

from typing import TYPE_CHECKING

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


__lowercase     : Dict       =						{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase     : Optional[Any]       =						['''DeiTFeatureExtractor''']
     __lowercase     : Tuple       =						['''DeiTImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase     : str       =						[
         '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DeiTForImageClassification''',
         '''DeiTForImageClassificationWithTeacher''',
         '''DeiTForMaskedImageModeling''',
         '''DeiTModel''',
         '''DeiTPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase     : Dict       =						[
         '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFDeiTForImageClassification''',
         '''TFDeiTForImageClassificationWithTeacher''',
         '''TFDeiTForMaskedImageModeling''',
         '''TFDeiTModel''',
         '''TFDeiTPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_deit import DeiTFeatureExtractor
          from .image_processing_deit import DeiTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deit import (
              DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DeiTForImageClassification,
              DeiTForImageClassificationWithTeacher,
              DeiTForMaskedImageModeling,
              DeiTModel,
              DeiTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_deit import (
              TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDeiTForImageClassification,
              TFDeiTForImageClassificationWithTeacher,
              TFDeiTForMaskedImageModeling,
              TFDeiTModel,
              TFDeiTPreTrainedModel,
          )


else:
     import sys

     __lowercase     : Any       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""





import fire

from utils import calculate_rouge, save_json



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=None,	**__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()]
					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()][: len(__snake_case      )]
					_UpperCamelCase									=    calculate_rouge(__snake_case,	__snake_case,	**__snake_case      )
					if save_path is not None:
										save_json(__snake_case,	__snake_case,	indent=__snake_case      )
					return metrics  # these print nicely


if __name__ == "__main__":
				fire.Fire(calculate_rouge_path)






