

'''simple docstring'''






import torch

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






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    'facebook/bart-large-mnli'
  SCREAMING_SNAKE_CASE					:						Tuple           =    (
      '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.'
  )
  SCREAMING_SNAKE_CASE					:						List[str]           =    'text_classifier'
  SCREAMING_SNAKE_CASE					:						int           =    AutoTokenizer
  SCREAMING_SNAKE_CASE					:						Any           =    AutoModelForSequenceClassification

  SCREAMING_SNAKE_CASE					:						Optional[int]           =    ['text', ['text']]
  SCREAMING_SNAKE_CASE					:						List[Any]           =    ['text']






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   super().setup()
   __lowercase													=				self.model.config
   __lowercase													=				-1
   for idx, label in config.idalabel.items():
    if label.lower().startswith('''entail'''    ):
     __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  Any      ,lowercase__     :  Optional[int]    ):
   __lowercase													=				labels
   return self.pre_processor(
       [text] * len(lowercase__    )      ,[F"This example is {label}" for label in labels]      ,return_tensors='''pt'''      ,padding='''max_length'''      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  Any    ):
   __lowercase													=				outputs.logits
   __lowercase													=				torch.argmax(logits[:, 2]    ).item()
   return self._labels[label_id]




'''simple docstring'''






def 	_A  (					A__ = 1000000   ):






 """simple docstring"""



 __lowercase													=				set(range(3  ,       A__  ,       2   )   )
 primes.add(2   )
 for p in range(3  ,       A__  ,       2   ):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p  ,       A__  ,       A__   )   )   )

 __lowercase													=				[float(A__   ) for n in range(limit + 1   )]

 for p in primes:
  for n in range(A__  ,       limit + 1  ,       A__   ):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]   )   )


if __name__ == "__main__":
 print(f'{solution() = }')

