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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				SCREAMING_SNAKE_CASE__		  =				[]
				for part_id in partition_order:
								SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  )							->   Dict:
				SCREAMING_SNAKE_CASE__		  =				pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
				SCREAMING_SNAKE_CASE__		  =				spark.range(100       ).repartition(1       )
				SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
				SCREAMING_SNAKE_CASE__		  =				spark.range(10       ).repartition(2       )
				SCREAMING_SNAKE_CASE__		  =				[1, 0]
				SCREAMING_SNAKE_CASE__		  =				_generate_iterable_examples(_lowerCAmelCase  ,					_lowerCAmelCase       )  # Reverse the partitions.
				SCREAMING_SNAKE_CASE__		  =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCAmelCase  ,					_lowerCAmelCase       )

				for i, (row_id, row_dict) in enumerate(generate_fn()       ):
								SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
				SCREAMING_SNAKE_CASE__		  =				spark.range(10       ).repartition(1       )
				SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
				SCREAMING_SNAKE_CASE__		  =				spark.range(30       ).repartition(3       )
				# Mock the generator so that shuffle reverses the partition indices.
				with patch("numpy.random.Generator"       ) as generator_mock:
								SCREAMING_SNAKE_CASE__		  =				lambda __UpperCAmelCase       : x.reverse()
								SCREAMING_SNAKE_CASE__		  =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCAmelCase  ,					[2, 1, 0]       )

								SCREAMING_SNAKE_CASE__		  =				SparkExamplesIterable(_lowerCAmelCase       ).shuffle_data_sources(_lowerCAmelCase       )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(_lowerCAmelCase       ):
												SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  )							->   int:
				SCREAMING_SNAKE_CASE__		  =				pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
				SCREAMING_SNAKE_CASE__		  =				spark.range(20       ).repartition(4       )

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

				# Partitions 1 and 3
				SCREAMING_SNAKE_CASE__		  =				SparkExamplesIterable(_lowerCAmelCase       ).shard_data_sources(worker_id=1  ,					num_workers=2       )
				assert shard_it_a.n_shards == 2
				SCREAMING_SNAKE_CASE__		  =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCAmelCase  ,					[1, 3]       )
				for i, (row_id, row_dict) in enumerate(_lowerCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
				SCREAMING_SNAKE_CASE__		  =				spark.range(100       ).repartition(1       )
				SCREAMING_SNAKE_CASE__		  =				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"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




