
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 ViltImageProcessor


class   __lowerCAmelCase		(		unittest.TestCase ):



      """simple docstring"""







      def __init__( self						:    List[Any]			,    _snake_case						:    Any			,    _snake_case						:    Optional[Any]=7			,    _snake_case						:    Optional[int]=3			,    _snake_case						:    List[str]=18			,    _snake_case						:    List[str]=30			,    _snake_case						:    List[str]=400			,    _snake_case						:    Optional[int]=True			,    _snake_case						:    Optional[Any]=None			,    _snake_case						:    int=2			,    _snake_case						:    int=True			,    _snake_case						:    Union[str, Any]=[0.5, 0.5, 0.5]			,    _snake_case						:    Optional[Any]=[0.5, 0.5, 0.5]			,    ):
          __lowercase						:     List[str]		      =    size if size is not None else {'''shortest_edge''': 30}
          __lowercase						:     Optional[Any]		      =    parent
          __lowercase						:     Optional[int]		      =    batch_size
          __lowercase						:     int		      =    num_channels
          __lowercase						:     Tuple		      =    image_size
          __lowercase						:     Any		      =    min_resolution
          __lowercase						:     Union[str, Any]		      =    max_resolution
          __lowercase						:     str		      =    do_resize
          __lowercase						:     Tuple		      =    size
          __lowercase						:     Tuple		      =    size_divisor
          __lowercase						:     Optional[Any]		      =    do_normalize
          __lowercase						:     Any		      =    image_mean
          __lowercase						:     Dict		      =    image_std




      def 						snake_case_     ( self						:    List[Any]			):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }




      def 						snake_case_     ( self						:    int			,    _snake_case						:    List[str]			,    _snake_case						:    List[Any]=False			):
          if not batched:
              __lowercase						:     int		      =    self.size['''shortest_edge''']
              __lowercase						:     Dict		      =    image_inputs[0]
              if isinstance(_snake_case			,    Image.Image			):
                  __lowercase      , __lowercase						:     Tuple		      =    image.size
              else:
                  __lowercase      , __lowercase						:     Tuple		      =    image.shape[1], image.shape[2]
              __lowercase						:     Optional[Any]		      =    size / min(_snake_case			,    _snake_case			)
              if h < w:
                  __lowercase      , __lowercase						:     Any		      =    size, scale * w
              else:
                  __lowercase      , __lowercase						:     List[str]		      =    scale * h, size

              __lowercase						:     str		      =    int((1333 / 800) * size			)
              if max(_snake_case			,    _snake_case			) > max_size:
                  __lowercase						:     List[Any]		      =    max_size / max(_snake_case			,    _snake_case			)
                  __lowercase						:     Tuple		      =    newh * scale
                  __lowercase						:     str		      =    neww * scale

              __lowercase      , __lowercase						:     List[Any]		      =    int(newh + 0.5			), int(neww + 0.5			)
              __lowercase      , __lowercase						:     Optional[Any]		      =    (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

          else:
              __lowercase						:     str		      =    []
              for image in image_inputs:
                  __lowercase      , __lowercase						:     List[str]		      =    self.get_expected_values([image]			)
                  expected_values.append((expected_height, expected_width)			)
              __lowercase						:     Union[str, Any]		      =    max(_snake_case			,    key=lambda _snake_case			: item[0]			)[0]
              __lowercase						:     Optional[int]		      =    max(_snake_case			,    key=lambda _snake_case			: item[1]			)[1]

          return expected_height, expected_width






@require_torch
@require_vision
class   __lowerCAmelCase		(		lowerCAmelCase_	,	unittest.TestCase ):



      """simple docstring"""







      A__		:     List[Any]       				=      ViltImageProcessor if is_vision_available() else None




      def 						snake_case_     ( self						:    int			):
          __lowercase						:     int		      =    ViltImageProcessingTester(self			)




      @property
      def 						snake_case_     ( self						:    Dict			):
          return self.image_processor_tester.prepare_image_processor_dict()




      def 						snake_case_     ( self						:    int			):
          __lowercase						:     Dict		      =    self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(_snake_case			,    '''image_mean'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''image_std'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_normalize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_resize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''size'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''size_divisor'''			)			)




      def 						snake_case_     ( self						:    str			):
          __lowercase						:     Optional[Any]		      =    self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size			,    {'''shortest_edge''': 30}			)

