#
# 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 sys
if sys.version >= '3':
    xrange = range

from pyspark import since
from pyspark import SparkContext
from pyspark.sql import DataFrame
from pyspark.ml import Estimator, Transformer, Model
from pyspark.ml.param import Params
from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol
from pyspark.ml.util import _jvm
from pyspark.ml.common import inherit_doc, _java2py, _py2java


class JavaWrapper(object):
    """
    Wrapper class for a Java companion object
    """
    def __init__(self, java_obj=None):
        super(JavaWrapper, self).__init__()
        self._java_obj = java_obj

    def __del__(self):
        if SparkContext._active_spark_context and self._java_obj is not None:
            SparkContext._active_spark_context._gateway.detach(self._java_obj)

    @classmethod
    def _create_from_java_class(cls, java_class, *args):
        """
        Construct this object from given Java classname and arguments
        """
        java_obj = JavaWrapper._new_java_obj(java_class, *args)
        return cls(java_obj)

    def _call_java(self, name, *args):
        m = getattr(self._java_obj, name)
        sc = SparkContext._active_spark_context
        java_args = [_py2java(sc, arg) for arg in args]
        return _java2py(sc, m(*java_args))

    @staticmethod
    def _new_java_obj(java_class, *args):
        """
        Returns a new Java object.
        """
        sc = SparkContext._active_spark_context
        java_obj = _jvm()
        for name in java_class.split("."):
            java_obj = getattr(java_obj, name)
        java_args = [_py2java(sc, arg) for arg in args]
        return java_obj(*java_args)

    @staticmethod
    def _new_java_array(pylist, java_class):
        """
        Create a Java array of given java_class type. Useful for
        calling a method with a Scala Array from Python with Py4J.
        If the param pylist is a 2D array, then a 2D java array will be returned.
        The returned 2D java array is a square, non-jagged 2D array that is big
        enough for all elements. The empty slots in the inner Java arrays will
        be filled with null to make the non-jagged 2D array.

        :param pylist:
          Python list to convert to a Java Array.
        :param java_class:
          Java class to specify the type of Array. Should be in the
          form of sc._gateway.jvm.* (sc is a valid Spark Context).
        :return:
          Java Array of converted pylist.

        Example primitive Java classes:
          - basestring -> sc._gateway.jvm.java.lang.String
          - int -> sc._gateway.jvm.java.lang.Integer
          - float -> sc._gateway.jvm.java.lang.Double
          - bool -> sc._gateway.jvm.java.lang.Boolean
        """
        sc = SparkContext._active_spark_context
        java_array = None
        if len(pylist) > 0 and isinstance(pylist[0], list):
            # If pylist is a 2D array, then a 2D java array will be created.
            # The 2D array is a square, non-jagged 2D array that is big enough for all elements.
            inner_array_length = 0
            for i in xrange(len(pylist)):
                inner_array_length = max(inner_array_length, len(pylist[i]))
            java_array = sc._gateway.new_array(java_class, len(pylist), inner_array_length)
            for i in xrange(len(pylist)):
                for j in xrange(len(pylist[i])):
                    java_array[i][j] = pylist[i][j]
        else:
            java_array = sc._gateway.new_array(java_class, len(pylist))
            for i in xrange(len(pylist)):
                java_array[i] = pylist[i]
        return java_array


@inherit_doc
class JavaParams(JavaWrapper, Params):
    """
    Utility class to help create wrapper classes from Java/Scala
    implementations of pipeline components.
    """
    #: The param values in the Java object should be
    #: synced with the Python wrapper in fit/transform/evaluate/copy.

    __metaclass__ = ABCMeta

    def _make_java_param_pair(self, param, value):
        """
        Makes a Java param pair.
        """
        sc = SparkContext._active_spark_context
        param = self._resolveParam(param)
        java_param = self._java_obj.getParam(param.name)
        java_value = _py2java(sc, value)
        return java_param.w(java_value)

    def _transfer_params_to_java(self):
        """
        Transforms the embedded params to the companion Java object.
        """
        pair_defaults = []
        for param in self.params:
            if self.isSet(param):
                pair = self._make_java_param_pair(param, self._paramMap[param])
                self._java_obj.set(pair)
            if self.hasDefault(param):
                pair = self._make_java_param_pair(param, self._defaultParamMap[param])
                pair_defaults.append(pair)
        if len(pair_defaults) > 0:
            sc = SparkContext._active_spark_context
            pair_defaults_seq = sc._jvm.PythonUtils.toSeq(pair_defaults)
            self._java_obj.setDefault(pair_defaults_seq)

