"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  Tuple   ,				*__lowerCAmelCase	:  str   ,				**__lowerCAmelCase	:  Optional[int]					):
      warnings.warn(
          """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
          """ use DeiTImageProcessor 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     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      []
  for part_id in partition_order:
    _UpperCAmelCase						 =      df.where(f'''SPARK_PARTITION_ID() = {part_id}'''		).collect()
    for row_idx, row in enumerate(lowercase		):
      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					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(1_00		).repartition(1		)
  _UpperCAmelCase						 =      Spark(lowercase		)
  # 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					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(10		).repartition(2		)
  _UpperCAmelCase						 =      [1, 0]
  _UpperCAmelCase						 =      _generate_iterable_examples(lowercase   ,lowercase		)  # Reverse the partitions.
  _UpperCAmelCase						 =      _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase   ,lowercase		)

  for i, (row_id, row_dict) in enumerate(generate_fn()		):
    _UpperCAmelCase ,				_UpperCAmelCase						 =      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					(			):







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






@require_not_windows
@require_dill_gt_0_3_2
def     __UpperCAmelCase					(			):







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

    _UpperCAmelCase						 =      SparkExamplesIterable(lowercase		).shuffle_data_sources(lowercase		)
    assert shuffled_it.n_shards == 3
    for i, (row_id, row_dict) in enumerate(lowercase		):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      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					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(20		).repartition(4		)

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

  # Partitions 1 and 3
  _UpperCAmelCase						 =      SparkExamplesIterable(lowercase		).shard_data_sources(worker_id=1   ,num_workers=2		)
  assert shard_it_a.n_shards == 2
  _UpperCAmelCase						 =      _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase   ,[1, 3]		)
  for i, (row_id, row_dict) in enumerate(lowercase		):
    _UpperCAmelCase ,				_UpperCAmelCase						 =      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					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(1_00		).repartition(1		)
  _UpperCAmelCase						 =      Spark(lowercase		)
  # 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() == 1_00