          __lowercase						:     Dict		      =    self.image_processing_class.from_dict(self.image_processor_dict			,    size=42			)
          self.assertEqual(image_processor.size			,    {'''shortest_edge''': 42}			)




      def 						snake_case_     ( self						:    List[str]			):
          pass




      def 						snake_case_     ( self						:    int			):
          # Initialize image_processing
          __lowercase						:     Optional[Any]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          __lowercase						:     Any		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    Image.Image			)

          # Test not batched input
          __lowercase						:     Dict		      =    image_processing(image_inputs[0]			,    return_tensors='''pt'''			).pixel_values

          __lowercase      , __lowercase						:     Optional[int]		      =    self.image_processor_tester.get_expected_values(_snake_case			)
          self.assertEqual(
              encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

          # Test batched
          __lowercase						:     List[Any]		      =    image_processing(_snake_case			,    return_tensors='''pt'''			).pixel_values

          __lowercase      , __lowercase						:     List[Any]		      =    self.image_processor_tester.get_expected_values(_snake_case			,    batched=_snake_case			)
          self.assertEqual(
              encoded_images.shape			,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,    )




      def 						snake_case_     ( self						:    str			):
          # Initialize image_processing
          __lowercase						:     Optional[int]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          __lowercase						:     Optional[int]		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    numpify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    np.ndarray			)

          # Test not batched input
          __lowercase						:     Any		      =    image_processing(image_inputs[0]			,    return_tensors='''pt'''			).pixel_values

          __lowercase      , __lowercase						:     int		      =    self.image_processor_tester.get_expected_values(_snake_case			)
          self.assertEqual(
              encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

          # Test batched
          __lowercase						:     Tuple		      =    image_processing(_snake_case			,    return_tensors='''pt'''			).pixel_values

          __lowercase      , __lowercase						:     int		      =    self.image_processor_tester.get_expected_values(_snake_case			,    batched=_snake_case			)
          self.assertEqual(
              encoded_images.shape			,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,    )




      def 						snake_case_     ( self						:    Optional[int]			):
          # Initialize image_processing
          __lowercase						:     str		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          __lowercase						:     List[str]		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    torchify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    torch.Tensor			)

          # Test not batched input
          __lowercase						:     Optional[int]		      =    image_processing(image_inputs[0]			,    return_tensors='''pt'''			).pixel_values

          __lowercase      , __lowercase						:     List[str]		      =    self.image_processor_tester.get_expected_values(_snake_case			)
          self.assertEqual(
              encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

          # Test batched
          __lowercase						:     Optional[Any]		      =    image_processing(_snake_case			,    return_tensors='''pt'''			).pixel_values

          __lowercase      , __lowercase						:     Union[str, Any]		      =    self.image_processor_tester.get_expected_values(_snake_case			,    batched=_snake_case			)
          self.assertEqual(
              encoded_images.shape			,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,    )




from __future__ import annotations

from math import pi, pow


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  float:
    if side_length < 0:
        raise ValueError('''vol_cube() only accepts non-negative values'''		)
    return pow(__lowerCAmelCase		,   3		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or radius < 0:
        raise ValueError('''vol_spherical_cap() only accepts non-negative values'''		)
    # Volume is 1/3 pi * height squared * (3 * radius - height)
    return 1 / 3 * pi * pow(__lowerCAmelCase		,   2		) * (3 * radius - height)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if radius_a < 0 or radius_a < 0 or centers_distance < 0:
        raise ValueError('''vol_spheres_intersect() only accepts non-negative values'''		)
    if centers_distance == 0:
        return vol_sphere(min(__lowerCAmelCase		,   __lowerCAmelCase		)		)

    __lowercase						:     Dict		      =    (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )
    __lowercase						:     Optional[int]		      =    (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )

    return vol_spherical_cap(__lowerCAmelCase		,   __lowerCAmelCase		) + vol_spherical_cap(__lowerCAmelCase		,   __lowerCAmelCase		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:

    if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
        raise ValueError(
            '''vol_spheres_union() only accepts non-negative values, non-zero radius'''		)

    if centers_distance == 0:
        return vol_sphere(max(__lowerCAmelCase		,   __lowerCAmelCase		)		)

    return (
        vol_sphere(__lowerCAmelCase		)
        + vol_sphere(__lowerCAmelCase		)
        - vol_spheres_intersect(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)
    )


