"""Cache utilities for precomputed pipeline results.

This module provides functions for loading and saving cached pipeline results,
enabling instant display of results for example images without running the
expensive pipeline (GDINO+SAM segmentation, feature extraction, matching) on CPU.

Cache Structure (v2.0 - supports filtering):
    cached_results/
    ├── {image_stem}_{extractor}/
    │   ├── predictions.json          # ALL matches with location/body_part
    │   ├── segmentation.png          # Segmentation visualization
    │   ├── cropped.png               # Cropped snow leopard image
    │   ├── keypoints.png             # Extracted keypoints visualization
    │   └── pairwise/
    │       ├── {catalog_id}.npz      # NPZ data for ALL matches
    │       └── ...                   # (visualizations generated on-demand)
"""

import copy
import json
import logging
from pathlib import Path

import numpy as np
from PIL import Image

from snowleopard_reid.visualization import (
    draw_matched_keypoints,
    draw_side_by_side_comparison,
)

logger = logging.getLogger(__name__)

# Cache directory - use absolute path based on module location
# This ensures consistent path resolution regardless of cwd
PROJECT_ROOT = Path(__file__).parent.parent.parent
CACHE_DIR = PROJECT_ROOT / "cached_results"


def get_cache_key(image_path: Path | str, extractor: str) -> str:
    """Generate cache key from image stem and extractor.

    Args:
        image_path: Path to the query image
        extractor: Feature extractor name (e.g., 'sift', 'superpoint')

    Returns:
        Cache key string in format "{image_stem}_{extractor}"
    """
    image_path = Path(image_path)
    return f"{image_path.stem}_{extractor}"


def get_cache_dir(image_path: Path | str, extractor: str) -> Path:
    """Get cache directory for an image/extractor combination.

    Args:
        image_path: Path to the query image
        extractor: Feature extractor name

    Returns:
        Path to the cache directory
    """
    return CACHE_DIR / get_cache_key(image_path, extractor)


def is_cached(image_path: Path | str, extractor: str) -> bool:
    """Check if results are cached for this image/extractor combination.

    Args:
        image_path: Path to the query image
        extractor: Feature extractor name

    Returns:
        True if all required cache files exist
    """
    cache_dir = get_cache_dir(image_path, extractor)
    predictions_file = cache_dir / "predictions.json"

    if not predictions_file.exists():
        return False

    # Check for required visualization files
    required_files = [
        "segmentation.png",
        "cropped.png",
        "keypoints.png",
    ]

    for filename in required_files:
        if not (cache_dir / filename).exists():
            return False

    return True


def load_cached_results(image_path: Path | str, extractor: str) -> dict:
    """Load all cached results for an image/extractor combination.

    Args:
        image_path: Path to the query image
        extractor: Feature extractor name

    Returns:
        Dictionary containing:
            - predictions: Full pipeline predictions dict
            - segmentation_image: PIL Image of segmentation overlay
            - cropped_image: PIL Image of cropped snow leopard
            - keypoints_image: PIL Image of extracted keypoints
            - pairwise_dir: Path to directory with match visualizations

    Raises:
        FileNotFoundError: If cache files don't exist
    """
    cache_dir = get_cache_dir(image_path, extractor)

    if not cache_dir.exists():
        raise FileNotFoundError(f"Cache directory not found: {cache_dir}")

    predictions_file = cache_dir / "predictions.json"
    if not predictions_file.exists():
        raise FileNotFoundError(f"Predictions file not found: {predictions_file}")

    # Load predictions JSON
    with open(predictions_file) as f:
        predictions = json.load(f)

    # Load visualization images
    segmentation_image = Image.open(cache_dir / "segmentation.png")
    cropped_image = Image.open(cache_dir / "cropped.png")
    keypoints_image = Image.open(cache_dir / "keypoints.png")

    return {
        "predictions": predictions,
        "segmentation_image": segmentation_image,
        "cropped_image": cropped_image,
        "keypoints_image": keypoints_image,
        "pairwise_dir": cache_dir / "pairwise",
    }


def load_cached_match_visualizations(
    pairwise_dir: Path,
    matches: list[dict],
) -> tuple[dict, dict]:
    """Load cached match and clean comparison visualizations.

