


'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


__UpperCAmelCase					       =							logging.get_logger(__name__)




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							None:
														warnings.warn(
														    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
														    ''' Please use MobileViTImageProcessor instead.'''      ,					lowerCamelCase_      ,					)
														super().__init__(*lowerCamelCase_      ,					**lowerCamelCase_     )





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

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


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Union[str, Any]:
							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 A : x.reverse()
														lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[2, 1, 0] )

														lowerCAmelCase__      =			SparkExamplesIterable(A ).shuffle_data_sources(A )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(A ):
																					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       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(20 ).repartition(4 )

							# Partitions 0 and 2
							lowerCAmelCase__      =			SparkExamplesIterable(A ).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(A				,					[0, 2] )
							for i, (row_id, row_dict) in enumerate(A ):
														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(A ).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(A				,					[1, 3] )
							for i, (row_id, row_dict) in enumerate(A ):
														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       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# 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