





# 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]







from math import isclose, sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						tuple[float, float, float]:






				"""simple docstring"""


				snake_case_						:   Dict           =  point_y / 4 / point_x
				snake_case_						:   List[str]           =  2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				snake_case_						:   Union[str, Any]           =  (1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				snake_case_						:   Tuple           =  (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				snake_case_						:   Union[str, Any]           =  outgoing_gradient**2 + 4
				snake_case_						:   Tuple           =  2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				snake_case_						:   Optional[Any]           =  (point_y - outgoing_gradient * point_x) ** 2 - 100

				snake_case_						:   Dict           =  (
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)
				snake_case_						:   Optional[int]           =  (
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				snake_case_						:   Any           =  x_minus if isclose(_UpperCamelCase			,    _UpperCamelCase			) else x_plus
				snake_case_						:   int           =  point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient






def 					lowerCamelCase_						(    _UpperCamelCase = 1.4			,    _UpperCamelCase = -9.6			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  0
				snake_case_						:   float           =  first_x_coord
				snake_case_						:   float           =  first_y_coord
				snake_case_						:   float           =  (10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  next_point(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								num_reflections += 1

				return num_reflections


if __name__ == "__main__":
	print(F'''{solution() = }''')
