



"""simple docstring"""






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     _lowercase       (						__snake_case      ,__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						Optional[int]				= []
			for part_id in partition_order:
						__lowerCAmelCase   :						List[Any]				= df.where(F"""SPARK_PARTITION_ID() = {part_id}"""			).collect()
						for row_idx, row in enumerate(__snake_case			):
									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     _lowercase       (						)		->     Optional[Any]:
			__lowerCAmelCase   :						int				= pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
			__lowerCAmelCase   :						Union[str, Any]				= spark.range(100			).repartition(1			)
			__lowerCAmelCase   :						Dict				= Spark(__snake_case			)
			# 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     _lowercase       (						)		->     Tuple:
			__lowerCAmelCase   :						Dict				= pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
			__lowerCAmelCase   :						Optional[int]				= spark.range(10			).repartition(2			)
			__lowerCAmelCase   :						Dict				= [1, 0]
			__lowerCAmelCase   :						Dict				= _generate_iterable_examples(__snake_case      ,__snake_case			)  # Reverse the partitions.
			__lowerCAmelCase   :						Optional[int]				= _get_expected_row_ids_and_row_dicts_for_partition_order(__snake_case      ,__snake_case			)

			for i, (row_id, row_dict) in enumerate(generate_fn()			):
						__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= 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     _lowercase       (						)		->     Dict:
			__lowerCAmelCase   :						str				= pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
			__lowerCAmelCase   :						Optional[Any]				= spark.range(10			).repartition(1			)
			__lowerCAmelCase   :						Any				= SparkExamplesIterable(__snake_case			)
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(__snake_case			):
						assert row_id == F"""0_{i}"""
						assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def     _lowercase       (						)		->     int:
			__lowerCAmelCase   :						int				= pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
			__lowerCAmelCase   :						List[Any]				= spark.range(30			).repartition(3			)
			# Mock the generator so that shuffle reverses the partition indices.
			with patch("numpy.random.Generator"			) as generator_mock:
						__lowerCAmelCase   :						Dict				= lambda __snake_case			: x.reverse()
						__lowerCAmelCase   :						List[Any]				= _get_expected_row_ids_and_row_dicts_for_partition_order(__snake_case      ,[2, 1, 0]			)

						__lowerCAmelCase   :						Tuple				= SparkExamplesIterable(__snake_case			).shuffle_data_sources(__snake_case			)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(__snake_case			):
									__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= 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     _lowercase       (						)		->     Union[str, Any]:
			__lowerCAmelCase   :						List[Any]				= pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
			__lowerCAmelCase   :						Optional[int]				= spark.range(20			).repartition(4			)

			# Partitions 0 and 2
			__lowerCAmelCase   :						List[Any]				= SparkExamplesIterable(__snake_case			).shard_data_sources(worker_id=0      ,num_workers=2			)
			assert shard_it_a.n_shards == 2
			__lowerCAmelCase   :						List[Any]				= _get_expected_row_ids_and_row_dicts_for_partition_order(__snake_case      ,[0, 2]			)
			for i, (row_id, row_dict) in enumerate(__snake_case			):
						__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= 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   :						Optional[Any]				= SparkExamplesIterable(__snake_case			).shard_data_sources(worker_id=1      ,num_workers=2			)
			assert shard_it_a.n_shards == 2
			__lowerCAmelCase   :						List[Any]				= _get_expected_row_ids_and_row_dicts_for_partition_order(__snake_case      ,[1, 3]			)
			for i, (row_id, row_dict) in enumerate(__snake_case			):
						__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= 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     _lowercase       (						)		->     Any:
			__lowerCAmelCase   :						Optional[int]				= pyspark.sql.SparkSession.builder.master("local[*]"			).appName("pyspark"			).getOrCreate()
			__lowerCAmelCase   :						Any				= spark.range(100			).repartition(1			)
			__lowerCAmelCase   :						int				= Spark(__snake_case			)
			# 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 logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale