

'''simple docstring'''
from typing import Dict

from .base import GenericTensor, Pipeline







class 							A__   (	A__  ):





  def 						A  (		self				:     Optional[int]							, _a				:     int=None							, _a				:     Optional[Any]=None							, _a				:     Tuple=None							, **_a				:     int			)     ->   Union[str, Any]:





       '''simple docstring'''






       if tokenize_kwargs is None:
            _SCREAMING_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)'			)
            _SCREAMING_SNAKE_CASE							   =truncation

       _SCREAMING_SNAKE_CASE							   =tokenize_kwargs

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

       return preprocess_params, {}, postprocess_params





  def 						A  (		self				:     Union[str, Any]							, _a				:     int							, **_a				:     int			)     ->   Dict[str, GenericTensor]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.framework
       _SCREAMING_SNAKE_CASE							   =self.tokenizer(_a							, return_tensors=_a							, **_a			)
       return model_inputs





  def 						A  (		self				:     Optional[Any]							, _a				:     Optional[int]			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.model(**_a			)
       return model_outputs





  def 						A  (		self				:     Tuple							, _a				:     Optional[int]							, _a				:     Optional[int]=False			)     ->   str:





       '''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				:     Tuple							, *_a				:     List[Any]							, **_a				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






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



'''simple docstring'''
import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)
