


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 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:Dict 							=       []
      for part_id in partition_order:
            a						:str 							=       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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Union[str, Any] 							=       pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
      a						:List[Any] 							=       spark.range(100						).repartition(1						)
      a						:Any 							=       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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:int 							=       pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
      a						:Tuple 							=       spark.range(10						).repartition(2						)
      a						:Optional[Any] 							=       [1, 0]
      a						:Any 							=       _generate_iterable_examples(UpperCAmelCase_ ,  UpperCAmelCase_						)  # Reverse the partitions.
      a						:int 							=       _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ ,  UpperCAmelCase_						)

      for i, (row_id, row_dict) in enumerate(generate_fn()						):
            a							,				a						:int 							=       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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:int 							=       pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
      a						:List[str] 							=       spark.range(10						).repartition(1						)
      a						:str 							=       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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:List[Any] 							=       pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
      a						:Dict 							=       spark.range(30						).repartition(3						)
      # Mock the generator so that shuffle reverses the partition indices.
      with patch('''numpy.random.Generator'''						) as generator_mock:
            a						:Optional[int] 							=       lambda UpperCAmelCase_						: x.reverse()
            a						:Tuple 							=       _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ ,  [2, 1, 0]						)

            a						:str 							=       SparkExamplesIterable(UpperCAmelCase_						).shuffle_data_sources(UpperCAmelCase_						)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(UpperCAmelCase_						):
                  a							,				a						:str 							=       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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:List[str] 							=       pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
      a						:Optional[Any] 							=       spark.range(20						).repartition(4						)

      # Partitions 0 and 2
      a						:Tuple 							=       SparkExamplesIterable(UpperCAmelCase_						).shard_data_sources(worker_id=0 ,  num_workers=2						)
      assert shard_it_a.n_shards == 2
      a						:List[str] 							=       _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ ,  [0, 2]						)
      for i, (row_id, row_dict) in enumerate(UpperCAmelCase_						):
            a							,				a						:List[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
      a						:Tuple 							=       SparkExamplesIterable(UpperCAmelCase_						).shard_data_sources(worker_id=1 ,  num_workers=2						)
      assert shard_it_a.n_shards == 2
      a						:Optional[int] 							=       _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ ,  [1, 3]						)
      for i, (row_id, row_dict) in enumerate(UpperCAmelCase_						):
            a							,				a						: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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:List[Any] 							=       pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
      a						:Dict 							=       spark.range(100						).repartition(1						)
      a						:Dict 							=       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() == 100







from __future__ import annotations

from fractions import Fraction




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						int							)			->   bool:
				return (
				    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
				)




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   list[str]:
				__lowerCamelCase    					=	[]
				__lowerCamelCase    					=	11
				__lowerCamelCase    					=	int('''1''' + '''0''' * digit_len							)
				for num in range(__lowerCAmelCase		,  __lowerCAmelCase							):
								while den <= 99:
												if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																if is_digit_cancelling(__lowerCAmelCase		,  __lowerCAmelCase							):
																				solutions.append(f'''{num}/{den}'''							)
												den += 1
								num += 1
								__lowerCamelCase    					=	10
				return solutions




def  __magic_name__ (   __lowerCAmelCase	:						int = 2							)			->   int:
				__lowerCamelCase    					=	1.0
				for fraction in fraction_list(__lowerCAmelCase							):
								__lowerCamelCase    					=	Fraction(__lowerCAmelCase							)
								result *= frac.denominator / frac.numerator
				return int(__lowerCAmelCase							)


if __name__ == "__main__":
					print(solution())



