#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from abc import ABCMeta, abstractmethod

import copy

from pyspark import since
from pyspark.ml.param import Params
from pyspark.ml.param.shared import *
from pyspark.ml.common import inherit_doc
from pyspark.sql.functions import udf
from pyspark.sql.types import StructField, StructType, DoubleType


@inherit_doc
class Estimator(Params):
    """
    Abstract class for estimators that fit models to data.

    .. versionadded:: 1.3.0
    """

    __metaclass__ = ABCMeta

    @abstractmethod
    def _fit(self, dataset):
        """
        Fits a model to the input dataset. This is called by the default implementation of fit.

        :param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
        :returns: fitted model
        """
        raise NotImplementedError()

    @since("1.3.0")
    def fit(self, dataset, params=None):
        """
        Fits a model to the input dataset with optional parameters.

        :param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
        :param params: an optional param map that overrides embedded params. If a list/tuple of
                       param maps is given, this calls fit on each param map and returns a list of
                       models.
        :returns: fitted model(s)
        """
        if params is None:
            params = dict()
        if isinstance(params, (list, tuple)):
            return [self.fit(dataset, paramMap) for paramMap in params]
        elif isinstance(params, dict):
            if params:
                return self.copy(params)._fit(dataset)
            else:
                return self._fit(dataset)
        else:
            raise ValueError("Params must be either a param map or a list/tuple of param maps, "
                             "but got %s." % type(params))


@inherit_doc
class Transformer(Params):
    """
    Abstract class for transformers that transform one dataset into another.

    .. versionadded:: 1.3.0
    """

    __metaclass__ = ABCMeta

    @abstractmethod
    def _transform(self, dataset):
        """
        Transforms the input dataset.

        :param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
        :returns: transformed dataset
        """
        raise NotImplementedError()

    @since("1.3.0")
    def transform(self, dataset, params=None):
        """
        Transforms the input dataset with optional parameters.

        :param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
        :param params: an optional param map that overrides embedded params.
        :returns: transformed dataset
        """
        if params is None:
            params = dict()
        if isinstance(params, dict):
            if params:
                return self.copy(params)._transform(dataset)
            else:
                return self._transform(dataset)
        else:
            raise ValueError("Params must be a param map but got %s." % type(params))


@inherit_doc
class Model(Transformer):
    """
    Abstract class for models that are fitted by estimators.

    .. versionadded:: 1.4.0
    """

    __metaclass__ = ABCMeta


@inherit_doc
class UnaryTransformer(HasInputCol, HasOutputCol, Transformer):
    """
    Abstract class for transformers that take one input column, apply transformation,
    and output the result as a new column.

    .. versionadded:: 2.3.0
    """

    @abstractmethod
    def createTransformFunc(self):
        """
        Creates the transform function using the given param map. The input param map already takes
        account of the embedded param map. So the param values should be determined
        solely by the input param map.
        """
        raise NotImplementedError()

    @abstractmethod
    def outputDataType(self):
        """
        Returns the data type of the output column.
        """
        raise NotImplementedError()

    @abstractmethod
    def validateInputType(self, inputType):
        """
        Validates the input type. Throw an exception if it is invalid.
        """
        raise NotImplementedError()

    def transformSchema(self, schema):
        inputType = schema[self.getInputCol()].dataType
        self.validateInputType(inputType)
        if self.getOutputCol() in schema.names:
            raise ValueError("Output column %s already exists." % self.getOutputCol())
        outputFields = copy.copy(schema.fields)
        outputFields.append(StructField(self.getOutputCol(),
                                        self.outputDataType(),
                                        nullable=False))
        return StructType(outputFields)

    def _transform(self, dataset):
        self.transformSchema(dataset.schema)
        transformUDF = udf(self.createTransformFunc(), self.outputDataType())
        transformedDataset = dataset.withColumn(self.getOutputCol(),
                                                transformUDF(dataset[self.getInputCol()]))
        return transformedDataset
