text: #
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  # 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 distutils.version import LooseVersion
  
  import numpy as np
  import pandas as pd
  
  from pyspark import pandas as ps
  from pyspark.pandas import set_option, reset_option
  from pyspark.pandas.numpy_compat import unary_np_spark_mappings, binary_np_spark_mappings
  from pyspark.testing.pandasutils import PandasOnSparkTestCase
  from pyspark.testing.sqlutils import SQLTestUtils
  
  
  class NumPyCompatTest(PandasOnSparkTestCase, SQLTestUtils):
      blacklist = [
          # Koalas does not currently support
          "conj",
          "conjugate",
          "isnat",
          "matmul",
          "frexp",
          # Values are close enough but tests failed.
          "arccos",
          "exp",
          "expm1",
          "log",  # flaky
          "log10",  # flaky
          "log1p",  # flaky
          "modf",
          "floor_divide",  # flaky
          # Results seem inconsistent in a different version of, I (Hyukjin) suspect, PyArrow.
          # From PyArrow 0.15, seems it returns the correct results via PySpark. Probably we
          # can enable it later when Koalas switches to PyArrow 0.15 completely.
          "left_shift",
      ]
  
      @property
      def pdf(self):
          return pd.DataFrame(
              {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
              index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
          )
  
      @property
      def kdf(self):
          return ps.from_pandas(self.pdf)
  
      def test_np_add_series(self):
          kdf = self.kdf
          pdf = self.pdf
  
          if LooseVersion(pd.__version__) < LooseVersion("0.25"):
              self.assert_eq(np.add(kdf.a, kdf.b), np.add(pdf.a, pdf.b).rename())
          else:
              self.assert_eq(np.add(kdf.a, kdf.b), np.add(pdf.a, pdf.b))
  
          kdf = self.kdf
          pdf = self.pdf
          self.assert_eq(np.add(kdf.a, 1), np.add(pdf.a, 1))
  
      def test_np_add_index(self):
          k_index = self.kdf.index
          p_index = self.pdf.index
          self.assert_eq(np.add(k_index, k_index), np.add(p_index, p_index))
  
      def test_np_unsupported_series(self):
          kdf = self.kdf
          with self.assertRaisesRegex(NotImplementedError, "pandas.*not.*support.*sqrt.*"):
              np.sqrt(kdf.a, kdf.b)
  
      def test_np_unsupported_frame(self):
          kdf = self.kdf
          with self.assertRaisesRegex(NotImplementedError, "on-Spark.*not.*support.*sqrt.*"):
              np.sqrt(kdf, kdf)
  
      def test_np_spark_compat_series(self):
          # Use randomly generated dataFrame
          pdf = pd.DataFrame(
              np.random.randint(-100, 100, size=(np.random.randint(100), 2)), columns=["a", "b"]
          )
          pdf2 = pd.DataFrame(
              np.random.randint(-100, 100, size=(len(pdf), len(pdf.columns))), columns=["a", "b"]
          )
          kdf = ps.from_pandas(pdf)
          kdf2 = ps.from_pandas(pdf2)
  
          for np_name, spark_func in unary_np_spark_mappings.items():
              np_func = getattr(np, np_name)
              if np_name not in self.blacklist:
                  try:
                      # unary ufunc
                      self.assert_eq(np_func(pdf.a), np_func(kdf.a), almost=True)
                  except Exception as e:
                      raise AssertionError("Test in '%s' function was failed." % np_name) from e
  
          for np_name, spark_func in binary_np_spark_mappings.items():
              np_func = getattr(np, np_name)
              if np_name not in self.blacklist:
                  try:
                      # binary ufunc
                      if LooseVersion(pd.__version__) < LooseVersion("0.25"):
                          self.assert_eq(
                              np_func(pdf.a, pdf.b).rename(), np_func(kdf.a, kdf.b), almost=True
                          )
                      else:
                          self.assert_eq(np_func(pdf.a, pdf.b), np_func(kdf.a, kdf.b), almost=True)
                      self.assert_eq(np_func(pdf.a, 1), np_func(kdf.a, 1), almost=True)
                  except Exception as e:
                      raise AssertionError("Test in '%s' function was failed." % np_name) from e
  
          # Test only top 5 for now. 'compute.ops_on_diff_frames' option increases too much time.
          try:
              set_option("compute.ops_on_diff_frames", True)
              for np_name, spark_func in list(binary_np_spark_mappings.items())[:5]:
                  np_func = getattr(np, np_name)
                  if np_name not in self.blacklist:
                      try:
                          # binary ufunc
                          if LooseVersion(pd.__version__) < LooseVersion("0.25"):
                              self.assert_eq(
                                  np_func(pdf.a, pdf2.b).sort_index().rename(),
                                  np_func(kdf.a, kdf2.b).sort_index(),
                                  almost=True,
                              )
                          else:
                              self.assert_eq(
                                  np_func(pdf.a, pdf2.b).sort_index(),
                                  np_func(kdf.a, kdf2.b).sort_index(),
                                  almost=True,
                              )
                      except Exception as e:
                          raise AssertionError("Test in '%s' function was failed." % np_name) from e
          finally:
              reset_option("compute.ops_on_diff_frames")
  
      def test_np_spark_compat_frame(self):
          # Use randomly generated dataFrame
          pdf = pd.DataFrame(
              np.random.randint(-100, 100, size=(np.random.randint(100), 2)), columns=["a", "b"]
          )
          pdf2 = pd.DataFrame(
              np.random.randint(-100, 100, size=(len(pdf), len(pdf.columns))), columns=["a", "b"]
          )
          kdf = ps.from_pandas(pdf)
          kdf2 = ps.from_pandas(pdf2)
  
          for np_name, spark_func in unary_np_spark_mappings.items():
              np_func = getattr(np, np_name)
              if np_name not in self.blacklist:
                  try:
                      # unary ufunc
                      self.assert_eq(np_func(pdf), np_func(kdf), almost=True)
                  except Exception as e:
                      raise AssertionError("Test in '%s' function was failed." % np_name) from e
  
          for np_name, spark_func in binary_np_spark_mappings.items():
              np_func = getattr(np, np_name)
              if np_name not in self.blacklist:
                  try:
                      # binary ufunc
                      self.assert_eq(np_func(pdf, pdf), np_func(kdf, kdf), almost=True)
                      self.assert_eq(np_func(pdf, 1), np_func(kdf, 1), almost=True)
                  except Exception as e:
                      raise AssertionError("Test in '%s' function was failed." % np_name) from e
  
          # Test only top 5 for now. 'compute.ops_on_diff_frames' option increases too much time.
          try:
              set_option("compute.ops_on_diff_frames", True)
              for np_name, spark_func in list(binary_np_spark_mappings.items())[:5]:
                  np_func = getattr(np, np_name)
                  if np_name not in self.blacklist:
                      try:
                          # binary ufunc
                          self.assert_eq(
                              np_func(pdf, pdf2).sort_index(),
                              np_func(kdf, kdf2).sort_index(),
                              almost=True,
                          )
  
                      except Exception as e:
                          raise AssertionError("Test in '%s' function was failed." % np_name) from e
          finally:
              reset_option("compute.ops_on_diff_frames")
  
  
  if __name__ == "__main__":
      import unittest
      from pyspark.pandas.tests.test_numpy_compat import *  # noqa: F401
  
      try:
          import xmlrunner  # type: ignore[import]
          testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
      except ImportError:
          testRunner = None
      unittest.main(testRunner=testRunner, verbosity=2)
  