

'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowerCamelCase    =  {
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    =  ['LEDTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    =  [
							    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'LEDForConditionalGeneration',
							    'LEDForQuestionAnswering',
							    'LEDForSequenceClassification',
							    'LEDModel',
							    'LEDPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    =  ['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
							from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
							from .tokenization_led import LEDTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_led_fast import LEDTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_led import (
														    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LEDForConditionalGeneration,
														    LEDForQuestionAnswering,
														    LEDForSequenceClassification,
														    LEDModel,
														    LEDPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
							import sys

							_lowerCamelCase    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''







from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     HfArgumentParser(snake_case_	)
	try:
		_lowercase							=     parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		_lowercase							=     """Arg --no_{0} is no longer used, please use --no-{0} instead."""
		_lowercase							=     """ """.join(str(snake_case_	).split(""" """	)[:-1]	)
		_lowercase							=     """"""
		_lowercase							=     eval(str(snake_case_	).split(""" """	)[-1]	)
		_lowercase							=     []
		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(snake_case_	)
		if len(snake_case_	) > 0:
			_lowercase							=     full_error_msg + begin_error_msg + str(snake_case_	)
		raise ValueError(snake_case_	)

	_lowercase							=     PyTorchBenchmark(args=snake_case_	)
	benchmark.run()


if __name__ == "__main__":
							main()




