






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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

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




							def __init__(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=7 ,							SCREAMING_SNAKE_CASE_     :   str=3 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=18 ,							SCREAMING_SNAKE_CASE_     :   Dict=30 ,							SCREAMING_SNAKE_CASE_     :   List[str]=400 ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							SCREAMING_SNAKE_CASE_     :   List[str]=None ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Any=[0.48_145_466, 0.4_578_275, 0.40_821_073] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=True ,							):
											lowerCAmelCase__      			=						size if size is not None else {'''height''': 224, '''width''': 224}
											lowerCAmelCase__      			=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						num_channels
											lowerCAmelCase__      			=						image_size
											lowerCAmelCase__      			=						min_resolution
											lowerCAmelCase__      			=						max_resolution
											lowerCAmelCase__      			=						do_resize
											lowerCAmelCase__      			=						size
											lowerCAmelCase__      			=						do_center_crop
											lowerCAmelCase__      			=						crop_size
											lowerCAmelCase__      			=						do_normalize
											lowerCAmelCase__      			=						image_mean
											lowerCAmelCase__      			=						image_std
											lowerCAmelCase__      			=						do_convert_rgb




							def 						__snake_case					(   self     :   Tuple	):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_convert_rgb": self.do_convert_rgb,
											}




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=False ,							SCREAMING_SNAKE_CASE_     :   Tuple=False ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=False	):
											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
															lowerCAmelCase__      			=						[]
															for i in range(self.batch_size	):
																			image_inputs.append(
																			    np.random.randint(
																			        255 ,							size=(self.num_channels, self.max_resolution, self.max_resolution) ,							dtype=np.uinta	)	)
											else:
															lowerCAmelCase__      			=						[]
															for i in range(self.batch_size	):
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						np.random.choice(np.arange(self.min_resolution ,							self.max_resolution	) ,							2	)
																			image_inputs.append(np.random.randint(255 ,							size=(self.num_channels, width, height) ,							dtype=np.uinta	)	)

											if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															lowerCAmelCase__      			=						[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_ ,							0 ,							-1	)	) for x in image_inputs]

											if torchify:
															lowerCAmelCase__      			=						[torch.from_numpy(SCREAMING_SNAKE_CASE_	) for x in image_inputs]

											return image_inputs



@require_torch
@require_vision
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Tuple		      =    ChineseCLIPImageProcessor if is_vision_available() else None




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						ChineseCLIPImageProcessingTester(self ,							do_center_crop=SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   Optional[Any]	):
											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(SCREAMING_SNAKE_CASE_ ,							'''do_resize'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''size'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_center_crop'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''center_crop'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_normalize'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''image_mean'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''image_std'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_convert_rgb'''	)	)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.image_processing_class.from_dict(self.image_processor_dict	)
											self.assertEqual(image_processor.size ,							{'''height''': 224, '''width''': 224}	)
											self.assertEqual(image_processor.crop_size ,							{'''height''': 18, '''width''': 18}	)

											lowerCAmelCase__      			=						self.image_processing_class.from_dict(self.image_processor_dict ,							size=42 ,							crop_size=84	)
											self.assertEqual(image_processor.size ,							{'''shortest_edge''': 42}	)
											self.assertEqual(image_processor.crop_size ,							{'''height''': 84, '''width''': 84}	)




							def 						__snake_case					(   self     :   str	):
											pass




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

											# Test not batched input
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)

											# Test batched
											lowerCAmelCase__      			=						image_processing(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)




							def 						__snake_case					(   self     :   Tuple	):
											# Initialize image_processing
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											lowerCAmelCase__      			=						self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ ,							numpify=SCREAMING_SNAKE_CASE_	)
											for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,							np.ndarray	)

											# Test not batched input
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)

											# Test batched
											lowerCAmelCase__      			=						image_processing(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)




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

											# Test not batched input
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)

											# Test batched
											lowerCAmelCase__      			=						image_processing(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)



@require_torch
@require_vision
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Any		      =    ChineseCLIPImageProcessor if is_vision_available() else None




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						ChineseCLIPImageProcessingTester(self ,							num_channels=4 ,							do_center_crop=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						3




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




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_resize'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''size'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_center_crop'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''center_crop'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_normalize'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''image_mean'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''image_std'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_convert_rgb'''	)	)




							def 						__snake_case					(   self     :   Any	):
											pass




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

											# Test not batched input
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        1,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)

											# Test batched
											lowerCAmelCase__      			=						image_processing(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).pixel_values
											self.assertEqual(
											    encoded_images.shape ,							(
											        self.image_processor_tester.batch_size,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,							)













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() = }''')





