


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

      for i, (row_id, row_dict) in enumerate(generate_fn()):
            A_   ,					A_						:  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      lowerCamelCase					(      ):
      A_						:  str          =				pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
      A_						:  Optional[int]          =				spark.range(10).repartition(1)
      A_						:  Union[str, Any]          =				SparkExamplesIterable(lowerCamelCase)
      assert it.n_shards == 1
      for i, (row_id, row_dict) in enumerate(lowerCamelCase):
            assert row_id == F'0_{i}'
            assert row_dict == {"id": i}




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

            A_						:  Any          =				SparkExamplesIterable(lowerCamelCase).shuffle_data_sources(lowerCamelCase)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(lowerCamelCase):
                  A_   ,					A_						:  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      lowerCamelCase					(      ):
      A_						:  str          =				pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
      A_						:  Tuple          =				spark.range(20).repartition(4)

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

      # Partitions 1 and 3
      A_						:  str          =				SparkExamplesIterable(lowerCamelCase).shard_data_sources(worker_id=1							,						num_workers=2)
      assert shard_it_a.n_shards == 2
      A_						:  str          =				_get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase							,						[1, 3])
      for i, (row_id, row_dict) in enumerate(lowerCamelCase):
            A_   ,					A_						:  Union[str, 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      lowerCamelCase					(      ):
      A_						:  int          =				pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
      A_						:  Tuple          =				spark.range(100).repartition(1)
      A_						:  Optional[int]          =				Spark(lowerCamelCase)
      # 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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""deberta-v2"""






  def __init__(							self :       Optional[Any]	,_a :       Union[str, Any]=128100	,_a :       Optional[int]=1536	,_a :       Dict=24	,_a :       int=24	,_a :       Tuple=6144	,_a :       Union[str, Any]="gelu"	,_a :       List[Any]=0.1	,_a :       Dict=0.1	,_a :       int=512	,_a :       int=0	,_a :       int=0.02	,_a :       int=1e-7	,_a :       List[str]=False	,_a :       Union[str, Any]=-1	,_a :       List[Any]=0	,_a :       Optional[Any]=True	,_a :       Tuple=None	,_a :       Any=0	,_a :       int="gelu"	,**_a :       Any	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  Union[str, Any]          =				hidden_size
        A_						:  Dict          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[Any]          =				hidden_act
        A_						:  Optional[int]          =				hidden_dropout_prob
        A_						:  Dict          =				attention_probs_dropout_prob
        A_						:  int          =				max_position_embeddings
        A_						:  Any          =				type_vocab_size
        A_						:  List[Any]          =				initializer_range
        A_						:  int          =				relative_attention
        A_						:  Tuple          =				max_relative_positions
        A_						:  int          =				pad_token_id
        A_						:  Tuple          =				position_biased_input

        # Backwards compatibility
        if type(_a ) == str:
              A_						:  str          =				[x.strip() for x in pos_att_type.lower().split("""|""" )]

        A_						:  Any          =				pos_att_type
        A_						:  Optional[int]          =				vocab_size
        A_						:  Tuple          =				layer_norm_eps

        A_						:  Any          =				kwargs.get("""pooler_hidden_size"""	,_a )
        A_						:  Union[str, Any]          =				pooler_dropout
        A_						:  List[Any]          =				pooler_hidden_act







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        if self.task == "multiple-choice":
              A_						:  Any          =				{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              A_						:  Any          =				{0: """batch""", 1: """sequence"""}
        if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
        else:
              return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return 12






  def        _a							(							self :       int	,_a :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,_a :       int = -1	,_a :       int = -1	,_a :       int = -1	,_a :       bool = False	,_a :       Optional["TensorType"] = None	,_a :       int = 3	,_a :       int = 40	,_a :       int = 40	,_a :       "PreTrainedTokenizerBase" = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(preprocessor=_a	,framework=_a )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
        return dummy_inputs
