


"""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:     Dict		,   _a:     int							)->		List[str]:
      '''simple docstring'''


      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						(				)->		List[str]:
      '''simple docstring'''


      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						(				)->		List[Any]:
      '''simple docstring'''


      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[int]:
      '''simple docstring'''


      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						(				)->		Tuple:
      '''simple docstring'''


      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						(				)->		Optional[int]:
      '''simple docstring'''


      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						(				)->		Optional[Any]:
      '''simple docstring'''


      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







"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



