





'''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 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''facebook/bart-large-mnli'''
     lowerCAmelCase		      =	(
         '''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.'''
     )
     lowerCAmelCase		      =	'''text_classifier'''
     lowerCAmelCase		      =	AutoTokenizer
     lowerCAmelCase		      =	AutoModelForSequenceClassification

     lowerCAmelCase		      =	['''text''', ['''text''']]
     lowerCAmelCase		      =	['''text''']


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setup()
          __A						:			Union[str, Any]								=	self.model.config
          __A						:			Any								=	-1
          for idx, label in config.idalabel.items():
               if label.lower().startswith('entail'):
                    __A						:			Any								=	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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	labels
          return self.pre_processor(
              [text] * len(_UpperCAmelCase)						,     [F'This example is {label}' for label in labels]						,     return_tensors='pt'						,     padding='max_length'						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






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






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

     def get_matched_characters(__snake_case  :			str			,							__snake_case  :			str      ) -> str:
          __A						:			Optional[int]								=	[]
          __A						:			Optional[Any]								=	min(len(_stra      )			,							len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               __A						:			str								=	int(max(0			,							i - limit      )      )
               __A						:			str								=	int(min(i + limit + 1			,							len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(__snake_case      )
                    __A						:			Dict								=	f'{_stra[0:_stra.index(__snake_case      )]} {_stra[_stra.index(__snake_case      ) + 1:]}'

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

     # transposition
     __A						:			Tuple								=	(
         len([(ca, ca) for ca, ca in zip(__snake_case			,							__snake_case      ) if ca != ca]      ) // 2
     )

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	0
     for ca, ca in zip(stra[:4]			,							stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler('''hello''', '''world'''))