
'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

lowercase			:				Any    		=			TypeVar('T')





class  A				(							Generic[T]						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						data
         A       :   Node[T] | None					=						None





     def __str__(							self	) ->				str:






         """simple docstring"""






         return F'{self.data}'





class  A				(							Generic[T]						):





     def __init__(							self	) ->				None:






         """simple docstring"""






         A       :   Node[T] | None					=						None





     def __iter__(							self	) ->				Iterator[T]:






         """simple docstring"""






         A       :   Dict					=						self.top
         while node:
             yield node.data
             A       :   List[Any]					=						node.next





     def __str__(							self	) ->				str:






         """simple docstring"""






         return "->".join([str(SCREAMING_SNAKE_CASE	) for item in self]	)





     def __len__(							self	) ->				int:






         """simple docstring"""






         return len(tuple(iter(self	)	)	)





     def 				__lowerCAmelCase							(							self	) ->				bool:






         """simple docstring"""






         return self.top is None





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				None:






         """simple docstring"""






         A       :   List[Any]					=						Node(SCREAMING_SNAKE_CASE	)
         if not self.is_empty():
             A       :   int					=						self.top
         A       :   int					=						node





     def 				__lowerCAmelCase							(							self	) ->				T:






         """simple docstring"""






         if self.is_empty():
             raise IndexError('''pop from empty stack'''	)
         assert isinstance(self.top						,			SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						self.top
         A       :   Dict					=						self.top.next
         return pop_node.data





     def 				__lowerCAmelCase							(							self	) ->				T:






         """simple docstring"""






         if self.is_empty():
             raise IndexError('''peek from empty stack'''	)

         assert self.top is not None
         return self.top.data





     def 				__lowerCAmelCase							(							self	) ->				None:






         """simple docstring"""






         A       :   Union[str, Any]					=						None


if __name__ == "__main__":
      from doctest import testmod

      testmod()











"""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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _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 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().setup()
     __A    :						List[str]						 =     self.model.config
     __A    :						int						 =     -1
     for idx, label in config.idalabel.items():
         if label.lower().startswith('''entail'''  ):
             __A    :						List[str]						 =     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 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Union[str, 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 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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




