


from math import pi, sqrt, tan

def 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      a__:		float	)  ->		float:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		float	)  ->		float:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      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'	)
							_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__			(      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 	lowerCAmelCase__			(      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(lowerCAmelCase__      ,  2	) * torus_radius * tube_radius

def 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      a__:		float	)  ->		float:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      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'	)
							_UpperCAmelCase					=      (sidea + sidea + sidea) / 2
							_UpperCAmelCase					=      sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)	)
							return area

def 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      a__:		float	)  ->		float:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      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 	lowerCAmelCase__			(      a__:		int      ,  a__:		float	)  ->		float:


							'''simple docstring'''





							if not isinstance(lowerCAmelCase__      ,  lowerCAmelCase__	) or sides < 3:
														raise ValueError(
														    'area_reg_polygon() only accepts integers greater than or \\nequal to three as number of sides'	)
							elif length < 0:
														raise ValueError(
														    'area_reg_polygon() only accepts non-negative values as \\nlength 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) = }''')




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_UpperCAmelCase					=      image_std if image_std is not None else IMAGENET_STANDARD_STD
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return resize(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									return normalize(_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=      do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=      rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=      do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=      image_std if image_std is not None else self.image_std

									_UpperCAmelCase					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_SNAKE_CASE						):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

									if do_resize and size is None or resample is None:
																raise ValueError('Size and resample must be specified if do_resize is True.'						)

									if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'						)

									if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

									if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

									# All transformations expect numpy arrays.
									_UpperCAmelCase					=      [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

									if do_resize:
																_UpperCAmelCase					=      [self.resize(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE      ,   resample=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_center_crop:
																_UpperCAmelCase					=      [self.center_crop(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_rescale:
																_UpperCAmelCase					=      [self.rescale(image=_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_normalize:
																_UpperCAmelCase					=      [self.normalize(image=_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
