# // 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 spaces
import subprocess
import os
import sys

# --- ETAPA 1: Clonar o Repositório do GitHub ---
repo_name = "SeedVR"
if not os.path.exists(repo_name):
    print(f"Clonando o repositório {repo_name} do GitHub...")
    subprocess.run(f"git clone https://github.com/ByteDance-Seed/{repo_name}.git", shell=True, check=True)

# --- ETAPA 2: Mudar para o Diretório e Configurar o Ambiente ---
os.chdir(repo_name)
print(f"Diretório de trabalho alterado para: {os.getcwd()}")

sys.path.insert(0, os.path.abspath('.'))
print(f"Diretório atual adicionado ao sys.path.")

# --- ETAPA 3: Instalar Dependências Corretamente ---
python_executable = sys.executable

# CORREÇÃO: Forçar uma versão do NumPy < 2.0 para evitar conflitos de compatibilidade.
print("Instalando NumPy compatível...")
subprocess.run([python_executable, "-m", "pip", "install", "numpy<2.0"], check=True)

# Filtrar requirements.txt para evitar conflitos com torch/torchvision pré-instalados
print("Filtrando requirements.txt...")
with open("requirements.txt", "r") as f_in, open("filtered_requirements.txt", "w") as f_out:
    for line in f_in:
        if not line.strip().startswith(('torch', 'torchvision')):
            f_out.write(line)

print("Instalando dependências filtradas...")
subprocess.run([python_executable, "-m", "pip", "install", "-r", "filtered_requirements.txt"], check=True)

print("Instalando flash-attn...")
subprocess.run([python_executable, "-m", "pip", "install", "flash-attn==2.5.9.post1", "--no-build-isolation"], check=True)

from pathlib import Path
from urllib.parse import urlparse
from torch.hub import download_url_to_file, get_dir

def load_file_from_url(url, model_dir='.', progress=True, file_name=None):
    os.makedirs(model_dir, exist_ok=True)
    if not file_name:
        parts = urlparse(url)
        file_name = os.path.basename(parts.path)
    cached_file = os.path.join(model_dir, file_name)
    if not os.path.exists(cached_file):
        print(f'Baixando: "{url}" para {cached_file}\n')
        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
    return cached_file

apex_url = 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/apex-0.1-cp310-cp310-linux_x86_64.whl'
apex_wheel_path = load_file_from_url(url=apex_url)
print("Instalando Apex a partir do wheel baixado...")
subprocess.run([python_executable, "-m", "pip", "install", "--force-reinstall", "--no-cache-dir", apex_wheel_path], check=True)
print("✅ Configuração do Apex concluída.")

# --- ETAPA 4: Baixar os Modelos Pré-treinados ---
print("Baixando modelos pré-treinados...")
import torch

pretrain_model_url = {
    'vae': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/ema_vae.pth',
    'dit': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/seedvr2_ema_3b.pth',
    'pos_emb': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/pos_emb.pt',
    'neg_emb': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/neg_emb.pt',
}

Path('./ckpts').mkdir(exist_ok=True)
for key, url in pretrain_model_url.items():
    model_dir = './ckpts' if key in ['vae', 'dit'] else '.'
    load_file_from_url(url=url, model_dir=model_dir)


# --- ETAPA 5: Executar a Aplicação Principal ---
import mediapy
from einops import rearrange
from omegaconf import OmegaConf
import datetime
from tqdm import tqdm
import gc
from PIL import Image
import gradio as gr
import uuid
import mimetypes
import torchvision.transforms as T
from torchvision.transforms import Compose, Lambda, Normalize
from torchvision.io.video import read_video

from data.image.transforms.divisible_crop import DivisibleCrop
from data.image.transforms.na_resize import NaResize
from data.video.transforms.rearrange import Rearrange
from common.config import load_config
from common.distributed import init_torch
from common.seed import set_seed
from projects.video_diffusion_sr.infer import VideoDiffusionInfer
from common.distributed.ops import sync_data

os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "12355"
os.environ["RANK"] = str(0)
os.environ["WORLD_SIZE"] = str(1)

use_colorfix = os.path.exists("projects/video_diffusion_sr/color_fix.py")
if use_colorfix:
    from projects.video_diffusion_sr.color_fix import wavelet_reconstruction

