
"""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			)	->  List[Any]:







							"""simple docstring"""


							__snake_case        =							[]
							for part_id in partition_order:
														__snake_case        =							df.where(F'''SPARK_PARTITION_ID() = {part_id}'''			).collect()
														for row_idx, row in enumerate(_UpperCamelCase			):
																					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 (       )	->  str:







							"""simple docstring"""


							__snake_case        =							pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
							__snake_case        =							spark.range(1_00			).repartition(1			)
							__snake_case        =							Spark(_UpperCamelCase			)
							# 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 (       )	->  int:







							"""simple docstring"""


							__snake_case        =							pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
							__snake_case        =							spark.range(10			).repartition(2			)
							__snake_case        =							[1, 0]
							__snake_case        =							_generate_iterable_examples(_UpperCamelCase				,     _UpperCamelCase			)  # Reverse the partitions.
							__snake_case        =							_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCamelCase				,     _UpperCamelCase			)

							for i, (row_id, row_dict) in enumerate(generate_fn()			):
														__snake_case      , __snake_case        =							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 (       )	->  List[Any]:







							"""simple docstring"""


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







							"""simple docstring"""


							__snake_case        =							pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
							__snake_case        =							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        =							lambda SCREAMING_SNAKE_CASE			: x.reverse()
														__snake_case        =							_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCamelCase				,     [2, 1, 0]			)

														__snake_case        =							SparkExamplesIterable(_UpperCamelCase			).shuffle_data_sources(_UpperCamelCase			)
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(_UpperCamelCase			):
																					__snake_case      , __snake_case        =							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]:







							"""simple docstring"""


							__snake_case        =							pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
							__snake_case        =							spark.range(20			).repartition(4			)

							# Partitions 0 and 2
							__snake_case        =							SparkExamplesIterable(_UpperCamelCase			).shard_data_sources(worker_id=0				,     num_workers=2			)
							assert shard_it_a.n_shards == 2
							__snake_case        =							_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCamelCase				,     [0, 2]			)
							for i, (row_id, row_dict) in enumerate(_UpperCamelCase			):
														__snake_case      , __snake_case        =							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        =							SparkExamplesIterable(_UpperCamelCase			).shard_data_sources(worker_id=1				,     num_workers=2			)
							assert shard_it_a.n_shards == 2
							__snake_case        =							_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCamelCase				,     [1, 3]			)
							for i, (row_id, row_dict) in enumerate(_UpperCamelCase			):
														__snake_case      , __snake_case        =							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 (       )	->  List[str]:







							"""simple docstring"""


							__snake_case        =							pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
							__snake_case        =							spark.range(1_00			).repartition(1			)
							__snake_case        =							Spark(_UpperCamelCase			)
							# 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() == 1_00








__magic_name__   :  Optional[Any]        =							0  # The first color of the flag.
__magic_name__   :  List[str]        =							1  # The second color of the flag.
__magic_name__   :  Dict        =							2  # The third color of the flag.
__magic_name__   :  Union[str, Any]        =							(red, white, blue)
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if not sequence:
            return []
      if len(_UpperCamelCase) == 1:
            return list(_UpperCamelCase)
      UpperCamelCase					       =  0
      UpperCamelCase					       =  len(_UpperCamelCase) - 1
      UpperCamelCase					       =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  UpperCamelCase					       =  F'The elements inside the sequence must contains only {colors} values'
                  raise ValueError(_UpperCamelCase)
      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __magic_name__   :  str        =							input('''Enter numbers separated by commas:\n''').strip()
       __magic_name__   :  Union[str, Any]        =							[int(item.strip()) for item in user_input.split(''',''')]
       print(F'{dutch_national_flag_sort(unsorted)}')



