# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // 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 os
import torch
import mediapy
from einops import rearrange
from omegaconf import OmegaConf
print(os.getcwd())
import datetime
from tqdm import tqdm
from models.dit import na
import gc

from data.image.transforms.divisible_crop import DivisibleCrop
from data.image.transforms.na_resize import NaResize
from data.video.transforms.rearrange import Rearrange
if os.path.exists("./projects/video_diffusion_sr/color_fix.py"):
    from projects.video_diffusion_sr.color_fix import wavelet_reconstruction
    use_colorfix=True
else:
    use_colorfix = False
    print('Note!!!!!! Color fix is not avaliable!')
from torchvision.transforms import Compose, Lambda, Normalize
from torchvision.io.video import read_video
import argparse


from common.distributed import (
    get_device,
    init_torch,
)

from common.distributed.advanced import (
    get_data_parallel_rank,
    get_data_parallel_world_size,
    get_sequence_parallel_rank,
    get_sequence_parallel_world_size,
    init_sequence_parallel,
)

from projects.video_diffusion_sr.infer import VideoDiffusionInfer
from common.config import load_config
from common.distributed.ops import sync_data
from common.seed import set_seed
from common.partition import partition_by_groups, partition_by_size


def configure_sequence_parallel(sp_size):
    if sp_size > 1:
        init_sequence_parallel(sp_size)

def configure_runner(sp_size):
    config_path = os.path.join('./configs_7b', 'main.yaml')
    config = load_config(config_path)
    runner = VideoDiffusionInfer(config)
    OmegaConf.set_readonly(runner.config, False)
    
    init_torch(cudnn_benchmark=False, timeout=datetime.timedelta(seconds=3600))
    configure_sequence_parallel(sp_size)
    runner.configure_dit_model(device="cuda", checkpoint='./ckpts/seedvr_ema_7b.pth')
    runner.configure_vae_model()
    # Set memory limit.
    if hasattr(runner.vae, "set_memory_limit"):
        runner.vae.set_memory_limit(**runner.config.vae.memory_limit)
    return runner

def generation_step(runner, text_embeds_dict, cond_latents):
    def _move_to_cuda(x):
        return [i.to(get_device()) for i in x]

    noises = [torch.randn_like(latent) for latent in cond_latents]
    aug_noises = [torch.randn_like(latent) for latent in cond_latents]
    print(f"Generating with noise shape: {noises[0].size()}.")
    noises, aug_noises, cond_latents = sync_data((noises, aug_noises, cond_latents), 0)
    noises, aug_noises, cond_latents = list(
        map(lambda x: _move_to_cuda(x), (noises, aug_noises, cond_latents))
    )
    cond_noise_scale = 0.1

    def _add_noise(x, aug_noise):
        t = (
            torch.tensor([1000.0], device=get_device())
            * cond_noise_scale
        )
        shape = torch.tensor(x.shape[1:], device=get_device())[None]
        t = runner.timestep_transform(t, shape)
        print(
            f"Timestep shifting from"
            f" {1000.0 * cond_noise_scale} to {t}."
        )
        x = runner.schedule.forward(x, aug_noise, t)
        return x

    conditions = [
        runner.get_condition(
            noise,
            task="sr",
            latent_blur=_add_noise(latent_blur, aug_noise),
        )
        for noise, aug_noise, latent_blur in zip(noises, aug_noises, cond_latents)
    ]

    with torch.no_grad(), torch.autocast("cuda", torch.bfloat16, enabled=True):
        video_tensors = runner.inference(
            noises=noises,
            conditions=conditions,
            dit_offload=True,
            **text_embeds_dict,
        )

    samples = [
        (
            rearrange(video[:, None], "c t h w -> t c h w")
            if video.ndim == 3
            else rearrange(video, "c t h w -> t c h w")
        )
        for video in video_tensors
    ]
    del video_tensors

    return samples

def generation_loop(runner, video_path='./test_videos', output_dir='./results', batch_size=1, cfg_scale=6.5, cfg_rescale=0.0, sample_steps=50, seed=666, res_h=1280, res_w=720, sp_size=1):

    def _build_pos_and_neg_prompt():
        # read positive prompt
        positive_text = "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, \
        hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, \
        skin pore detailing, hyper sharpness, perfect without deformations."
        # read negative prompt
        negative_text = "painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, \
        CG Style, 3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, \
        signature, jpeg artifacts, deformed, lowres, over-smooth"
        return positive_text, negative_text

    def _build_test_prompts(video_path):
        positive_text, negative_text = _build_pos_and_neg_prompt()
        original_videos = []
        prompts = {}
        video_list = os.listdir(video_path)
        for f in video_list:
            if f.endswith(".mp4"):
                original_videos.append(f)
                prompts[f] = positive_text
        print(f"Total prompts to be generated: {len(original_videos)}")
        return original_videos, prompts, negative_text

    def _extract_text_embeds():
        # Text encoder forward.
        positive_prompts_embeds = []
        for texts_pos in tqdm(original_videos_local):
            text_pos_embeds = torch.load('pos_emb.pt')
            text_neg_embeds = torch.load('neg_emb.pt')

            positive_prompts_embeds.append(
                {"texts_pos": [text_pos_embeds], "texts_neg": [text_neg_embeds]}
            )
        gc.collect()
        torch.cuda.empty_cache()
        return positive_prompts_embeds

    def cut_videos(videos, sp_size):
        t = videos.size(1)
        if t <= 4 * sp_size:
            print(f"Cut input video size: {videos.size()}")
            padding = [videos[:, -1].unsqueeze(1)] * (4 * sp_size - t + 1)
            padding = torch.cat(padding, dim=1)
            videos = torch.cat([videos, padding], dim=1)
            return videos
        if (t - 1) % (4 * sp_size) == 0:
            return videos
        else:
            padding = [videos[:, -1].unsqueeze(1)] * (
                4 * sp_size - ((t - 1) % (4 * sp_size))
            )
            padding = torch.cat(padding, dim=1)
            videos = torch.cat([videos, padding], dim=1)
            assert (videos.size(1) - 1) % (4 * sp_size) == 0
            return videos

