torch.stack(frames, dim=0)
        frames = rearrange(frames, "T C H W -> C T H W")
        return frames

def encode_scenes(scenes_path, text_encoder_path, vae_path,output_dir):
    """编码所有场景的视频"""

    encoder = VideoEncoder(text_encoder_path, vae_path)
    encoder = encoder.cuda()
    encoder.pipe.device = "cuda"
    
    processed_count = 0

    processed_chunk_count = 0

    prompt_emb = 0

    os.makedirs(output_dir,exist_ok=True)
    chunk_size = 300
    for i, scene_name in tqdm(enumerate(os.listdir(scenes_path)),total=len(os.listdir(scenes_path))):
        # print('index-----:',type(i))
        # if i < 3000 :#or i >=2000:
        #     # print('index-----:',i)
        #     continue
            # print('index:',i)
        print('index:',i)
        scene_dir = os.path.join(scenes_path, scene_name)
        
        # save_dir = os.path.join(output_dir,scene_name.split('.')[0])
        # print('in:',scene_dir)
        # print('out:',save_dir)
        
        if not scene_dir.endswith(".mp4"):# or os.path.isdir(output_dir):
            continue
        

        scene_cam_path = scene_dir.replace(".mp4", ".npz")
        if not os.path.exists(scene_cam_path):
            continue

        with np.load(scene_cam_path) as data:  
            cam_data = data.files
            cam_emb = {k: data[k].cpu() if isinstance(data[k], torch.Tensor) else data[k] for k in cam_data}
        # with open(scene_cam_path, 'rb') as f:
        #     cam_data = np.load(f)  # 此时cam_data仅包含数据，无文件句柄引用

        video_name = scene_name[:-4].split('_')[0]
        start_frame = int(scene_name[:-4].split('_')[1])
        end_frame = int(scene_name[:-4].split('_')[2])

        sampled_range = range(start_frame, end_frame , chunk_size)
        sampled_frames = list(sampled_range)

        sampled_chunk_end = sampled_frames[0] + 300
        start_str = f"{sampled_frames[0]:07d}"
        end_str = f"{sampled_chunk_end:07d}"

        chunk_name = f"{video_name}_{start_str}_{end_str}"
        save_chunk_path = os.path.join(output_dir,chunk_name,"encoded_video.pth")
        
        if os.path.exists(save_chunk_path):
            print(f"Video {video_name} already encoded, skipping...")
            continue
        
        # 加载视频
        video_path = scene_dir
        if not os.path.exists(video_path):
            print(f"Video not found: {video_path}")
            continue
        
        video_frames = encoder.load_video_frames(video_path)
        if video_frames is None:
            print(f"Failed to load video: {video_path}")
            continue
            
        video_frames = video_frames.unsqueeze(0).to("cuda", dtype=torch.bfloat16)
        print('video shape:',video_frames.shape)
        
        
        
        # print(sampled_frames)

        print(f"Encoding scene {scene_name}...")
        for sampled_chunk_start in sampled_frames:
            sampled_chunk_end = sampled_chunk_start + 300
            start_str = f"{sampled_chunk_start:07d}"
            end_str = f"{sampled_chunk_end:07d}"
            
            # 生成保存目录名（假设video_name已定义）
            chunk_name = f"{video_name}_{start_str}_{end_str}"
            save_chunk_dir = os.path.join(output_dir,chunk_name)

            os.makedirs(save_chunk_dir,exist_ok=True)  
            print(f"Encoding chunk {chunk_name}...")

            encoded_path = os.path.join(save_chunk_dir, "encoded_video.pth")

            if os.path.exists(encoded_path):
                print(f"Chunk {chunk_name} already encoded, skipping...")
                continue
            
            
            chunk_frames = video_frames[:,:, sampled_chunk_start - start_frame : sampled_chunk_end - start_frame,...]
            # print('extrinsic:',cam_emb['extrinsic'].shape)
            chunk_cam_emb ={'extrinsic':cam_emb['extrinsic'][sampled_chunk_start - start_frame : sampled_chunk_end - start_frame],
                            'intrinsic':cam_emb['intrinsic']}

            # print('chunk shape:',chunk_frames.shape)

            with torch.no_grad():
                latents = encoder.pipe.encode_video(chunk_frames, **encoder.tiler_kwargs)[0]
                
                # 编码文本
                # if processed_count == 0:
                #     print('encode prompt!!!')
                #     prompt_emb = encoder.pipe.encode_prompt("A video of a scene shot using a pedestrian's front camera while walking")
                #     del encoder.pipe.prompter
                # pdb.set_trace()
                # 保存编码结果
                encoded_data = {
                    "latents": latents.cpu(),
                    # "prompt_emb": {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in prompt_emb.items()},
                    "cam_emb": chunk_cam_emb
                }
                # pdb.set_trace()
                torch.save(encoded_data, encoded_path)
                print(f"Saved encoded data: {encoded_path}")
            processed_chunk_count += 1

        processed_count += 1

        print("Encoded scene numebr:",processed_count)
        print("Encoded chunk numebr:",processed_chunk_count)

        # os.makedirs(save_dir,exist_ok=True)  
        # # 检查是否已编码
        # encoded_path = os.path.join(save_dir, "encoded_video.pth")
        # if os.path.exists(encoded_path):
        #     print(f"Scene {scene_name} already encoded, skipping...")
        #     continue
        
        # 加载场景信息

        
        
        # try:
        # print(f"Encoding scene {scene_name}...")
        
        # 加载和编码视频
        
        # 编码视频
        # with torch.no_grad():
        #     latents = encoder.pipe.encode_video(video_frames, **encoder.tiler_kwargs)[0]
            
        #     # 编码文本
        #     if processed_count == 0:
        #         print('encode prompt!!!')
        #         prompt_emb = encoder.pipe.encode_prompt("A video of a scene shot using a pedestrian's front camera while walking")
        #         del encoder.pipe.prompter
        #     # pdb.set_trace()
        #     # 保存编码结果
        #     encoded_data = {
        #         "latents": latents.cpu(),
        #         #"prompt_emb": {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in prompt_emb.items()},
        #         "cam_emb": cam_emb
        #     }
        #     # pdb.set_trace()
        #     torch.save(encoded_data, encoded_path)
        #     print(f"Saved encoded data: {encoded_path}")
        #     processed_count += 1
                
        # except Exception as e:
        #     print(f"Error encoding scene {scene_name}: {e}")
        #     continue
    
    print(f"Encoding completed! Processed {processed_count} scenes.")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--scenes_path", type=str, default="")
    parser.add_argument("--text_encoder_path", type=str, 
                       default="models/Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth")
    parser.add_argument("--vae_path", type=str,
                       default="models/Wan-AI/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth")
    
    parser.add_argument("--output_dir",type=str,
                       default="")

    args = parser.parse_args()
    encode_scenes(args.scenes_path, args.text_encoder_path, args.vae_path,args.output_dir)
