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  # Licensed to the Apache Software Foundation (ASF) under one or more
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  # 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.
  #
  
  import unittest
  
  from pyspark.sql.utils import AnalysisException
  from pyspark.sql.functions import array, explode, col, lit, mean, min, max, rank, \
      udf, pandas_udf, PandasUDFType
  from pyspark.sql.window import Window
  from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
      pandas_requirement_message, pyarrow_requirement_message
  from pyspark.testing.utils import QuietTest
  
  if have_pandas:
      from pandas.testing import assert_frame_equal
  
  
  @unittest.skipIf(
      not have_pandas or not have_pyarrow,
      pandas_requirement_message or pyarrow_requirement_message)  # type: ignore[arg-type]
  class WindowPandasUDFTests(ReusedSQLTestCase):
      @property
      def data(self):
          return self.spark.range(10).toDF('id') \
              .withColumn("vs", array([lit(i * 1.0) + col('id') for i in range(20, 30)])) \
              .withColumn("v", explode(col('vs'))) \
              .drop('vs') \
              .withColumn('w', lit(1.0))
  
      @property
      def python_plus_one(self):
          @udf('double')
          def plus_one(v):
              assert isinstance(v, float)
              return v + 1
          return plus_one
  
      @property
      def pandas_scalar_time_two(self):
          return pandas_udf(lambda v: v * 2, 'double')
  
      @property
      def pandas_agg_count_udf(self):
          @pandas_udf('long', PandasUDFType.GROUPED_AGG)
          def count(v):
              return len(v)
          return count
  
      @property
      def pandas_agg_mean_udf(self):
          @pandas_udf('double', PandasUDFType.GROUPED_AGG)
          def avg(v):
              return v.mean()
          return avg
  
      @property
      def pandas_agg_max_udf(self):
          @pandas_udf('double', PandasUDFType.GROUPED_AGG)
          def max(v):
              return v.max()
          return max
  
      @property
      def pandas_agg_min_udf(self):
          @pandas_udf('double', PandasUDFType.GROUPED_AGG)
          def min(v):
              return v.min()
          return min
  
      @property
      def unbounded_window(self):
          return Window.partitionBy('id') \
              .rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing).orderBy('v')
  
      @property
      def ordered_window(self):
          return Window.partitionBy('id').orderBy('v')
  
      @property
      def unpartitioned_window(self):
          return Window.partitionBy()
  
      @property
      def sliding_row_window(self):
          return Window.partitionBy('id').orderBy('v').rowsBetween(-2, 1)
  
      @property
      def sliding_range_window(self):
          return Window.partitionBy('id').orderBy('v').rangeBetween(-2, 4)
  
      @property
      def growing_row_window(self):
          return Window.partitionBy('id').orderBy('v').rowsBetween(Window.unboundedPreceding, 3)
  
      @property
      def growing_range_window(self):
          return Window.partitionBy('id').orderBy('v') \
              .rangeBetween(Window.unboundedPreceding, 4)
  
      @property
      def shrinking_row_window(self):
          return Window.partitionBy('id').orderBy('v').rowsBetween(-2, Window.unboundedFollowing)
  
      @property
      def shrinking_range_window(self):
          return Window.partitionBy('id').orderBy('v') \
              .rangeBetween(-3, Window.unboundedFollowing)
  
      def test_simple(self):
          df = self.data
          w = self.unbounded_window
  
          mean_udf = self.pandas_agg_mean_udf
  
          result1 = df.withColumn('mean_v', mean_udf(df['v']).over(w))
          expected1 = df.withColumn('mean_v', mean(df['v']).over(w))
  
          result2 = df.select(mean_udf(df['v']).over(w))
          expected2 = df.select(mean(df['v']).over(w))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
          assert_frame_equal(expected2.toPandas(), result2.toPandas())
  
      def test_multiple_udfs(self):
          df = self.data
          w = self.unbounded_window
  
          result1 = df.withColumn('mean_v', self.pandas_agg_mean_udf(df['v']).over(w)) \
              .withColumn('max_v', self.pandas_agg_max_udf(df['v']).over(w)) \
              .withColumn('min_w', self.pandas_agg_min_udf(df['w']).over(w))
  
          expected1 = df.withColumn('mean_v', mean(df['v']).over(w)) \
              .withColumn('max_v', max(df['v']).over(w)) \
              .withColumn('min_w', min(df['w']).over(w))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
      def test_replace_existing(self):
          df = self.data
          w = self.unbounded_window
  
          result1 = df.withColumn('v', self.pandas_agg_mean_udf(df['v']).over(w))
          expected1 = df.withColumn('v', mean(df['v']).over(w))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
      def test_mixed_sql(self):
          df = self.data
          w = self.unbounded_window
          mean_udf = self.pandas_agg_mean_udf
  
          result1 = df.withColumn('v', mean_udf(df['v'] * 2).over(w) + 1)
          expected1 = df.withColumn('v', mean(df['v'] * 2).over(w) + 1)
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
      def test_mixed_udf(self):
          df = self.data
          w = self.unbounded_window
  
          plus_one = self.python_plus_one
          time_two = self.pandas_scalar_time_two
          mean_udf = self.pandas_agg_mean_udf
  
          result1 = df.withColumn(
              'v2',
              plus_one(mean_udf(plus_one(df['v'])).over(w)))
          expected1 = df.withColumn(
              'v2',
              plus_one(mean(plus_one(df['v'])).over(w)))
  
          result2 = df.withColumn(
              'v2',
              time_two(mean_udf(time_two(df['v'])).over(w)))
          expected2 = df.withColumn(
              'v2',
              time_two(mean(time_two(df['v'])).over(w)))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
          assert_frame_equal(expected2.toPandas(), result2.toPandas())
  
