

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {
    '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     a__  (    a_	):
			"""simple docstring"""







			__lowerCamelCase       				=				"deberta-v2"


			def __init__(			self					,					lowercase=128100					,					lowercase=1536					,					lowercase=24					,					lowercase=24					,					lowercase=6144					,					lowercase="gelu"					,					lowercase=0.1					,					lowercase=0.1					,					lowercase=512					,					lowercase=0					,					lowercase=0.02					,					lowercase=1e-7					,					lowercase=False					,					lowercase=-1					,					lowercase=0					,					lowercase=True					,					lowercase=None					,					lowercase=0					,					lowercase="gelu"					,					**lowercase					,					)     ->  Dict:
							'''simple docstring'''






							super().__init__(**_lowerCamelCase				)

							A__						=	hidden_size
							A__						=	num_hidden_layers
							A__						=	num_attention_heads
							A__						=	intermediate_size
							A__						=	hidden_act
							A__						=	hidden_dropout_prob
							A__						=	attention_probs_dropout_prob
							A__						=	max_position_embeddings
							A__						=	type_vocab_size
							A__						=	initializer_range
							A__						=	relative_attention
							A__						=	max_relative_positions
							A__						=	pad_token_id
							A__						=	position_biased_input

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

							A__						=	pos_att_type
							A__						=	vocab_size
							A__						=	layer_norm_eps

							A__						=	kwargs.get("pooler_hidden_size"					,					_lowerCamelCase				)
							A__						=	pooler_dropout
							A__						=	pooler_hidden_act

class     a__  (    a_	):
			"""simple docstring"""







			@property
			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							if self.task == "multiple-choice":
											A__						=	{0: "batch", 1: "choice", 2: "sequence"}
							else:
											A__						=	{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    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							return 12


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = -1					,					lowercase = -1					,					lowercase = -1					,					lowercase = False					,					lowercase = None					,					lowercase = 3					,					lowercase = 40					,					lowercase = 40					,					lowercase = None					,					)     ->  Any:
							'''simple docstring'''






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









"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


