
'''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					(	_lowerCamelCase    :							List[str]	,   _lowerCamelCase    :							Any		)     ->    Dict:
  _lowerCAmelCase							:	Dict 					=       []
  for part_id in partition_order:
    _lowerCAmelCase							:	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:
  _lowerCAmelCase							:	List[str] 					=       pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _lowerCAmelCase							:	str 					=       spark.range(1_00		).repartition(1		)
  _lowerCAmelCase							:	Any 					=       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					(	)     ->    List[Any]:
  _lowerCAmelCase							:	List[str] 					=       pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _lowerCAmelCase							:	Union[str, Any] 					=       spark.range(10		).repartition(2		)
  _lowerCAmelCase							:	int 					=       [1, 0]
  _lowerCAmelCase							:	List[str] 					=       _generate_iterable_examples(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__		)  # Reverse the partitions.
  _lowerCAmelCase							:	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()		):
    _lowerCAmelCase							:	Optional[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 						_UpperCAmelCase					(	)     ->    Union[str, Any]:
  _lowerCAmelCase							:	Any 					=       pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _lowerCAmelCase							:	List[Any] 					=       spark.range(10		).repartition(1		)
  _lowerCAmelCase							:	Optional[Any] 					=       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					(	)     ->    Optional[Any]:
  _lowerCAmelCase							:	Optional[int] 					=       pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _lowerCAmelCase							:	Tuple 					=       spark.range(30		).repartition(3		)
  # Mock the generator so that shuffle reverses the partition indices.
  with patch("""numpy.random.Generator"""		) as generator_mock:
    _lowerCAmelCase							:	Tuple 					=       lambda _lowerCamelCase		: x.reverse()
    _lowerCAmelCase							:	int 					=       _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE__	,   [2, 1, 0]		)

    _lowerCAmelCase							:	List[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__		):
      _lowerCAmelCase							:	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					(	)     ->    int:
  _lowerCAmelCase							:	Union[str, Any] 					=       pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _lowerCAmelCase							:	Optional[int] 					=       spark.range(20		).repartition(4		)

  # Partitions 0 and 2
  _lowerCAmelCase							:	Optional[int] 					=       SparkExamplesIterable(SCREAMING_SNAKE_CASE__		).shard_data_sources(worker_id=0	,   num_workers=2		)
  assert shard_it_a.n_shards == 2
  _lowerCAmelCase							:	Tuple 					=       _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__		):
    _lowerCAmelCase							:	List[str] 					=       expected_row_ids_and_row_dicts_a[i]
    assert row_id == expected_row_id
    assert row_dict == expected_row_dict

  # Partitions 1 and 3
  _lowerCAmelCase							:	int 					=       SparkExamplesIterable(SCREAMING_SNAKE_CASE__		).shard_data_sources(worker_id=1	,   num_workers=2		)
  assert shard_it_a.n_shards == 2
  _lowerCAmelCase							:	Any 					=       _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__		):
    _lowerCAmelCase							:	str 					=       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]:
  _lowerCAmelCase							:	List[Any] 					=       pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _lowerCAmelCase							:	int 					=       spark.range(1_00		).repartition(1		)
  _lowerCAmelCase							:	str 					=       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() == 1_00




'''simple docstring'''
import numpy as np




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							np.ndarray:


		'''simple docstring'''


		return 1 / (1 + np.exp(-vector			))




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							np.ndarray:


		'''simple docstring'''


		return vector * sigmoid(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


