






'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''facebook/bart-large-mnli'''
      UpperCamelCase_             =      (
          '''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_             =      '''text_classifier'''
      UpperCamelCase_             =      AutoTokenizer
      UpperCamelCase_             =      AutoModelForSequenceClassification

      UpperCamelCase_             =      ['''text''', ['''text''']]
      UpperCamelCase_             =      ['''text''']






      def 					A__   (			self      :		List[Any]		)	->	Optional[int]:


       '''simple docstring'''

       super().setup()
       lowercase       : Dict						=self.model.config
       lowercase       : Tuple						=-1
       for idx, label in config.idalabel.items():
        if label.lower().startswith('''entail'''		):
         lowercase       : int						=int(UpperCAmelCase		)
       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]  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		List[str]		)	->	Union[str, Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

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











'''simple docstring'''



from __future__ import annotations


def 				lowercase_					(     __A			:							list[list[int]]      )							->   int:



 """simple docstring"""




 for i in range(1       ,   len(matrix[0]      )      ):
  matrix[0][i] += matrix[0][i - 1]

 # preprocessing the first column
 for i in range(1       ,   len(__A      )      ):
  matrix[i][0] += matrix[i - 1][0]

 # updating the path cost for current position
 for i in range(1       ,   len(__A      )      ):
  for j in range(1       ,   len(matrix[0]      )      ):
   matrix[i][j] += min(matrix[i - 1][j]       ,   matrix[i][j - 1]      )

 return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()



