






'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import GLPNImageProcessor
class  lowerCAmelCase_       (				unittest.TestCase					):




							def __init__(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   int=7 ,							SCREAMING_SNAKE_CASE_     :   List[str]=3 ,							SCREAMING_SNAKE_CASE_     :   Tuple=18 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=30 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=400 ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Tuple=32 ,							SCREAMING_SNAKE_CASE_     :   List[str]=True ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						num_channels
											lowerCAmelCase__      			=						image_size
											lowerCAmelCase__      			=						min_resolution
											lowerCAmelCase__      			=						max_resolution
											lowerCAmelCase__      			=						do_resize
											lowerCAmelCase__      			=						size_divisor
											lowerCAmelCase__      			=						do_rescale




							def 						__snake_case					(   self     :   List[str]	):
											return {
											    "do_resize": self.do_resize,
											    "size_divisor": self.size_divisor,
											    "do_rescale": self.do_rescale,
											}



@require_torch
@require_vision
class  lowerCAmelCase_       (				UpperCAmelCase_     ,    unittest.TestCase					):
							UpperCamelCase_		:str		      =    GLPNImageProcessor if is_vision_available() else None




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						GLPNImageProcessingTester(self	)




							@property
							def 						__snake_case					(   self     :   str	):
											return self.image_processor_tester.prepare_image_processor_dict()




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											self.assertTrue(hasattr(_lowercase ,							'''do_resize'''	)	)
											self.assertTrue(hasattr(_lowercase ,							'''size_divisor'''	)	)
											self.assertTrue(hasattr(_lowercase ,							'''resample'''	)	)
											self.assertTrue(hasattr(_lowercase ,							'''do_rescale'''	)	)




							def 						__snake_case					(   self     :   Tuple	):
											pass




							def 						__snake_case					(   self     :   int	):
											# Initialize image_processing
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											lowerCAmelCase__      			=						prepare_image_inputs(self.image_processor_tester ,							equal_resolution=_lowercase	)
											for image in image_inputs:
															self.assertIsInstance(_lowercase ,							Image.Image	)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0	)
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0	)




							def 						__snake_case					(   self     :   List[Any]	):
											# Initialize image_processing
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											lowerCAmelCase__      			=						prepare_image_inputs(self.image_processor_tester ,							equal_resolution=_lowercase ,							numpify=_lowercase	)
											for image in image_inputs:
															self.assertIsInstance(_lowercase ,							np.ndarray	)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0	)
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0	)




							def 						__snake_case					(   self     :   Any	):
											# Initialize image_processing
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											lowerCAmelCase__      			=						prepare_image_inputs(self.image_processor_tester ,							equal_resolution=_lowercase ,							torchify=_lowercase	)
											for image in image_inputs:
															self.assertIsInstance(_lowercase ,							torch.Tensor	)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0	)
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0	)













from math import isclose, sqrt





def 	lowerCAmelCase_		(lowercase__						:       float     ,					lowercase__						:       float     ,					lowercase__						:       float       )		->    tuple[float, float, float]:


				'''simple docstring'''




				lowerCAmelCase__      			=						point_y / 4 / point_x
				lowerCAmelCase__      			=						2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				lowerCAmelCase__      			=						(1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				lowerCAmelCase__      			=						(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
				lowerCAmelCase__      			=						outgoing_gradient**2 + 4
				lowerCAmelCase__      			=						2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				lowerCAmelCase__      			=						(point_y - outgoing_gradient * point_x) ** 2 - 1_00

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

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

				return next_x, next_y, outgoing_gradient





def 	lowerCAmelCase_		(lowercase__						:       float = 1.4     ,					lowercase__						:       float = -9.6       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						0
				lowerCAmelCase__      			=						first_x_coord
				lowerCAmelCase__      			=						first_y_coord
				lowerCAmelCase__      			=						(10.1 - point_y) / (0.0 - point_x)

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

				return num_reflections


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





