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  #    http://www.apache.org/licenses/LICENSE-2.0
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  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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  #
  
  import datetime
  import shutil
  import tempfile
  import time
  
  from pyspark.sql import Row
  from pyspark.sql.functions import lit
  from pyspark.sql.types import StructType, StructField, DecimalType, BinaryType
  from pyspark.testing.sqlutils import ReusedSQLTestCase, UTCOffsetTimezone
  
  
  class SerdeTests(ReusedSQLTestCase):
  
      def test_serialize_nested_array_and_map(self):
          d = [Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})]
          rdd = self.sc.parallelize(d)
          df = self.spark.createDataFrame(rdd)
          row = df.head()
          self.assertEqual(1, len(row.l))
          self.assertEqual(1, row.l[0].a)
          self.assertEqual("2", row.d["key"].d)
  
          l = df.rdd.map(lambda x: x.l).first()
          self.assertEqual(1, len(l))
          self.assertEqual('s', l[0].b)
  
          d = df.rdd.map(lambda x: x.d).first()
          self.assertEqual(1, len(d))
          self.assertEqual(1.0, d["key"].c)
  
          row = df.rdd.map(lambda x: x.d["key"]).first()
          self.assertEqual(1.0, row.c)
          self.assertEqual("2", row.d)
  
      def test_select_null_literal(self):
          df = self.spark.sql("select null as col")
          self.assertEqual(Row(col=None), df.first())
  
      def test_struct_in_map(self):
          d = [Row(m={Row(i=1): Row(s="")})]
          df = self.sc.parallelize(d).toDF()
          k, v = list(df.head().m.items())[0]
          self.assertEqual(1, k.i)
          self.assertEqual("", v.s)
  
      def test_filter_with_datetime(self):
          time = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000)
          date = time.date()
          row = Row(date=date, time=time)
          df = self.spark.createDataFrame([row])
          self.assertEqual(1, df.filter(df.date == date).count())
          self.assertEqual(1, df.filter(df.time == time).count())
          self.assertEqual(0, df.filter(df.date > date).count())
          self.assertEqual(0, df.filter(df.time > time).count())
  
      def test_filter_with_datetime_timezone(self):
          dt1 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(0))
          dt2 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(1))
          row = Row(date=dt1)
          df = self.spark.createDataFrame([row])
          self.assertEqual(0, df.filter(df.date == dt2).count())
          self.assertEqual(1, df.filter(df.date > dt2).count())
          self.assertEqual(0, df.filter(df.date < dt2).count())
  
      def test_time_with_timezone(self):
          day = datetime.date.today()
          now = datetime.datetime.now()
          ts = time.mktime(now.timetuple())
          # class in __main__ is not serializable
          from pyspark.testing.sqlutils import UTCOffsetTimezone
          utc = UTCOffsetTimezone()
          utcnow = datetime.datetime.utcfromtimestamp(ts)  # without microseconds
          # add microseconds to utcnow (keeping year,month,day,hour,minute,second)
          utcnow = datetime.datetime(*(utcnow.timetuple()[:6] + (now.microsecond, utc)))
          df = self.spark.createDataFrame([(day, now, utcnow)])
          day1, now1, utcnow1 = df.first()
          self.assertEqual(day1, day)
          self.assertEqual(now, now1)
          self.assertEqual(now, utcnow1)
  
      # regression test for SPARK-19561
      def test_datetime_at_epoch(self):
          epoch = datetime.datetime.fromtimestamp(0)
          df = self.spark.createDataFrame([Row(date=epoch)])
          first = df.select('date', lit(epoch).alias('lit_date')).first()
          self.assertEqual(first['date'], epoch)
          self.assertEqual(first['lit_date'], epoch)
  
      def test_decimal(self):
          from decimal import Decimal
          schema = StructType([StructField("decimal", DecimalType(10, 5))])
          df = self.spark.createDataFrame([(Decimal("3.14159"),)], schema)
          row = df.select(df.decimal + 1).first()
          self.assertEqual(row[0], Decimal("4.14159"))
          tmpPath = tempfile.mkdtemp()
          shutil.rmtree(tmpPath)
          df.write.parquet(tmpPath)
          df2 = self.spark.read.parquet(tmpPath)
          row = df2.first()
          self.assertEqual(row[0], Decimal("3.14159"))
  
      def test_BinaryType_serialization(self):
          # Pyrolite version <= 4.9 could not serialize BinaryType with Python3 SPARK-17808
          # The empty bytearray is test for SPARK-21534.
          schema = StructType([StructField('mybytes', BinaryType())])
          data = [[bytearray(b'here is my data')],
                  [bytearray(b'and here is some more')],
                  [bytearray(b'')]]
          df = self.spark.createDataFrame(data, schema=schema)
          df.collect()
  
      def test_int_array_serialization(self):
          # Note that this test seems dependent on parallelism.
          data = self.spark.sparkContext.parallelize([[1, 2, 3, 4]] * 100, numSlices=12)
          df = self.spark.createDataFrame(data, "array<integer>")
          self.assertEqual(len(list(filter(lambda r: None in r.value, df.collect()))), 0)
  
      def test_bytes_as_binary_type(self):
          df = self.spark.createDataFrame([[b"abcd"]], "col binary")
          self.assertEqual(df.first().col, bytearray(b'abcd'))
  
  
  if __name__ == "__main__":
      import unittest
      from pyspark.sql.tests.test_serde 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)
  