'''simple docstring'''







import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool


class  lowerCAmelCase_						(       a__      ):





  """simple docstring"""



  a_       :Dict												="""facebook/bart-large-mnli"""
  a_       :Union[str, Any]												=(
      """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
      """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
      """It returns the most likely label in the list of provided `labels` for the input text."""
  )
  a_       :Optional[Any]												="""text_classifier"""
  a_       :Dict												=AutoTokenizer
  a_       :Dict												=AutoModelForSequenceClassification

  a_       :str												=["""text""", ["""text"""]]
  a_       :Optional[int]												=["""text"""]

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       super().setup()
       __a       =   self.model.config
       __a       =   -1
       for idx, label in config.idalabel.items():
            if label.lower().startswith("""entail"""						):
                 __a       =   int(lowercase__						)
       if self.entailment_id == -1:
            raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""						)

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __a       =   labels
       return self.pre_processor(
           [text] * len(lowercase__						)				,							[f'''This example is {label}''' for label in labels]				,							return_tensors="""pt"""				,							padding="""max_length"""				,							)


  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       __a       =   outputs.logits
       __a       =   torch.argmax(logits[:, 2]						).item()
       return self._labels[label_id]










'''simple docstring'''







from __future__ import annotations




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  )				->					tuple[str, float]:
     """simple docstring"""




     if (stress, tangential_force, area).count(0		) != 1:
          raise ValueError("""You cannot supply more or less than 2 values"""		)
     elif stress < 0:
          raise ValueError("""Stress cannot be negative"""		)
     elif tangential_force < 0:
          raise ValueError("""Tangential Force cannot be negative"""		)
     elif area < 0:
          raise ValueError("""Area cannot be negative"""		)
     elif stress == 0:
          return (
              "stress",
              tangential_force / area,
          )
     elif tangential_force == 0:
          return (
              "tangential_force",
              stress * area,
          )
     else:
          return (
              "area",
              tangential_force / stress,
          )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






