import subprocess
# not sure why it works in the original space but says "pip not found" in mine
#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

from huggingface_hub import snapshot_download, hf_hub_download

snapshot_download(
    repo_id="Wan-AI/Wan2.1-T2V-1.3B",
    local_dir="wan_models/Wan2.1-T2V-1.3B",
    local_dir_use_symlinks=False,
    resume_download=True,
    repo_type="model" 
)

hf_hub_download(
    repo_id="gdhe17/Self-Forcing",
    filename="checkpoints/self_forcing_dmd.pt",
    local_dir=".",              
    local_dir_use_symlinks=False 
)

import os
import re
import random
import argparse
import hashlib
import urllib.request
import time
from PIL import Image
import torch
import gradio as gr
from omegaconf import OmegaConf
from tqdm import tqdm
import imageio
import av
import uuid

from pipeline import CausalInferencePipeline
from demo_utils.constant import ZERO_VAE_CACHE
from demo_utils.vae_block3 import VAEDecoderWrapper
from utils.wan_wrapper import WanDiffusionWrapper, WanTextEncoder

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM #, BitsAndBytesConfig
import numpy as np

device = "cuda" if torch.cuda.is_available() else "cpu"

# --- Argument Parsing ---
parser = argparse.ArgumentParser(description="Gradio Demo for Self-Forcing with Frame Streaming")
parser.add_argument('--port', type=int, default=7860, help="Port to run the Gradio app on.")
parser.add_argument('--host', type=str, default='0.0.0.0', help="Host to bind the Gradio app to.")
parser.add_argument("--checkpoint_path", type=str, default='./checkpoints/self_forcing_dmd.pt', help="Path to the model checkpoint.")
parser.add_argument("--config_path", type=str, default='./configs/self_forcing_dmd.yaml', help="Path to the model config.")
parser.add_argument('--share', action='store_true', help="Create a public Gradio link.")
parser.add_argument('--trt', action='store_true', help="Use TensorRT optimized VAE decoder.")
parser.add_argument('--fps', type=float, default=15.0, help="Playback FPS for frame streaming.")
args = parser.parse_args()

gpu = "cuda"

try:
    config = OmegaConf.load(args.config_path)
    default_config = OmegaConf.load("configs/default_config.yaml")
    config = OmegaConf.merge(default_config, config)
except FileNotFoundError as e:
    print(f"Error loading config file: {e}\n. Please ensure config files are in the correct path.")
    exit(1)

# Initialize Models
print("Initializing models...")
text_encoder = WanTextEncoder()
transformer = WanDiffusionWrapper(is_causal=True)

try:
    state_dict = torch.load(args.checkpoint_path, map_location="cpu")
    transformer.load_state_dict(state_dict.get('generator_ema', state_dict.get('generator')))
except FileNotFoundError as e:
    print(f"Error loading checkpoint: {e}\nPlease ensure the checkpoint '{args.checkpoint_path}' exists.")
    exit(1)

text_encoder.eval().to(dtype=torch.float16).requires_grad_(False)
transformer.eval().to(dtype=torch.float16).requires_grad_(False)

text_encoder.to(gpu)
transformer.to(gpu)

APP_STATE = {
    "torch_compile_applied": False,
    "fp8_applied": False,
    "current_use_taehv": False,
    "current_vae_decoder": None,
}

# I've tried to enable it, but I didn't notice a significant performance improvement..
ENABLE_TORCH_COMPILATION = False

# “default”: The default mode, used when no mode parameter is specified. It provides a good balance between performance and overhead.
# “reduce-overhead”: Minimizes Python-related overhead using CUDA graphs. However, it may increase memory usage.
# “max-autotune”: Uses Triton or template-based matrix multiplications on supported devices. It takes longer to compile but optimizes for the fastest possible execution. On GPUs it enables CUDA graphs by default.
# “max-autotune-no-cudagraphs”: Similar to “max-autotune”, but without CUDA graphs.
TORCH_COMPILATION_MODE = "default"

# Apply torch.compile for maximum performance
if not APP_STATE["torch_compile_applied"] and ENABLE_TORCH_COMPILATION:
    print("🚀 Applying torch.compile for speed optimization...")
    transformer.compile(mode=TORCH_COMPILATION_MODE)
    APP_STATE["torch_compile_applied"] = True
    print("✅ torch.compile applied to transformer")

def frames_to_ts_file(frames, filepath, fps = 15):
    """
    Convert frames directly to .ts file using PyAV.
    
