"""Image deduplication utilities using perceptual hashing."""

from typing import Optional

import imagehash
from PIL import Image as PILImage


def calculate_phash(image_path: str) -> Optional[str]:
    """
    Calculate perceptual hash for an image.

    Args:
        image_path: Path to image file

    Returns:
        Hex string of perceptual hash, or None if failed
    """
    try:
        with PILImage.open(image_path) as img:
            return str(imagehash.phash(img))
    except Exception:
        return None


def calculate_dhash(image_path: str) -> Optional[str]:
    """
    Calculate difference hash for an image.
    Faster but less accurate than phash.

    Args:
        image_path: Path to image file

    Returns:
        Hex string of difference hash, or None if failed
    """
    try:
        with PILImage.open(image_path) as img:
            return str(imagehash.dhash(img))
    except Exception:
        return None


def hashes_are_similar(hash1: str, hash2: str, threshold: int = 10) -> bool:
    """
    Check if two hashes are similar (potential duplicates).

    Args:
        hash1: First hash string
        hash2: Second hash string
        threshold: Maximum Hamming distance (default 10)

    Returns:
        True if hashes are similar
    """
    try:
        h1 = imagehash.hex_to_hash(hash1)
        h2 = imagehash.hex_to_hash(hash2)
        return (h1 - h2) <= threshold
    except Exception:
        return False


def hamming_distance(hash1: str, hash2: str) -> int:
    """
    Calculate Hamming distance between two hashes.

    Args:
        hash1: First hash string
        hash2: Second hash string

    Returns:
        Hamming distance (0 = identical, higher = more different)
    """
    try:
        h1 = imagehash.hex_to_hash(hash1)
        h2 = imagehash.hex_to_hash(hash2)
        return int(h1 - h2)
    except Exception:
        return 64  # Maximum distance