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 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase=sys.maxsize   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[str]        =       "bilinear"
          __snake_case   :				Tuple        =       max_size
          __snake_case   :				Dict        =       short_edge_length
  
  
  
  
  
  
  
        def __call__( self				,						UpperCAmelCase   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       []
          for img in imgs:
            __snake_case    ,   __snake_case   :				Tuple        =       img.shape[:2]
            # later: provide list and randomly choose index for resize
            __snake_case   :				List[Any]        =       np.random.randint(self.short_edge_length[0]				,						self.short_edge_length[1] + 1   )
            if size == 0:
              return img
            __snake_case   :				Any        =       size * 1.0 / min(UpperCAmelCase				,						UpperCAmelCase   )
            if h < w:
              __snake_case    ,   __snake_case   :				Optional[int]        =       size, scale * w
            else:
              __snake_case    ,   __snake_case   :				str        =       scale * h, size
            if max(UpperCAmelCase				,						UpperCAmelCase   ) > self.max_size:
              __snake_case   :				Union[str, Any]        =       self.max_size * 1.0 / max(UpperCAmelCase				,						UpperCAmelCase   )
              __snake_case   :				Optional[Any]        =       newh * scale
              __snake_case   :				List[Any]        =       neww * scale
            __snake_case   :				List[Any]        =       int(neww + 0.5   )
            __snake_case   :				Optional[Any]        =       int(newh + 0.5   )
  
            if img.dtype == np.uinta:
              __snake_case   :				List[str]        =       Image.fromarray(UpperCAmelCase   )
              __snake_case   :				int        =       pil_image.resize((neww, newh)				,						PILImageResampling.BILINEAR   )
              __snake_case   :				List[Any]        =       np.asarray(UpperCAmelCase   )
            else:
              __snake_case   :				Union[str, Any]        =       img.permute(2				,						0				,						1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
              __snake_case   :				str        =       nn.functional.interpolate(
                  UpperCAmelCase				,						(newh, neww)				,						mode=self.interp_method				,						align_corners=UpperCAmelCase   ).squeeze(0   )
            img_augs.append(UpperCAmelCase   )
  
          return img_augs
  
  
  
  
  class 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				str        =       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,						cfg.INPUT.MAX_SIZE_TEST   )
          __snake_case   :				Dict        =       cfg.INPUT.FORMAT
          __snake_case   :				Union[str, Any]        =       cfg.SIZE_DIVISIBILITY
          __snake_case   :				Dict        =       cfg.PAD_VALUE
          __snake_case   :				Optional[Any]        =       cfg.INPUT.MAX_SIZE_TEST
          __snake_case   :				List[str]        =       cfg.MODEL.DEVICE
          __snake_case   :				Dict        =       torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,						1				,						1   )
          __snake_case   :				Optional[int]        =       torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,						1				,						1   )
          __snake_case   :				List[Any]        =       lambda UpperCAmelCase   : (x - self.pixel_mean) / self.pixel_std
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				str        =       tuple(max(UpperCAmelCase   ) for s in zip(*[img.shape for img in images]   )   )
          __snake_case   :				Optional[Any]        =       [im.shape[-2:] for im in images]
          __snake_case   :				Tuple        =       [
              nn.functional.pad(
                  UpperCAmelCase				,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,						value=self.pad_value				,						)
              for size, im in zip(UpperCAmelCase				,						UpperCAmelCase   )
          ]
  
          return torch.stack(UpperCAmelCase   ), torch.tensor(UpperCAmelCase   )
  
  
  
  
  
  
  
        def __call__( self				,						UpperCAmelCase				,						UpperCAmelCase=False   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          with torch.no_grad():
            if not isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
              __snake_case   :				Tuple        =       [images]
            if single_image:
              assert len(UpperCAmelCase   ) == 1
            for i in range(len(UpperCAmelCase   )   ):
              if isinstance(images[i]				,						torch.Tensor   ):
                images.insert(UpperCAmelCase				,						images.pop(UpperCAmelCase   ).to(self.device   ).float()   )
              elif not isinstance(images[i]				,						torch.Tensor   ):
                images.insert(
                    UpperCAmelCase				,						torch.as_tensor(img_tensorize(images.pop(UpperCAmelCase   )				,						input_format=self.input_format   )   )
                    .to(self.device   )
                    .float()				,						)
              # resize smallest edge
            __snake_case   :				Union[str, Any]        =       torch.tensor([im.shape[:2] for im in images]   )
            __snake_case   :				Tuple        =       self.aug(UpperCAmelCase   )
            # 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
            __snake_case   :				Dict        =       [self.normalizer(UpperCAmelCase   ) for x in images]
            # now pad them to do the following operations
            __snake_case    ,   __snake_case   :				str        =       self.pad(UpperCAmelCase   )
            # Normalize
  
            if self.size_divisibility > 0:
              raise NotImplementedError()
            # pad
            __snake_case   :				Tuple        =       torch.true_divide(UpperCAmelCase				,						UpperCAmelCase   )
            if single_image:
              return images[0], sizes[0], scales_yx[0]
            else:
              return images, sizes, scales_yx
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    List[str]   ,     lowercase       :    Tuple				) ->    Optional[Any]:
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Union[str, Any]   ,     lowercase       :    Tuple[int, int]				) ->    Optional[Any]:
    assert torch.isfinite(lowercase				).all(), "Box tensor contains infinite or NaN!"
    __snake_case    ,   __snake_case   :				Optional[Any]        =       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: 
  
  
  
  
  
  import torch
  from torch import autograd
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       inputs.numel()
          __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
          if sigmoid:
            __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
          else:
            __snake_case   :				Optional[int]        =       (inputs > threshold).type(inputs.type()   )
          if mask.sum() < nb_min:
            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
            __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
            __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None, None
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[int]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[int]        =       mask.flatten()
          __snake_case   :				Union[str, Any]        =       0
          __snake_case   :				Any        =       1
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[Any]        =       mask.flatten()
          __snake_case   :				Tuple        =       0
          __snake_case   :				Any        =       1
          return mask
  
  