    Args:
        frames: List of numpy arrays (HWC, RGB, uint8)
        filepath: Output file path
        fps: Frames per second
    
    Returns:
        The filepath of the created file
    """
    if not frames:
        return filepath
    
    height, width = frames[0].shape[:2]
    
    # Create container for MPEG-TS format
    container = av.open(filepath, mode='w', format='mpegts')
    
    # Add video stream with optimized settings for streaming
    stream = container.add_stream('h264', rate=fps)
    stream.width = width
    stream.height = height
    stream.pix_fmt = 'yuv420p'
    
    # Optimize for low latency streaming
    stream.options = {
        'preset': 'ultrafast',
        'tune': 'zerolatency', 
        'crf': '23',
        'profile': 'baseline',
        'level': '3.0'
    }
    
    try:
        for frame_np in frames:
            frame = av.VideoFrame.from_ndarray(frame_np, format='rgb24')
            frame = frame.reformat(format=stream.pix_fmt)
            for packet in stream.encode(frame):
                container.mux(packet)
        
        for packet in stream.encode():
            container.mux(packet)
            
    finally:
        container.close()
    
    return filepath

def initialize_vae_decoder(use_taehv=False, use_trt=False):
    if use_trt:
        from demo_utils.vae import VAETRTWrapper
        print("Initializing TensorRT VAE Decoder...")
        vae_decoder = VAETRTWrapper()
        APP_STATE["current_use_taehv"] = False
    elif use_taehv:
        print("Initializing TAEHV VAE Decoder...")
        from demo_utils.taehv import TAEHV
        taehv_checkpoint_path = "checkpoints/taew2_1.pth"
        if not os.path.exists(taehv_checkpoint_path):
            print(f"Downloading TAEHV checkpoint to {taehv_checkpoint_path}...")
            os.makedirs("checkpoints", exist_ok=True)
            download_url = "https://github.com/madebyollin/taehv/raw/main/taew2_1.pth"
            try:
                urllib.request.urlretrieve(download_url, taehv_checkpoint_path)
            except Exception as e:
                raise RuntimeError(f"Failed to download taew2_1.pth: {e}")
        
        class DotDict(dict): __getattr__ = dict.get
        
        class TAEHVDiffusersWrapper(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.dtype = torch.float16
                self.taehv = TAEHV(checkpoint_path=taehv_checkpoint_path).to(self.dtype)
                self.config = DotDict(scaling_factor=1.0)
            def decode(self, latents, return_dict=None):
                return self.taehv.decode_video(latents, parallel=not LOW_MEMORY).mul_(2).sub_(1)
        
        vae_decoder = TAEHVDiffusersWrapper()
        APP_STATE["current_use_taehv"] = True
    else:
        print("Initializing Default VAE Decoder...")
        vae_decoder = VAEDecoderWrapper()
        try:
            vae_state_dict = torch.load('wan_models/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth', map_location="cpu")
            decoder_state_dict = {k: v for k, v in vae_state_dict.items() if 'decoder.' in k or 'conv2' in k}
            vae_decoder.load_state_dict(decoder_state_dict)
        except FileNotFoundError:
            print("Warning: Default VAE weights not found.")
        APP_STATE["current_use_taehv"] = False

    vae_decoder.eval().to(dtype=torch.float16).requires_grad_(False).to(gpu)
    
    # Apply torch.compile to VAE decoder if enabled (following demo.py pattern)
    if APP_STATE["torch_compile_applied"] and not use_taehv and not use_trt:
        print("🚀 Applying torch.compile to VAE decoder...")
        vae_decoder.compile(mode=TORCH_COMPILATION_MODE)
        print("✅ torch.compile applied to VAE decoder")
    
    APP_STATE["current_vae_decoder"] = vae_decoder
    print(f"✅ VAE decoder initialized: {'TAEHV' if use_taehv else 'Default VAE'}")

# Initialize with default VAE
initialize_vae_decoder(use_taehv=False, use_trt=args.trt)

pipeline = CausalInferencePipeline(
    config, device=gpu, generator=transformer, text_encoder=text_encoder, 
    vae=APP_STATE["current_vae_decoder"]
)

pipeline.to(dtype=torch.float16).to(gpu)

@torch.no_grad()
def video_generation_handler_streaming(prompt, seed=42, fps=15, width=400, height=224, duration=5, buffering=2):
    """
    Generator function that yields .ts video chunks using PyAV for streaming.
    Now optimized for block-based processing.
    """
    if seed == -1: 
        seed = random.randint(0, 2**32 - 1)
    
    print(f"🎬 Starting PyAV streaming: '{prompt}', seed: {seed}, duration: {duration}s, buffering: {buffering}s")
    
    # Buffering delay
    if buffering > 0:
        buffering_status_html = (
            f"<div style='padding: 10px; border: 1px solid #ffc107; background: #fff3cd; border-radius: 8px; font-family: sans-serif;'>"
            f"  <p style='margin: 0 0 8px 0; font-size: 16px; font-weight: bold;'>⏳ Buffering...</p>"
            f"  <p style='margin: 0; color: #856404; font-size: 14px;'>Waiting {buffering} seconds before starting stream</p>"
            f"</div>"
        )
        yield None, buffering_status_html
        time.sleep(buffering)
    
    # Setup
    conditional_dict = text_encoder(text_prompts=[prompt])
    for key, value in conditional_dict.items():
        conditional_dict[key] = value.to(dtype=torch.float16)
    
    rnd = torch.Generator(gpu).manual_seed(int(seed))
    pipeline._initialize_kv_cache(1, torch.float16, device=gpu)
    pipeline._initialize_crossattn_cache(1, torch.float16, device=gpu)
    noise = torch.randn([1, 21, 16, 60, 104], device=gpu, dtype=torch.float16, generator=rnd)
    
    vae_cache, latents_cache = None, None
    if not APP_STATE["current_use_taehv"] and not args.trt:
        vae_cache = [c.to(device=gpu, dtype=torch.float16) for c in ZERO_VAE_CACHE]

    # Calculate number of blocks based on duration
    # Current setup generates approximately 5 seconds with 7 blocks
    # So we scale proportionally
    base_duration = 5.0  # seconds
    base_blocks = 7
    num_blocks = max(1, int(base_blocks * duration / base_duration))
    
    current_start_frame = 0
    all_num_frames = [pipeline.num_frame_per_block] * num_blocks
    
    total_frames_yielded = 0
    
    # Ensure temp directory exists
    os.makedirs("gradio_tmp", exist_ok=True)
    
    # Generation loop
    for idx, current_num_frames in enumerate(all_num_frames):
        print(f"📦 Processing block {idx+1}/{num_blocks}")
        
        noisy_input = noise[:, current_start_frame : current_start_frame + current_num_frames]

        # Denoising steps
        for step_idx, current_timestep in enumerate(pipeline.denoising_step_list):
            timestep = torch.ones([1, current_num_frames], device=noise.device, dtype=torch.int64) * current_timestep
            _, denoised_pred = pipeline.generator(
                noisy_image_or_video=noisy_input, conditional_dict=conditional_dict,
                timestep=timestep, kv_cache=pipeline.kv_cache1,
                crossattn_cache=pipeline.crossattn_cache,
                current_start=current_start_frame * pipeline.frame_seq_length
            )
            if step_idx < len(pipeline.denoising_step_list) - 1:
                next_timestep = pipeline.denoising_step_list[step_idx + 1]
                noisy_input = pipeline.scheduler.add_noise(
                    denoised_pred.flatten(0, 1), torch.randn_like(denoised_pred.flatten(0, 1)),
                    next_timestep * torch.ones([1 * current_num_frames], device=noise.device, dtype=torch.long)
                ).unflatten(0, denoised_pred.shape[:2])

        if idx < len(all_num_frames) - 1:
            pipeline.generator(
                noisy_image_or_video=denoised_pred, conditional_dict=conditional_dict,
                timestep=torch.zeros_like(timestep), kv_cache=pipeline.kv_cache1,
                crossattn_cache=pipeline.crossattn_cache,
                current_start=current_start_frame * pipeline.frame_seq_length,
            )

        # Decode to pixels
        if args.trt:
            pixels, vae_cache = pipeline.vae.forward(denoised_pred.half(), *vae_cache)
        elif APP_STATE["current_use_taehv"]:
            if latents_cache is None: 
                latents_cache = denoised_pred
            else:
                denoised_pred = torch.cat([latents_cache, denoised_pred], dim=1)
                latents_cache = denoised_pred[:, -3:]
            pixels = pipeline.vae.decode(denoised_pred)
        else:
            pixels, vae_cache = pipeline.vae(denoised_pred.half(), *vae_cache)
            
        # Handle frame skipping
        if idx == 0 and not args.trt: 
            pixels = pixels[:, 3:]
        elif APP_STATE["current_use_taehv"] and idx > 0: 
            pixels = pixels[:, 12:]

        print(f"🔍 DEBUG Block {idx}: Pixels shape after skipping: {pixels.shape}")

        # Process all frames from this block at once
        all_frames_from_block = []
        for frame_idx in range(pixels.shape[1]):
            frame_tensor = pixels[0, frame_idx]
            
            # Convert to numpy (HWC, RGB, uint8)
            frame_np = torch.clamp(frame_tensor.float(), -1., 1.) * 127.5 + 127.5
            frame_np = frame_np.to(torch.uint8).cpu().numpy()
            frame_np = np.transpose(frame_np, (1, 2, 0))  # CHW -> HWC
            
            all_frames_from_block.append(frame_np)
            total_frames_yielded += 1
            
            # Yield status update for each frame (cute tracking!)
            blocks_completed = idx
            current_block_progress = (frame_idx + 1) / pixels.shape[1]
            total_progress = (blocks_completed + current_block_progress) / num_blocks * 100
            
            # Cap at 100% to avoid going over
            total_progress = min(total_progress, 100.0)
            
            frame_status_html = (
                f"<div style='padding: 10px; border: 1px solid #ddd; border-radius: 8px; font-family: sans-serif;'>"
                f"  <p style='margin: 0 0 8px 0; font-size: 16px; font-weight: bold;'>Generating Video...</p>"
                f"  <div style='background: #e9ecef; border-radius: 4px; width: 100%; overflow: hidden;'>"
                f"    <div style='width: {total_progress:.1f}%; height: 20px; background-color: #0d6efd; transition: width 0.2s;'></div>"
                f"  </div>"
                f"  <p style='margin: 8px 0 0 0; color: #555; font-size: 14px; text-align: right;'>"
                f"    Block {idx+1}/{num_blocks}   |   Frame {total_frames_yielded}   |   {total_progress:.1f}%"
                f"  </p>"
                f"</div>"
            )
            
            # Yield None for video but update status (frame-by-frame tracking)
            yield None, frame_status_html

        # Encode entire block as one chunk immediately
        if all_frames_from_block:
            print(f"📹 Encoding block {idx} with {len(all_frames_from_block)} frames")
            
            try:
                chunk_uuid = str(uuid.uuid4())[:8]
                ts_filename = f"block_{idx:04d}_{chunk_uuid}.ts"
                ts_path = os.path.join("gradio_tmp", ts_filename)
                
                frames_to_ts_file(all_frames_from_block, ts_path, fps)
                
                # Calculate final progress for this block
                total_progress = (idx + 1) / num_blocks * 100
                
                # Yield the actual video chunk
                yield ts_path, gr.update()
                
            except Exception as e:
                print(f"⚠️ Error encoding block {idx}: {e}")
                import traceback
                traceback.print_exc()
                    
        current_start_frame += current_num_frames
    
    # Final completion status
    final_status_html = (
        f"<div style='padding: 16px; border: 1px solid #198754; background: linear-gradient(135deg, #d1e7dd, #f8f9fa); border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);'>"
        f"  <div style='display: flex; align-items: center; margin-bottom: 8px;'>"
        f"    <span style='font-size: 24px; margin-right: 12px;'>🎉</span>"
        f"    <h4 style='margin: 0; color: #0f5132; font-size: 18px;'>Stream Complete!</h4>"
        f"  </div>"
        f"  <div style='background: rgba(255,255,255,0.7); padding: 8px; border-radius: 4px;'>"
        f"    <p style='margin: 0; color: #0f5132; font-weight: 500;'>"
        f"      📊 Generated {total_frames_yielded} frames across {num_blocks} blocks"
        f"    </p>"
        f"    <p style='margin: 4px 0 0 0; color: #0f5132; font-size: 14px;'>"
        f"      🎬 Playback: {fps} FPS • 📁 Format: MPEG-TS/H.264"
        f"    </p>"
        f"  </div>"
        f"</div>"
    )
    yield None, final_status_html
    print(f"✅ PyAV streaming complete! {total_frames_yielded} frames across {num_blocks} blocks")