    Args:
        pairwise_dir: Path to pairwise visualizations directory
        matches: List of match dictionaries with rank and catalog_id

    Returns:
        Tuple of (match_visualizations, clean_comparison_visualizations)
        Both are dicts mapping rank -> PIL Image
    """
    match_visualizations = {}
    clean_comparison_visualizations = {}

    for match in matches:
        rank = match["rank"]
        catalog_id = match["catalog_id"]

        # Load match visualization
        match_path = pairwise_dir / f"rank_{rank:02d}_{catalog_id}_match.png"
        if match_path.exists():
            match_visualizations[rank] = Image.open(match_path)

        # Load clean comparison visualization
        clean_path = pairwise_dir / f"rank_{rank:02d}_{catalog_id}_clean.png"
        if clean_path.exists():
            clean_comparison_visualizations[rank] = Image.open(clean_path)

    return match_visualizations, clean_comparison_visualizations


def save_cache_results(
    image_path: Path | str,
    extractor: str,
    predictions: dict,
    segmentation_image: Image.Image,
    cropped_image: Image.Image,
    keypoints_image: Image.Image,
    match_visualizations: dict[int, Image.Image],
    clean_comparison_visualizations: dict[int, Image.Image],
    matches: list[dict],
) -> Path:
    """Save pipeline results to cache.

    Args:
        image_path: Path to the original query image
        extractor: Feature extractor name
        predictions: Full pipeline predictions dictionary
        segmentation_image: PIL Image of segmentation overlay
        cropped_image: PIL Image of cropped snow leopard
        keypoints_image: PIL Image of extracted keypoints
        match_visualizations: Dict mapping rank -> match visualization PIL Image
        clean_comparison_visualizations: Dict mapping rank -> clean comparison PIL Image
        matches: List of match dictionaries with rank and catalog_id

    Returns:
        Path to the cache directory
    """
    cache_dir = get_cache_dir(image_path, extractor)
    cache_dir.mkdir(parents=True, exist_ok=True)

    # Save predictions JSON
    predictions_file = cache_dir / "predictions.json"
    with open(predictions_file, "w") as f:
        json.dump(predictions, f, indent=2)
    logger.info(f"Saved predictions: {predictions_file}")

    # Save visualization images
    segmentation_image.save(cache_dir / "segmentation.png")
    cropped_image.save(cache_dir / "cropped.png")
    keypoints_image.save(cache_dir / "keypoints.png")
    logger.info(f"Saved visualization images to {cache_dir}")

    # Save pairwise match visualizations
    pairwise_dir = cache_dir / "pairwise"
    pairwise_dir.mkdir(exist_ok=True)

    for match in matches:
        rank = match["rank"]
        catalog_id = match["catalog_id"]

        # Save match visualization
        if rank in match_visualizations:
            match_path = pairwise_dir / f"rank_{rank:02d}_{catalog_id}_match.png"
            match_visualizations[rank].save(match_path)

        # Save clean comparison visualization
        if rank in clean_comparison_visualizations:
            clean_path = pairwise_dir / f"rank_{rank:02d}_{catalog_id}_clean.png"
            clean_comparison_visualizations[rank].save(clean_path)

    logger.info(f"Saved {len(match_visualizations)} pairwise visualizations")

    return cache_dir


def clear_cache(image_path: Path | str = None, extractor: str = None) -> None:
    """Clear cache directory.

    Args:
        image_path: If provided, only clear cache for this image
        extractor: If provided with image_path, only clear specific cache
    """
    import shutil

    if image_path and extractor:
        # Clear specific cache
        cache_dir = get_cache_dir(image_path, extractor)
        if cache_dir.exists():
            shutil.rmtree(cache_dir)
            logger.info(f"Cleared cache: {cache_dir}")
    elif CACHE_DIR.exists():
        # Clear all caches
        shutil.rmtree(CACHE_DIR)
        logger.info(f"Cleared all caches: {CACHE_DIR}")


def get_cache_summary() -> dict:
    """Get summary of cached results.

    Returns:
        Dictionary with cache statistics
    """
    if not CACHE_DIR.exists():
        return {"total_cached": 0, "total_size_mb": 0, "cached_items": []}

    cached_items = []
    total_size = 0

    for cache_dir in CACHE_DIR.iterdir():
        if cache_dir.is_dir():
            # Calculate size
            size = sum(f.stat().st_size for f in cache_dir.rglob("*") if f.is_file())
            total_size += size

            # Parse cache key
            parts = cache_dir.name.rsplit("_", 1)
            if len(parts) == 2:
                image_stem, extractor = parts
            else:
                image_stem, extractor = cache_dir.name, "unknown"

            cached_items.append({
                "image_stem": image_stem,
                "extractor": extractor,
                "size_mb": size / (1024 * 1024),
                "path": str(cache_dir),
            })

    return {
        "total_cached": len(cached_items),
        "total_size_mb": total_size / (1024 * 1024),
        "cached_items": cached_items,
    }


def filter_cached_matches(
    all_matches: list[dict],
    filter_locations: list[str] | None = None,
    filter_body_parts: list[str] | None = None,
    top_k: int = 5,
) -> list[dict]:
    """Filter cached matches by location/body_part and return top-k.

