




from math import pi, sqrt, tan

def 			a   (_lowerCAmelCase     ):
							if side_length < 0:
														raise ValueError('''surface_area_cube() only accepts non-negative values'''     )
							return 6 * side_length**2

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							if length < 0 or breadth < 0 or height < 0:
														raise ValueError('''surface_area_cuboid() only accepts non-negative values'''     )
							return 2 * ((length * breadth) + (breadth * height) + (length * height))

def 			a   (_lowerCAmelCase     ):
							if radius < 0:
														raise ValueError('''surface_area_sphere() only accepts non-negative values'''     )
							return 4 * pi * radius**2

def 			a   (_lowerCAmelCase     ):
							if radius < 0:
														raise ValueError('''surface_area_hemisphere() only accepts non-negative values'''     )
							return 3 * pi * radius**2

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cone() only accepts non-negative values'''     )
							return pi * radius * (radius + (height**2 + radius**2) ** 0.5)

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError(
														    '''surface_area_conical_frustum() only accepts non-negative values'''     )
							SCREAMING_SNAKE_CASE_   	=    (height**2 + (radius_a - radius_a) ** 2) ** 0.5
							return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cylinder() only accepts non-negative values'''     )
							return 2 * pi * radius * (height + radius)

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if torus_radius < 0 or tube_radius < 0:
														raise ValueError('''surface_area_torus() only accepts non-negative values'''     )
							if torus_radius < tube_radius:
														raise ValueError(
														    '''surface_area_torus() does not support spindle or self intersecting tori'''     )
							return 4 * pow(_lowerCAmelCase  ,    2     ) * torus_radius * tube_radius

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if length < 0 or width < 0:
														raise ValueError('''area_rectangle() only accepts non-negative values'''     )
							return length * width

def 			a   (_lowerCAmelCase     ):
							if side_length < 0:
														raise ValueError('''area_square() only accepts non-negative values'''     )
							return side_length**2

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if base < 0 or height < 0:
														raise ValueError('''area_triangle() only accepts non-negative values'''     )
							return (base * height) / 2

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							if sidea < 0 or sidea < 0 or sidea < 0:
														raise ValueError('''area_triangle_three_sides() only accepts non-negative values'''     )
							elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
														raise ValueError('''Given three sides do not form a triangle'''     )
							SCREAMING_SNAKE_CASE_   	=    (sidea + sidea + sidea) / 2
							SCREAMING_SNAKE_CASE_   	=    sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)     )
							return area

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if base < 0 or height < 0:
														raise ValueError('''area_parallelogram() only accepts non-negative values'''     )
							return base * height

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							if basea < 0 or basea < 0 or height < 0:
														raise ValueError('''area_trapezium() only accepts non-negative values'''     )
							return 1 / 2 * (basea + basea) * height

def 			a   (_lowerCAmelCase     ):
							if radius < 0:
														raise ValueError('''area_circle() only accepts non-negative values'''     )
							return pi * radius**2

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if radius_x < 0 or radius_y < 0:
														raise ValueError('''area_ellipse() only accepts non-negative values'''     )
							return pi * radius_x * radius_y

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if diagonal_a < 0 or diagonal_a < 0:
														raise ValueError('''area_rhombus() only accepts non-negative values'''     )
							return 1 / 2 * diagonal_a * diagonal_a

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if not isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ) or sides < 3:
														raise ValueError(
														    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'''     )
							elif length < 0:
														raise ValueError(
														    '''area_reg_polygon() only accepts non-negative values as \
length of a side'''     )
							return (sides * length**2) / (4 * tan(pi / sides     ))
							return (sides * length**2) / (4 * tan(pi / sides     ))


