






import torch

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






class 			_UpperCamelCase(       __SCREAMING_SNAKE_CASE		):
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							'''facebook/bart-large-mnli'''
		__SCREAMING_SNAKE_CASE  :       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.'''
		)
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							'''text_classifier'''
		__SCREAMING_SNAKE_CASE  :       Any							=							AutoTokenizer
		__SCREAMING_SNAKE_CASE  :       Union[str, Any]							=							AutoModelForSequenceClassification

		__SCREAMING_SNAKE_CASE  :       str							=							['''text''', ['''text''']]
		__SCREAMING_SNAKE_CASE  :       Tuple							=							['''text''']

		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



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

		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    int    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



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



		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Any    ):



					'''simple docstring'''



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










"""simple docstring"""






from math import sqrt




def 			snake_case		(		lowerCAmelCase_ = 1000000	)						->				int:
  _snake_case   	=      0
  _snake_case   	=      0
  _snake_case   	=      42

  while num_cuboids <= limit:
    max_cuboid_size += 1
    for sum_shortest_sides in range(2  ,						2 * max_cuboid_size + 1	):
      if sqrt(sum_shortest_sides**2 + max_cuboid_size**2	).is_integer():
        num_cuboids += (
            min(lowerCAmelCase_  ,						sum_shortest_sides // 2	)
            - max(1  ,						sum_shortest_sides - max_cuboid_size	)
            + 1
        )

  return max_cuboid_size


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





