# SPDX-License-Identifier: Apache-2.0

import sys
import unittest
import numpy
import pandas
from pyspark.ml.feature import DCT
from pyspark.ml.linalg import Vectors
from onnx.defs import onnx_opset_version
from onnxconverter_common.onnx_ex import DEFAULT_OPSET_NUMBER
from onnxmltools import convert_sparkml
from onnxmltools.convert.common.data_types import FloatTensorType
from tests.sparkml.sparkml_test_utils import save_data_models, run_onnx_model, compare_results
from tests.sparkml import SparkMlTestCase


TARGET_OPSET = min(DEFAULT_OPSET_NUMBER, onnx_opset_version())


class TestSparkmlDCT(SparkMlTestCase):

    @unittest.skipIf(sys.version_info < (3, 8),
                     reason="pickle fails on python 3.7")
    def test_dct(self):
        data = self.spark.createDataFrame(
            [(Vectors.dense([5.0, 8.0, 6.0]),)],
            ["vec"])
        model = DCT(inverse=False, inputCol="vec", outputCol="resultVec")
        # the input name should match that of what inputCol
        feature_count = data.first()[0].size
        model_onnx = convert_sparkml(model, 'Sparkml DCT', [('vec', FloatTensorType([None, feature_count]))],
                                     target_opset=TARGET_OPSET)
        self.assertTrue(model_onnx is not None)

        # run the model
        predicted = model.transform(data)
        expected = predicted.toPandas().resultVec.apply(lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32)
        data_np = data.toPandas().vec.apply(lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32)

        paths = save_data_models(data_np, expected, model, model_onnx, basename="SparkmlDCT")
        onnx_model_path = paths[-1]
        output, output_shapes = run_onnx_model(['resultVec'], data_np, onnx_model_path)
        compare_results(expected, output, decimal=5)


if __name__ == "__main__":
    unittest.main()
