"""tool_cache.py — V6: tool cache for repeated invocations.

Cache LRU para resultados de ferramentas. Reduz computação redundante
quando a mesma entrada é processada múltiplas vezes.
"""
from __future__ import annotations
import hashlib
import time
from typing import Optional, Dict, Any, Callable
from collections import OrderedDict

import torch


class ToolCache:
    """V6: cache LRU para ferramentas.

    Usage:
        cache = ToolCache(max_size=1024, ttl_seconds=3600)
        result = cache.get_or_compute("tool_name", input_tensor, compute_fn)
    """
    def __init__(
        self,
        max_size: int = 1024,
        ttl_seconds: float = 3600.0,
        hash_fn: Optional[Callable] = None,
    ):
        self.max_size = max_size
        self.ttl_seconds = ttl_seconds
        self.hash_fn = hash_fn or self._default_hash
        self._cache: OrderedDict[str, tuple] = OrderedDict()  # key -> (value, timestamp)
        self._stats = {"hits": 0, "misses": 0, "evictions": 0}

    @staticmethod
    def _default_hash(key: Any) -> str:
        if isinstance(key, torch.Tensor):
            key = key.detach().cpu().numpy().tobytes()
        elif isinstance(key, (list, tuple)):
            key = str(key)
        return hashlib.sha256(str(key).encode("utf-8")).hexdigest()

    def _make_key(self, tool_name: str, input_key: Any) -> str:
        return f"{tool_name}:{self.hash_fn(input_key)}"

    def get_or_compute(
        self,
        tool_name: str,
        input_key: Any,
        compute_fn: Callable,
    ) -> Any:
        key = self._make_key(tool_name, input_key)
        now = time.time()
        # Check cache
        if key in self._cache:
            value, ts = self._cache[key]
            if now - ts < self.ttl_seconds:
                self._cache.move_to_end(key)
                self._stats["hits"] += 1
                return value
            else:
                del self._cache[key]
        # Compute
        value = compute_fn()
        self._cache[key] = (value, now)
        self._stats["misses"] += 1
        # Evict LRU
        while len(self._cache) > self.max_size:
            self._cache.popitem(last=False)
            self._stats["evictions"] += 1
        return value

    def invalidate(self, tool_name: str) -> None:
        """Remove todas as entradas de uma ferramenta."""
        keys_to_remove = [k for k in self._cache if k.startswith(f"{tool_name}:")]
        for k in keys_to_remove:
            del self._cache[k]

    def clear(self) -> None:
        self._cache.clear()
        self._stats = {"hits": 0, "misses": 0, "evictions": 0}

    def get_stats(self) -> Dict[str, Any]:
        total = self._stats["hits"] + self._stats["misses"]
        hit_rate = self._stats["hits"] / max(1, total)
        return {
            **self._stats,
            "hit_rate": hit_rate,
            "size": len(self._cache),
            "max_size": self.max_size,
        }


__all__ = ["ToolCache"]