    def _transfer_param_map_to_java(self, pyParamMap):
        """
        Transforms a Python ParamMap into a Java ParamMap.
        """
        paramMap = JavaWrapper._new_java_obj("org.apache.spark.ml.param.ParamMap")
        for param in self.params:
            if param in pyParamMap:
                pair = self._make_java_param_pair(param, pyParamMap[param])
                paramMap.put([pair])
        return paramMap

    def _create_params_from_java(self):
        """
        SPARK-10931: Temporary fix to create params that are defined in the Java obj but not here
        """
        java_params = list(self._java_obj.params())
        from pyspark.ml.param import Param
        for java_param in java_params:
            java_param_name = java_param.name()
            if not hasattr(self, java_param_name):
                param = Param(self, java_param_name, java_param.doc())
                setattr(param, "created_from_java_param", True)
                setattr(self, java_param_name, param)
                self._params = None  # need to reset so self.params will discover new params

    def _transfer_params_from_java(self):
        """
        Transforms the embedded params from the companion Java object.
        """
        sc = SparkContext._active_spark_context
        for param in self.params:
            if self._java_obj.hasParam(param.name):
                java_param = self._java_obj.getParam(param.name)
                # SPARK-14931: Only check set params back to avoid default params mismatch.
                if self._java_obj.isSet(java_param):
                    value = _java2py(sc, self._java_obj.getOrDefault(java_param))
                    self._set(**{param.name: value})
                # SPARK-10931: Temporary fix for params that have a default in Java
                if self._java_obj.hasDefault(java_param) and not self.isDefined(param):
                    value = _java2py(sc, self._java_obj.getDefault(java_param)).get()
                    self._setDefault(**{param.name: value})

    def _transfer_param_map_from_java(self, javaParamMap):
        """
        Transforms a Java ParamMap into a Python ParamMap.
        """
        sc = SparkContext._active_spark_context
        paramMap = dict()
        for pair in javaParamMap.toList():
            param = pair.param()
            if self.hasParam(str(param.name())):
                paramMap[self.getParam(param.name())] = _java2py(sc, pair.value())
        return paramMap

    @staticmethod
    def _empty_java_param_map():
        """
        Returns an empty Java ParamMap reference.
        """
        return _jvm().org.apache.spark.ml.param.ParamMap()

    def _to_java(self):
        """
        Transfer this instance's Params to the wrapped Java object, and return the Java object.
        Used for ML persistence.

        Meta-algorithms such as Pipeline should override this method.

        :return: Java object equivalent to this instance.
        """
        self._transfer_params_to_java()
        return self._java_obj

    @staticmethod
    def _from_java(java_stage):
        """
        Given a Java object, create and return a Python wrapper of it.
        Used for ML persistence.

        Meta-algorithms such as Pipeline should override this method as a classmethod.
        """
        def __get_class(clazz):
            """
            Loads Python class from its name.
            """
            parts = clazz.split('.')
            module = ".".join(parts[:-1])
            m = __import__(module)
            for comp in parts[1:]:
                m = getattr(m, comp)
            return m
        stage_name = java_stage.getClass().getName().replace("org.apache.spark", "pyspark")
        # Generate a default new instance from the stage_name class.
        py_type = __get_class(stage_name)
        if issubclass(py_type, JavaParams):
            # Load information from java_stage to the instance.
            py_stage = py_type()
            py_stage._java_obj = java_stage

            # SPARK-10931: Temporary fix so that persisted models would own params from Estimator
            if issubclass(py_type, JavaModel):
                py_stage._create_params_from_java()

            py_stage._resetUid(java_stage.uid())
            py_stage._transfer_params_from_java()
        elif hasattr(py_type, "_from_java"):
            py_stage = py_type._from_java(java_stage)
        else:
            raise NotImplementedError("This Java stage cannot be loaded into Python currently: %r"
                                      % stage_name)
        return py_stage

    def copy(self, extra=None):
        """
        Creates a copy of this instance with the same uid and some
        extra params. This implementation first calls Params.copy and
        then make a copy of the companion Java pipeline component with
        extra params. So both the Python wrapper and the Java pipeline
        component get copied.

