




'''simple docstring'''






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Tuple:
      snake_case__       :   Union[str, Any]	      =    []
      for part_id in partition_order:
            snake_case__       :   Dict	      =    df.where(f"SPARK_PARTITION_ID() = {part_id}"		).collect()
            for row_idx, row in enumerate(__SCREAMING_SNAKE_CASE		):
                  expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict())		)
      return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def 		UpperCamelCase__      (		)    ->   int:
      snake_case__       :   Optional[int]	      =    pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
      snake_case__       :   str	      =    spark.range(100		).repartition(1		)
      snake_case__       :   Tuple	      =    Spark(__SCREAMING_SNAKE_CASE		)
      # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
      # that each partition can hold 2 rows.
      spark_builder._repartition_df_if_needed(max_shard_size=16		)
      # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
      assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def 		UpperCamelCase__      (		)    ->   str:
      snake_case__       :   List[Any]	      =    pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
      snake_case__       :   Tuple	      =    spark.range(10		).repartition(2		)
      snake_case__       :   List[Any]	      =    [1, 0]
      snake_case__       :   str	      =    _generate_iterable_examples(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)  # Reverse the partitions.
      snake_case__       :   Optional[int]	      =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)

      for i, (row_id, row_dict) in enumerate(generate_fn()		):
            snake_case__      ,							snake_case__       :   Optional[Any]	      =    expected_row_ids_and_row_dicts[i]
            assert row_id == expected_row_id
            assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def 		UpperCamelCase__      (		)    ->   Optional[Any]:
      snake_case__       :   List[Any]	      =    pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
      snake_case__       :   List[str]	      =    spark.range(10		).repartition(1		)
      snake_case__       :   Optional[int]	      =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE		)
      assert it.n_shards == 1
      for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE		):
            assert row_id == f"0_{i}"
            assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def 		UpperCamelCase__      (		)    ->   int:
      snake_case__       :   Optional[Any]	      =    pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
      snake_case__       :   List[str]	      =    spark.range(30		).repartition(3		)
      # Mock the generator so that shuffle reverses the partition indices.
      with patch('numpy.random.Generator'		) as generator_mock:
            snake_case__       :   Optional[int]	      =    lambda __SCREAMING_SNAKE_CASE		: x.reverse()
            snake_case__       :   Optional[Any]	      =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE				,      [2, 1, 0]		)

            snake_case__       :   Optional[Any]	      =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE		).shuffle_data_sources(__SCREAMING_SNAKE_CASE		)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE		):
                  snake_case__      ,							snake_case__       :   Tuple	      =    expected_row_ids_and_row_dicts[i]
                  assert row_id == expected_row_id
                  assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def 		UpperCamelCase__      (		)    ->   Union[str, Any]:
      snake_case__       :   Union[str, Any]	      =    pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
      snake_case__       :   int	      =    spark.range(20		).repartition(4		)

      # Partitions 0 and 2
      snake_case__       :   int	      =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE		).shard_data_sources(worker_id=0				,      num_workers=2		)
      assert shard_it_a.n_shards == 2
      snake_case__       :   List[str]	      =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE				,      [0, 2]		)
      for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE		):
            snake_case__      ,							snake_case__       :   Union[str, Any]	      =    expected_row_ids_and_row_dicts_a[i]
            assert row_id == expected_row_id
            assert row_dict == expected_row_dict

      # Partitions 1 and 3
      snake_case__       :   Tuple	      =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE		).shard_data_sources(worker_id=1				,      num_workers=2		)
      assert shard_it_a.n_shards == 2
      snake_case__       :   Optional[int]	      =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE				,      [1, 3]		)
      for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE		):
            snake_case__      ,							snake_case__       :   Union[str, Any]	      =    expected_row_ids_and_row_dicts_a[i]
            assert row_id == expected_row_id
            assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def 		UpperCamelCase__      (		)    ->   Union[str, Any]:
      snake_case__       :   Dict	      =    pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
      snake_case__       :   Dict	      =    spark.range(100		).repartition(1		)
      snake_case__       :   Any	      =    Spark(__SCREAMING_SNAKE_CASE		)
      # Choose a small max_shard_size for maximum partitioning.
      spark_builder._repartition_df_if_needed(max_shard_size=1		)
      # The new number of partitions should not be greater than the number of rows.
      assert spark_builder.df.rdd.getNumPartitions() == 100






'''simple docstring'''






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__      ,							snake_case__       :   Optional[int]	      =    [], []
      while len(__SCREAMING_SNAKE_CASE		) > 1:
            snake_case__      ,							snake_case__       :   Tuple	      =    min(__SCREAMING_SNAKE_CASE		), max(__SCREAMING_SNAKE_CASE		)
            start.append(__SCREAMING_SNAKE_CASE		)
            end.append(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
      end.reverse()
      return start + collection + end


if __name__ == "__main__":
      A_			      =		input("Enter numbers separated by a comma:\n").strip()
      A_			      =		[int(item) for item in user_input.split(",")]
      print(*merge_sort(unsorted), sep=",")
