"""
Vision-based compression module for enhanced Glyph compression.

This module simulates DeepSeek-OCR-like compression by using available
vision models to further compress Glyph-rendered images.
"""

import time
from pathlib import Path
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass
import base64
import hashlib

from ..utils.structured_logging import get_logger
from ..media.types import MediaContent, MediaType, ContentFormat


@dataclass
class VisionCompressionResult:
    """Result from vision-based compression."""
    original_images: int
    compressed_tokens: int
    compression_ratio: float
    quality_score: float
    processing_time: float
    method: str
    metadata: Dict[str, Any]


class VisionCompressor:
    """
    Vision-based compressor that further compresses Glyph-rendered images.

    This simulates DeepSeek-OCR's approach by using vision models to
    create compressed representations of text-heavy images.
    """

    def __init__(self, provider: str = "ollama", model: str = "llama3.2-vision"):
        """
        Initialize vision compressor.

        Args:
            provider: Vision model provider to use
            model: Vision model for compression
        """
        self.logger = get_logger(self.__class__.__name__)
        self.provider = provider
        self.model = model

        # Compression configurations based on quality targets
        self.compression_modes = {
            "conservative": {
                "target_ratio": 2.0,
                "quality_threshold": 0.95,
                "description": "Minimal compression, high quality"
            },
            "balanced": {
                "target_ratio": 5.0,
                "quality_threshold": 0.90,
                "description": "Balanced compression and quality"
            },
            "aggressive": {
                "target_ratio": 10.0,
                "quality_threshold": 0.85,
                "description": "Maximum compression, acceptable quality"
            }
        }

    def compress_images(
        self,
        glyph_images: List[MediaContent],
        mode: str = "balanced",
        original_tokens: int = None
    ) -> VisionCompressionResult:
        """
        Compress Glyph-rendered images using vision model.

        Args:
            glyph_images: List of Glyph-rendered images
            mode: Compression mode (conservative/balanced/aggressive)
            original_tokens: Original token count before Glyph compression

        Returns:
            VisionCompressionResult with compression metrics
        """
        start_time = time.time()

        if mode not in self.compression_modes:
            raise ValueError(f"Invalid mode: {mode}. Use: {list(self.compression_modes.keys())}")

        config = self.compression_modes[mode]

        self.logger.info(f"Starting vision compression: {len(glyph_images)} images, mode={mode}")

        # Simulate vision-based compression
        # In a real implementation, this would:
        # 1. Pass images through a vision encoder (like DeepSeek's SAM+CLIP)
        # 2. Apply learned compression to reduce token count
        # 3. Return compressed vision tokens

        # For simulation, we calculate compressed tokens based on target ratio
        original_image_tokens = len(glyph_images) * 1500  # Approximate tokens per image

        # Apply vision compression based on mode
        if mode == "conservative":
            # Minimal compression - combine adjacent images
            compressed_tokens = original_image_tokens // config["target_ratio"]
            quality_score = 0.95

        elif mode == "balanced":
            # Balanced - more aggressive merging and compression
            compressed_tokens = original_image_tokens // config["target_ratio"]
            quality_score = 0.92

        else:  # aggressive
            # Maximum compression - extreme token reduction
            compressed_tokens = original_image_tokens // config["target_ratio"]
            quality_score = 0.88

        # Calculate overall compression ratio
        if original_tokens:
            overall_ratio = original_tokens / compressed_tokens
        else:
            overall_ratio = original_image_tokens / compressed_tokens

        processing_time = time.time() - start_time

        # Create result
        result = VisionCompressionResult(
            original_images=len(glyph_images),
            compressed_tokens=int(compressed_tokens),
            compression_ratio=overall_ratio,
            quality_score=quality_score,
            processing_time=processing_time,
            method=f"vision_{mode}",
            metadata={
                "provider": self.provider,
                "model": self.model,
                "mode": mode,
                "target_ratio": config["target_ratio"],
                "quality_threshold": config["quality_threshold"]
            }
        )

        self.logger.info(
            f"Vision compression complete: {overall_ratio:.1f}x ratio, "
            f"{quality_score:.2%} quality, {compressed_tokens} tokens"
        )

        return result

    def compress_with_ocr(
        self,
        glyph_images: List[MediaContent],
        extract_text: bool = True
    ) -> Tuple[VisionCompressionResult, Optional[str]]:
        """
        Compress images and optionally extract text (OCR).

        This simulates DeepSeek-OCR's dual capability:
        1. Compress images to vision tokens
        2. Extract text from images

        Args:
            glyph_images: List of Glyph-rendered images
            extract_text: Whether to extract text from images

        Returns:
            Tuple of (compression result, extracted text)
        """
        # Compress images
        result = self.compress_images(glyph_images, mode="balanced")

        extracted_text = None
        if extract_text:
            # In a real implementation, this would use OCR
            # For now, we'll indicate that text extraction is possible
            extracted_text = "[Text extraction would occur here with real OCR model]"
            self.logger.info("Text extraction completed (simulated)")

        return result, extracted_text

    def adaptive_compress(
        self,
        glyph_images: List[MediaContent],
        original_tokens: int,
        target_ratio: float = 30.0,
        min_quality: float = 0.85
    ) -> VisionCompressionResult:
        """
        Adaptively compress to achieve target compression ratio.

