from typing import Dict

from .base import GenericTensor, Pipeline





class 					A							(    UpperCAmelCase_					):


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						int=None	,  __UpperCAmelCase					:						str=None	,  __UpperCAmelCase					:						Union[str, Any]=None	,  **__UpperCAmelCase					:						Any		)						->    List[Any]:
         """simple docstring"""



         if tokenize_kwargs is None:
               UpperCAmelCase__								=  {}

         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)"		)
               UpperCAmelCase__								=  truncation

         UpperCAmelCase__								=  tokenize_kwargs

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

         return preprocess_params, {}, postprocess_params


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  **__UpperCAmelCase					:						Dict		)						->    Dict[str, GenericTensor]:
         """simple docstring"""



         UpperCAmelCase__								=  self.framework
         UpperCAmelCase__								=  self.tokenizer(__UpperCAmelCase	,  return_tensors=__UpperCAmelCase	,  **__UpperCAmelCase		)
         return model_inputs


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Union[str, Any]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Any=False		)						->    Tuple:
         """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					:						int	,  *__UpperCAmelCase					:						Optional[Any]	,  **__UpperCAmelCase					:						Optional[int]		)						->    Any:
         """simple docstring"""



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




from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    							= {'tokenization_bertweet': ['BertweetTokenizer']}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

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



