






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_						     =  {
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_						     =  [
		    """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """EsmForMaskedLM""",
		    """EsmForSequenceClassification""",
		    """EsmForTokenClassification""",
		    """EsmModel""",
		    """EsmPreTrainedModel""",
		]
		lowerCamelCase_						     =  ["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_						     =  [
		    """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFEsmForMaskedLM""",
		    """TFEsmForSequenceClassification""",
		    """TFEsmForTokenClassification""",
		    """TFEsmModel""",
		    """TFEsmPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
		from .tokenization_esm import EsmTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_esm import (
				    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EsmForMaskedLM,
				    EsmForSequenceClassification,
				    EsmForTokenClassification,
				    EsmModel,
				    EsmPreTrainedModel,
				)
				from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_esm import (
				    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEsmForMaskedLM,
				    TFEsmForSequenceClassification,
				    TFEsmForTokenClassification,
				    TFEsmModel,
				    TFEsmPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase_						     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)











from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
def      lowerCamelCase					(						)	->			Union[str, Any]:
							lowerCAmelCase_										=     HfArgumentParser(a_	)
							try:
														lowerCAmelCase_										=     parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowerCAmelCase_										=     'Arg --no_{0} is no longer used, please use --no-{0} instead.'
														lowerCAmelCase_										=     ' '.join(str(a_	).split(' '	)[:-1]	)
														lowerCAmelCase_										=     ''
														lowerCAmelCase_										=     eval(str(a_	).split(' '	)[-1]	)
														lowerCAmelCase_										=     []
														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(a_	)
														if len(a_	) > 0:
																					lowerCAmelCase_										=     full_error_msg + begin_error_msg + str(a_	)
														raise ValueError(a_	)

							lowerCAmelCase_										=     PyTorchBenchmark(args=a_	)
							benchmark.run()


if __name__ == "__main__":
		main()



