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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
#
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# pylint: disable=unused-argument
# pylint: disable=invalid-name
# pylint: disable=too-many-ancestors
# pylint: disable=no-member
from typing import List, Optional

import pyspark.sql.functions as F
import tensorflow as tf
from pyspark import keyword_only
from pyspark.ml.param import Param, Params, TypeConverters
from pyspark.sql import DataFrame
from pyspark.sql.types import DataType, DoubleType, FloatType, IntegerType, LongType

from kamae.spark.params import SingleInputSingleOutputParams
from kamae.spark.utils import single_input_single_output_scalar_transform
from kamae.tensorflow.layers import RoundToDecimalLayer

from .base import BaseTransformer


class RoundToDecimalParams(Params):
    """
    Mixin class containing decimals parameter needed for rounding transform layers.
    """

    decimals = Param(
        Params._dummy(),
        "decimals",
        "Number of decimals to round to",
        typeConverter=TypeConverters.toInt,
    )

    def setDecimals(self, value: int) -> "RoundToDecimalParams":
        """
        Sets the decimals parameter.

        :param value: Number of decimals to round to.
        :returns: Instance of class mixed in.
        """
        if value < 0:
            raise ValueError("decimals must be a non-negative integer")
        return self._set(decimals=value)

    def getDecimals(self) -> int:
        """
        Gets the decimals parameter.

        :returns: Number of decimals to round to.
        """
        return self.getOrDefault(self.decimals)


class RoundToDecimalTransformer(
    BaseTransformer,
    SingleInputSingleOutputParams,
    RoundToDecimalParams,
):
    """
    Round Spark Transformer for use in Spark pipelines.
    This transformer rounds the input column to the nearest decimal using the
    specified number of decimals.
    """

    @keyword_only
    def __init__(
        self,
        inputCol: Optional[str] = None,
        outputCol: Optional[str] = None,
        inputDtype: Optional[str] = None,
        outputDtype: Optional[str] = None,
        layerName: Optional[str] = None,
        decimals: Optional[int] = None,
    ) -> None:
        """
        Initializes an RoundTransformer transformer.

        :param inputCol: Input column name.
        :param outputCol: Output column name.
        :param inputDtype: Input data type to cast input column to before
        transforming.
        :param outputDtype: Output data type to cast the output column to after
        transforming.
        :param layerName: Name of the layer. Used as the name of the tensorflow layer
        in the keras model. If not set, we use the uid of the Spark transformer.
        :param decimals: Number of decimals to round to.
        :returns: None - class instantiated.
        """
        super().__init__()
        kwargs = self._input_kwargs
        self.setParams(**kwargs)

    @property
    def compatible_dtypes(self) -> Optional[List[DataType]]:
        """
        List of compatible data types for the layer.
        If the computation can be performed on any data type, return None.

        :returns: List of compatible data types for the layer.
        """
        return [FloatType(), DoubleType(), IntegerType(), LongType()]

    def _transform(self, dataset: DataFrame) -> DataFrame:
        """
        Transforms the input dataset. Creates a new column with name `outputCol`,
        which applies the rounding operation to the input column.

        :param dataset: Pyspark dataframe to transform.
        :returns: Transformed pyspark dataframe.
        """

        input_datatype = self.get_column_datatype(
            dataset=dataset, column_name=self.getInputCol()
        )
        output_col = single_input_single_output_scalar_transform(
            input_col=F.col(self.getInputCol()),
            input_col_datatype=input_datatype,
            func=lambda x: F.round(x, scale=self.getDecimals()),
        )
        return dataset.withColumn(self.getOutputCol(), output_col)

    def get_tf_layer(self) -> tf.keras.layers.Layer:
        """
        Gets the tensorflow layer for the round transformer.

        :returns: Tensorflow keras layer with name equal to the layerName parameter that
         performs a rounding operation.
        """
        return RoundToDecimalLayer(
            name=self.getLayerName(),
            input_dtype=self.getInputTFDtype(),
            output_dtype=self.getOutputTFDtype(),
            decimals=self.getDecimals(),
        )