def configure_runner():
    config = load_config('configs_3b/main.yaml')
    runner = VideoDiffusionInfer(config)
    OmegaConf.set_readonly(runner.config, False)
    init_torch(cudnn_benchmark=False, timeout=datetime.timedelta(seconds=3600))
    runner.configure_dit_model(device="cuda", checkpoint='ckpts/seedvr2_ema_3b.pth')
    runner.configure_vae_model()
    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("cuda") for i in x]
    noises, aug_noises = [torch.randn_like(l) for l in cond_latents], [torch.randn_like(l) for l in cond_latents]
    noises, aug_noises, cond_latents = sync_data((noises, aug_noises, cond_latents), 0)
    noises, aug_noises, cond_latents = map(_move_to_cuda, (noises, aug_noises, cond_latents))
    def _add_noise(x, aug_noise):
        t = torch.tensor([100.0], device="cuda")
        shape = torch.tensor(x.shape[1:], device="cuda")[None]
        t = runner.timestep_transform(t, shape)
        return runner.schedule.forward(x, aug_noise, t)
    conditions = [runner.get_condition(n, task="sr", latent_blur=_add_noise(l, an)) for n, an, l 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, **text_embeds_dict)
    return [rearrange(v, "c t h w -> t c h w") for v in video_tensors]

@spaces.GPU
def generation_loop(video_path, seed=666, fps_out=24):
    if video_path is None: return None, None, None
    runner = configure_runner()
    # Adicionado `weights_only=True` para segurança e para suprimir o aviso
    text_embeds = {
        "texts_pos": [torch.load('pos_emb.pt', weights_only=True).to("cuda")], 
        "texts_neg": [torch.load('neg_emb.pt', weights_only=True).to("cuda")]
    }
    runner.configure_diffusion()
    set_seed(int(seed))
    os.makedirs("output", exist_ok=True)
    
    # CORREÇÃO: Fornecer os argumentos que faltam para NaResize.
    res_h, res_w = 1280, 720
    transform = Compose([
        NaResize(resolution=(res_h * res_w)**0.5, mode="area", 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")
    ])

    media_type, _ = mimetypes.guess_type(video_path)
    is_video = media_type and media_type.startswith("video")
    
    if is_video:
        video, _, _ = read_video(video_path, output_format="TCHW")
        video = video[:121] / 255.0
        output_path = os.path.join("output", f"{uuid.uuid4()}.mp4")
    else:
        video = T.ToTensor()(Image.open(video_path).convert("RGB")).unsqueeze(0)
        output_path = os.path.join("output", f"{uuid.uuid4()}.png")

    cond_latents = [transform(video.to("cuda"))]
    ori_length = cond_latents[0].size(2)
    cond_latents = runner.vae_encode(cond_latents)
    samples = generation_step(runner, text_embeds, cond_latents)
    sample = samples[0][:ori_length].cpu()
    sample = rearrange(sample, "t c h w -> t h w c").clip(-1, 1).add(1).mul(127.5).byte().numpy()

    if is_video:
        mediapy.write_video(output_path, sample, fps=fps_out)
        return None, output_path, output_path
    else:
        mediapy.write_image(output_path, sample[0])
        return output_path, None, output_path

with gr.Blocks(title="SeedVR") as demo:
    gr.HTML(f"""
        
        <p><b>Demonstração oficial do Gradio</b> para 
        <a href='https://github.com/ByteDance-Seed/SeedVR' target='_blank'>
        <b>SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training</b></a>.<br>
        🔥 <b>SeedVR2</b> é um algoritmo de restauração de imagem e vídeo em um passo para conteúdo do mundo real e AIGC.
        </p>
    """)
    with gr.Row():
        input_file = gr.File(label="Carregar Imagem ou Vídeo")
        with gr.Column():
            seed = gr.Number(label="Seed", value=42)
            fps = gr.Number(label="FPS de Saída", value=24)
    run_button = gr.Button("Executar")
    output_image = gr.Image(label="Imagem de Saída")
    output_video = gr.Video(label="Vídeo de Saída")
    download_link = gr.File(label="Baixar Resultado")
    run_button.click(fn=generation_loop, inputs=[input_file, seed, fps], outputs=[output_image, output_video, download_link])
    

demo.queue().launch(share=True)