import os
import pickle
import faiss
import numpy as np
from typing import List, Dict, Any, Optional, Tuple
from tqdm import tqdm

from src.utils.config import DATA_DIR, EMBEDDINGS_DIR
from src.embeddings.embedder import TextEmbedder

class DocumentProcessor:
    """
    Handles document loading, chunking, and processing operations.
    """
    
    def __init__(self, data_dir: str = DATA_DIR, embeddings_dir: str = EMBEDDINGS_DIR):
        """
        Initialize the document processor.
        
        Args:
            data_dir: Directory containing the document files
            embeddings_dir: Directory for storing embeddings and indexes
        """
        self.data_dir = data_dir
        self.embeddings_dir = embeddings_dir
        self.embedder = TextEmbedder()
        
        # Create directories if they don't exist
        os.makedirs(data_dir, exist_ok=True)
        os.makedirs(embeddings_dir, exist_ok=True)
    
    def process_documents(self, doc_chunks: List[Dict[str, Any]], save: bool = True) -> Tuple[List[Dict[str, Any]], List[List[float]]]:
        """
        Process document chunks by generating embeddings and creating a FAISS index.
        
        Args:
            doc_chunks: List of document chunks to process
            save: Whether to save the processed data to disk
            
        Returns:
            Tuple containing the document chunks and their embeddings
        """
        print(f"Processing {len(doc_chunks)} document chunks...")
        
        # Extract text chunks for embedding
        texts = [chunk["chunk"] for chunk in doc_chunks]
        
        # Generate embeddings
        print("Generating embeddings...")
        embeddings = self.embedder.get_embeddings_for_texts(texts)
        
        # Save the results if requested
        if save:
            self._save_processed_data(doc_chunks, embeddings)
        
        return doc_chunks, embeddings
    
    def create_faiss_index(self, embeddings: List[List[float]], save: bool = True) -> faiss.Index:
        """
        Create a FAISS index from the document embeddings.
        
        Args:
            embeddings: List of embedding vectors
            save: Whether to save the index to disk
            
        Returns:
            FAISS index
        """
        print("Creating FAISS index...")
        
        # Convert embeddings to numpy array
        embedding_array = np.array(embeddings, dtype='float32')
        
        # Get dimensions
        vector_dimension = embedding_array.shape[1]
        
        # Create the index
        index = faiss.IndexFlatL2(vector_dimension)
        index.add(embedding_array)
        
        print(f"Created FAISS index with {index.ntotal} vectors of dimension {vector_dimension}")
        
        # Save the index if requested
        if save:
            index_path = os.path.join(self.embeddings_dir, "faiss_index.index")
            faiss.write_index(index, index_path)
            print(f"FAISS index saved to {index_path}")
        
        return index
    
    def _save_processed_data(self, doc_chunks: List[Dict[str, Any]], embeddings: List[List[float]]) -> None:
        """
        Save the processed document chunks and embeddings to disk.
        
        Args:
            doc_chunks: List of document chunks
            embeddings: List of embedding vectors
        """
        # Save document chunks
        chunks_path = os.path.join(self.data_dir, "doc_chunks.pkl")
        with open(chunks_path, "wb") as f:
            pickle.dump(doc_chunks, f)
        print(f"Document chunks saved to {chunks_path}")
        
        # Save embeddings
        embeddings_path = os.path.join(self.embeddings_dir, "embeddings.pkl")
        with open(embeddings_path, "wb") as f:
            pickle.dump(embeddings, f)
        print(f"Embeddings saved to {embeddings_path}")
    
    def load_processed_data(self) -> Tuple[List[Dict[str, Any]], List[List[float]], faiss.Index]:
        """
        Load processed document chunks, embeddings, and FAISS index from disk.
        
        Returns:
            Tuple containing document chunks, embeddings, and FAISS index
        """
        # Load document chunks
        chunks_path = os.path.join(self.data_dir, "doc_chunks.pkl")
        with open(chunks_path, "rb") as f:
            doc_chunks = pickle.load(f)
        print(f"Document chunks loaded from {chunks_path}")
        
        # Load embeddings
        embeddings_path = os.path.join(self.embeddings_dir, "embeddings.pkl")
        with open(embeddings_path, "rb") as f:
            embeddings = pickle.load(f)
        print(f"Embeddings loaded from {embeddings_path}")
        
        # Load FAISS index
        index_path = os.path.join(self.embeddings_dir, "faiss_index.index")
        index = faiss.read_index(index_path)
        print(f"FAISS index loaded from {index_path}")
        
        return doc_chunks, embeddings, index