


"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__UpperCamelCase						=      {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[Any]								=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
		SCREAMING_SNAKE_CASE_						:     str								=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

		if model_mapping is not None:
				SCREAMING_SNAKE_CASE_						:     int								=     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
		if tf_model_mapping is not None:
				SCREAMING_SNAKE_CASE_						:     List[Any]								=     {
				    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
				}







		@require_torch
		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					pipeline(
								    task='text-classification'						,       model='hf-internal-testing/tiny-random-distilbert'						,       framework='pt'				)

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'LABEL_0', 'score': 0.5_04}]				)

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'						,       top_k=2				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]				)

								SCREAMING_SNAKE_CASE													=					text_classifier(['This is great !', 'This is bad']						,       top_k=2				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [
								        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
								        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
								    ]						,       )

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'						,       top_k=1				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'LABEL_0', 'score': 0.5_04}]				)

								# Legacy behavior
								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'						,       return_all_scores=lowerCAmelCase__				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'LABEL_0', 'score': 0.5_04}]				)

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'						,       return_all_scores=lowerCAmelCase__				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]]				)

								SCREAMING_SNAKE_CASE													=					text_classifier(['This is great !', 'Something else']						,       return_all_scores=lowerCAmelCase__				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [
								        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
								        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
								    ]						,       )

								SCREAMING_SNAKE_CASE													=					text_classifier(['This is great !', 'Something else']						,       return_all_scores=lowerCAmelCase__				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [
								        {'label': 'LABEL_0', 'score': 0.5_04},
								        {'label': 'LABEL_0', 'score': 0.5_04},
								    ]						,       )







		@require_torch
		def 							__A						(	self				)     ->							List[Any]:
								import torch

								SCREAMING_SNAKE_CASE													=					pipeline(
								    task='text-classification'						,       model='hf-internal-testing/tiny-random-distilbert'						,       framework='pt'						,       device=torch.device('cpu'				)						,       )

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'LABEL_0', 'score': 0.5_04}]				)







		@require_tf
		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					pipeline(
								    task='text-classification'						,       model='hf-internal-testing/tiny-random-distilbert'						,       framework='tf'				)

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'LABEL_0', 'score': 0.5_04}]				)







		@slow
		@require_torch
		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					pipeline('text-classification'				)

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'POSITIVE', 'score': 1.0}]				)
								SCREAMING_SNAKE_CASE													=					text_classifier('This is bad !'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'NEGATIVE', 'score': 1.0}]				)
								SCREAMING_SNAKE_CASE													=					text_classifier('Birds are a type of animal'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'POSITIVE', 'score': 0.9_88}]				)







		@slow
		@require_tf
		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					pipeline('text-classification'						,       framework='tf'				)

								SCREAMING_SNAKE_CASE													=					text_classifier('This is great !'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'POSITIVE', 'score': 1.0}]				)
								SCREAMING_SNAKE_CASE													=					text_classifier('This is bad !'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'NEGATIVE', 'score': 1.0}]				)
								SCREAMING_SNAKE_CASE													=					text_classifier('Birds are a type of animal'				)
								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': 'POSITIVE', 'score': 0.9_88}]				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					TextClassificationPipeline(model=lowerCAmelCase__						,       tokenizer=lowerCAmelCase__				)
								return text_classifier, ["HuggingFace is in", "This is another test"]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							int:
								SCREAMING_SNAKE_CASE													=					text_classifier.model
								# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
								SCREAMING_SNAKE_CASE													=					'HuggingFace is in'
								SCREAMING_SNAKE_CASE													=					text_classifier(lowerCAmelCase__				)

								self.assertEqual(nested_simplify(lowerCAmelCase__				)						,       [{'label': ANY(lowerCAmelCase__				), 'score': ANY(lowerCAmelCase__				)}]				)
								self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()				)

