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# The ASF licenses this file to You under the Apache License, Version 2.0
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import numpy as np

from pyspark import keyword_only
from pyspark.ml import Estimator, Model, Transformer, UnaryTransformer
from pyspark.ml.evaluation import Evaluator
from pyspark.ml.param import Param, Params, TypeConverters
from pyspark.ml.param.shared import HasMaxIter, HasRegParam
from pyspark.ml.classification import Classifier, ClassificationModel
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWritable
from pyspark.ml.wrapper import _java2py  # type: ignore
from pyspark.sql import DataFrame, SparkSession
from pyspark.sql.types import DoubleType
from pyspark.testing.utils import ReusedPySparkTestCase as PySparkTestCase


def check_params(test_self, py_stage, check_params_exist=True):
    """
    Checks common requirements for :py:class:`PySpark.ml.Params.params`:

      - set of params exist in Java and Python and are ordered by names
      - param parent has the same UID as the object's UID
      - default param value from Java matches value in Python
      - optionally check if all params from Java also exist in Python
    """
    py_stage_str = "%s %s" % (type(py_stage), py_stage)
    if not hasattr(py_stage, "_to_java"):
        return
    java_stage = py_stage._to_java()
    if java_stage is None:
        return
    test_self.assertEqual(py_stage.uid, java_stage.uid(), msg=py_stage_str)
    if check_params_exist:
        param_names = [p.name for p in py_stage.params]
        java_params = list(java_stage.params())
        java_param_names = [jp.name() for jp in java_params]
        test_self.assertEqual(
            param_names, sorted(java_param_names),
            "Param list in Python does not match Java for %s:\nJava = %s\nPython = %s"
            % (py_stage_str, java_param_names, param_names))
    for p in py_stage.params:
        test_self.assertEqual(p.parent, py_stage.uid)
        java_param = java_stage.getParam(p.name)
        py_has_default = py_stage.hasDefault(p)
        java_has_default = java_stage.hasDefault(java_param)
        test_self.assertEqual(py_has_default, java_has_default,
                              "Default value mismatch of param %s for Params %s"
                              % (p.name, str(py_stage)))
        if py_has_default:
            if p.name == "seed":
                continue  # Random seeds between Spark and PySpark are different
            java_default = _java2py(test_self.sc,
                                    java_stage.clear(java_param).getOrDefault(java_param))
            py_stage.clear(p)
            py_default = py_stage.getOrDefault(p)
            # equality test for NaN is always False
            if isinstance(java_default, float) and np.isnan(java_default):
                java_default = "NaN"
                py_default = "NaN" if np.isnan(py_default) else "not NaN"
            test_self.assertEqual(
                java_default, py_default,
                "Java default %s != python default %s of param %s for Params %s"
                % (str(java_default), str(py_default), p.name, str(py_stage)))


class SparkSessionTestCase(PySparkTestCase):
    @classmethod
    def setUpClass(cls):
        PySparkTestCase.setUpClass()
        cls.spark = SparkSession(cls.sc)

    @classmethod
    def tearDownClass(cls):
        PySparkTestCase.tearDownClass()
        cls.spark.stop()


class MockDataset(DataFrame):

    def __init__(self):
        self.index = 0


class HasFake(Params):

    def __init__(self):
        super(HasFake, self).__init__()
        self.fake = Param(self, "fake", "fake param")

    def getFake(self):
        return self.getOrDefault(self.fake)


class MockTransformer(Transformer, HasFake):

    def __init__(self):
        super(MockTransformer, self).__init__()
        self.dataset_index = None

    def _transform(self, dataset):
        self.dataset_index = dataset.index
        dataset.index += 1
        return dataset


class MockUnaryTransformer(UnaryTransformer, DefaultParamsReadable, DefaultParamsWritable):

    shift = Param(Params._dummy(),  # type: ignore
                  "shift", "The amount by which to shift " +
                  "data in a DataFrame",
                  typeConverter=TypeConverters.toFloat)

    def __init__(self, shiftVal=1):
        super(MockUnaryTransformer, self).__init__()
        self._setDefault(shift=1)
        self._set(shift=shiftVal)

    def getShift(self):
        return self.getOrDefault(self.shift)

    def setShift(self, shift):
        self._set(shift=shift)

    def createTransformFunc(self):
        shiftVal = self.getShift()
        return lambda x: x + shiftVal

    def outputDataType(self):
        return DoubleType()

    def validateInputType(self, inputType):
        if inputType != DoubleType():
            raise TypeError("Bad input type: {}. ".format(inputType) +
                            "Requires Double.")


class MockEstimator(Estimator, HasFake):

    def __init__(self):
        super(MockEstimator, self).__init__()
        self.dataset_index = None

    def _fit(self, dataset):
        self.dataset_index = dataset.index
        model = MockModel()
        self._copyValues(model)
        return model


class MockModel(MockTransformer, Model, HasFake):
    pass


class _DummyLogisticRegressionParams(HasMaxIter, HasRegParam):
    def setMaxIter(self, value):
        return self._set(maxIter=value)

    def setRegParam(self, value):
        return self._set(regParam=value)


# This is a dummy LogisticRegression used in test for python backend estimator/model
class DummyLogisticRegression(Classifier, _DummyLogisticRegressionParams,
                              DefaultParamsReadable, DefaultParamsWritable):
    @keyword_only
    def __init__(self, *, featuresCol="features", labelCol="label", predictionCol="prediction",
                 maxIter=100, regParam=0.0, rawPredictionCol="rawPrediction"):
        super(DummyLogisticRegression, self).__init__()
        kwargs = self._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    def setParams(self, *, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.0, rawPredictionCol="rawPrediction"):
        kwargs = self._input_kwargs
        self._set(**kwargs)
        return self

    def _fit(self, dataset):
        # Do nothing but create a dummy model
        return self._copyValues(DummyLogisticRegressionModel())


class DummyLogisticRegressionModel(ClassificationModel, _DummyLogisticRegressionParams,
                                   DefaultParamsReadable, DefaultParamsWritable):

    def __init__(self):
        super(DummyLogisticRegressionModel, self).__init__()

    def _transform(self, dataset):
        # A dummy transform impl which always predict label 1
        from pyspark.sql.functions import array, lit
        from pyspark.ml.functions import array_to_vector
        rawPredCol = self.getRawPredictionCol()
        if rawPredCol:
            dataset = dataset.withColumn(
                rawPredCol, array_to_vector(array(lit(-100.0), lit(100.0))))
        predCol = self.getPredictionCol()
        if predCol:
            dataset = dataset.withColumn(predCol, lit(1.0))

        return dataset

    @property
    def numClasses(self):
        # a dummy implementation for test.
        return 2

    @property
    def intercept(self):
        # a dummy implementation for test.
        return 0.0

    # This class only used in test. The following methods/properties are not used in tests.

    @property
    def coefficients(self):
        raise NotImplementedError()

    def predictRaw(self, value):
        raise NotImplementedError()

    def numFeatures(self):
        raise NotImplementedError()

    def predict(self, value):
        raise NotImplementedError()


class DummyEvaluator(Evaluator, DefaultParamsReadable, DefaultParamsWritable):

    def _evaluate(self, dataset):
        # a dummy implementation for test.
        return 1.0
