"""
Voice prompt caching utilities.
"""

import hashlib
import logging
import torch
from pathlib import Path
from typing import Optional, Union, Dict, Any

from .. import config

logger = logging.getLogger(__name__)


def _get_cache_dir() -> Path:
    """Get cache directory from config."""
    return config.get_cache_dir()


# In-memory cache - can store dict (voice prompt) or tensor (legacy)
_memory_cache: dict[str, Union[torch.Tensor, Dict[str, Any]]] = {}


def get_cache_key(audio_path: str, reference_text: str) -> str:
    """
    Generate cache key from audio file and reference text.

    Args:
        audio_path: Path to audio file
        reference_text: Reference text

    Returns:
        Cache key (MD5 hash)
    """
    # Read audio file
    with open(audio_path, "rb") as f:
        audio_bytes = f.read()

    # Combine audio bytes and text
    combined = audio_bytes + reference_text.encode("utf-8")

    # Generate hash
    return hashlib.md5(combined).hexdigest()


def get_cached_voice_prompt(
    cache_key: str,
) -> Optional[Union[torch.Tensor, Dict[str, Any]]]:
    """
    Get cached voice prompt if available.

    Args:
        cache_key: Cache key

    Returns:
        Cached voice prompt (dict or tensor) or None
    """
    # Check in-memory cache
    if cache_key in _memory_cache:
        return _memory_cache[cache_key]

    # Check disk cache
    cache_file = _get_cache_dir() / f"{cache_key}.prompt"
    if cache_file.exists():
        try:
            prompt = torch.load(cache_file, weights_only=True)
            _memory_cache[cache_key] = prompt
            return prompt
        except Exception:
            # Cache file corrupted, delete it
            cache_file.unlink()

    return None


def cache_voice_prompt(
    cache_key: str,
    voice_prompt: Union[torch.Tensor, Dict[str, Any]],
) -> None:
    """
    Cache voice prompt to memory and disk.

    Args:
        cache_key: Cache key
        voice_prompt: Voice prompt (dict or tensor)
    """
    # Store in memory
    _memory_cache[cache_key] = voice_prompt

    # Store on disk (torch.save can handle both dicts and tensors)
    cache_file = _get_cache_dir() / f"{cache_key}.prompt"
    torch.save(voice_prompt, cache_file)


def clear_voice_prompt_cache() -> int:
    """
    Clear all voice prompt caches (memory and disk).

    Returns:
        Number of cache files deleted
    """
    # Clear memory cache
    _memory_cache.clear()

    # Clear disk cache
    cache_dir = _get_cache_dir()
    deleted_count = 0

    if cache_dir.exists():
        # Delete prompt cache files
        for cache_file in cache_dir.glob("*.prompt"):
            try:
                cache_file.unlink()
                deleted_count += 1
            except Exception as e:
                logger.warning("Failed to delete cache file %s: %s", cache_file, e)

        # Delete combined audio files
        for audio_file in cache_dir.glob("combined_*.wav"):
            try:
                audio_file.unlink()
                deleted_count += 1
            except Exception as e:
                logger.warning("Failed to delete combined audio file %s: %s", audio_file, e)

    return deleted_count


def clear_profile_cache(profile_id: str) -> int:
    """
    Clear cache files for a specific profile.

    Args:
        profile_id: Profile ID

    Returns:
        Number of cache files deleted
    """
    cache_dir = _get_cache_dir()
    deleted_count = 0

    if cache_dir.exists():
        # Delete combined audio files for this profile
        pattern = f"combined_{profile_id}_*.wav"
        for audio_file in cache_dir.glob(pattern):
            try:
                audio_file.unlink()
                deleted_count += 1
            except Exception as e:
                logger.warning("Failed to delete combined audio file %s: %s", audio_file, e)

    return deleted_count