import logging
from pathlib import Path
from typing import Dict, Type

from llama_index.core.readers import StringIterableReader
from llama_index.core.readers.base import BaseReader
from llama_index.core.readers.json import JSONReader
from llama_index.core.schema import Document

logger = logging.getLogger(__name__)


# Inspired by the `llama_index.core.readers.file.base` module
def _try_loading_included_file_formats() -> Dict[str, Type[BaseReader]]:
    try:
        from llama_index.readers.file.docs import DocxReader, HWPReader, PDFReader
        from llama_index.readers.file.epub import EpubReader
        from llama_index.readers.file.image import ImageReader
        from llama_index.readers.file.ipynb import IPYNBReader
        from llama_index.readers.file.markdown import MarkdownReader
        from llama_index.readers.file.mbox import MboxReader
        from llama_index.readers.file.tabular import PandasCSVReader
        from llama_index.readers.file.slides import PptxReader
        from llama_index.readers.file.video_audio import VideoAudioReader
    except ImportError:
        raise ImportError("`llama-index-readers-file` package not found")

    default_file_reader_cls: Dict[str, Type[BaseReader]] = {
        ".hwp": HWPReader,
        ".pdf": PDFReader,
        ".docx": DocxReader,
        ".pptx": PptxReader,
        ".ppt": PptxReader,
        ".pptm": PptxReader,
        ".jpg": ImageReader,
        ".png": ImageReader,
        ".jpeg": ImageReader,
        ".mp3": VideoAudioReader,
        ".mp4": VideoAudioReader,
        ".csv": PandasCSVReader,
        ".epub": EpubReader,
        ".md": MarkdownReader,
        ".mbox": MboxReader,
        ".ipynb": IPYNBReader,
    }
    return default_file_reader_cls


# Patching the default file reader to support other file types
FILE_READER_CLS = _try_loading_included_file_formats()
FILE_READER_CLS.update(
    {
        ".json": JSONReader,
    }
)


class IngestionHelper:
    """Helper class to transform a file into a list of documents.

    This class should be used to transform a file into a list of documents.
    These methods are thread-safe (and multiprocessing-safe).
    """

    @staticmethod
    def transform_file_into_documents(
        file_name: str, file_data: Path
    ) -> list[Document]:
        documents = IngestionHelper._load_file_to_documents(file_name, file_data)
        for document in documents:
            document.metadata["file_name"] = file_name
        IngestionHelper._exclude_metadata(documents)
        return documents

    @staticmethod
    def _load_file_to_documents(file_name: str, file_data: Path) -> list[Document]:
        logger.debug("Transforming file_name=%s into documents", file_name)
        extension = Path(file_name).suffix
        reader_cls = FILE_READER_CLS.get(extension)
        if reader_cls is None:
            logger.debug(
                "No reader found for extension=%s, using default string reader",
                extension,
            )
            # Read as a plain text
            string_reader = StringIterableReader()
            return string_reader.load_data([file_data.read_text()])

        logger.debug("Specific reader found for extension=%s", extension)
        return reader_cls().load_data(file_data)

    @staticmethod
    def _exclude_metadata(documents: list[Document]) -> None:
        logger.debug("Excluding metadata from count=%s documents", len(documents))
        for document in documents:
            document.metadata["doc_id"] = document.doc_id
            # We don't want the Embeddings search to receive this metadata
            document.excluded_embed_metadata_keys = ["doc_id"]
            # We don't want the LLM to receive these metadata in the context
            document.excluded_llm_metadata_keys = ["file_name", "doc_id", "page_label"]