    # classifier-free guidance
    runner.config.diffusion.cfg.scale = cfg_scale
    runner.config.diffusion.cfg.rescale = cfg_rescale
    # sampling steps
    runner.config.diffusion.timesteps.sampling.steps = sample_steps
    runner.configure_diffusion()

    # set random seed
    set_seed(seed, same_across_ranks=True)
    os.makedirs(output_dir, exist_ok=True)
    tgt_path = output_dir

    # get test prompts
    original_videos, _, _ = _build_test_prompts(video_path)

    # divide the prompts into different groups
    original_videos_group = partition_by_groups(
        original_videos,
        get_data_parallel_world_size() // get_sequence_parallel_world_size(),
    )
    # store prompt mapping
    original_videos_local = original_videos_group[
        get_data_parallel_rank() // get_sequence_parallel_world_size()
    ]
    original_videos_local = partition_by_size(original_videos_local, batch_size)

    # pre-extract the text embeddings
    positive_prompts_embeds = _extract_text_embeds()

    video_transform = Compose(
        [
            NaResize(
                resolution=(
                    res_h * res_w
                )
                ** 0.5,
                mode="area",
                # Upsample image, model only trained for high res.
                downsample_only=False,
            ),
            Lambda(lambda x: torch.clamp(x, 0.0, 1.0)),
            DivisibleCrop((16, 16)),
            Normalize(0.5, 0.5),
            Rearrange("t c h w -> c t h w"),
        ]
    )

    # generation loop
    for videos, text_embeds in tqdm(zip(original_videos_local, positive_prompts_embeds)):
        # read condition latents
        cond_latents = []
        for video in videos:
            video = (
                read_video(
                   os.path.join(video_path, video), output_format="TCHW"
                )[0]
                / 255.0
            )
            print(f"Read video size: {video.size()}")
            cond_latents.append(video_transform(video.to(get_device())))

        ori_lengths = [video.size(1) for video in cond_latents]
        input_videos = cond_latents
        cond_latents = [cut_videos(video, sp_size) for video in cond_latents]

        runner.dit.to("cpu")
        print(f"Encoding videos: {list(map(lambda x: x.size(), cond_latents))}")
        runner.vae.to(get_device())
        cond_latents = runner.vae_encode(cond_latents)
        runner.vae.to("cpu")
        runner.dit.to(get_device())

        for i, emb in enumerate(text_embeds["texts_pos"]):
            text_embeds["texts_pos"][i] = emb.to(get_device())
        for i, emb in enumerate(text_embeds["texts_neg"]):
            text_embeds["texts_neg"][i] = emb.to(get_device())

        samples = generation_step(runner, text_embeds, cond_latents=cond_latents)
        runner.dit.to("cpu")
        del cond_latents

        # dump samples to the output directory
        if get_sequence_parallel_rank() == 0:
            for path, input, sample, ori_length in zip(
                videos, input_videos, samples, ori_lengths
            ):
                if ori_length < sample.shape[0]:
                    sample = sample[:ori_length]
                filename = os.path.join(tgt_path, os.path.basename(path))
                # color fix
                input = (
                    rearrange(input[:, None], "c t h w -> t c h w")
                    if input.ndim == 3
                    else rearrange(input, "c t h w -> t c h w")
                )
                if use_colorfix:
                    sample = wavelet_reconstruction(
                        sample.to("cpu"), input[: sample.size(0)].to("cpu")
                    )
                else:
                    sample = sample.to("cpu")
                sample = (
                    rearrange(sample[:, None], "t c h w -> t h w c")
                    if sample.ndim == 3
                    else rearrange(sample, "t c h w -> t h w c")
                )
                sample = sample.clip(-1, 1).mul_(0.5).add_(0.5).mul_(255).round()
                sample = sample.to(torch.uint8).numpy()

                if sample.shape[0] == 1:
                    mediapy.write_image(filename, sample.squeeze(0))
                else:
                    mediapy.write_video(
                        filename, sample, fps=24
                    )
        gc.collect()
        torch.cuda.empty_cache()

if __name__ == "__main__":
    parser = argparse.ArgumentParser() 
    parser.add_argument("--video_path", type=str, default="./test_videos")
    parser.add_argument("--output_dir", type=str, default="./results")
    parser.add_argument("--cfg_scale", type=float, default=6.5)
    parser.add_argument("--sample_steps", type=int, default=50)
    parser.add_argument("--seed", type=int, default=666)
    parser.add_argument("--res_h", type=int, default=720)
    parser.add_argument("--res_w", type=int, default=1280)
    parser.add_argument("--sp_size", type=int, default=1)
    args = parser.parse_args()

    runner = configure_runner(args.sp_size)
    generation_loop(runner, **vars(args))