        :param extra: Extra parameters to copy to the new instance
        :return: Copy of this instance
        """
        if extra is None:
            extra = dict()
        that = super(JavaParams, self).copy(extra)
        if self._java_obj is not None:
            that._java_obj = self._java_obj.copy(self._empty_java_param_map())
            that._transfer_params_to_java()
        return that


@inherit_doc
class JavaEstimator(JavaParams, Estimator):
    """
    Base class for :py:class:`Estimator`s that wrap Java/Scala
    implementations.
    """

    __metaclass__ = ABCMeta

    @abstractmethod
    def _create_model(self, java_model):
        """
        Creates a model from the input Java model reference.
        """
        raise NotImplementedError()

    def _fit_java(self, dataset):
        """
        Fits a Java model to the input dataset.

        :param dataset: input dataset, which is an instance of
                        :py:class:`pyspark.sql.DataFrame`
        :param params: additional params (overwriting embedded values)
        :return: fitted Java model
        """
        self._transfer_params_to_java()
        return self._java_obj.fit(dataset._jdf)

    def _fit(self, dataset):
        java_model = self._fit_java(dataset)
        model = self._create_model(java_model)
        return self._copyValues(model)


@inherit_doc
class JavaTransformer(JavaParams, Transformer):
    """
    Base class for :py:class:`Transformer`s that wrap Java/Scala
    implementations. Subclasses should ensure they have the transformer Java object
    available as _java_obj.
    """

    __metaclass__ = ABCMeta

    def _transform(self, dataset):
        self._transfer_params_to_java()
        return DataFrame(self._java_obj.transform(dataset._jdf), dataset.sql_ctx)


@inherit_doc
class JavaModel(JavaTransformer, Model):
    """
    Base class for :py:class:`Model`s that wrap Java/Scala
    implementations. Subclasses should inherit this class before
    param mix-ins, because this sets the UID from the Java model.
    """

    __metaclass__ = ABCMeta

    def __init__(self, java_model=None):
        """
        Initialize this instance with a Java model object.
        Subclasses should call this constructor, initialize params,
        and then call _transfer_params_from_java.

        This instance can be instantiated without specifying java_model,
        it will be assigned after that, but this scenario only used by
        :py:class:`JavaMLReader` to load models.  This is a bit of a
        hack, but it is easiest since a proper fix would require
        MLReader (in pyspark.ml.util) to depend on these wrappers, but
        these wrappers depend on pyspark.ml.util (both directly and via
        other ML classes).
        """
        super(JavaModel, self).__init__(java_model)
        if java_model is not None:

            # SPARK-10931: This is a temporary fix to allow models to own params
            # from estimators. Eventually, these params should be in models through
            # using common base classes between estimators and models.
            self._create_params_from_java()

            self._resetUid(java_model.uid())


@inherit_doc
class JavaPredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):
    """
    (Private) Trait for parameters for prediction (regression and classification)
    """
    pass


@inherit_doc
class JavaPredictor(JavaEstimator, JavaPredictorParams):
    """
    (Private) Java Estimator for prediction tasks (regression and classification).
    """

    @since("3.0.0")
    def setLabelCol(self, value):
        """
        Sets the value of :py:attr:`labelCol`.
        """
        return self._set(labelCol=value)

    @since("3.0.0")
    def setFeaturesCol(self, value):
        """
        Sets the value of :py:attr:`featuresCol`.
        """
        return self._set(featuresCol=value)

    @since("3.0.0")
    def setPredictionCol(self, value):
        """
        Sets the value of :py:attr:`predictionCol`.
        """
        return self._set(predictionCol=value)


@inherit_doc
class JavaPredictionModel(JavaModel, JavaPredictorParams):
    """
    (Private) Java Model for prediction tasks (regression and classification).
    """

    @since("3.0.0")
    def setFeaturesCol(self, value):
        """
        Sets the value of :py:attr:`featuresCol`.
        """
        return self._set(featuresCol=value)

    @since("3.0.0")
    def setPredictionCol(self, value):
        """
        Sets the value of :py:attr:`predictionCol`.
        """
        return self._set(predictionCol=value)

    @property
    @since("2.1.0")
    def numFeatures(self):
        """
        Returns the number of features the model was trained on. If unknown, returns -1
        """
        return self._call_java("numFeatures")

    @since("3.0.0")
    def predict(self, value):
        """
        Predict label for the given features.
        """
        return self._call_java("predict", value)
