





import torch

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







class 	__lowerCAmelCase      (       A_	):
   _UpperCamelCase			:    Any  		=					"""facebook/bart-large-mnli"""
   _UpperCamelCase			:    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."""
   )
   _UpperCamelCase			:    List[Any]  		=					"""text_classifier"""
   _UpperCamelCase			:    Any  		=					AutoTokenizer
   _UpperCamelCase			:    List[str]  		=					AutoModelForSequenceClassification

   _UpperCamelCase			:    Union[str, Any]  		=					["""text""", ["""text"""]]
   _UpperCamelCase			:    Dict  		=					["""text"""]





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





      super().setup()
      a__						:    List[Any]   		=	self.model.config
      a__						:    Union[str, Any]   		=	-1
      for idx, label in config.idalabel.items():
         if label.lower().startswith("entail"   ):
            a__						:    Tuple   		=	int(snake_case   )
      if self.entailment_id == -1:
         raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	labels
      return self.pre_processor(
          [text] * len(snake_case   )       ,      [F"""This example is {label}""" for label in labels]       ,      return_tensors="pt"       ,      padding="max_length"       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	outputs.logits
      a__						:    List[Any]   		=	torch.argmax(logits[:, 2]   ).item()
      return self._labels[label_id]





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		len(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		len(__snake_case   )
   _lowerCamelCase	:					int   =		[[False for _ in range(m + 1   )] for _ in range(n + 1   )]
   _lowerCamelCase	:					Union[str, Any]   =		True
   for i in range(__snake_case   ):
      for j in range(m + 1   ):
         if dp[i][j]:
            if j < m and a[i].upper() == b[j]:
               _lowerCamelCase	:					Tuple   =		True
            if a[i].islower():
               _lowerCamelCase	:					Tuple   =		True
   return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