      def test_without_partitionBy(self):
          df = self.data
          w = self.unpartitioned_window
          mean_udf = self.pandas_agg_mean_udf
  
          result1 = df.withColumn('v2', mean_udf(df['v']).over(w))
          expected1 = df.withColumn('v2', mean(df['v']).over(w))
  
          result2 = df.select(mean_udf(df['v']).over(w))
          expected2 = df.select(mean(df['v']).over(w))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
          assert_frame_equal(expected2.toPandas(), result2.toPandas())
  
      def test_mixed_sql_and_udf(self):
          df = self.data
          w = self.unbounded_window
          ow = self.ordered_window
          max_udf = self.pandas_agg_max_udf
          min_udf = self.pandas_agg_min_udf
  
          result1 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min_udf(df['v']).over(w))
          expected1 = df.withColumn('v_diff', max(df['v']).over(w) - min(df['v']).over(w))
  
          # Test mixing sql window function and window udf in the same expression
          result2 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min(df['v']).over(w))
          expected2 = expected1
  
          # Test chaining sql aggregate function and udf
          result3 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
              .withColumn('min_v', min(df['v']).over(w)) \
              .withColumn('v_diff', col('max_v') - col('min_v')) \
              .drop('max_v', 'min_v')
          expected3 = expected1
  
          # Test mixing sql window function and udf
          result4 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
              .withColumn('rank', rank().over(ow))
          expected4 = df.withColumn('max_v', max(df['v']).over(w)) \
              .withColumn('rank', rank().over(ow))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
          assert_frame_equal(expected2.toPandas(), result2.toPandas())
          assert_frame_equal(expected3.toPandas(), result3.toPandas())
          assert_frame_equal(expected4.toPandas(), result4.toPandas())
  
      def test_array_type(self):
          df = self.data
          w = self.unbounded_window
  
          array_udf = pandas_udf(lambda x: [1.0, 2.0], 'array<double>', PandasUDFType.GROUPED_AGG)
          result1 = df.withColumn('v2', array_udf(df['v']).over(w))
          self.assertEqual(result1.first()['v2'], [1.0, 2.0])
  
      def test_invalid_args(self):
          df = self.data
          w = self.unbounded_window
  
          with QuietTest(self.sc):
              with self.assertRaisesRegex(
                      AnalysisException,
                      '.*not supported within a window function'):
                  foo_udf = pandas_udf(lambda x: x, 'v double', PandasUDFType.GROUPED_MAP)
                  df.withColumn('v2', foo_udf(df['v']).over(w))
  
      def test_bounded_simple(self):
          from pyspark.sql.functions import mean, max, min, count
  
          df = self.data
          w1 = self.sliding_row_window
          w2 = self.shrinking_range_window
  
          plus_one = self.python_plus_one
          count_udf = self.pandas_agg_count_udf
          mean_udf = self.pandas_agg_mean_udf
          max_udf = self.pandas_agg_max_udf
          min_udf = self.pandas_agg_min_udf
  
          result1 = df.withColumn('mean_v', mean_udf(plus_one(df['v'])).over(w1)) \
              .withColumn('count_v', count_udf(df['v']).over(w2)) \
              .withColumn('max_v',  max_udf(df['v']).over(w2)) \
              .withColumn('min_v', min_udf(df['v']).over(w1))
  
          expected1 = df.withColumn('mean_v', mean(plus_one(df['v'])).over(w1)) \
              .withColumn('count_v', count(df['v']).over(w2)) \
              .withColumn('max_v', max(df['v']).over(w2)) \
              .withColumn('min_v', min(df['v']).over(w1))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
      def test_growing_window(self):
          from pyspark.sql.functions import mean
  
          df = self.data
          w1 = self.growing_row_window
          w2 = self.growing_range_window
  
          mean_udf = self.pandas_agg_mean_udf
  
          result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
              .withColumn('m2', mean_udf(df['v']).over(w2))
  
          expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
              .withColumn('m2', mean(df['v']).over(w2))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
      def test_sliding_window(self):
          from pyspark.sql.functions import mean
  
          df = self.data
          w1 = self.sliding_row_window
          w2 = self.sliding_range_window
  
          mean_udf = self.pandas_agg_mean_udf
  
          result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
              .withColumn('m2', mean_udf(df['v']).over(w2))
  
          expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
              .withColumn('m2', mean(df['v']).over(w2))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
      def test_shrinking_window(self):
          from pyspark.sql.functions import mean
  
          df = self.data
          w1 = self.shrinking_row_window
          w2 = self.shrinking_range_window
  
          mean_udf = self.pandas_agg_mean_udf
  
          result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
              .withColumn('m2', mean_udf(df['v']).over(w2))
  
          expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
              .withColumn('m2', mean(df['v']).over(w2))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
      def test_bounded_mixed(self):
          from pyspark.sql.functions import mean, max
  
          df = self.data
          w1 = self.sliding_row_window
          w2 = self.unbounded_window
  
          mean_udf = self.pandas_agg_mean_udf
          max_udf = self.pandas_agg_max_udf
  
          result1 = df.withColumn('mean_v', mean_udf(df['v']).over(w1)) \
              .withColumn('max_v', max_udf(df['v']).over(w2)) \
              .withColumn('mean_unbounded_v', mean_udf(df['v']).over(w1))
  
          expected1 = df.withColumn('mean_v', mean(df['v']).over(w1)) \
              .withColumn('max_v', max(df['v']).over(w2)) \
              .withColumn('mean_unbounded_v', mean(df['v']).over(w1))
  
          assert_frame_equal(expected1.toPandas(), result1.toPandas())
  
  
  if __name__ == "__main__":
      from pyspark.sql.tests.test_pandas_udf_window 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)
  