#!/usr/bin/env python3
import os
import glob
from typing import List
from multiprocessing import Pool
from tqdm import tqdm

from langchain.document_loaders import (
    CSVLoader,
    EverNoteLoader,
    PyMuPDFLoader,
    TextLoader,
    UnstructuredEPubLoader,
    UnstructuredHTMLLoader,
    UnstructuredMarkdownLoader,
    UnstructuredODTLoader,
    UnstructuredPowerPointLoader,
    UnstructuredWordDocumentLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.docstore.document import Document

from config import (
    CHROMA_SETTINGS,
    DOCUMENTS_PATH,
    PERSIST_DIRECTORY,
    CHUNK_SIZE,
    CHUNK_OVERLAP,
)

# Map file extensions to document loaders and their arguments
LOADER_MAPPING = {
    ".csv": (CSVLoader, {}),
    ".doc": (UnstructuredWordDocumentLoader, {}),
    ".docx": (UnstructuredWordDocumentLoader, {}),
    ".enex": (EverNoteLoader, {}),
    ".epub": (UnstructuredEPubLoader, {}),
    ".html": (UnstructuredHTMLLoader, {}),
    ".md": (UnstructuredMarkdownLoader, {}),
    ".odt": (UnstructuredODTLoader, {}),
    ".pdf": (PyMuPDFLoader, {}),
    ".ppt": (UnstructuredPowerPointLoader, {}),
    ".pptx": (UnstructuredPowerPointLoader, {}),
    ".txt": (TextLoader, {"encoding": "utf8"}),
}


def load_single_document(file_path: str) -> List[Document]:
    print(file_path)
    ext = "." + file_path.rsplit(".", 1)[-1]
    if ext in LOADER_MAPPING:
        loader_class, loader_args = LOADER_MAPPING[ext]
        loader = loader_class(file_path, **loader_args)
        return loader.load()

    raise ValueError(f"Unsupported file extension '{ext}'")


def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
    """
    Loads all documents from the source documents directory, ignoring specified files
    """
    all_files = []
    for ext in LOADER_MAPPING:
        all_files.extend(
            glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
        )
    filtered_files = [
        file_path for file_path in all_files if file_path not in ignored_files
    ]

    with Pool(processes=os.cpu_count()) as pool:
        results = []
        with tqdm(
            total=len(filtered_files), desc="Loading new documents", ncols=80
        ) as pbar:
            for i, docs in enumerate(
                pool.imap_unordered(load_single_document, filtered_files)
            ):
                results.extend(docs)
                pbar.update()

    return results


def process_documents(ignored_files: List[str] = []) -> List[Document]:
    """
    Load documents and split in chunks
    """
    print(f"Loading documents from {DOCUMENTS_PATH}")
    documents = load_documents(DOCUMENTS_PATH, ignored_files)
    if not documents:
        print("No new documents to load")
        return []
    print(f"Loaded {len(documents)} new documents from {DOCUMENTS_PATH}")
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP
    )
    texts = text_splitter.split_documents(documents)
    print(f"Split into {len(texts)} chunks of text (max. {CHUNK_SIZE} tokens each)")
    return texts


def does_vectorstore_exist(persist_directory: str) -> bool:
    """
    Checks if vectorstore exists
    """
    if os.path.exists(os.path.join(persist_directory, "index")):
        if os.path.exists(
            os.path.join(persist_directory, "chroma-collections.parquet")
        ) and os.path.exists(
            os.path.join(persist_directory, "chroma-embeddings.parquet")
        ):
            list_index_files = glob.glob(os.path.join(persist_directory, "index/*.bin"))
            list_index_files += glob.glob(
                os.path.join(persist_directory, "index/*.pkl")
            )
            # At least 3 documents are needed in a working vectorstore
            if len(list_index_files) > 3:
                return True
    return False


def create_vectorstore():
    # Create embeddings
    embeddings = OpenAIEmbeddings()

    if does_vectorstore_exist(PERSIST_DIRECTORY):
        # Update and store locally vectorstore
        print(f"Appending to existing vectorstore at {PERSIST_DIRECTORY}")
        db = Chroma(
            persist_directory=PERSIST_DIRECTORY,
            embedding_function=embeddings,
            client_settings=CHROMA_SETTINGS,
        )
        collection = db.get()
        texts = process_documents(
            [metadata["source"] for metadata in collection["metadatas"]]
        )

        if not texts:
            return

        print(f"Creating embeddings. May take some minutes...")
        db.add_documents(texts)
    else:
        # Create and store locally vectorstore
        print("Creating new vectorstore")
        texts = process_documents()

        if not texts:
            return

        print(f"Creating embeddings. May take some minutes...")
        db = Chroma.from_documents(
            texts,
            embeddings,
            persist_directory=PERSIST_DIRECTORY,
            client_settings=CHROMA_SETTINGS,
        )
    db.persist()
    db = None

    print(f"Ingestion complete!")