# SPDX-License-Identifier: Apache-2.0

import sys
import unittest
import numpy
import pandas
from pyspark.ml.feature import VectorSlicer
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 TestSparkmlVectorSlicer(SparkMlTestCase):

    @unittest.skipIf(sys.version_info < (3, 8),
                     reason="pickle fails on python 3.7")
    def test_vector_slicer(self):
        data = self.spark.createDataFrame([
            (Vectors.dense([-2.0, 2.3, 0.0, 0.0, 1.0]), ),
            (Vectors.dense([0.0, 0.0, 0.0, 0.0, 0.0]), ),
            (Vectors.dense([0.6, -1.1, -3.0, 4.5, 3.3]), )], ["features"])
        model = VectorSlicer(inputCol="features", outputCol="sliced", indices=[1, 4])

        feature_count = data.first()[0].array.size
        model_onnx = convert_sparkml(model, 'Sparkml VectorSlicer',
                                     [('features', 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().sliced.apply(lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32)
        data_np = data.toPandas().features.apply(lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32)
        paths = save_data_models(data_np, expected, model, model_onnx, basename="SparkmlVectorSlicer")
        onnx_model_path = paths[-1]
        output, output_shapes = run_onnx_model(['sliced'], data_np, onnx_model_path)
        compare_results(expected, output, decimal=5)


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