        Args:
            glyph_images: List of Glyph-rendered images
            original_tokens: Original token count
            target_ratio: Target compression ratio
            min_quality: Minimum acceptable quality

        Returns:
            Best compression result achieving targets
        """
        self.logger.info(f"Adaptive compression: target ratio={target_ratio:.1f}x, min quality={min_quality:.2%}")

        # Try different modes to find best fit
        best_result = None

        for mode in ["aggressive", "balanced", "conservative"]:
            result = self.compress_images(glyph_images, mode, original_tokens)

            # Check if this meets our criteria
            if result.quality_score >= min_quality:
                if best_result is None or result.compression_ratio > best_result.compression_ratio:
                    best_result = result

                # If we've achieved target, stop
                if result.compression_ratio >= target_ratio:
                    break

        if best_result is None:
            # Fall back to conservative if nothing meets quality threshold
            best_result = self.compress_images(glyph_images, "conservative", original_tokens)

        self.logger.info(
            f"Adaptive compression selected: {best_result.method}, "
            f"{best_result.compression_ratio:.1f}x ratio, {best_result.quality_score:.2%} quality"
        )

        return best_result


class HybridCompressionPipeline:
    """
    Hybrid compression pipeline combining Glyph and vision compression.

    This implements the theoretical Glyph → Vision Compressor pipeline
    to achieve higher compression ratios.
    """

    def __init__(
        self,
        vision_provider: str = "ollama",
        vision_model: str = "llama3.2-vision"
    ):
        """
        Initialize hybrid compression pipeline.

        Args:
            vision_provider: Provider for vision compression
            vision_model: Model for vision compression
        """
        self.logger = get_logger(self.__class__.__name__)
        self.vision_compressor = VisionCompressor(vision_provider, vision_model)

        # Import Glyph processor
        from .glyph_processor import GlyphProcessor
        from .config import GlyphConfig

        # Configure Glyph for optimal vision compression
        config = GlyphConfig()
        config.enabled = True
        config.min_token_threshold = 1000

        self.glyph_processor = GlyphProcessor(config=config)

    def compress(
        self,
        text: str,
        target_ratio: float = 30.0,
        min_quality: float = 0.85
    ) -> Dict[str, Any]:
        """
        Compress text using hybrid Glyph + Vision pipeline.

        Args:
            text: Text to compress
            target_ratio: Target compression ratio
            min_quality: Minimum acceptable quality

        Returns:
            Dictionary with compression results, metrics, AND media content
        """
        start_time = time.time()

        # Calculate original tokens
        from ..utils.token_utils import TokenUtils
        original_tokens = TokenUtils.estimate_tokens(text, "gpt-4o")

        self.logger.info(f"Starting hybrid compression: {original_tokens} tokens")

        # Stage 1: Glyph compression
        self.logger.info("Stage 1: Glyph rendering...")
        glyph_start = time.time()

        try:
            glyph_images = self.glyph_processor.process_text(
                text,
                provider="openai",
                model="gpt-4o",
                user_preference="always"
            )
            glyph_time = time.time() - glyph_start

            # Calculate Glyph compression
            glyph_tokens = len(glyph_images) * 1500  # Approximate
            glyph_ratio = original_tokens / glyph_tokens if glyph_tokens > 0 else 1.0

            self.logger.info(
                f"Glyph complete: {len(glyph_images)} images, "
                f"{glyph_ratio:.1f}x compression, {glyph_time:.2f}s"
            )

        except Exception as e:
            self.logger.error(f"Glyph compression failed: {e}")
            raise

        # Stage 2: Vision compression
        self.logger.info("Stage 2: Vision compression...")
        vision_start = time.time()

        vision_result = self.vision_compressor.adaptive_compress(
            glyph_images,
            original_tokens,
            target_ratio=target_ratio,
            min_quality=min_quality
        )

        vision_time = time.time() - vision_start

        # Calculate total compression
        total_time = time.time() - start_time
        total_ratio = vision_result.compression_ratio  # Already calculated from original

        # Compile results - NOW INCLUDING THE ACTUAL MEDIA
        results = {
            "success": True,
            "media": glyph_images,  # ADD THE ACTUAL COMPRESSED IMAGES HERE
            "original_tokens": original_tokens,
            "final_tokens": vision_result.compressed_tokens,
            "total_compression_ratio": total_ratio,
            "total_quality_score": vision_result.quality_score,
            "total_processing_time": total_time,
            "stages": {
                "glyph": {
                    "images": len(glyph_images),
                    "tokens": glyph_tokens,
                    "ratio": glyph_ratio,
                    "time": glyph_time
                },
                "vision": {
                    "tokens": vision_result.compressed_tokens,
                    "ratio": vision_result.compression_ratio,
                    "quality": vision_result.quality_score,
                    "mode": vision_result.metadata["mode"],
                    "time": vision_time
                }
            },
            "metadata": {
                "pipeline": "hybrid_glyph_vision",
                "vision_provider": self.vision_compressor.provider,
                "vision_model": self.vision_compressor.model,
                "timestamp": time.time()
            }
        }

        self.logger.info(
            f"Hybrid compression complete: {total_ratio:.1f}x total compression, "
            f"{vision_result.quality_score:.2%} quality, {total_time:.2f}s"
        )

        return results