


"""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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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# 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_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			"facebook/bart-large-mnli"
	snake_case__				:  str    					=			(
	    "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."
	)
	snake_case__				:  Tuple    					=			"text_classifier"
	snake_case__				:  Any    					=			AutoTokenizer
	snake_case__				:  Optional[Any]    					=			AutoModelForSequenceClassification

	snake_case__				:  Any    					=			["text", ["text"]]
	snake_case__				:  Optional[int]    					=			["text"]




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	List[Any]:
						super().setup()
						__SCREAMING_SNAKE_CASE						   =				self.model.config
						__SCREAMING_SNAKE_CASE						   =				-1
						for idx, label in config.idalabel.items():
											if label.lower().startswith("entail"   ):
																__SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				Union[str, Any]			,			UpperCAmelCase__			:				Tuple   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				labels
						return self.pre_processor(
						    [text] * len(UpperCAmelCase__   )			,			[F"""This example is {label}""" for label in labels]			,			return_tensors="pt"			,			padding="max_length"			,			)




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				List[str]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				outputs.logits
						__SCREAMING_SNAKE_CASE						   =				torch.argmax(logits[:, 2]   ).item()
						return self._labels[label_id]




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
