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 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=sys.maxsize		):
            UpperCAmelCase__			:     str										= """bilinear"""
            UpperCAmelCase__			:     Dict										= max_size
            UpperCAmelCase__			:     List[str]										= short_edge_length
  
  
  
  
       def __call__(							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     List[str]										= []
            for img in imgs:
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Any										= img.shape[:2]
                 # later: provide list and randomly choose index for resize
                 UpperCAmelCase__			:     int										= np.random.randint(self.short_edge_length[0]  ,	self.short_edge_length[1] + 1		)
                 if size == 0:
                      return img
                 UpperCAmelCase__			:     Optional[int]										= size * 1.0 / min(_lowerCAmelCase  ,	_lowerCAmelCase		)
                 if h < w:
                      UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[Any]										= size, scale * w
                 else:
                      UpperCAmelCase__			,		UpperCAmelCase__			:     Any										= scale * h, size
                 if max(_lowerCAmelCase  ,	_lowerCAmelCase		) > self.max_size:
                      UpperCAmelCase__			:     Optional[Any]										= self.max_size * 1.0 / max(_lowerCAmelCase  ,	_lowerCAmelCase		)
                      UpperCAmelCase__			:     Tuple										= newh * scale
                      UpperCAmelCase__			:     str										= neww * scale
                 UpperCAmelCase__			:     List[str]										= int(neww + 0.5		)
                 UpperCAmelCase__			:     Tuple										= int(newh + 0.5		)
  
                 if img.dtype == np.uinta:
                      UpperCAmelCase__			:     int										= Image.fromarray(_lowerCAmelCase		)
                      UpperCAmelCase__			:     Union[str, Any]										= pil_image.resize((neww, newh)  ,	PILImageResampling.BILINEAR		)
                      UpperCAmelCase__			:     Tuple										= np.asarray(_lowerCAmelCase		)
                 else:
                      UpperCAmelCase__			:     List[Any]										= img.permute(2  ,	0  ,	1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
                      UpperCAmelCase__			:     Union[str, Any]										= nn.functional.interpolate(
                          _lowerCAmelCase  ,	(newh, neww)  ,	mode=self.interp_method  ,	align_corners=_lowerCAmelCase		).squeeze(0		)
                 img_augs.append(_lowerCAmelCase		)
  
            return img_augs
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Optional[Any]										= ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,	cfg.INPUT.MAX_SIZE_TEST		)
            UpperCAmelCase__			:     Optional[Any]										= cfg.INPUT.FORMAT
            UpperCAmelCase__			:     Optional[Any]										= cfg.SIZE_DIVISIBILITY
            UpperCAmelCase__			:     Any										= cfg.PAD_VALUE
            UpperCAmelCase__			:     Union[str, Any]										= cfg.INPUT.MAX_SIZE_TEST
            UpperCAmelCase__			:     List[str]										= cfg.MODEL.DEVICE
            UpperCAmelCase__			:     Tuple										= torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)  ,	1  ,	1		)
            UpperCAmelCase__			:     Any										= torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)  ,	1  ,	1		)
            UpperCAmelCase__			:     List[str]										= lambda _lowerCAmelCase		: (x - self.pixel_mean) / self.pixel_std
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     int										= tuple(max(_lowerCAmelCase		) for s in zip(*[img.shape for img in images]		)		)
            UpperCAmelCase__			:     List[Any]										= [im.shape[-2:] for im in images]
            UpperCAmelCase__			:     Dict										= [
                nn.functional.pad(
                    _lowerCAmelCase  ,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]  ,	value=self.pad_value  ,	)
                for size, im in zip(_lowerCAmelCase  ,	_lowerCAmelCase		)
            ]
  
            return torch.stack(_lowerCAmelCase		), torch.tensor(_lowerCAmelCase		)
  
  
  
  
       def __call__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=False		):
            with torch.no_grad():
                 if not isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                      UpperCAmelCase__			:     Any										= [images]
                 if single_image:
                      assert len(_lowerCAmelCase		) == 1
                 for i in range(len(_lowerCAmelCase		)		):
                      if isinstance(images[i]  ,	torch.Tensor		):
                           images.insert(_lowerCAmelCase  ,	images.pop(_lowerCAmelCase		).to(self.device		).float()		)
                      elif not isinstance(images[i]  ,	torch.Tensor		):
                           images.insert(
                               _lowerCAmelCase  ,	torch.as_tensor(img_tensorize(images.pop(_lowerCAmelCase		)  ,	input_format=self.input_format		)		)
                               .to(self.device		)
                               .float()  ,	)
              # resize smallest edge
                 UpperCAmelCase__			:     int										= torch.tensor([im.shape[:2] for im in images]		)
                 UpperCAmelCase__			:     Any										= self.aug(_lowerCAmelCase		)
                 # 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(_lowerCAmelCase		) for x in images]
                 # now pad them to do the following operations
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Dict										= self.pad(_lowerCAmelCase		)
                 # Normalize
  
                 if self.size_divisibility > 0:
                      raise NotImplementedError()
                 # pad
                 UpperCAmelCase__			:     Optional[Any]										= torch.true_divide(_lowerCAmelCase  ,	_lowerCAmelCase		)
                 if single_image:
                      return images[0], sizes[0], scales_yx[0]
                 else:
                      return images, sizes, scales_yx
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:
  
       '''simple docstring'''
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes
  
  
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> List[Any]:
  
       '''simple docstring'''
       assert torch.isfinite(__lowerCamelCase					).all(), "Box tensor contains infinite or NaN!"
       UpperCAmelCase__			,		UpperCAmelCase__			:     str										= box_size
       tensor[:, 0].clamp_(min=0   ,       max=__lowerCamelCase					)
       tensor[:, 1].clamp_(min=0   ,       max=__lowerCamelCase					)
       tensor[:, 2].clamp_(min=0   ,       max=__lowerCamelCase					)
       tensor[:, 3].clamp_(min=0   ,       max=__lowerCamelCase					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from ..utils import requires_backends
  
  
  SCREAMING_SNAKE_CASE__		:       int   							=							None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(self  ,	["""detectron2"""]		)
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(cls  ,	["""detectron2"""]		)
  
  
  
  
  
  
  