


'''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_     (       A__   :  str				,       A__   :  str   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[Any]     =  []
  for part_id in partition_order:
    lowerCAmelCase_     : Tuple     =  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 							UpperCamelCase_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : List[Any]     =  pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
  lowerCAmelCase_     : Tuple     =  spark.range(1_00   ).repartition(1   )
  lowerCAmelCase_     : str     =  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 							UpperCamelCase_     (       ):
  '''simple docstring'''





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

  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 							UpperCamelCase_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : str     =  pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
  lowerCAmelCase_     : Dict     =  spark.range(10   ).repartition(1   )
  lowerCAmelCase_     : List[str]     =  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 							UpperCamelCase_     (       ):
  '''simple docstring'''





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

    lowerCAmelCase_     : Any     =  SparkExamplesIterable(A__   ).shuffle_data_sources(A__   )
    assert shuffled_it.n_shards == 3
    for i, (row_id, row_dict) in enumerate(A__   ):
      lowerCAmelCase_,				lowerCAmelCase_     : 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_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
  lowerCAmelCase_     : Union[str, Any]     =  spark.range(20   ).repartition(4   )

  # Partitions 0 and 2
  lowerCAmelCase_     : int     =  SparkExamplesIterable(A__   ).shard_data_sources(worker_id=0				,       num_workers=2   )
  assert shard_it_a.n_shards == 2
  lowerCAmelCase_     : Optional[int]     =  _get_expected_row_ids_and_row_dicts_for_partition_order(A__				,       [0, 2]   )
  for i, (row_id, row_dict) in enumerate(A__   ):
    lowerCAmelCase_,				lowerCAmelCase_     : int     =  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(A__   ).shard_data_sources(worker_id=1				,       num_workers=2   )
  assert shard_it_a.n_shards == 2
  lowerCAmelCase_     : Optional[int]     =  _get_expected_row_ids_and_row_dicts_for_partition_order(A__				,       [1, 3]   )
  for i, (row_id, row_dict) in enumerate(A__   ):
    lowerCAmelCase_,				lowerCAmelCase_     : List[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_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : List[str]     =  pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
  lowerCAmelCase_     : int     =  spark.range(1_00   ).repartition(1   )
  lowerCAmelCase_     : Dict     =  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() == 1_00







'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   :    List[Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roberta-prelayernorm'

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Tuple=5_02_65       ,		lowerCamelCase       :    Optional[int]=7_68       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Optional[int]="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Optional[int]=5_12       ,		lowerCamelCase       :    Union[str, Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    Any=1E-12       ,		lowerCamelCase       :    int=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    Optional[Any]="absolute"       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    Tuple=None       ,		**lowerCamelCase       :    int       ,		)  ->    Optional[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : List[Any]     =  num_attention_heads
      lowerCAmelCase_     : List[str]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : Tuple     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Optional[int]     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : Union[str, Any]     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Dict     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)



