






from math import pi, sqrt, tan







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     float:
     if radius < 0:
          raise ValueError("surface_area_sphere() only accepts non-negative values"      )
     return 4 * pi * radius**2







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              "surface_area_conical_frustum() only accepts non-negative values"      )
     UpperCamelCase__      :       Tuple	    =  (height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     if length < 0 or width < 0:
          raise ValueError("area_rectangle() only accepts non-negative values"      )
     return length * width







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     if base < 0 or height < 0:
          raise ValueError("area_triangle() only accepts non-negative values"      )
     return (base * height) / 2







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     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__      :       Dict	    =  (sidea + sidea + sidea) / 2
     UpperCamelCase__      :       Tuple	    =  sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)      )
     return area







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     if base < 0 or height < 0:
          raise ValueError("area_parallelogram() only accepts non-negative values"      )
     return base * height







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     float:
     if radius < 0:
          raise ValueError("area_circle() only accepts non-negative values"      )
     return pi * radius**2







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     if radius_x < 0 or radius_y < 0:
          raise ValueError("area_ellipse() only accepts non-negative values"      )
     return pi * radius_x * radius_y







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     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 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       __a  (   unittest.TestCase	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[Any]=7							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Dict=18							,       SCREAMING_SNAKE_CASE :       str=30							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4_00							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       int=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  parent
      UpperCamelCase__      :       Dict	    =  batch_size
      UpperCamelCase__      :       Optional[Any]	    =  num_channels
      UpperCamelCase__      :       int	    =  image_size
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       List[str]	    =  max_resolution
      UpperCamelCase__      :       Union[str, Any]	    =  do_resize
      UpperCamelCase__      :       Optional[Any]	    =  size_divisor
      UpperCamelCase__      :       int	    =  do_rescale







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }







@require_torch
@require_vision
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	List[Any]				=			GLPNImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  GLPNImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "resample"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_rescale"						)						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       Image.Image						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       List[Any]	    =  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  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Optional[Any]	    =  prepare_image_inputs(self.image_processor_tester							,       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 (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       str	    =  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  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       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 (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       Dict	    =  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						)