




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


UpperCAmelCase_		:					Optional[int]          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Tuple          =  {
    "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   (      __lowerCamelCase      ):
						UpperCamelCase      =       'deberta-v2'

						def __init__(		self :str     ,			__snake_case :Tuple=12_81_00     ,			__snake_case :Any=15_36     ,			__snake_case :Union[str, Any]=24     ,			__snake_case :Any=24     ,			__snake_case :Union[str, Any]=61_44     ,			__snake_case :Union[str, Any]="gelu"     ,			__snake_case :List[Any]=0.1     ,			__snake_case :Any=0.1     ,			__snake_case :str=5_12     ,			__snake_case :List[Any]=0     ,			__snake_case :int=0.02     ,			__snake_case :str=1E-7     ,			__snake_case :List[Any]=False     ,			__snake_case :Optional[Any]=-1     ,			__snake_case :Any=0     ,			__snake_case :Any=True     ,			__snake_case :int=None     ,			__snake_case :Optional[Any]=0     ,			__snake_case :int="gelu"     ,			**__snake_case :str     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

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

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

										__magic_name__						:						Tuple										=pos_att_type
										__magic_name__						:						Union[str, Any]										=vocab_size
										__magic_name__						:						int										=layer_norm_eps

										__magic_name__						:						Any										=kwargs.get("""pooler_hidden_size"""     ,			__snake_case			)
										__magic_name__						:						Union[str, Any]										=pooler_dropout
										__magic_name__						:						Optional[Any]										=pooler_hidden_act




class 		__A   (      __lowerCamelCase      ):

						@property
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						int										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[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 :Union[str, Any]			):
										'''simple docstring'''

										return 12


						def      A__    (		self :Tuple     ,			__snake_case :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional["TensorType"] = None     ,			__snake_case :int = 3     ,			__snake_case :int = 40     ,			__snake_case :int = 40     ,			__snake_case :"PreTrainedTokenizerBase" = None     ,			):
										'''simple docstring'''

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






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


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for shard in shards:
						for i in range(a	):
									yield {"i": i, "shard": shard}


def    a      ( )     ->List[str]:




			'''simple docstring'''


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

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

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

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

			SCREAMING_SNAKE_CASE     						= 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()