"""
Diffusion Pipeline Service

Orchestrates the complete image enhancement pipeline:
1. Load and preprocess input image
2. Run Stable Diffusion img2img with optional LoRA
3. Apply upscaling
4. Apply post-processing effects
"""
import logging
from pathlib import Path
from typing import Optional, Callable
import torch
from PIL import Image
from diffusers import (
    StableDiffusionImg2ImgPipeline,
    AutoencoderKL,
    DPMSolverMultistepScheduler
)

from backend.config import settings, get_lora_path
from backend.services.upscaler import upscale_image
from backend.services.postprocess import postprocess_image


logger = logging.getLogger(__name__)


class DiffusionPipelineManager:
    """
    Manages the Stable Diffusion pipeline for image enhancement
    
    This class handles lazy loading of models and provides a clean
    interface for image-to-image enhancement.
    """
    
    def __init__(self):
        self.pipe = None
        self.lora_loaded = False
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        logger.info(f"Pipeline will use device: {self.device}")
        logger.info(f"CUDA available: {torch.cuda.is_available()}")
        if torch.cuda.is_available():
            logger.info(f"CUDA device: {torch.cuda.get_device_name(0)}")
    
    def load_pipeline(self):
        """
        Load the Stable Diffusion img2img pipeline with optimizations
        
        This method initializes the Stable Diffusion pipeline with:
        - Mixed precision for faster inference
        - Memory-efficient attention
        - Optional VAE for better image quality
        """
        if self.pipe is not None:
            return
        
        logger.info("Loading Stable Diffusion 1.5 pipeline...")
        
        try:
            # Determine dtype based on device
            dtype = torch.float32  # Always use float32 for CPU compatibility
            
            logger.info(f"Loading model with dtype: {dtype}, device: {self.device}")
            
            # Initialize the pipeline (SD 1.5) - let diffusers handle all components
            self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
                settings.BASE_MODEL,
                torch_dtype=dtype,
                safety_checker=None,
                requires_safety_checker=False,
                low_cpu_mem_usage=False  # Load everything properly
            )
            
            # Optimize scheduler
            self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(
                self.pipe.scheduler.config
            )
            
            # Move to device
            self.pipe = self.pipe.to(self.device)
            
            # Enable memory optimizations
            if self.device == "cuda":
                if settings.ENABLE_ATTENTION_SLICING:
                    self.pipe.enable_attention_slicing()
                    logger.info("Enabled attention slicing")
                if settings.ENABLE_VAE_SLICING:
                    self.pipe.enable_vae_slicing()
                    logger.info("Enabled VAE slicing")
            
            logger.info("Pipeline loaded successfully")
            logger.info(f"Model: {settings.BASE_MODEL}")
            logger.info(f"Device: {self.device}")
            
        except Exception as e:
            logger.error(f"Failed to load pipeline: {e}", exc_info=True)
            raise
    
    def load_lora(self, lora_path: Path):
        """
        Load a custom LoRA adapter into the pipeline
        
        Args:
            lora_path: Path to the LoRA weights file (.safetensors)
        """
        if self.pipe is None:
            self.load_pipeline()
        
        try:
            logger.info(f"Loading LoRA from: {lora_path}")
            self.pipe.load_lora_weights(str(lora_path.parent), weight_name=lora_path.name)
            self.lora_loaded = True
            logger.info("LoRA loaded successfully")
            
        except Exception as e:
            logger.warning(f"Failed to load LoRA (continuing without it): {e}")
            self.lora_loaded = False
    
    def enhance(
        self,
        image: Image.Image,
        prompt: str,
        negative_prompt: str,
        strength: float,
        guidance_scale: float,
        num_inference_steps: int = 30,
        progress_callback: Optional[Callable[[int, str], None]] = None
    ) -> Image.Image:
        """
        Run image-to-image enhancement
        
        Args:
            image: Input PIL Image
            prompt: Positive prompt
            negative_prompt: Negative prompt
            strength: Denoising strength (0-1)
            guidance_scale: CFG scale
            num_inference_steps: Number of diffusion steps
            progress_callback: Optional callback for progress updates
            
        Returns:
            Enhanced PIL Image
        """
        if self.pipe is None:
            if progress_callback:
                progress_callback(30, "Loading AI model...")
            self.load_pipeline()
        
        if progress_callback:
            progress_callback(40, "Preprocessing image...")
        
        logger.info(f"Running enhancement - Strength: {strength}, Guidance: {guidance_scale}")
        
        # Ensure image is in RGB mode
        if image.mode != "RGB":
            image = image.convert("RGB")
        
        # Resize if too large
        max_size = settings.MAX_IMAGE_SIZE
        if max(image.size) > max_size:
            ratio = max_size / max(image.size)
            new_size = tuple(int(dim * ratio) for dim in image.size)
            image = image.resize(new_size, Image.LANCZOS)
            logger.info(f"Resized input to: {new_size}")
        
        if progress_callback:
            progress_callback(50, f"Starting generation ({num_inference_steps} steps)...")
        