								SCREAMING_SNAKE_CASE													=					['HuggingFace is in ', 'Paris is in France']
								SCREAMING_SNAKE_CASE													=					text_classifier(lowerCAmelCase__				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [{'label': ANY(lowerCAmelCase__				), 'score': ANY(lowerCAmelCase__				)}, {'label': ANY(lowerCAmelCase__				), 'score': ANY(lowerCAmelCase__				)}]						,       )
								self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()				)
								self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()				)

								# Forcing to get all results with `top_k=None`
								# This is NOT the legacy format
								SCREAMING_SNAKE_CASE													=					text_classifier(lowerCAmelCase__						,       top_k=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					len(model.config.idalabel.values()				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [[{'label': ANY(lowerCAmelCase__				), 'score': ANY(lowerCAmelCase__				)}] * N, [{'label': ANY(lowerCAmelCase__				), 'score': ANY(lowerCAmelCase__				)}] * N]						,       )

								SCREAMING_SNAKE_CASE													=					{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
								SCREAMING_SNAKE_CASE													=					text_classifier(lowerCAmelCase__				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       {'label': ANY(lowerCAmelCase__				), 'score': ANY(lowerCAmelCase__				)}						,       )
								self.assertTrue(outputs['label'] in model.config.idalabel.values()				)

								# This might be used a text pair, but tokenizer + pipe interaction
								# makes it hard to understand that it's not using the pair properly
								# https://github.com/huggingface/transformers/issues/17305
								# We disabled this usage instead as it was outputting wrong outputs.
								SCREAMING_SNAKE_CASE													=					[['HuggingFace is in ', 'Paris is in France']]
								with self.assertRaises(lowerCAmelCase__				):
														text_classifier(lowerCAmelCase__				)

								# This used to be valid for doing text pairs
								# We're keeping it working because of backward compatibility
								SCREAMING_SNAKE_CASE													=					text_classifier([[['HuggingFace is in ', 'Paris is in France']]]				)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       [{'label': ANY(lowerCAmelCase__				), 'score': ANY(lowerCAmelCase__				)}]						,       )
								self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()				)






"""simple docstring"""




from math import isclose, sqrt


def     lowercase			(SCREAMING_SNAKE_CASE_		:				float ,			SCREAMING_SNAKE_CASE_		:				float ,			SCREAMING_SNAKE_CASE_		:				float  )   ->						tuple[float, float, float]:
						SCREAMING_SNAKE_CASE													=					point_y / 4 / point_x
						SCREAMING_SNAKE_CASE													=					2 * normal_gradient / (1 + normal_gradient * normal_gradient)
						SCREAMING_SNAKE_CASE													=					(1 - normal_gradient * normal_gradient) / (
						    1 + normal_gradient * normal_gradient
						)
						SCREAMING_SNAKE_CASE													=					(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
						SCREAMING_SNAKE_CASE													=					outgoing_gradient**2 + 4
						SCREAMING_SNAKE_CASE													=					2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
						SCREAMING_SNAKE_CASE													=					(point_y - outgoing_gradient * point_x) ** 2 - 1_00

						SCREAMING_SNAKE_CASE													=					(
						    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
						) / (2 * quadratic_term)
						SCREAMING_SNAKE_CASE													=					(
						    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
						) / (2 * quadratic_term)

						# two solutions, one of which is our input point
						SCREAMING_SNAKE_CASE													=					x_minus if isclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) else x_plus
						SCREAMING_SNAKE_CASE													=					point_y + outgoing_gradient * (next_x - point_x)

						return next_x, next_y, outgoing_gradient


def     lowercase			(SCREAMING_SNAKE_CASE_		:				float = 1.4 ,			SCREAMING_SNAKE_CASE_		:				float = -9.6  )   ->						int:
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					first_x_coord
						SCREAMING_SNAKE_CASE													=					first_y_coord
						SCREAMING_SNAKE_CASE													=					(10.1 - point_y) / (0.0 - point_x)

						while not (-0.01 <= point_x <= 0.01 and point_y > 0):
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					next_point(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												num_reflections += 1

						return num_reflections


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