    Args:
        all_matches: List of all cached match dictionaries
        filter_locations: List of locations to filter by (e.g., ["skycrest_valley"])
        filter_body_parts: List of body parts to filter by (e.g., ["head", "right_flank"])
        top_k: Number of top matches to return after filtering

    Returns:
        List of filtered and re-ranked match dictionaries
    """
    # Make a deep copy to avoid modifying the original
    filtered = [copy.deepcopy(m) for m in all_matches]

    if filter_locations:
        filtered = [m for m in filtered if m.get("location") in filter_locations]

    if filter_body_parts:
        filtered = [m for m in filtered if m.get("body_part") in filter_body_parts]

    # Re-sort by wasserstein (descending - higher is better)
    filtered = sorted(filtered, key=lambda x: x.get("wasserstein", 0), reverse=True)

    # Re-assign ranks for the filtered top-k
    for i, match in enumerate(filtered[:top_k]):
        match["rank"] = i + 1

    return filtered[:top_k]


def generate_visualizations_from_npz(
    pairwise_dir: Path,
    matches: list[dict],
    cropped_image_path: Path | str,
) -> tuple[dict, dict]:
    """Generate match visualizations on-demand from cached NPZ data.

    Args:
        pairwise_dir: Path to directory containing NPZ pairwise data files
        matches: List of filtered match dictionaries with catalog_id and filepath
        cropped_image_path: Path to the cropped query image

    Returns:
        Tuple of (match_visualizations, clean_comparison_visualizations)
        Both are dicts mapping rank -> PIL Image
    """
    match_visualizations = {}
    clean_comparison_visualizations = {}

    cropped_image_path = Path(cropped_image_path)

    for match in matches:
        rank = match["rank"]
        catalog_id = match["catalog_id"]

        # Resolve relative path to absolute (handles both relative and absolute paths)
        filepath = match["filepath"]
        if not Path(filepath).is_absolute():
            catalog_image_path = PROJECT_ROOT / filepath
        else:
            catalog_image_path = Path(filepath)

        # Look for NPZ file by catalog_id
        npz_path = pairwise_dir / f"{catalog_id}.npz"

        if npz_path.exists():
            try:
                pairwise_data = np.load(npz_path, allow_pickle=True)

                # Generate matched keypoints visualization
                match_viz = draw_matched_keypoints(
                    query_image_path=cropped_image_path,
                    catalog_image_path=catalog_image_path,
                    query_keypoints=pairwise_data["query_keypoints"],
                    catalog_keypoints=pairwise_data["catalog_keypoints"],
                    match_scores=pairwise_data["match_scores"],
                    max_matches=100,
                )
                match_visualizations[rank] = match_viz

                # Generate clean side-by-side comparison
                clean_viz = draw_side_by_side_comparison(
                    query_image_path=cropped_image_path,
                    catalog_image_path=catalog_image_path,
                )
                clean_comparison_visualizations[rank] = clean_viz

            except Exception as e:
                logger.warning(
                    f"Failed to generate visualization for {catalog_id}: {e}"
                )
        else:
            logger.warning(f"NPZ file not found for {catalog_id}: {npz_path}")

    return match_visualizations, clean_comparison_visualizations


def extract_location_body_part_from_filepath(filepath: str) -> tuple[str, str]:
    """Extract location and body_part from catalog image filepath.

    Expected filepath format:
        .../database/{location}/{individual}/images/{body_part}/{filename}

    Args:
        filepath: Path to catalog image

    Returns:
        Tuple of (location, body_part)
    """
    parts = Path(filepath).parts

    # Find "database" in path and extract location (next part) and body_part
    try:
        db_idx = parts.index("database")
        location = parts[db_idx + 1] if db_idx + 1 < len(parts) else "unknown"

        # Find "images" in path and get body_part (next part)
        img_idx = parts.index("images")
        body_part = parts[img_idx + 1] if img_idx + 1 < len(parts) else "unknown"

        return location, body_part
    except (ValueError, IndexError):
        return "unknown", "unknown"