
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 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   List[Any]  )   ->			str:






						'''simple docstring'''

						_lowercase       :	Tuple           =   []
						for part_id in partition_order:
												_lowercase       :	str           =   df.where(f"""SPARK_PARTITION_ID() = {part_id}"""  ).collect()
												for row_idx, row in enumerate(_UpperCAmelCase  ):
																		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 		UpperCamelCase     (       )   ->			List[str]:






						'''simple docstring'''

						_lowercase       :	List[str]           =   pyspark.sql.SparkSession.builder.master("local[*]"  ).appName("pyspark"  ).getOrCreate()
						_lowercase       :	Dict           =   spark.range(100  ).repartition(1  )
						_lowercase       :	int           =   Spark(_UpperCAmelCase  )
						# 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 		UpperCamelCase     (       )   ->			Any:






						'''simple docstring'''

						_lowercase       :	List[str]           =   pyspark.sql.SparkSession.builder.master("local[*]"  ).appName("pyspark"  ).getOrCreate()
						_lowercase       :	Any           =   spark.range(10  ).repartition(2  )
						_lowercase       :	int           =   [1, 0]
						_lowercase       :	Tuple           =   _generate_iterable_examples(_UpperCAmelCase ,			_UpperCAmelCase  )  # Reverse the partitions.
						_lowercase       :	Optional[int]           =   _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase ,			_UpperCAmelCase  )

						for i, (row_id, row_dict) in enumerate(generate_fn()  ):
												_lowercase      ,    _lowercase       :	Union[str, Any]           =   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 		UpperCamelCase     (       )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Optional[int]           =   pyspark.sql.SparkSession.builder.master("local[*]"  ).appName("pyspark"  ).getOrCreate()
						_lowercase       :	List[str]           =   spark.range(10  ).repartition(1  )
						_lowercase       :	List[str]           =   SparkExamplesIterable(_UpperCAmelCase  )
						assert it.n_shards == 1
						for i, (row_id, row_dict) in enumerate(_UpperCAmelCase  ):
												assert row_id == f"""0_{i}"""
												assert row_dict == {"id": i}



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






						'''simple docstring'''

						_lowercase       :	str           =   pyspark.sql.SparkSession.builder.master("local[*]"  ).appName("pyspark"  ).getOrCreate()
						_lowercase       :	int           =   spark.range(30  ).repartition(3  )
						# Mock the generator so that shuffle reverses the partition indices.
						with patch("numpy.random.Generator"  ) as generator_mock:
												_lowercase       :	Dict           =   lambda _UpperCAmelCase  : x.reverse()
												_lowercase       :	Optional[int]           =   _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase ,			[2, 1, 0]  )

												_lowercase       :	str           =   SparkExamplesIterable(_UpperCAmelCase  ).shuffle_data_sources(_UpperCAmelCase  )
												assert shuffled_it.n_shards == 3
												for i, (row_id, row_dict) in enumerate(_UpperCAmelCase  ):
																		_lowercase      ,    _lowercase       :	Union[str, Any]           =   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 		UpperCamelCase     (       )   ->			List[str]:






						'''simple docstring'''

						_lowercase       :	Tuple           =   pyspark.sql.SparkSession.builder.master("local[*]"  ).appName("pyspark"  ).getOrCreate()
						_lowercase       :	int           =   spark.range(20  ).repartition(4  )

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

						# Partitions 1 and 3
						_lowercase       :	Dict           =   SparkExamplesIterable(_UpperCAmelCase  ).shard_data_sources(worker_id=1 ,			num_workers=2  )
						assert shard_it_a.n_shards == 2
						_lowercase       :	Dict           =   _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase ,			[1, 3]  )
						for i, (row_id, row_dict) in enumerate(_UpperCAmelCase  ):
												_lowercase      ,    _lowercase       :	Any           =   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 		UpperCamelCase     (       )   ->			List[Any]:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   pyspark.sql.SparkSession.builder.master("local[*]"  ).appName("pyspark"  ).getOrCreate()
						_lowercase       :	List[Any]           =   spark.range(100  ).repartition(1  )
						_lowercase       :	Any           =   Spark(_UpperCAmelCase  )
						# 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






from __future__ import annotations



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str] | None = None  )   ->			list[list[str]]:






						'''simple docstring'''

						_lowercase       :	Dict           =   word_bank or []
						# create a table
						_lowercase       :	int           =   len(_UpperCAmelCase  ) + 1

						_lowercase       :	list[list[list[str]]]           =   []
						for _ in range(_UpperCAmelCase  ):
												table.append([]  )
						# seed value
						_lowercase       :	Optional[int]           =   [[]]  # because empty string has empty combination

						# iterate through the indices
						for i in range(_UpperCAmelCase  ):
												# condition
												if table[i] != []:
																		for word in word_bank:
																								# slice condition
																								if target[i : i + len(_UpperCAmelCase  )] == word:
																														_lowercase       :	list[list[str]]           =   [
																														    [word, *way] for way in table[i]
																														]
																														# adds the word to every combination the current position holds
																														# now,push that combination to the table[i+len(word)]
																														table[i + len(_UpperCAmelCase  )] += new_combinations

    # combinations are in reverse order so reverse for better output
						for combination in table[len(_UpperCAmelCase  )]:
												combination.reverse()

						return table[len(_UpperCAmelCase  )]


if __name__ == "__main__":
							print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
							print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
							print(
							    all_construct(
							        """hexagonosaurus""",
							        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
							    )
							)