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

			print("""[DEMO] Areas of various geometric shapes: \n""")
			print(f"""Rectangle: {area_rectangle(10, 20) = }""")
			print(f"""Square: {area_square(10) = }""")
			print(f"""Triangle: {area_triangle(10, 10) = }""")
			print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
			print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
			print(f"""Rhombus: {area_rhombus(10, 20) = }""")
			print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
			print(f"""Circle: {area_circle(20) = }""")
			print(f"""Ellipse: {area_ellipse(10, 20) = }""")
			print("""\nSurface Areas of various geometric shapes: \n""")
			print(f"""Cube: {surface_area_cube(20) = }""")
			print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
			print(f"""Sphere: {surface_area_sphere(20) = }""")
			print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
			print(f"""Cone: {surface_area_cone(10, 20) = }""")
			print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
			print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
			print(f"""Torus: {surface_area_torus(20, 10) = }""")
			print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
			print(f"""Square: {area_reg_polygon(4, 10) = }""")
			print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")








import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor






class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Union[str, Any]=7		,					_lowerCamelCase:   int=3		,					_lowerCamelCase:   Optional[int]=18		,					_lowerCamelCase:   Optional[Any]=30		,					_lowerCamelCase:   Any=4_00		,					_lowerCamelCase:   List[str]=True		,					_lowerCamelCase:   str=None		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   Union[str, Any]=False		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   Any=True		,					_lowerCamelCase:   List[str]=[0.5, 0.5, 0.5]		,					_lowerCamelCase:   Dict=[0.5, 0.5, 0.5]		,					):
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    batch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    min_resolution
											SCREAMING_SNAKE_CASE_   	=    max_resolution
											SCREAMING_SNAKE_CASE_   	=    do_resize
											SCREAMING_SNAKE_CASE_   	=    size if size is not None else {'''height''': 18, '''width''': 20}
											SCREAMING_SNAKE_CASE_   	=    do_thumbnail
											SCREAMING_SNAKE_CASE_   	=    do_align_axis
											SCREAMING_SNAKE_CASE_   	=    do_pad
											SCREAMING_SNAKE_CASE_   	=    do_normalize
											SCREAMING_SNAKE_CASE_   	=    image_mean
											SCREAMING_SNAKE_CASE_   	=    image_std





				def     _A							(							self:   str		):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}






@require_torch
@require_vision
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					DonutImageProcessor if is_vision_available() else None


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    DonutImageProcessingTester(self		)


				@property
				def     _A							(							self:   List[str]		):
											return self.image_processor_tester.prepare_image_processor_dict()


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_resize'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''size'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_thumbnail'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_align_long_axis'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_pad'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_normalize'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''image_mean'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''image_std'''		)		)


				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size		,					{'''height''': 18, '''width''': 20}		)

											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		)
											self.assertEqual(image_processor.size		,					{'''height''': 42, '''width''': 42}		)

											# Previous config had dimensions in (width, height) order
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		,					size=(42, 84)		)
											self.assertEqual(image_processor.size		,					{'''height''': 84, '''width''': 42}		)


				def     _A							(							self:   List[Any]		):
											pass


				@is_flaky()
				def     _A							(							self:   int		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											SCREAMING_SNAKE_CASE_   	=    prepare_image_inputs(self.image_processor_tester		,					equal_resolution=_lowerCamelCase		)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCamelCase		,					Image.Image		)

											# Test not batched input
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)

											# Test batched
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)


				@is_flaky()
				def     _A							(							self:   Optional[Any]		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE_   	=    prepare_image_inputs(self.image_processor_tester		,					equal_resolution=_lowerCamelCase		,					numpify=_lowerCamelCase		)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCamelCase		,					np.ndarray		)

											# Test not batched input
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)

											# Test batched
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)





				@is_flaky()
				def     _A							(							self:   List[str]		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE_   	=    prepare_image_inputs(self.image_processor_tester		,					equal_resolution=_lowerCamelCase		,					torchify=_lowerCamelCase		)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCamelCase		,					torch.Tensor		)

											# Test not batched input
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)

											# Test batched
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)


