'''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     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''facebook/bart-large-mnli'''
				UpperCAmelCase__							=							(
				    '''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.'''
				)
				UpperCAmelCase__							=							'''text_classifier'''
				UpperCAmelCase__							=							AutoTokenizer
				UpperCAmelCase__							=							AutoModelForSequenceClassification

				UpperCAmelCase__							=							['''text''', ['''text''']]
				UpperCAmelCase__							=							['''text''']





				def  snake_case__				(						self					:						Union[str, Any]					)							->Optional[int]:


										'''simple docstring'''




										super().setup()
										_UpperCamelCase			:			Optional[Any]   			=						self.model.config
										_UpperCamelCase			:			Tuple   			=						-1
										for idx, label in config.idalabel.items():
																if label.lower().startswith("entail"					):
																						_UpperCamelCase			:			Union[str, Any]   			=						int(lowercase__					)
										if self.entailment_id == -1:
																raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."					)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						int       ,		lowercase__					:						List[Any]					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						labels
										return self.pre_processor(
										    [text] * len(lowercase__					)       ,		[f'''This example is {label}''' for label in labels]       ,		return_tensors="pt"       ,		padding="max_length"       ,		)







				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						str					)							->Any:


										'''simple docstring'''




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



'''simple docstring'''





from __future__ import annotations

from cmath import sqrt
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			tuple[complex, complex]:




						'''simple docstring'''

						if a == 0:
												raise ValueError("Coefficient 'a' must not be zero."					)
						_UpperCamelCase			:			List[str]   			=						b * b - 4 * a * c

						_UpperCamelCase			:			Dict   			=						(-b + sqrt(UpperCAmelCase					)) / (2 * a)
						_UpperCamelCase			:			Optional[int]   			=						(-b - sqrt(UpperCAmelCase					)) / (2 * a)

						return (
						    root_a.real if not root_a.imag else root_a,
						    root_a.real if not root_a.imag else root_a,
						)
def 						__A							(     )     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						quadratic_roots(a=5     ,b=6     ,c=1					)
						print(f'''The solutions are: {solutiona} and {solutiona}'''					)


if __name__ == "__main__":
			main()


