


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 CLIPImageProcessor

class  UpperCamelCase__ ( unittest.TestCase			):






						def __init__(     self					:   List[Any],		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   Optional[Any]=7,		__lowerCamelCase					:   Any=3,		__lowerCamelCase					:   Optional[int]=18,		__lowerCamelCase					:   Dict=30,		__lowerCamelCase					:   int=4_00,		__lowerCamelCase					:   Any=True,		__lowerCamelCase					:   List[Any]=None,		__lowerCamelCase					:   Tuple=True,		__lowerCamelCase					:   Optional[Any]=None,		__lowerCamelCase					:   Any=True,		__lowerCamelCase					:   Optional[Any]=[0.4814_5466, 0.457_8275, 0.4082_1073],		__lowerCamelCase					:   Tuple=[0.2686_2954, 0.2613_0258, 0.2757_7711],		__lowerCamelCase					:   List[Any]=True,		)  -> Optional[int]:
											UpperCamelCase__							:  Any								=     size if size is not None else {'''shortest_edge''': 20}
											UpperCamelCase__							:  Union[str, Any]								=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
											UpperCamelCase__							:  Optional[int]								=     parent
											UpperCamelCase__							:  List[Any]								=     batch_size
											UpperCamelCase__							:  Tuple								=     num_channels
											UpperCamelCase__							:  Union[str, Any]								=     image_size
											UpperCamelCase__							:  List[Any]								=     min_resolution
											UpperCamelCase__							:  Tuple								=     max_resolution
											UpperCamelCase__							:  Tuple								=     do_resize
											UpperCamelCase__							:  str								=     size
											UpperCamelCase__							:  Any								=     do_center_crop
											UpperCamelCase__							:  Any								=     crop_size
											UpperCamelCase__							:  str								=     do_normalize
											UpperCamelCase__							:  List[Any]								=     image_mean
											UpperCamelCase__							:  Optional[Any]								=     image_std
											UpperCamelCase__							:  Optional[Any]								=     do_convert_rgb






						def 	__lowercase(     self					:   Optional[int]   )  -> str:
											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 	__lowercase(     self					:   Tuple,		__lowerCamelCase					:   Optional[int]=False,		__lowerCamelCase					:   Union[str, Any]=False,		__lowerCamelCase					:   Union[str, Any]=False   )  -> Any:
											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
																UpperCamelCase__							:  str								=     []
																for i in range(self.batch_size   ):
																					image_inputs.append(
																					    np.random.randint(
																					        2_55,		size=(self.num_channels, self.max_resolution, self.max_resolution),		dtype=np.uinta   )   )
											else:
																UpperCamelCase__							:  Union[str, Any]								=     []
																for i in range(self.batch_size   ):
																					UpperCamelCase__	,UpperCamelCase__							:  str								=     np.random.choice(np.arange(self.min_resolution,		self.max_resolution   ),		2   )
																					image_inputs.append(np.random.randint(2_55,		size=(self.num_channels, width, height),		dtype=np.uinta   )   )

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

											if torchify:
																UpperCamelCase__							:  Dict								=     [torch.from_numpy(lowerCamelCase__   ) for x in image_inputs]

											return image_inputs

@require_torch
@require_vision
class  UpperCamelCase__ ( __UpperCAmelCase							,						unittest.TestCase			):
						a__   :     Any								=						CLIPImageProcessor if is_vision_available() else None






						def 	__lowercase(     self					:   Optional[Any]   )  -> int:
											UpperCamelCase__							:  int								=     CLIPImageProcessingTester(self   )






						@property
						def 	__lowercase(     self					:   str   )  -> Optional[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()






						def 	__lowercase(     self					:   Union[str, Any]   )  -> Tuple:
											UpperCamelCase__							:  int								=     self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_resize'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''size'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_center_crop'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''center_crop'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_normalize'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''image_mean'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''image_std'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_convert_rgb'''   )   )






						def 	__lowercase(     self					:   Optional[int]   )  -> Optional[int]:
											UpperCamelCase__							:  Optional[int]								=     self.image_processing_class.from_dict(self.image_processor_dict   )
											self.assertEqual(image_processor.size,		{'''shortest_edge''': 20}   )
											self.assertEqual(image_processor.crop_size,		{'''height''': 18, '''width''': 18}   )

											UpperCamelCase__							:  Dict								=     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 	__lowercase(     self					:   Tuple   )  -> Any:
											pass






						def 	__lowercase(     self					:   str   )  -> Any:
											UpperCamelCase__							:  Optional[int]								=     self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											UpperCamelCase__							:  str								=     self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__   )
											for image in image_inputs:
																self.assertIsInstance(lowerCamelCase__,		Image.Image   )

											# Test not batched input
											UpperCamelCase__							:  List[str]								=     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
											UpperCamelCase__							:  int								=     image_processing(lowerCamelCase__,		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 	__lowercase(     self					:   List[Any]   )  -> Tuple:
											UpperCamelCase__							:  List[Any]								=     self.image_processing_class(**self.image_processor_dict   )
											# create random numpy tensors
											UpperCamelCase__							:  Dict								=     self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__,		numpify=lowerCamelCase__   )
											for image in image_inputs:
																self.assertIsInstance(lowerCamelCase__,		np.ndarray   )

											# Test not batched input
											UpperCamelCase__							:  List[Any]								=     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
											UpperCamelCase__							:  Union[str, Any]								=     image_processing(lowerCamelCase__,		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 	__lowercase(     self					:   List[str]   )  -> Union[str, Any]:
											UpperCamelCase__							:  Dict								=     self.image_processing_class(**self.image_processor_dict   )
											# create random PyTorch tensors
											UpperCamelCase__							:  List[Any]								=     self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__,		torchify=lowerCamelCase__   )
											for image in image_inputs:
																self.assertIsInstance(lowerCamelCase__,		torch.Tensor   )

											# Test not batched input
											UpperCamelCase__							:  Any								=     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
											UpperCamelCase__							:  Optional[int]								=     image_processing(lowerCamelCase__,		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  UpperCamelCase__ ( __UpperCAmelCase							,						unittest.TestCase			):
						a__   :     Optional[Any]								=						CLIPImageProcessor if is_vision_available() else None






						def 	__lowercase(     self					:   Optional[Any]   )  -> Union[str, Any]:
											UpperCamelCase__							:  Optional[Any]								=     CLIPImageProcessingTester(self,		num_channels=4   )
											UpperCamelCase__							:  Optional[Any]								=     3






						@property
						def 	__lowercase(     self					:   Optional[Any]   )  -> Union[str, Any]:
											return self.image_processor_tester.prepare_image_processor_dict()






						def 	__lowercase(     self					:   Tuple   )  -> Tuple:
											UpperCamelCase__							:  List[Any]								=     self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_resize'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''size'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_center_crop'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''center_crop'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_normalize'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''image_mean'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''image_std'''   )   )
											self.assertTrue(hasattr(lowerCamelCase__,		'''do_convert_rgb'''   )   )






						def 	__lowercase(     self					:   Optional[int]   )  -> Any:
											pass






						def 	__lowercase(     self					:   str   )  -> Tuple:
											UpperCamelCase__							:  Optional[int]								=     self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											UpperCamelCase__							:  List[Any]								=     self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__   )
											for image in image_inputs:
																self.assertIsInstance(lowerCamelCase__,		Image.Image   )

											# Test not batched input
											UpperCamelCase__							:  List[str]								=     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
											UpperCamelCase__							:  List[Any]								=     image_processing(lowerCamelCase__,		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 __future__ import annotations

from math import pi, pow







def 						_lowercase       (     __lowerCamelCase			:					int | float			)  ->   float:





     '''simple docstring'''





     if side_length < 0:
          raise ValueError('''vol_cube() only accepts non-negative values'''			)
     return pow(__lowerCamelCase		,3			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or radius < 0:
          raise ValueError('''vol_spherical_cap() only accepts non-negative values'''			)
     # Volume is 1/3 pi * height squared * (3 * radius - height)
     return 1 / 3 * pi * pow(__lowerCamelCase		,2			) * (3 * radius - height)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius_a < 0 or radius_a < 0 or centers_distance < 0:
          raise ValueError('''vol_spheres_intersect() only accepts non-negative values'''			)
     if centers_distance == 0:
          return vol_sphere(min(__lowerCamelCase		,__lowerCamelCase			)			)

     UpperCamelCase__							:  Dict								=     (
         (radius_a - radius_a + centers_distance)
         * (radius_a + radius_a - centers_distance)
         / (2 * centers_distance)
     )
     UpperCamelCase__							:  Tuple								=     (
         (radius_a - radius_a + centers_distance)
         * (radius_a + radius_a - centers_distance)
         / (2 * centers_distance)
     )

     return vol_spherical_cap(__lowerCamelCase		,__lowerCamelCase			) + vol_spherical_cap(__lowerCamelCase		,__lowerCamelCase			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
          raise ValueError(
              '''vol_spheres_union() only accepts non-negative values, non-zero radius'''			)

     if centers_distance == 0:
          return vol_sphere(max(__lowerCamelCase		,__lowerCamelCase			)			)

     return (
         vol_sphere(__lowerCamelCase			)
         + vol_sphere(__lowerCamelCase			)
         - vol_spheres_intersect(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
     )







