"""Module to train and choose between structured and unstructured data labelers."""
from __future__ import annotations

import os

import pandas as pd
import pkg_resources

from .. import data_readers
from .base_data_labeler import BaseDataLabeler, TrainableDataLabeler
from .base_model import BaseModel
from .data_processing import BaseDataPostprocessor, BaseDataPreprocessor

default_labeler_dir = pkg_resources.resource_filename("resources", "labelers")


def train_structured_labeler(
    data: None | pd.DataFrame,
    default_label: int = None,
    save_dirpath: str = None,
    epochs: int = 2,
) -> TrainableDataLabeler:
    """
    Use provided data to create and save a structured data labeler.

    :param data: data to be trained upon
    :type data: Union[None, pd.DataFrame]
    :param save_dirpath: path to save data labeler
    :type save_dirpath: Union[None, str]
    :param epochs: number of epochs to loop training the data
    :type epochs: int
    :return: structured data labeler
    :rtype: TrainableDataLabeler
    """
    if isinstance(data, data_readers.base_data.BaseData) and not isinstance(
        data, data_readers.text_data.TextData
    ):
        data = data.data
    elif isinstance(data, pd.DataFrame):
        pass
    else:
        raise TypeError(
            "Input data must be either a `pd.DataFrame` or a "
            "`data_profiler.Data` and not of type `TextData`."
        )
    if save_dirpath is not None:
        if not isinstance(save_dirpath, str):
            raise TypeError("The output dirpath must be a string.")
        elif not os.access(save_dirpath, os.W_OK):
            raise ValueError("The `save_dirpath` is not valid or not accessible.")

    # prep data
    value_label_df = data.reset_index(drop=True).melt()
    value_label_df.columns = [1, 0]  # labels=1, values=0 in that order
    value_label_df = value_label_df.astype(str)

    data_labeler: TrainableDataLabeler = DataLabeler(  # type: ignore
        labeler_type="structured", trainable=True
    )
    labels = value_label_df[1].unique().tolist()

    # set default label to the data labeler pipeline
    if default_label:
        params = {"default_label": default_label}
        data_labeler.set_params(  # pylint: disable=no-member
            {"preprocessor": params, "model": params, "postprocessor": params}
        )

    data_labeler.fit(  # pylint: disable=no-member
        x=value_label_df[0], y=value_label_df[1], labels=labels, epochs=epochs
    )
    if save_dirpath:
        data_labeler.save_to_disk(save_dirpath)  # pylint: disable=no-member
    return data_labeler


class UnstructuredDataLabeler(BaseDataLabeler):
    """BaseDataLabeler subclass specified as unstructured with internal variable."""

    _default_model_loc: str = "unstructured_model"


class StructuredDataLabeler(BaseDataLabeler):
    """BaseDataLabeler subclass specified as structured with internal variable."""

    _default_model_loc: str = "structured_model"


class DataLabeler:
    """Wrapper class for choosing between structured and unstructured labeler."""

    labeler_classes = dict(
        structured=StructuredDataLabeler,
        unstructured=UnstructuredDataLabeler,
    )

    def __new__(  # type: ignore
        cls,
        labeler_type: str,
        dirpath: str = None,
        load_options: dict = None,
        trainable: bool = False,
    ) -> BaseDataLabeler:
        """
        Create structured and unstructred data labeler objects.

        :param dirpath: Path to load data labeler
        :type dirpath: str
        :param load_options: Optional arguments to include for load.
        :type load_options: Dict
        :param trainable: variable to dictate whether you want a trainable data
            labeler
        :type trainable: bool
        :return:
        """
        data_labeler = cls.labeler_classes.get(labeler_type, None)
        if data_labeler is None:
            raise ValueError(
                "No DataLabeler class types matched the input, `{}`. "
                "Allowed types {}.".format(
                    labeler_type, list(cls.labeler_classes.keys())
                )
            )
        if trainable:
            if dirpath is None:
                dirpath = os.path.join(
                    default_labeler_dir, data_labeler._default_model_loc
                )
            return TrainableDataLabeler(dirpath, load_options)
        return data_labeler(dirpath, load_options)

    @classmethod
    def load_from_library(cls, name: str, trainable: bool = False) -> BaseDataLabeler:
        """
        Load the data labeler from the data labeler zoo in the library.

        :param name: name of the data labeler.
        :type name: str
        :param trainable: variable to dictate whether you want a trainable data
            labeler
        :type trainable: bool
        :return: DataLabeler class
        """
        if trainable:
            return TrainableDataLabeler.load_from_library(name)
        return BaseDataLabeler.load_from_library(name)

    @classmethod
    def load_from_disk(
        cls, dirpath: str, load_options: dict = None, trainable: bool = False
    ) -> BaseDataLabeler:
        """
        Load the data labeler from a saved location on disk.

        :param dirpath: path to data labeler files.
        :type dirpath: str
        :param load_options: optional arguments to include for load i.e. class
                             for model or processors
        :param trainable: variable to dictate whether you want a trainable data
            labeler
        :type trainable: bool
        :type load_options: dict
        :return: DataLabeler class
        """
        if trainable:
            return TrainableDataLabeler.load_from_disk(dirpath, load_options)
        return BaseDataLabeler.load_from_disk(dirpath, load_options)

    @classmethod
    def load_with_components(
        cls,
        preprocessor: BaseDataPreprocessor,
        model: BaseModel,
        postprocessor: BaseDataPostprocessor,
        trainable: bool = False,
    ) -> BaseDataLabeler:
        """
        Load the data labeler from a its set of components.

        :param preprocessor: processor to set as the preprocessor
        :type preprocessor: data_processing.BaseDataPreprocessor
        :param model: model to use within the data labeler
        :type model: base_model.BaseModel
        :param postprocessor: processor to set as the postprocessor
        :type postprocessor: data_processing.BaseDataPostprocessor
        :param trainable: variable to dictate whether you want a trainable data
            labeler
        :type trainable: bool
        :return:
        """
        if trainable:
            return TrainableDataLabeler.load_with_components(
                preprocessor, model, postprocessor
            )
        return BaseDataLabeler.load_with_components(preprocessor, model, postprocessor)