        # Callback to track diffusion progress
        def step_callback(pipe, step_index, timestep, callback_kwargs):
            if progress_callback:
                # Calculate progress: 50-85% range mapped to steps
                progress = 50 + int((step_index / num_inference_steps) * 35)
                progress_callback(progress, f"Step {step_index+1}/{num_inference_steps}")
            return callback_kwargs
        
        # Run inference
        with torch.inference_mode():
            result = self.pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                image=image,
                strength=strength,
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,
                callback_on_step_end=step_callback if progress_callback else None
            ).images[0]
        
        if progress_callback:
            progress_callback(85, "Finalizing...")
        
        return result
    
    def unload(self):
        """
        Unload the pipeline to free memory
        """
        if self.pipe is not None:
            del self.pipe
            self.pipe = None
            self.lora_loaded = False
            torch.cuda.empty_cache()
            logger.info("Pipeline unloaded")


# Global pipeline instance (singleton pattern)
_pipeline_manager = None


def get_pipeline_manager() -> DiffusionPipelineManager:
    """
    Get or create the global pipeline manager instance
    
    Returns:
        DiffusionPipelineManager instance
    """
    global _pipeline_manager
    if _pipeline_manager is None:
        _pipeline_manager = DiffusionPipelineManager()
    return _pipeline_manager


def preprocess_image(image_path: Path) -> Image.Image:
    """
    Load and preprocess an input image
    
    This function:
    - Loads the image
    - Converts to RGB
    - Normalizes exposure/gamma if needed
    
    Args:
        image_path: Path to input image
        
    Returns:
        Preprocessed PIL Image
    """
    logger.info(f"Loading image from: {image_path}")
    
    image = Image.open(image_path)
    
    # Convert to RGB if necessary
    if image.mode != "RGB":
        image = image.convert("RGB")
    
    # Optional: Add gamma/exposure normalization here
    # This would analyze the histogram and adjust brightness
    
    return image


def enhance_image(
    input_image_path: Path,
    output_path: Path,
    strength: float = settings.IMG2IMG_STRENGTH,
    guidance_scale: float = settings.GUIDANCE_SCALE,
    custom_prompt: Optional[str] = None,
    use_upscaler: bool = True,
    use_postprocess: bool = True,
    progress_callback: Optional[Callable[[int, str], None]] = None
) -> Path:
    """
    Complete image enhancement pipeline
    
    This is the main entry point for enhancing images. It orchestrates:
    1. Image preprocessing
    2. Stable Diffusion img2img with optional LoRA
    3. Upscaling (optional)
    4. Post-processing (optional)
    
    Args:
        input_image_path: Path to input image
        output_path: Path to save enhanced image
        strength: Image-to-image strength
        guidance_scale: CFG scale
        custom_prompt: Optional custom prompt
        use_upscaler: Whether to apply upscaling
        use_postprocess: Whether to apply post-processing
        progress_callback: Optional callback for progress updates
        
    Returns:
        Path to the saved enhanced image
    """
    try:
        # Step 1: Load and preprocess
        if progress_callback:
            progress_callback(25, "Loading and preprocessing image...")
        logger.info("Step 1/4: Preprocessing image")
        image = preprocess_image(input_image_path)
        
        # Step 2: Get pipeline and load LoRA if available
        logger.info("Step 2/4: Running diffusion model")
        pipeline = get_pipeline_manager()
        
        lora_path = get_lora_path()
        if lora_path and not pipeline.lora_loaded:
            pipeline.load_lora(lora_path)
        
        # Determine prompt
        prompt = custom_prompt if custom_prompt else settings.DEFAULT_PROMPT
        
        # Run enhancement
        enhanced = pipeline.enhance(
            image=image,
            prompt=prompt,
            negative_prompt=settings.NEGATIVE_PROMPT,
            strength=strength,
            guidance_scale=guidance_scale,
            num_inference_steps=settings.NUM_INFERENCE_STEPS,
            progress_callback=progress_callback
        )
        
        # Step 3: Upscaling
        if use_upscaler:
            if progress_callback:
                progress_callback(92, "Upscaling image...")
            logger.info("Step 3/4: Upscaling image")
            enhanced = upscale_image(enhanced, scale=settings.UPSCALE_FACTOR)
        else:
            logger.info("Step 3/4: Skipping upscaling")
        
        # Step 4: Post-processing
        if use_postprocess:
            if progress_callback:
                progress_callback(94, "Applying final touches...")
            logger.info("Step 4/4: Applying post-processing")
            enhanced = postprocess_image(enhanced)
        else:
            logger.info("Step 4/4: Skipping post-processing")
        
        # Save result
        enhanced.save(output_path, quality=95, optimize=True)
        logger.info(f"Enhanced image saved to: {output_path}")
        
        return output_path
        
    except Exception as e:
        logger.error(f"Enhancement pipeline failed: {e}", exc_info=True)
        raise