



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase				:							int								={
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Any								=["""DebertaV2TokenizerFast"""]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Any								=[
	    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFDebertaV2ForMaskedLM""",
	    """TFDebertaV2ForQuestionAnswering""",
	    """TFDebertaV2ForSequenceClassification""",
	    """TFDebertaV2ForTokenClassification""",
	    """TFDebertaV2Model""",
	    """TFDebertaV2PreTrainedModel""",
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							str								=[
	    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """DebertaV2ForMaskedLM""",
	    """DebertaV2ForMultipleChoice""",
	    """DebertaV2ForQuestionAnswering""",
	    """DebertaV2ForSequenceClassification""",
	    """DebertaV2ForTokenClassification""",
	    """DebertaV2Model""",
	    """DebertaV2PreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_deberta_va import (
	    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DebertaVaConfig,
	    DebertaVaOnnxConfig,
	)
	from .tokenization_deberta_va import DebertaVaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deberta_va import (
		    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDebertaVaForMaskedLM,
		    TFDebertaVaForQuestionAnswering,
		    TFDebertaVaForSequenceClassification,
		    TFDebertaVaForTokenClassification,
		    TFDebertaVaModel,
		    TFDebertaVaPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deberta_va import (
		    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DebertaVaForMaskedLM,
		    DebertaVaForMultipleChoice,
		    DebertaVaForQuestionAnswering,
		    DebertaVaForSequenceClassification,
		    DebertaVaForTokenClassification,
		    DebertaVaModel,
		    DebertaVaPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase				:							Dict								=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				HfArgumentParser(_lowerCAmelCase)
					try:
										UpperCamelCase_											=				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										UpperCamelCase_											=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										UpperCamelCase_											=				" ".join(str(_lowerCAmelCase).split(" ")[:-1])
										UpperCamelCase_											=				""
										UpperCamelCase_											=				eval(str(_lowerCAmelCase).split(" ")[-1])
										UpperCamelCase_											=				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:])
															else:
																				wrong_args.append(_lowerCAmelCase)
										if len(_lowerCAmelCase) > 0:
															UpperCamelCase_											=				full_error_msg + begin_error_msg + str(_lowerCAmelCase)
										raise ValueError(_lowerCAmelCase)

					UpperCamelCase_											=				PyTorchBenchmark(args=_lowerCAmelCase)
					benchmark.run()


if __name__ == "__main__":
	main()
