




'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline





class      lowerCAmelCase (			__A):


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            if tokenize_kwargs is None:
                 __snake_case            =	{}

            if truncation is not None:
                 if "truncation" in tokenize_kwargs:
                      raise ValueError(
                          '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''	)
                 __snake_case            =	truncation

            __snake_case            =	tokenize_kwargs

            __snake_case            =	{}
            if return_tensors is not None:
                 __snake_case            =	return_tensors

            return preprocess_params, {}, postprocess_params


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.framework
            __snake_case            =	self.tokenizer(__A     ,	return_tensors=__A     ,	**__A	)
            return model_inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.model(**__A	)
            return model_outputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False	)   ->	int:



            '''simple docstring'''




            if return_tensors:
                 return model_outputs[0]
            if self.framework == "pt":
                 return model_outputs[0].tolist()
            elif self.framework == "tf":
                 return model_outputs[0].numpy().tolist()




       def __call__( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            return super().__call__(*__A     ,	**__A	)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							    =							{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							['MLukeTokenizer']

if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mluke import MLukeTokenizer


else:
  import sys

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



