import os
import tensorflow as tf

from ml4ir.base.features.feature_config import FeatureConfig
from ml4ir.base.data import tfrecord_reader, tfrecord_writer
from ml4ir.base.io.file_io import FileIO

from typing import List

# Constants
TFRECORD_FILE = "file_0.tfrecord"


def read(
    data_dir: str,
    feature_config: FeatureConfig,
    tfrecord_type: str,
    tfrecord_dir: str,
    file_io: FileIO,
    data_compression: str = None,
    batch_size: int = 128,
    preprocessing_keys_to_fns: dict = {},
    use_part_files: bool = False,
    max_sequence_size: int = 25,
    parse_tfrecord: bool = True,
    logger=None,
    **kwargs
) -> tf.data.TFRecordDataset:
    """
    Create a TFRecordDataset from directory of CSV files using the FeatureConfig

    Current execution plan:
        1. Load CSVs as pandas dataframes
        2. Convert each query into tf.train.SequenceExample protobufs
        3. Write the protobufs into a .tfrecord file
        4. Load .tfrecord file into a TFRecordDataset and parse the protobufs

    Parameters
    ----------
    data_dir : str
        Path to directory containing csv files to read
    feature_config : FeatureConfig object
        FeatureConfig object that defines the features to be loaded in the dataset
        and the preprocessing functions to be applied to each of them
    tfrecord_dir : str
        Path to directory where the serialized .tfrecord files will be stored
    data_compression: str
        Type of data compression used for the input data files.
        NOTE - Not implemented for CSV reader.
    batch_size : int
        value specifying the size of the data batch
    use_part_files : bool
        load dataset from part files checked using "part-" prefix
    max_sequence_size : int
        value specifying max number of records per query
    logger : Logger object
        logging handler to print and save status messages

    Returns
    -------
    `TFRecordDataset` object
        tensorflow TFRecordDataset loaded from the CSV file
    """
    csv_files: List[str] = file_io.get_files_in_directory(
        data_dir,
        extension="" if use_part_files else ".csv",
        prefix="part-" if use_part_files else "",
    )

    # Create a directory for storing tfrecord files
    file_io.make_directory(tfrecord_dir, clear_dir=True)

    # Write tfrecord files
    tfrecord_writer.write_from_files(
        csv_files=csv_files,
        tfrecord_file=os.path.join(tfrecord_dir, TFRECORD_FILE),
        feature_config=feature_config,
        tfrecord_type=tfrecord_type,
        file_io=file_io,
        logger=logger,
    )

    dataset = tfrecord_reader.read(
        data_dir=tfrecord_dir,
        feature_config=feature_config,
        tfrecord_type=tfrecord_type,
        max_sequence_size=max_sequence_size,
        batch_size=batch_size,
        preprocessing_keys_to_fns=preprocessing_keys_to_fns,
        parse_tfrecord=parse_tfrecord,
        file_io=file_io,
        logger=logger,
    )

    return dataset