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if width < 0 or height < 0 or length < 0:
        raise ValueError('''vol_cuboid() only accepts non-negative values'''		)
    return float(width * height * length		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or area_of_base < 0:
        raise ValueError('''vol_cone() only accepts non-negative values'''		)
    return area_of_base * height / 3.0


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or radius < 0:
        raise ValueError('''vol_right_circ_cone() only accepts non-negative values'''		)
    return pi * pow(__lowerCAmelCase		,   2		) * height / 3.0


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or area_of_base < 0:
        raise ValueError('''vol_prism() only accepts non-negative values'''		)
    return float(area_of_base * height		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or area_of_base < 0:
        raise ValueError('''vol_pyramid() only accepts non-negative values'''		)
    return area_of_base * height / 3.0


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  float:
    if radius < 0:
        raise ValueError('''vol_sphere() only accepts non-negative values'''		)
    # Volume is 4/3 * pi * radius cubed
    return 4 / 3 * pi * pow(__lowerCAmelCase		,   3		)


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  float:
    if radius < 0:
        raise ValueError('''vol_hemisphere() only accepts non-negative values'''		)
    # Volume is radius cubed * pi * 2/3
    return pow(__lowerCAmelCase		,   3		) * pi * 2 / 3


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or radius < 0:
        raise ValueError('''vol_circular_cylinder() only accepts non-negative values'''		)
    # Volume is radius squared * height * pi
    return pow(__lowerCAmelCase		,   2		) * height * pi


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    # Volume - (outer_radius squared - inner_radius squared) * pi * height
    if inner_radius < 0 or outer_radius < 0 or height < 0:
        raise ValueError(
            '''vol_hollow_circular_cylinder() only accepts non-negative values'''		)
    if outer_radius <= inner_radius:
        raise ValueError('''outer_radius must be greater than inner_radius'''		)
    return pi * (pow(__lowerCAmelCase		,   2		) - pow(__lowerCAmelCase		,   2		)) * height


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    # Volume is 1/3 * pi * height *
    #           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
    if radius_a < 0 or radius_a < 0 or height < 0:
        raise ValueError('''vol_conical_frustum() only accepts non-negative values'''		)
    return (
        1
        / 3
        * pi
        * height
        * (pow(__lowerCAmelCase		,   2		) + pow(__lowerCAmelCase		,   2		) + radius_a * radius_a)
    )


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if torus_radius < 0 or tube_radius < 0:
        raise ValueError('''vol_torus() only accepts non-negative values'''		)
    return 2 * pow(__lowerCAmelCase		,   2		) * torus_radius * pow(__lowerCAmelCase		,   2		)


def       UpperCAmelCase_					(  )	->  Optional[Any]:
    print('''Volumes:'''		)
    print(F'Cube: {vol_cube(2		) = }'		)  # = 8
    print(F'Cuboid: {vol_cuboid(2		,   2		,   2		) = }'		)  # = 8
    print(F'Cone: {vol_cone(2		,   2		) = }'		)  # ~= 1.33
    print(F'Right Circular Cone: {vol_right_circ_cone(2		,   2		) = }'		)  # ~= 8.38
    print(F'Prism: {vol_prism(2		,   2		) = }'		)  # = 4
    print(F'Pyramid: {vol_pyramid(2		,   2		) = }'		)  # ~= 1.33
    print(F'Sphere: {vol_sphere(2		) = }'		)  # ~= 33.5
    print(F'Hemisphere: {vol_hemisphere(2		) = }'		)  # ~= 16.75
    print(F'Circular Cylinder: {vol_circular_cylinder(2		,   2		) = }'		)  # ~= 25.1
    print(F'Torus: {vol_torus(2		,   2		) = }'		)  # ~= 157.9
    print(F'Conical Frustum: {vol_conical_frustum(2		,   2		,   4		) = }'		)  # ~= 58.6
    print(F'Spherical cap: {vol_spherical_cap(1		,   2		) = }'		)  # ~= 5.24
    print(F'Spheres intersetion: {vol_spheres_intersect(2		,   2		,   1		) = }'		)  # ~= 21.21
    print(F'Spheres union: {vol_spheres_union(2		,   2		,   1		) = }'		)  # ~= 45.81
    print(
        F'Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1		,   2		,   3		) = }'		)  # ~= 28.3


if __name__ == "__main__":
 main()


