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

					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(     ):
					__a		 =       pyspark.sql.SparkSession.builder.master('local[*]'						).appName('pyspark'						).getOrCreate()
					__a		 =       spark.range(10						).repartition(1						)
					__a		 =       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 							lowerCAmelCase(     ):
					__a		 =       pyspark.sql.SparkSession.builder.master('local[*]'						).appName('pyspark'						).getOrCreate()
					__a		 =       spark.range(30						).repartition(3						)
					# Mock the generator so that shuffle reverses the partition indices.
					with patch('numpy.random.Generator'						) as generator_mock:
										__a		 =       lambda __lowerCamelCase						: x.reverse()
										__a		 =       _get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCamelCase				,    [2, 1, 0]						)

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

					# Partitions 0 and 2
					__a		 =       SparkExamplesIterable(__lowerCamelCase						).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(__lowerCamelCase				,    [0, 2]						)
					for i, (row_id, row_dict) in enumerate(__lowerCamelCase						):
										__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(__lowerCamelCase						).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(__lowerCamelCase				,    [1, 3]						)
					for i, (row_id, row_dict) in enumerate(__lowerCamelCase						):
										__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(     ):
					__a		 =       pyspark.sql.SparkSession.builder.master('local[*]'						).appName('pyspark'						).getOrCreate()
					__a		 =       spark.range(100						).repartition(1						)
					__a		 =       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





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# Return True if there is node that has not iterated.
					__a		 =       [False] * len(__lowerCamelCase						)
					__a		 =       []
					queue.append(__lowerCamelCase						)
					__a		 =       True

					while queue:
										__a		 =       queue.pop(0						)
										for ind in range(len(graph[u]						)						):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(__lowerCamelCase						)
																				__a		 =       True
																				__a		 =       u

					return visited[t]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# This array is filled by BFS and to store path
					__a		 =       [-1] * (len(__lowerCamelCase						))
					__a		 =       0
					while bfs(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       float('Inf'						)
										__a		 =       sink

										while s != source:
															# Find the minimum value in select path
															__a		 =       min(__lowerCamelCase				,    graph[parent[s]][s]						)
															__a		 =       parent[s]

										max_flow += path_flow
										__a		 =       sink

										while v != source:
															__a		 =       parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															__a		 =       parent[v]
					return max_flow


lowerCamelCase_    :		str							=	[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

lowerCamelCase_						,  lowerCamelCase_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




