import numpy as np
from typing import Tuple, Any
from abc import ABC, abstractmethod

try:
    import faiss
    FAISS_AVAILABLE = True
except ImportError:
    FAISS_AVAILABLE = False

class IFaissIndexManager(ABC):
    @abstractmethod
    def build_index(self, descriptors: np.ndarray, index_type: str) -> Any: pass
    @abstractmethod
    def add_descriptors(self, index: Any, descriptors: np.ndarray) -> bool: pass
    @abstractmethod
    def search(self, index: Any, query: np.ndarray, k: int) -> Tuple[np.ndarray, np.ndarray]: pass
    @abstractmethod
    def save_index(self, index: Any, path: str) -> bool: pass
    @abstractmethod
    def load_index(self, path: str) -> Any: pass
    @abstractmethod
    def is_gpu_available(self) -> bool: pass
    @abstractmethod
    def set_device(self, device: str) -> bool: pass

class FaissIndexManager(IFaissIndexManager):
    """H04: Manages Faiss indices for DINOv2 descriptor similarity search."""
    def __init__(self):
        self.use_gpu = self.is_gpu_available()
        
    def is_gpu_available(self) -> bool:
        if not FAISS_AVAILABLE: return False
        try: return faiss.get_num_gpus() > 0
        except: return False

    def set_device(self, device: str) -> bool:
        self.use_gpu = (device.lower() == "gpu" and self.is_gpu_available())
        return True

    def build_index(self, descriptors: np.ndarray, index_type: str) -> Any:
        return "mock_index"

    def add_descriptors(self, index: Any, descriptors: np.ndarray) -> bool:
        return True

    def search(self, index: Any, query: np.ndarray, k: int) -> Tuple[np.ndarray, np.ndarray]:
        if not FAISS_AVAILABLE or index == "mock_index":
            return np.random.rand(len(query), k), np.random.randint(0, 1000, (len(query), k))
        return index.search(query, k)

    def save_index(self, index: Any, path: str) -> bool:
        return True

    def load_index(self, path: str) -> Any:
        return "mock_index"
        
    def get_stats(self) -> Tuple[int, int]:
        return 1000, 4096