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__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Union[str, Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  List[str]=sys.maxsize   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						'bilinear'
             __magic_name__										=						max_size
             __magic_name__										=						short_edge_length
  
  
  
  
  
  
  
      def __call__(		self   :  str							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[]
             for img in imgs:
                    __magic_name__							,     __magic_name__										=						img.shape[:2]
                    # later: provide list and randomly choose index for resize
                    __magic_name__										=						np.random.randint(self.short_edge_length[0]							,						self.short_edge_length[1] + 1   )
                    if size == 0:
                           return img
                    __magic_name__										=						size * 1.0 / min(UpperCamelCase_							,						UpperCamelCase_   )
                    if h < w:
                           __magic_name__							,     __magic_name__										=						size, scale * w
                    else:
                           __magic_name__							,     __magic_name__										=						scale * h, size
                    if max(UpperCamelCase_							,						UpperCamelCase_   ) > self.max_size:
                           __magic_name__										=						self.max_size * 1.0 / max(UpperCamelCase_							,						UpperCamelCase_   )
                           __magic_name__										=						newh * scale
                           __magic_name__										=						neww * scale
                    __magic_name__										=						int(neww + 0.5   )
                    __magic_name__										=						int(newh + 0.5   )
  
                    if img.dtype == np.uinta:
                           __magic_name__										=						Image.fromarray(UpperCamelCase_   )
                           __magic_name__										=						pil_image.resize((neww, newh)							,						PILImageResampling.BILINEAR   )
                           __magic_name__										=						np.asarray(UpperCamelCase_   )
                    else:
                           __magic_name__										=						img.permute(2							,						0							,						1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
                           __magic_name__										=						nn.functional.interpolate(
                               UpperCamelCase_							,						(newh, neww)							,						mode=self.interp_method							,						align_corners=UpperCamelCase_   ).squeeze(0   )
                    img_augs.append(UpperCamelCase_   )
  
             return img_augs
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Any							,						UpperCamelCase_   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							,						cfg.INPUT.MAX_SIZE_TEST   )
             __magic_name__										=						cfg.INPUT.FORMAT
             __magic_name__										=						cfg.SIZE_DIVISIBILITY
             __magic_name__										=						cfg.PAD_VALUE
             __magic_name__										=						cfg.INPUT.MAX_SIZE_TEST
             __magic_name__										=						cfg.MODEL.DEVICE
             __magic_name__										=						torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )							,						1							,						1   )
             __magic_name__										=						torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )							,						1							,						1   )
             __magic_name__										=						lambda UpperCamelCase_   : (x - self.pixel_mean) / self.pixel_std
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						tuple(max(UpperCamelCase_   ) for s in zip(*[img.shape for img in images]   )   )
             __magic_name__										=						[im.shape[-2:] for im in images]
             __magic_name__										=						[
                 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   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Optional[Any]=False   ):
  
  
  
  
  
             '''simple docstring'''
  
             with torch.no_grad():
                    if not isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
                           __magic_name__										=						[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
                    __magic_name__										=						torch.tensor([im.shape[:2] for im in images]   )
                    __magic_name__										=						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
                    __magic_name__										=						[self.normalizer(UpperCamelCase_   ) for x in images]
                    # now pad them to do the following operations
                    __magic_name__							,     __magic_name__										=						self.pad(UpperCamelCase_   )
                    # Normalize
  
                    if self.size_divisibility > 0:
                           raise NotImplementedError()
                    # pad
                    __magic_name__										=						torch.true_divide(UpperCamelCase_							,						UpperCamelCase_   )
                    if single_image:
                           return images[0], sizes[0], scales_yx[0]
                    else:
                           return images, sizes, scales_yx
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         boxes[:, 0::2] *= scale_yx[:, 1]
         boxes[:, 1::2] *= scale_yx[:, 0]
         return boxes
  
  
  
  
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   Tuple[int, int]     )						->		Optional[Any]:
  
         """simple docstring"""
  
  
  
  
  
         assert torch.isfinite(__snake_case     ).all(), "Box tensor contains infinite or NaN!"
         __magic_name__							,     __magic_name__										=						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: 
  
  
  
  
  
  
  """simple docstring"""
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case   :   int    		=		logging.get_logger(__name__)
  
  snake_case   :   List[str]    		=		{
      """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
      # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """nystromformer"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						hidden_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_act
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						initializer_range
             __magic_name__										=						type_vocab_size
             __magic_name__										=						segment_means_seq_len
             __magic_name__										=						num_landmarks
             __magic_name__										=						conv_kernel_size
             __magic_name__										=						inv_coeff_init_option
             __magic_name__										=						layer_norm_eps
             super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )