"""Hunyuan3D-2.1 LOCAL generation using your L4 GPU."""

# CRITICAL: Import spaces BEFORE torch/CUDA packages
import spaces

import torch
from pathlib import Path
from PIL import Image

from core.config import QualityPreset
from utils.memory import MemoryManager


class HunyuanLocalGenerator:
    """Generates 3D models using Hunyuan3D-2.1 LOCALLY on your L4 GPU."""
    
    def __init__(self):
        self.memory_manager = MemoryManager()
        self.pipeline = None
        self._model_loaded = False
    
    def _load_model(self):
        """Load Hunyuan3D-2.1 model (lazy loading)."""
        if self._model_loaded:
            return
        
        print("[Hunyuan3D Local] Loading model from HuggingFace Hub...")
        
        try:
            # Use diffusers pipeline (Hunyuan3D is based on diffusers)
            from diffusers import DiffusionPipeline
            
            # Load model from HuggingFace Hub
            self.pipeline = DiffusionPipeline.from_pretrained(
                'tencent/Hunyuan3D-2.1',
                torch_dtype=torch.float16,
                device_map="auto",
                trust_remote_code=True  # Required for custom models
            )
            
            print("[Hunyuan3D Local] Model loaded successfully!")
            self._model_loaded = True
            
        except Exception as e:
            print(f"[Hunyuan3D Local] Failed to load model: {e}")
            print("[Hunyuan3D Local] Falling back to API client...")
            # Model loading failed, we'll need to use API client
            self._model_loaded = False
            raise RuntimeError(
                f"Failed to load Hunyuan3D-2.1 model locally: {e}\n"
                f"The model may not support direct loading via diffusers.\n"
                f"Falling back to API client (external space)."
            )
    
    @spaces.GPU(duration=120)
    def generate(
        self,
        image_path: Path,
        preset: QualityPreset,
        output_dir: Path
    ) -> Path:
        """
        Generate 3D model from 2D image using LOCAL Hunyuan3D.
        
        Args:
            image_path: Path to input image
            preset: Quality preset with generation parameters
            output_dir: Directory to save output
        
        Returns:
            Path to generated GLB file
        """
        try:
            print(f"[Hunyuan3D Local] Generating 3D model: {preset.name} quality")
            print(f"[Hunyuan3D Local] Input image: {image_path}")
            print(f"[Hunyuan3D Local] Settings: steps={preset.hunyuan_steps}, guidance={preset.hunyuan_guidance}, octree={preset.octree_resolution}")
            
            # Validate input image exists
            if not image_path.exists():
                raise FileNotFoundError(f"Input image not found: {image_path}")
            
            # Load model (lazy loading)
            self._load_model()
            
            # Load image
            print(f"[Hunyuan3D Local] Loading image...")
            image = Image.open(image_path).convert('RGB')
            
            # Generate 3D model
            print(f"[Hunyuan3D Local] Generating mesh...")
            result = self.pipeline(
                image=image,
                num_inference_steps=preset.hunyuan_steps,
                guidance_scale=preset.hunyuan_guidance,
                octree_resolution=preset.octree_resolution,
                seed=1234
            )
            
            # Extract mesh (result is a list with mesh as first element)
            if not result or len(result) == 0:
                raise ValueError("Hunyuan3D returned empty result")
            
            mesh = result[0]
            print(f"[Hunyuan3D Local] Mesh generated successfully")
            
            # Save as GLB
            output_path = output_dir / f"hunyuan_{int(Path(image_path).stem.split('_')[-1])}.glb"
            mesh.export(str(output_path))
            
            print(f"[Hunyuan3D Local] Model saved: {output_path}")
            
            # Cleanup
            import gc
            gc.collect()
            torch.cuda.empty_cache()
            
            return output_path
            
        except Exception as e:
            import traceback
            error_details = traceback.format_exc()
            print(f"[Hunyuan3D Local] ERROR: {e}")
            print(f"[Hunyuan3D Local] Full traceback:\n{error_details}")
            
            # Provide helpful error message
            if "out of memory" in str(e).lower():
                raise RuntimeError(
                    f"GPU out of memory. Try using a lower quality preset (Fast or Balanced)."
                ) from e
            elif "model" in str(e).lower() and "not found" in str(e).lower():
                raise RuntimeError(
                    f"Hunyuan3D model not found. Check requirements.txt includes:\n"
                    f"  git+https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1.git"
                ) from e
            else:
                raise RuntimeError(
                    f"Hunyuan3D generation failed: {e}. Check logs for details."
                ) from e