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