



'''simple docstring'''






from typing import Dict

from .base import GenericTensor, Pipeline



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def _snake_case (							self  ,a_=None  ,a_=None  ,a_=None  ,**a_							)		->   Dict:
        if tokenize_kwargs is None:
               _UpperCAmelCase  :						Union[str, Any]			     = {}

        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  :						List[Any]			     = truncation

        _UpperCAmelCase  :						List[str]			     = tokenize_kwargs

        _UpperCAmelCase  :						Any			     = {}
        if return_tensors is not None:
               _UpperCAmelCase  :						Optional[Any]			     = return_tensors

        return preprocess_params, {}, postprocess_params







 def _snake_case (							self  ,a_  ,**a_							)		->   Dict[str, GenericTensor]:
        _UpperCAmelCase  :						Dict			     = self.framework
        _UpperCAmelCase  :						Any			     = self.tokenizer(a_  ,return_tensors=a_  ,**a_							)
        return model_inputs







 def _snake_case (							self  ,a_							)		->   int:
        _UpperCAmelCase  :						Optional[Any]			     = self.model(**a_							)
        return model_outputs







 def _snake_case (							self  ,a_  ,a_=False							)		->   Dict:
        # [0] is the first available tensor, logits or last_hidden_state.
        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  ,*a_  ,**a_							)		->   List[Any]:
        return super().__call__(*a_  ,**a_							)








'''simple docstring'''






from torch import nn



class 							lowercase       (  nn.Module				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_							)		->   List[Any]:
        super().__init__()
        _UpperCAmelCase  :						Dict			     = class_size
        _UpperCAmelCase  :						Union[str, Any]			     = embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        _UpperCAmelCase  :						List[Any]			     = nn.Linear(a_  ,a_							)







 def _snake_case (							self  ,a_							)		->   Tuple:
        # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
        # hidden_state = self.mlp2(hidden_state)
        _UpperCAmelCase  :						Optional[int]			     = self.mlp(a_							)
        return logits



