





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 (							__a       :int   ,					__a       :Union[str, Any]   )		->    Tuple:






		"""simple docstring"""

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






		"""simple docstring"""

		A__  				=       pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
		A__  				=       spark.range(1_0_0   ).repartition(1   )
		A__  				=       Spark(__a   )
		# 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        __lowerCamelCase (							)		->    Optional[Any]:






		"""simple docstring"""

		A__  				=       pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
		A__  				=       spark.range(1_0   ).repartition(2   )
		A__  				=       [1, 0]
		A__  				=       _generate_iterable_examples(__a   ,					__a   )  # Reverse the partitions.
		A__  				=       _get_expected_row_ids_and_row_dicts_for_partition_order(__a   ,					__a   )

		for i, (row_id, row_dict) in enumerate(generate_fn()   ):
				A__				,      A__  				=       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 (							)		->    Dict:






		"""simple docstring"""

		A__  				=       pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
		A__  				=       spark.range(1_0   ).repartition(1   )
		A__  				=       SparkExamplesIterable(__a   )
		assert it.n_shards == 1
		for i, (row_id, row_dict) in enumerate(__a   ):
				assert row_id == F'0_{i}'
				assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def        __lowerCamelCase (							)		->    List[str]:






		"""simple docstring"""

		A__  				=       pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
		A__  				=       spark.range(3_0   ).repartition(3   )
		# Mock the generator so that shuffle reverses the partition indices.
		with patch("""numpy.random.Generator"""   ) as generator_mock:
				A__  				=       lambda __a   : x.reverse()
				A__  				=       _get_expected_row_ids_and_row_dicts_for_partition_order(__a   ,					[2, 1, 0]   )

				A__  				=       SparkExamplesIterable(__a   ).shuffle_data_sources(__a   )
				assert shuffled_it.n_shards == 3
				for i, (row_id, row_dict) in enumerate(__a   ):
						A__				,      A__  				=       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 (							)		->    Optional[int]:






		"""simple docstring"""

		A__  				=       pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
		A__  				=       spark.range(2_0   ).repartition(4   )

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






		"""simple docstring"""

		A__  				=       pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
		A__  				=       spark.range(1_0_0   ).repartition(1   )
		A__  				=       Spark(__a   )
		# 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










import math







def        __lowerCamelCase (							__a       :int   )		->    bool:






		"""simple docstring"""

		A__  				=       math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

		return exponent == int(__a   )







def        __lowerCamelCase (							__a       :float = 1 / 1_2_3_4_5   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       0

		A__  				=       3
		while True:
				A__  				=       (integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(__a   ):
						A__  				=       int(__a   )
						total_partitions += 1
						if check_partition_perfect(__a   ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(__a   )
				integer += 1


if __name__ == "__main__":
					print(F'''{solution() = }''')



