'''simple docstring'''





from math import pi, sqrt, tan





def       lowerCAmelCase_							(   snake_case_			:					float     )       -> float:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     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       lowerCAmelCase_							(   snake_case_			:					float     )       -> float:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					float     )       -> float:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     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       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              "surface_area_conical_frustum() only accepts non-negative values"     )
     UpperCAmelCase_			       =  (height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if radius < 0 or height < 0:
          raise ValueError("surface_area_cylinder() only accepts non-negative values"     )
     return 2 * pi * radius * (height + radius)





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     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(snake_case_   ,       2     ) * torus_radius * tube_radius





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if length < 0 or width < 0:
          raise ValueError("area_rectangle() only accepts non-negative values"     )
     return length * width





def       lowerCAmelCase_							(   snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if side_length < 0:
          raise ValueError("area_square() only accepts non-negative values"     )
     return side_length**2





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if base < 0 or height < 0:
          raise ValueError("area_triangle() only accepts non-negative values"     )
     return (base * height) / 2





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     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"     )
     UpperCAmelCase_			       =  (sidea + sidea + sidea) / 2
     UpperCAmelCase_			       =  sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)     )
     return area





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if base < 0 or height < 0:
          raise ValueError("area_parallelogram() only accepts non-negative values"     )
     return base * height





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     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       lowerCAmelCase_							(   snake_case_			:					float     )       -> float:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if radius_x < 0 or radius_y < 0:
          raise ValueError("area_ellipse() only accepts non-negative values"     )
     return pi * radius_x * radius_y





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     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       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					float     )       -> float:


     '''simple docstring'''




     if not isinstance(snake_case_   ,       snake_case_     ) 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
import requests

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

						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCamelCase		       =      False

if is_vision_available():
						from PIL import Image

						from transformers import PixaStructImageProcessor


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=18 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=400 ,					__UpperCAmelCase=None ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											a__	:							str      						= size if size is not None else {"height": 20, "width": 20}
											a__	:							Optional[int]      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							List[Any]      						= num_channels
											a__	:							Union[str, Any]      						= image_size
											a__	:							List[str]      						= min_resolution
											a__	:							int      						= max_resolution
											a__	:							int      						= size
											a__	:							List[str]      						= do_normalize
											a__	:							Any      						= do_convert_rgb
											a__	:							Optional[Any]      						= [512, 1024, 2048, 4096]
											a__	:							Any      						= patch_size if patch_size is not None else {"height": 16, "width": 16}



							def 		_A	(							self				):







											"""simple docstring"""



											return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
											a__	:							Union[str, Any]      						= Image.open(requests.get(__UpperCAmelCase ,					stream=__UpperCAmelCase				).raw				).convert("RGB"				)
											return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,  reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,  )
@require_torch
@require_vision
class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[str] =       PixaStructImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= PixaStructImageProcessingTester(self				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.image_processor_tester.prepare_dummy_image()

											a__	:							Any      						= self.image_processing_class(**self.image_processor_dict				)
											a__	:							List[str]      						= 2048

											a__	:							Union[str, Any]      						= image_processor(__UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				)
											self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,					torch.tensor(0.0_6_0_6				) ,					atol=1E-3 ,					rtol=1E-3				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							str      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							List[Any]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							Optional[Any]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[Any]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Any      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							Optional[int]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											a__	:							int      						= True

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															with self.assertRaises(__UpperCAmelCase				):
																			a__	:							List[str]      						= image_processor(
																			    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches

															a__	:							Tuple      						= "Hello"

															a__	:							Union[str, Any]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase ,					header_text=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[int]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase ,					header_text=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.image_processing_class(**self.image_processor_dict				)
											# create random numpy tensors
											a__	:							Tuple      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase ,					numpify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					np.ndarray				)

											a__	:							List[str]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							Any      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							str      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											a__	:							Any      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase ,					torchify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					torch.Tensor				)

											# Test not batched input
											a__	:							Optional[Any]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							str      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[int]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,  reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,  )
@require_torch
@require_vision
class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[str] =       PixaStructImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= PixaStructImageProcessingTester(self ,					num_channels=4				)
											a__	:							Tuple      						= 3



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Union[str, Any]      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							str      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * (self.image_processor_tester.num_channels - 1)
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							List[str]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							int      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)


