





# 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  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Union[str, Any]	      =				'''facebook/bart-large-mnli'''
			lowerCamelCase_					:					List[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.'''
			)
			lowerCamelCase_					:					Dict	      =				'''text_classifier'''
			lowerCamelCase_					:					List[str]	      =				AutoTokenizer
			lowerCamelCase_					:					Optional[int]	      =				AutoModelForSequenceClassification

			lowerCamelCase_					:					int	      =				['''text''', ['''text''']]
			lowerCamelCase_					:					Union[str, Any]	      =				['''text''']






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							super().setup()
							snake_case_						:   int           =  self.model.config
							snake_case_						:   Optional[int]           =  -1
							for idx, label in config.idalabel.items():
											if label.lower().startswith('''entail'''   ):
															snake_case_						:   Dict           =  int(__magic_name__   )
							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 ,							__magic_name__ ,							__magic_name__   )					->       Any:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       str:


							'''simple docstring'''



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







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				if number > 0:
								raise ValueError('''input must be a negative integer'''			)
				snake_case_						:   List[str]           =  len(bin(_UpperCamelCase			)[3:]			)
				snake_case_						:   str           =  bin(abs(_UpperCamelCase			) - (1 << binary_number_length)			)[3:]
				snake_case_						:   Dict           =  (
				    (
				        '''1'''
				        + '''0''' * (binary_number_length - len(_UpperCamelCase			))
				        + twos_complement_number
				    )
				    if number < 0
				    else '''0'''
				)
				return "0b" + twos_complement_number


if __name__ == "__main__":
	import doctest

	doctest.testmod()
