

'''simple docstring'''





import torch

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





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				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    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										super().setup()
										lowercase   :       Optional[int]								=self.model.config
										lowercase   :       Tuple								=-1
										for idx, label in config.idalabel.items():
																if label.lower().startswith('''entail'''      ):
																						lowercase   :       Tuple								=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    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Any								=labels
										return self.pre_processor(
										    [text] * len(UpperCAmelCase__      )		,				[F'''This example is {label}''' for label in labels]		,				return_tensors='''pt'''		,				padding='''max_length'''		,				)







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=outputs.logits
										lowercase   :       List[Any]								=torch.argmax(logits[:, 2]      ).item()
										return self._labels[label_id]








'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


UpperCamelCase_							=					logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Union[str, Any]		,				*UpperCAmelCase__    :		Dict		,				**UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										warnings.warn(
										    '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
										    ''' use CLIPImageProcessor instead.'''		,				UpperCAmelCase__		,				)
										super().__init__(*UpperCAmelCase__		,				**UpperCAmelCase__      )





