code: 
  
  
  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 OwlViTImageProcessor
  
  
  class 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):
  
  
       def __init__(     self       ,				A_       ,				A_=7       ,				A_=3       ,				A_=18       ,				A_=30       ,				A_=4_00       ,				A_=True       ,				A_=None       ,				A_=True       ,				A_=None       ,				A_=True       ,				A_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,				A_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,				A_=True       ,				):
         _UpperCAmelCase		:       List[Any]       =    parent
         _UpperCAmelCase		:       Tuple       =    batch_size
         _UpperCAmelCase		:       Dict       =    num_channels
         _UpperCAmelCase		:       List[str]       =    image_size
         _UpperCAmelCase		:       Any       =    min_resolution
         _UpperCAmelCase		:       int       =    max_resolution
         _UpperCAmelCase		:       str       =    do_resize
         _UpperCAmelCase		:       Optional[int]       =    size if size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase		:       Any       =    do_center_crop
         _UpperCAmelCase		:       Dict       =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase		:       Dict       =    do_normalize
         _UpperCAmelCase		:       Optional[int]       =    image_mean
         _UpperCAmelCase		:       Union[str, Any]       =    image_std
         _UpperCAmelCase		:       Tuple       =    do_convert_rgb
  
  
  
  
       def 					__snake_case(     self						):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_convert_rgb": self.do_convert_rgb,
         }
  
  
  @require_torch
  @require_vision
  class 		_SCREAMING_SNAKE_CASE    (	A       ,  unittest.TestCase    ):
       __SCREAMING_SNAKE_CASE    				=						OwlViTImageProcessor if is_vision_available() else None
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       int       =    OwlViTImageProcessingTester(self						)
  
  
       @property
       def 					__snake_case(     self						):
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[Any]       =    self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(A_       ,				"""do_resize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""size"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_normalize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_mean"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_std"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_convert_rgb"""						)						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size       ,				{"""height""": 18, """width""": 18}						)
         self.assertEqual(image_processor.crop_size       ,				{"""height""": 18, """width""": 18}						)
  
         _UpperCAmelCase		:       List[str]       =    self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42       ,				crop_size=84						)
         self.assertEqual(image_processor.size       ,				{"""height""": 42, """width""": 42}						)
         self.assertEqual(image_processor.crop_size       ,				{"""height""": 84, """width""": 84}						)
  
  
       def 					__snake_case(     self						):
         # Initialize image_processing
         _UpperCAmelCase		:       Any       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         _UpperCAmelCase		:       int       =    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=A_						)
  
         for image in image_inputs:
           self.assertIsInstance(A_       ,				Image.Image						)
  
         # Test not batched input
         _UpperCAmelCase		:       Dict       =    image_processing(image_inputs[0]       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
         # Test batched
         _UpperCAmelCase		:       Optional[int]       =    image_processing(A_       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
  
       def 					__snake_case(     self						):
         # Initialize image_processing
         _UpperCAmelCase		:       List[str]       =    self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         _UpperCAmelCase		:       Tuple       =    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=A_       ,				numpify=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				np.ndarray						)
  
         # Test not batched input
         _UpperCAmelCase		:       Tuple       =    image_processing(image_inputs[0]       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
         # Test batched
         _UpperCAmelCase		:       str       =    image_processing(A_       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
  
  
  
       def 					__snake_case(     self						):
         # Initialize image_processing
         _UpperCAmelCase		:       str       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         _UpperCAmelCase		:       str       =    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=A_       ,				torchify=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				torch.Tensor						)
  
         # Test not batched input
         _UpperCAmelCase		:       Tuple       =    image_processing(image_inputs[0]       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
         # Test batched
         _UpperCAmelCase		:       Union[str, Any]       =    image_processing(A_       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
  
  
  
  
  
style_context: 
  
  
  from itertools import count
  from math import asin, pi, sqrt
  
  
  
  
  def       a__						(		snake_case__				:       float    ):
    return (
        (1 - 2 * point) * sqrt(point - point**2    ) + 2 * point + asin(sqrt(1 - point    )    )
    ) / 4
  
  
  
  
  def       a__						(		snake_case__				:       int    ):
    _UpperCAmelCase		:       int       =    (circles_number + 1 - sqrt(2 * circles_number    )) / (
        2 * (circles_number**2 + 1)
    )
    _UpperCAmelCase		:       Union[str, Any]       =    circles_number * intersection_y
  
    _UpperCAmelCase		:       Tuple       =    intersection_x * intersection_y / 2
    _UpperCAmelCase		:       str       =    circle_bottom_arc_integral(
        1 / 2    ) - circle_bottom_arc_integral(snake_case__    )
  
    return triangle_area + concave_region_area
  
  
  
  
  def       a__						(		snake_case__				:       float = 1 / 1000    ):
    _UpperCAmelCase		:       Dict       =    (1 - pi / 4) / 4
  
    for n in count(1    ):
      if concave_triangle_area(snake_case__    ) / l_section_area < fraction:
        return n
  
    return -1
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  