import os
import argparse
import datetime
import PIL.Image
import numpy as np

import torch
import torch.distributed as dist

from transformers import AutoTokenizer, UMT5EncoderModel
from torchvision.io import write_video

from longcat_video.pipeline_longcat_video import LongCatVideoPipeline
from longcat_video.modules.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
from longcat_video.modules.autoencoder_kl_wan import AutoencoderKLWan
from longcat_video.modules.longcat_video_dit import LongCatVideoTransformer3DModel
from longcat_video.context_parallel import context_parallel_util
from longcat_video.context_parallel.context_parallel_util import init_context_parallel


def torch_gc():
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()

def generate(args):
    # case setup
    prompt = "realistic filming style, a person wearing a dark helmet, a deep-colored jacket, blue jeans, and bright yellow shoes rides a skateboard along a winding mountain road. The skateboarder starts in a standing position, then gradually lowers into a crouch, extending one hand to touch the road surface while maintaining a low center of gravity to navigate a sharp curve. After completing the turn, the skateboarder rises back to a standing position and continues gliding forward. The background features lush green hills flanking both sides of the road, with distant snow-capped mountain peaks rising against a clear, bright blue sky. The camera follows closely from behind, smoothly tracking the skateboarder’s movements and capturing the dynamic scenery along the route. The scene is shot in natural daylight, highlighting the vivid outdoor environment and the skateboarder’s fluid actions."
    negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
    num_segments = 11  # 1 minute video
    num_frames = 93
    num_cond_frames = 13

    # load parsed args
    checkpoint_dir = args.checkpoint_dir
    context_parallel_size = args.context_parallel_size
    enable_compile = args.enable_compile

    # prepare distributed environment
    rank = int(os.environ['RANK'])
    num_gpus = torch.cuda.device_count()
    local_rank = rank % num_gpus
    torch.cuda.set_device(local_rank)
    dist.init_process_group(backend="nccl", timeout=datetime.timedelta(seconds=3600*24))
    global_rank    = dist.get_rank()
    num_processes  = dist.get_world_size()

    # initialize context parallel before loading models
    init_context_parallel(context_parallel_size=context_parallel_size, global_rank=global_rank, world_size=num_processes)
    cp_size = context_parallel_util.get_cp_size()
    cp_split_hw = context_parallel_util.get_optimal_split(cp_size)

    tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir, subfolder="tokenizer", torch_dtype=torch.bfloat16)
    text_encoder = UMT5EncoderModel.from_pretrained(checkpoint_dir, subfolder="text_encoder", torch_dtype=torch.bfloat16)
    vae = AutoencoderKLWan.from_pretrained(checkpoint_dir, subfolder="vae", torch_dtype=torch.bfloat16)
    scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(checkpoint_dir, subfolder="scheduler", torch_dtype=torch.bfloat16)
    dit = LongCatVideoTransformer3DModel.from_pretrained(checkpoint_dir, subfolder="dit", cp_split_hw=cp_split_hw, torch_dtype=torch.bfloat16)

    if enable_compile:
        dit = torch.compile(dit)

    pipe = LongCatVideoPipeline(
        tokenizer = tokenizer,
        text_encoder = text_encoder,
        vae = vae,
        scheduler = scheduler,
        dit = dit,
    )
    pipe.to(local_rank)

    global_seed = 42
    seed = global_seed + global_rank

    generator = torch.Generator(device=local_rank)
    generator.manual_seed(seed)

    ### t2v (480p)
    output = pipe.generate_t2v(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=512,
        width=832,
        num_frames=num_frames,
        num_inference_steps=50,
        guidance_scale=4.0,
        generator=generator,
    )[0]

    if local_rank == 0:
        output_tensor = torch.from_numpy(np.array(output))
        output_tensor = (output_tensor * 255).clamp(0, 255).to(torch.uint8)
        write_video(f"output_long_video_0.mp4", output_tensor, fps=15, video_codec="libx264", options={"crf": f"{18}"})

    video = [(output[i] * 255).astype(np.uint8) for i in range(output.shape[0])]
    video = [PIL.Image.fromarray(img) for img in video]
    del output 
    torch_gc()

    target_size = video[0].size
    current_video = video

    ### long video
    all_generated_frames = video
    for segment_idx in range(num_segments):
        if local_rank == 0:
            print(f"Generating segment {segment_idx + 1}/{num_segments}...")
        
        output = pipe.generate_vc(
            video=current_video,
            prompt=prompt,
            negative_prompt=negative_prompt,
            resolution='480p', # 480p / 720p
            num_frames=num_frames,
            num_cond_frames=num_cond_frames,
            num_inference_steps=50,
            guidance_scale=4.0,
            generator=generator,
            use_kv_cache=True,
            offload_kv_cache=False,
            enhance_hf=True
        )[0]

        new_video = [(output[i] * 255).astype(np.uint8) for i in range(output.shape[0])]
        new_video = [PIL.Image.fromarray(img) for img in new_video]
        new_video = [frame.resize(target_size, PIL.Image.BICUBIC) for frame in new_video]
        del output

        all_generated_frames.extend(new_video[num_cond_frames:])

        current_video = new_video

        if local_rank == 0:
            output_tensor = torch.from_numpy(np.array(all_generated_frames))
            write_video(f"output_long_video_{segment_idx+1}.mp4", output_tensor, fps=15, video_codec="libx264", options={"crf": f"{18}"})
            del output_tensor

    ### long video refinement (720p)
    refinement_lora_path = os.path.join(checkpoint_dir, 'lora/refinement_lora.safetensors')
    pipe.dit.load_lora(refinement_lora_path, 'refinement_lora')
    pipe.dit.enable_loras(['refinement_lora'])
    pipe.dit.enable_bsa()

    if enable_compile:
        dit = torch.compile(dit)    

    torch_gc()
    cur_condition_video = None
    cur_num_cond_frames = 0
    start_id = 0
    all_refine_frames = []

    for segment_idx in range(num_segments+1):
        if local_rank == 0:
            print(f"Refine segment {segment_idx + 1}/{num_segments}...")

        output_refine = pipe.generate_refine(
            video=cur_condition_video,
            prompt='',
            stage1_video=all_generated_frames[start_id:start_id+num_frames],
            num_cond_frames=cur_num_cond_frames,
            num_inference_steps=50,
            generator=generator,
        )[0]

        new_video = [(output_refine[i] * 255).astype(np.uint8) for i in range(output_refine.shape[0])]
        new_video = [PIL.Image.fromarray(img) for img in new_video]
        del output_refine

        all_refine_frames.extend(new_video[cur_num_cond_frames:])
        cur_condition_video = new_video
        cur_num_cond_frames = num_cond_frames * 2
        start_id = start_id + num_frames - num_cond_frames
        
        if local_rank == 0:
            output_tensor = torch.from_numpy(np.array(all_refine_frames))
            write_video(f"output_longvideo_refine_{segment_idx}.mp4", output_tensor, fps=30, video_codec="libx264", options={"crf": f"{10}"})

def _parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--context_parallel_size",
        type=int,
        default=1,
    )
    parser.add_argument(
        "--checkpoint_dir",
        type=str,
        default=None,
    )
    parser.add_argument(
        '--enable_compile',
        action='store_true',
    )

    args = parser.parse_args()

    return args


if __name__ == "__main__":
    args = _parse_args()
    generate(args)