code: 
  
  
  import sys
  from typing import Tuple
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import nn
  
  from transformers.image_utils import PILImageResampling
  from utils import img_tensorize
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  
  
       def __init__(     self       ,				A_       ,				A_=sys.maxsize						):
         _UpperCAmelCase		:       List[Any]       =    """bilinear"""
         _UpperCAmelCase		:       Union[str, Any]       =    max_size
         _UpperCAmelCase		:       Dict       =    short_edge_length
  
  
  
  
       def __call__(     self       ,				A_						):
         _UpperCAmelCase		:       Union[str, Any]       =    []
         for img in imgs:
           _UpperCAmelCase,_UpperCAmelCase		:       str       =    img.shape[:2]
           # later: provide list and randomly choose index for resize
           _UpperCAmelCase		:       str       =    np.random.randint(self.short_edge_length[0]       ,				self.short_edge_length[1] + 1						)
           if size == 0:
             return img
           _UpperCAmelCase		:       Optional[Any]       =    size * 1.0 / min(A_       ,				A_						)
           if h < w:
             _UpperCAmelCase,_UpperCAmelCase		:       Any       =    size, scale * w
           else:
             _UpperCAmelCase,_UpperCAmelCase		:       Tuple       =    scale * h, size
           if max(A_       ,				A_						) > self.max_size:
             _UpperCAmelCase		:       List[str]       =    self.max_size * 1.0 / max(A_       ,				A_						)
             _UpperCAmelCase		:       Tuple       =    newh * scale
             _UpperCAmelCase		:       List[Any]       =    neww * scale
           _UpperCAmelCase		:       Any       =    int(neww + 0.5						)
           _UpperCAmelCase		:       Optional[Any]       =    int(newh + 0.5						)
  
           if img.dtype == np.uinta:
             _UpperCAmelCase		:       int       =    Image.fromarray(A_						)
             _UpperCAmelCase		:       Any       =    pil_image.resize((neww, newh)       ,				PILImageResampling.BILINEAR						)
             _UpperCAmelCase		:       Optional[int]       =    np.asarray(A_						)
           else:
             _UpperCAmelCase		:       List[str]       =    img.permute(2       ,				0       ,				1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
             _UpperCAmelCase		:       List[str]       =    nn.functional.interpolate(
                 A_       ,				(newh, neww)       ,				mode=self.interp_method       ,				align_corners=A_						).squeeze(0						)
           img_augs.append(A_						)
  
         return img_augs
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  
  
       def __init__(     self       ,				A_						):
         _UpperCAmelCase		:       Optional[int]       =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,				cfg.INPUT.MAX_SIZE_TEST						)
         _UpperCAmelCase		:       Union[str, Any]       =    cfg.INPUT.FORMAT
         _UpperCAmelCase		:       List[Any]       =    cfg.SIZE_DIVISIBILITY
         _UpperCAmelCase		:       List[str]       =    cfg.PAD_VALUE
         _UpperCAmelCase		:       Tuple       =    cfg.INPUT.MAX_SIZE_TEST
         _UpperCAmelCase		:       Optional[Any]       =    cfg.MODEL.DEVICE
         _UpperCAmelCase		:       Optional[int]       =    torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)       ,				1       ,				1						)
         _UpperCAmelCase		:       Optional[Any]       =    torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)       ,				1       ,				1						)
         _UpperCAmelCase		:       Tuple       =    lambda A_						: (x - self.pixel_mean) / self.pixel_std
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       List[Any]       =    tuple(max(A_						) for s in zip(*[img.shape for img in images]						)						)
         _UpperCAmelCase		:       Optional[int]       =    [im.shape[-2:] for im in images]
         _UpperCAmelCase		:       str       =    [
             nn.functional.pad(
                 A_       ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,				value=self.pad_value       ,				)
             for size, im in zip(A_       ,				A_						)
         ]
  
         return torch.stack(A_						), torch.tensor(A_						)
  
  
  
  
       def __call__(     self       ,				A_       ,				A_=False						):
         with torch.no_grad():
           if not isinstance(A_       ,				A_						):
             _UpperCAmelCase		:       Optional[int]       =    [images]
           if single_image:
             assert len(A_						) == 1
           for i in range(len(A_						)						):
             if isinstance(images[i]       ,				torch.Tensor						):
               images.insert(A_       ,				images.pop(A_						).to(self.device						).float()						)
             elif not isinstance(images[i]       ,				torch.Tensor						):
               images.insert(
                   A_       ,				torch.as_tensor(img_tensorize(images.pop(A_						)       ,				input_format=self.input_format						)						)
                   .to(self.device						)
                   .float()       ,				)
              # resize smallest edge
           _UpperCAmelCase		:       Any       =    torch.tensor([im.shape[:2] for im in images]						)
           _UpperCAmelCase		:       str       =    self.aug(A_						)
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           _UpperCAmelCase		:       Any       =    [self.normalizer(A_						) for x in images]
           # now pad them to do the following operations
           _UpperCAmelCase,_UpperCAmelCase		:       str       =    self.pad(A_						)
           # Normalize
  
           if self.size_divisibility > 0:
             raise NotImplementedError()
           # pad
           _UpperCAmelCase		:       Optional[Any]       =    torch.true_divide(A_       ,				A_						)
           if single_image:
             return images[0], sizes[0], scales_yx[0]
           else:
             return images, sizes, scales_yx
  
  
  
  
  def       a__						(		snake_case__				:       List[str]	,      snake_case__				:       Optional[int]    ):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes
  
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       Tuple[int, int]    ):
    assert torch.isfinite(snake_case__    ).all(), "Box tensor contains infinite or NaN!"
    _UpperCAmelCase,_UpperCAmelCase		:       Optional[int]       =    box_size
    tensor[:, 0].clamp_(min=0	,      max=snake_case__    )
    tensor[:, 1].clamp_(min=0	,      max=snake_case__    )
    tensor[:, 2].clamp_(min=0	,      max=snake_case__    )
    tensor[:, 3].clamp_(min=0	,      max=snake_case__    )
  
  
  
  
  
  
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() = }')
  
  
  
  
  
  