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if width < 0 or height < 0 or length < 0:
          raise ValueError('''vol_cuboid() only accepts non-negative values'''			)
     return float(width * height * length			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or area_of_base < 0:
          raise ValueError('''vol_cone() only accepts non-negative values'''			)
     return area_of_base * height / 3.0







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or radius < 0:
          raise ValueError('''vol_right_circ_cone() only accepts non-negative values'''			)
     return pi * pow(__lowerCamelCase		,2			) * height / 3.0







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or area_of_base < 0:
          raise ValueError('''vol_prism() only accepts non-negative values'''			)
     return float(area_of_base * height			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or area_of_base < 0:
          raise ValueError('''vol_pyramid() only accepts non-negative values'''			)
     return area_of_base * height / 3.0







def 						_lowercase       (     __lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius < 0:
          raise ValueError('''vol_sphere() only accepts non-negative values'''			)
     # Volume is 4/3 * pi * radius cubed
     return 4 / 3 * pi * pow(__lowerCamelCase		,3			)







def 						_lowercase       (     __lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius < 0:
          raise ValueError('''vol_hemisphere() only accepts non-negative values'''			)
     # Volume is radius cubed * pi * 2/3
     return pow(__lowerCamelCase		,3			) * pi * 2 / 3







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or radius < 0:
          raise ValueError('''vol_circular_cylinder() only accepts non-negative values'''			)
     # Volume is radius squared * height * pi
     return pow(__lowerCamelCase		,2			) * height * pi







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if inner_radius < 0 or outer_radius < 0 or height < 0:
          raise ValueError(
              '''vol_hollow_circular_cylinder() only accepts non-negative values'''			)
     if outer_radius <= inner_radius:
          raise ValueError('''outer_radius must be greater than inner_radius'''			)
     return pi * (pow(__lowerCamelCase		,2			) - pow(__lowerCamelCase		,2			)) * height







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError('''vol_conical_frustum() only accepts non-negative values'''			)
     return (
         1
         / 3
         * pi
         * height
         * (pow(__lowerCamelCase		,2			) + pow(__lowerCamelCase		,2			) + radius_a * radius_a)
     )







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if torus_radius < 0 or tube_radius < 0:
          raise ValueError('''vol_torus() only accepts non-negative values'''			)
     return 2 * pow(__lowerCamelCase		,2			) * torus_radius * pow(__lowerCamelCase		,2			)







def 						_lowercase       (     )  ->   Tuple:





     '''simple docstring'''





     print('''Volumes:'''			)
     print(F'Cube: {vol_cube(2			) = }'			)  # = 8
     print(F'Cuboid: {vol_cuboid(2		,2		,2			) = }'			)  # = 8
     print(F'Cone: {vol_cone(2		,2			) = }'			)  # ~= 1.33
     print(F'Right Circular Cone: {vol_right_circ_cone(2		,2			) = }'			)  # ~= 8.38
     print(F'Prism: {vol_prism(2		,2			) = }'			)  # = 4
     print(F'Pyramid: {vol_pyramid(2		,2			) = }'			)  # ~= 1.33
     print(F'Sphere: {vol_sphere(2			) = }'			)  # ~= 33.5
     print(F'Hemisphere: {vol_hemisphere(2			) = }'			)  # ~= 16.75
     print(F'Circular Cylinder: {vol_circular_cylinder(2		,2			) = }'			)  # ~= 25.1
     print(F'Torus: {vol_torus(2		,2			) = }'			)  # ~= 157.9
     print(F'Conical Frustum: {vol_conical_frustum(2		,2		,4			) = }'			)  # ~= 58.6
     print(F'Spherical cap: {vol_spherical_cap(1		,2			) = }'			)  # ~= 5.24
     print(F'Spheres intersetion: {vol_spheres_intersect(2		,2		,1			) = }'			)  # ~= 21.21
     print(F'Spheres union: {vol_spheres_union(2		,2		,1			) = }'			)  # ~= 45.81
     print(
         F'Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1		,2		,3			) = }'			)  # ~= 28.3


if __name__ == "__main__":
   main()




