
"""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 							A						(				snake_case      :List[str]				,					snake_case      :Optional[int]		)      ->	str:
 __UpperCamelCase				  =       []
 for part_id in partition_order:
  __UpperCamelCase				  =       df.where(f'SPARK_PARTITION_ID() = {part_id}'		).collect()
  for row_idx, row in enumerate(lowerCAmelCase__		):
   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 							A						(				)      ->	Any:
 __UpperCamelCase				  =       pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
 __UpperCamelCase				  =       spark.range(1_0_0		).repartition(1		)
 __UpperCamelCase				  =       Spark(lowerCAmelCase__		)
 # 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=1_6		)
 # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
 assert spark_builder.df.rdd.getNumPartitions() == 5_0






@require_not_windows
@require_dill_gt_0_3_2
def 							A						(				)      ->	int:
 __UpperCamelCase				  =       pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
 __UpperCamelCase				  =       spark.range(1_0		).repartition(2		)
 __UpperCamelCase				  =       [1, 0]
 __UpperCamelCase				  =       _generate_iterable_examples(lowerCAmelCase__				,					lowerCAmelCase__		)  # Reverse the partitions.
 __UpperCamelCase				  =       _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__				,					lowerCAmelCase__		)

 for i, (row_id, row_dict) in enumerate(generate_fn()		):
  __UpperCamelCase  ,	__UpperCamelCase				  =       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 							A						(				)      ->	Optional[int]:
 __UpperCamelCase				  =       pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
 __UpperCamelCase				  =       spark.range(1_0		).repartition(1		)
 __UpperCamelCase				  =       SparkExamplesIterable(lowerCAmelCase__		)
 assert it.n_shards == 1
 for i, (row_id, row_dict) in enumerate(lowerCAmelCase__		):
  assert row_id == f'0_{i}'
  assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def 							A						(				)      ->	Optional[Any]:
 __UpperCamelCase				  =       pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
 __UpperCamelCase				  =       spark.range(3_0		).repartition(3		)
 # Mock the generator so that shuffle reverses the partition indices.
 with patch('numpy.random.Generator'		) as generator_mock:
  __UpperCamelCase				  =       lambda snake_case		: x.reverse()
  __UpperCamelCase				  =       _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__				,					[2, 1, 0]		)

  __UpperCamelCase				  =       SparkExamplesIterable(lowerCAmelCase__		).shuffle_data_sources(lowerCAmelCase__		)
  assert shuffled_it.n_shards == 3
  for i, (row_id, row_dict) in enumerate(lowerCAmelCase__		):
   __UpperCamelCase  ,	__UpperCamelCase				  =       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 							A						(				)      ->	Union[str, Any]:
 __UpperCamelCase				  =       pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
 __UpperCamelCase				  =       spark.range(2_0		).repartition(4		)

 # Partitions 0 and 2
 __UpperCamelCase				  =       SparkExamplesIterable(lowerCAmelCase__		).shard_data_sources(worker_id=0				,					num_workers=2		)
 assert shard_it_a.n_shards == 2
 __UpperCamelCase				  =       _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__				,					[0, 2]		)
 for i, (row_id, row_dict) in enumerate(lowerCAmelCase__		):
  __UpperCamelCase  ,	__UpperCamelCase				  =       expected_row_ids_and_row_dicts_a[i]
  assert row_id == expected_row_id
  assert row_dict == expected_row_dict

 # Partitions 1 and 3
 __UpperCamelCase				  =       SparkExamplesIterable(lowerCAmelCase__		).shard_data_sources(worker_id=1				,					num_workers=2		)
 assert shard_it_a.n_shards == 2
 __UpperCamelCase				  =       _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__				,					[1, 3]		)
 for i, (row_id, row_dict) in enumerate(lowerCAmelCase__		):
  __UpperCamelCase  ,	__UpperCamelCase				  =       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 							A						(				)      ->	Optional[Any]:
 __UpperCamelCase				  =       pyspark.sql.SparkSession.builder.master('local[*]'		).appName('pyspark'		).getOrCreate()
 __UpperCamelCase				  =       spark.range(1_0_0		).repartition(1		)
 __UpperCamelCase				  =       Spark(lowerCAmelCase__		)
 # 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_0_0




"""simple docstring"""






from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[float]		) ->    Optional[Any]:
      '''simple docstring'''


      return np.maximum(0					,		lowerCAmelCase__		)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



