



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	:    Tuple				,   __lowerCamelCase	:    Any	)  ->						Dict:
					__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 					__lowerCAmelCase		(  )  ->						List[Any]:
					__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 					__lowerCAmelCase		(  )  ->						Dict:
					__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 					__lowerCAmelCase		(  )  ->						List[Any]:
					__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 					__lowerCAmelCase		(  )  ->						str:
					__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 					__lowerCAmelCase		(  )  ->						str:
					__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 					__lowerCAmelCase		(  )  ->						List[Any]:
					__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









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[int]	)  ->						int:
					if not numbers:
										return 0

					if not isinstance(__lowerCamelCase				,   (list, tuple)	) or not all(
					    isinstance(__lowerCamelCase				,   __lowerCamelCase	) for number in numbers	):
										raise ValueError("""numbers must be an iterable of integers"""	)

					__lowerCAmelCase	   =__lowerCAmelCase	   =__lowerCAmelCase	   =numbers[0]

					for i in range(1				,   len(__lowerCamelCase	)	):
										# update the maximum and minimum subarray products
										__lowerCAmelCase	   =numbers[i]
										if number < 0:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =min_till_now, max_till_now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   max_till_now * number	)
										__lowerCAmelCase	   =min(__lowerCamelCase				,   min_till_now * number	)

										# update the maximum product found till now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   __lowerCamelCase	)

					return max_prod




