
"""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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  []
			for part_id in partition_order:
						lowerCamelCase_					=  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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(100						).repartition(1						)
			lowerCamelCase_					=  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=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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(10						).repartition(2						)
			lowerCamelCase_					=  [1, 0]
			lowerCamelCase_					=  _generate_iterable_examples(lowerCAmelCase__	,lowerCAmelCase__						)  # Reverse the partitions.
			lowerCamelCase_					=  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__	,lowerCAmelCase__						)

			for i, (row_id, row_dict) in enumerate(generate_fn()						):
						lowerCamelCase_		, lowerCamelCase_					=  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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(10						).repartition(1						)
			lowerCamelCase_					=  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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(30						).repartition(3						)
			# Mock the generator so that shuffle reverses the partition indices.
			with patch('''numpy.random.Generator'''						) as generator_mock:
						lowerCamelCase_					=  lambda lowerCAmelCase__						: x.reverse()
						lowerCamelCase_					=  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__	,[2, 1, 0]						)

						lowerCamelCase_					=  SparkExamplesIterable(lowerCAmelCase__						).shuffle_data_sources(lowerCAmelCase__						)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(lowerCAmelCase__						):
									lowerCamelCase_		, lowerCamelCase_					=  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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(20						).repartition(4						)

			# Partitions 0 and 2
			lowerCamelCase_					=  SparkExamplesIterable(lowerCAmelCase__						).shard_data_sources(worker_id=0	,num_workers=2						)
			assert shard_it_a.n_shards == 2
			lowerCamelCase_					=  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__	,[0, 2]						)
			for i, (row_id, row_dict) in enumerate(lowerCAmelCase__						):
						lowerCamelCase_		, lowerCamelCase_					=  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_					=  SparkExamplesIterable(lowerCAmelCase__						).shard_data_sources(worker_id=1	,num_workers=2						)
			assert shard_it_a.n_shards == 2
			lowerCamelCase_					=  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__	,[1, 3]						)
			for i, (row_id, row_dict) in enumerate(lowerCAmelCase__						):
						lowerCamelCase_		, lowerCamelCase_					=  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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(100						).repartition(1						)
			lowerCamelCase_					=  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() == 100








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     return len(set(__A)) == len(__A)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