# --- Gradio UI Layout ---
with gr.Blocks(title="Self-Forcing Streaming Demo") as demo:
    gr.Markdown("# 🚀 Self-Forcing Video Generation")
    gr.Markdown("Real-time video generation with distilled Wan2-1 1.3B [[Model]](https://huggingface.co/gdhe17/Self-Forcing), [[Project page]](https://self-forcing.github.io), [[Paper]](https://huggingface.co/papers/2506.08009)")
    
    with gr.Row():
        with gr.Column(scale=2):
            with gr.Group():
                prompt = gr.Textbox(
                    label="Prompt", 
                    placeholder="A stylish woman walks down a Tokyo street...", 
                    lines=4,
                    value=""
                )

            start_btn = gr.Button("🎬 Start Streaming", variant="primary", size="lg")
            
            gr.Markdown("### ⚙️ Settings")
            with gr.Row():
                seed = gr.Number(
                    label="Seed", 
                    value=-1, 
                    info="Use -1 for random seed",
                    precision=0
                )
                fps = gr.Slider(
                    label="Playback FPS", 
                    minimum=1, 
                    maximum=30, 
                    value=args.fps, 
                    step=1,
                    visible=False,
                    info="Frames per second for playback"
                )
            
            with gr.Row():
                duration = gr.Slider(
                    label="Duration (seconds)",
                    minimum=1,
                    maximum=10,
                    value=5,
                    step=1,
                    info="Video duration in seconds"
                )
                buffering = gr.Slider(
                    label="Buffering (seconds)",
                    minimum=0,
                    maximum=5,
                    value=2,
                    step=0.5,
                    info="Wait time before starting stream"
                )
            
            with gr.Row():
                width = gr.Slider(
                    label="Width", 
                    minimum=320, 
                    maximum=720, 
                    value=400, 
                    step=8,
                    info="Video width in pixels (8px steps)"
                )
                height = gr.Slider(
                    label="Height", 
                    minimum=320, 
                    maximum=720, 
                    value=224, 
                    step=8,
                    info="Video height in pixels (8px steps)"
                )
            
        with gr.Column(scale=3):
            gr.Markdown("### 📺 Video Stream")

            streaming_video = gr.Video(
                label="Live Stream",
                streaming=True,
                loop=True,
                height=400,
                autoplay=True,
                show_label=False
            )
            
            status_display = gr.HTML(
                value=(
                    "<div style='text-align: center; padding: 20px; color: #666; border: 1px dashed #ddd; border-radius: 8px;'>"
                    "🎬 Ready to start streaming...<br>"
                    "<small>Configure your prompt and click 'Start Streaming'</small>"
                    "</div>"
                ),
                label="Generation Status"
            )

    # Connect the generator to the streaming video
    start_btn.click(
        fn=video_generation_handler_streaming,
        inputs=[prompt, seed, fps, width, height, duration, buffering],
        outputs=[streaming_video, status_display]
    )


# --- Launch App ---
if __name__ == "__main__":
    if os.path.exists("gradio_tmp"):
        import shutil
        shutil.rmtree("gradio_tmp")
    os.makedirs("gradio_tmp", exist_ok=True)
    
    print("🚀 Starting Self-Forcing Streaming Demo")
    print(f"📁 Temporary files will be stored in: gradio_tmp/")
    print(f"🎯 Chunk encoding: PyAV (MPEG-TS/H.264)")
    print(f"⚡ GPU acceleration: {gpu}")
    
    demo.queue().launch(
        server_name=args.host, 
        server_port=args.port, 
        share=args.share,
        show_error=True,
        max_threads=40,
        mcp_server=True
    )