

"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowercase   :	Optional[int]												=						{
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase   :	Tuple												=						['BlenderbotSmallTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase   :	int												=						[
				    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BlenderbotSmallForCausalLM',
				    'BlenderbotSmallForConditionalGeneration',
				    'BlenderbotSmallModel',
				    'BlenderbotSmallPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase   :	Tuple												=						[
				    'TFBlenderbotSmallForConditionalGeneration',
				    'TFBlenderbotSmallModel',
				    'TFBlenderbotSmallPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase   :	List[str]												=						[
				    'FlaxBlenderbotSmallForConditionalGeneration',
				    'FlaxBlenderbotSmallModel',
				    'FlaxBlenderbotSmallPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BlenderbotSmallConfig,
				    BlenderbotSmallOnnxConfig,
				)
				from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_blenderbot_small import (
								    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BlenderbotSmallForCausalLM,
								    BlenderbotSmallForConditionalGeneration,
								    BlenderbotSmallModel,
								    BlenderbotSmallPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_blenderbot_small import (
								    TFBlenderbotSmallForConditionalGeneration,
								    TFBlenderbotSmallModel,
								    TFBlenderbotSmallPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_blenderbot_small import (
								    FlaxBlenderbotSmallForConditionalGeneration,
								    FlaxBlenderbotSmallModel,
								    FlaxBlenderbotSmallPreTrainedModel,
								)

else:
				import sys

				_lowercase   :	str												=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


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


if __name__ == "__main__":
      main()



