






'''simple docstring'''






from math import pi, sqrt, tan




def        _SCREAMING_SNAKE_CASE  (				A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       float  ,		A  :       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        _SCREAMING_SNAKE_CASE  (				A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       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        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       float  ,		A  :       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'  )
   __snake_case				:      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  (				A  :       float  ,		A  :       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        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       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(A  ,		2  ) * torus_radius * tube_radius




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       float  ,		A  :       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'  )
   __snake_case				:      List[Any]     							= (sidea + sidea + sidea) / 2
   __snake_case				:      int     							= sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)  )
   return area




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       float  ,		A  :       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        _SCREAMING_SNAKE_CASE  (				A  :       float  )    -> float:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       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        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       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        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       float  )    -> float:







   """simple docstring"""
   if not isinstance(A  ,		A  ) 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(1_0, 2_0) = }''')
      print(f'''Square: {area_square(1_0) = }''')
      print(f'''Triangle: {area_triangle(1_0, 1_0) = }''')
      print(f'''Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }''')
      print(f'''Parallelogram: {area_parallelogram(1_0, 2_0) = }''')
      print(f'''Rhombus: {area_rhombus(1_0, 2_0) = }''')
      print(f'''Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }''')
      print(f'''Circle: {area_circle(2_0) = }''')
      print(f'''Ellipse: {area_ellipse(1_0, 2_0) = }''')
      print('''\nSurface Areas of various geometric shapes: \n''')
      print(f'''Cube: {surface_area_cube(2_0) = }''')
      print(f'''Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }''')
      print(f'''Sphere: {surface_area_sphere(2_0) = }''')
      print(f'''Hemisphere: {surface_area_hemisphere(2_0) = }''')
      print(f'''Cone: {surface_area_cone(1_0, 2_0) = }''')
      print(f'''Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }''')
      print(f'''Cylinder: {surface_area_cylinder(1_0, 2_0) = }''')
      print(f'''Torus: {surface_area_torus(2_0, 1_0) = }''')
      print(f'''Equilateral Triangle: {area_reg_polygon(3, 1_0) = }''')
      print(f'''Square: {area_reg_polygon(4, 1_0) = }''')
      print(f'''Reqular Pentagon: {area_reg_polygon(5, 1_0) = }''')







'''simple docstring'''






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							VQModel
   _snake_case        =							"""sample"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self					,   __a=(3_2, 3_2))			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= 4
      __snake_case				:      Optional[int]     							= 3

      __snake_case				:      str     							= floats_tensor((batch_size, num_channels) + sizes).to(__a)

      return {"sample": image}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= {
          'block_out_channels': [3_2, 6_4],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 3,
      }
      __snake_case				:      List[Any]     							= self.dummy_input
      return init_dict, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case							,__snake_case				:      List[Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy'					,   output_loading_info=__a)
      self.assertIsNotNone(__a)
      self.assertEqual(len(loading_info['missing_keys'])					,   0)

      model.to(__a)
      __snake_case				:      Any     							= model(**self.dummy_input)

      assert image is not None, "Make sure output is not None"







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy')
      model.to(__a).eval()

      torch.manual_seed(0)
      if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0)

      __snake_case				:      Tuple     							= torch.randn(1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size)
      __snake_case				:      Optional[int]     							= image.to(__a)
      with torch.no_grad():
         __snake_case				:      List[Any]     							= model(__a).sample

      __snake_case				:      int     							= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      __snake_case				:      int     							= torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143])
      # fmt: on
      self.assertTrue(torch.allclose(__a					,   __a					,   atol=1E-3))