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 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Tuple   ,       snake_case__:	Optional[int]   ,       snake_case__:	List[str]=sys.maxsize	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'bilinear'
         _lowercase     =	max_size
         _lowercase     =	short_edge_length
  
     def __call__(    self:	List[Any]   ,       snake_case__:	Optional[Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	[]
         for img in imgs:
             _lowercase ,    _lowercase     =	img.shape[:2]
             # later: provide list and randomly choose index for resize
             _lowercase     =	np.random.randint(self.short_edge_length[0]   ,       self.short_edge_length[1] + 1	)
             if size == 0:
                 return img
             _lowercase     =	size * 1.0 / min(snake_case__   ,       snake_case__	)
             if h < w:
                 _lowercase ,    _lowercase     =	size, scale * w
             else:
                 _lowercase ,    _lowercase     =	scale * h, size
             if max(snake_case__   ,       snake_case__	) > self.max_size:
                 _lowercase     =	self.max_size * 1.0 / max(snake_case__   ,       snake_case__	)
                 _lowercase     =	newh * scale
                 _lowercase     =	neww * scale
             _lowercase     =	int(neww + 0.5	)
             _lowercase     =	int(newh + 0.5	)
  
             if img.dtype == np.uinta:
                 _lowercase     =	Image.fromarray(snake_case__	)
                 _lowercase     =	pil_image.resize((neww, newh)   ,       PILImageResampling.BILINEAR	)
                 _lowercase     =	np.asarray(snake_case__	)
             else:
                 _lowercase     =	img.permute(2   ,       0   ,       1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
                 _lowercase     =	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 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	List[Any]   ,       snake_case__:	int	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]   ,       cfg.INPUT.MAX_SIZE_TEST	)
         _lowercase     =	cfg.INPUT.FORMAT
         _lowercase     =	cfg.SIZE_DIVISIBILITY
         _lowercase     =	cfg.PAD_VALUE
         _lowercase     =	cfg.INPUT.MAX_SIZE_TEST
         _lowercase     =	cfg.MODEL.DEVICE
         _lowercase     =	torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)   ,       1   ,       1	)
         _lowercase     =	torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)   ,       1   ,       1	)
         _lowercase     =	lambda snake_case__	: (x - self.pixel_mean) / self.pixel_std
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]   ,       snake_case__:	Dict	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	tuple(max(snake_case__	) for s in zip(*[img.shape for img in images]	)	)
         _lowercase     =	[im.shape[-2:] for im in images]
         _lowercase     =	[
             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:	Union[str, Any]   ,       snake_case__:	List[Any]   ,       snake_case__:	List[Any]=False	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         with torch.no_grad():
             if not isinstance(snake_case__   ,       snake_case__	):
                 _lowercase     =	[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
             _lowercase     =	torch.tensor([im.shape[:2] for im in images]	)
             _lowercase     =	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
             _lowercase     =	[self.normalizer(snake_case__	) for x in images]
             # now pad them to do the following operations
             _lowercase ,    _lowercase     =	self.pad(snake_case__	)
             # Normalize
  
             if self.size_divisibility > 0:
                 raise NotImplementedError()
             # pad
             _lowercase     =	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        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      assert torch.isfinite(_lowercase      ).all(), "Box tensor contains infinite or NaN!"
      _lowercase ,    _lowercase     =	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: 
  
  
  
  
  
  
  from typing import List, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
      # See all LED models at https://huggingface.co/models?filter=led
  }
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""led"""
     __lowerCAmelCase      =						{
         """num_attention_heads""": """encoder_attention_heads""",
         """hidden_size""": """d_model""",
         """attention_probs_dropout_prob""": """attention_dropout""",
         """initializer_range""": """init_std""",
     }
  
     def __init__(    self:	int   ,       snake_case__:	str=50_265   ,       snake_case__:	Dict=16_384   ,       snake_case__:	str=1_024   ,       snake_case__:	Tuple=12   ,       snake_case__:	int=4_096   ,       snake_case__:	Union[str, Any]=16   ,       snake_case__:	int=12   ,       snake_case__:	Union[str, Any]=4_096   ,       snake_case__:	Optional[Any]=16   ,       snake_case__:	str=0.0   ,       snake_case__:	Optional[Any]=0.0   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]="gelu"   ,       snake_case__:	Optional[int]=1_024   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	int=0.0   ,       snake_case__:	List[str]=0.0   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=2   ,       snake_case__:	int=0.0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	Tuple=0   ,       snake_case__:	Union[str, Any]=2   ,       snake_case__:	Union[List[int], int] = 512   ,       **snake_case__:	int   ,       )  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	vocab_size
         _lowercase     =	max_encoder_position_embeddings
         _lowercase     =	max_decoder_position_embeddings
         _lowercase     =	d_model
         _lowercase     =	encoder_ffn_dim
         _lowercase     =	encoder_layers
         _lowercase     =	encoder_attention_heads
         _lowercase     =	decoder_ffn_dim
         _lowercase     =	decoder_layers
         _lowercase     =	decoder_attention_heads
         _lowercase     =	dropout
         _lowercase     =	attention_dropout
         _lowercase     =	activation_dropout
         _lowercase     =	activation_function
         _lowercase     =	init_std
         _lowercase     =	encoder_layerdrop
         _lowercase     =	decoder_layerdrop
         _lowercase     =	classifier_dropout
         _lowercase     =	use_cache
         _lowercase     =	encoder_layers
         _lowercase     =	attention_window
  
         super().__init__(
             pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__   ,       is_encoder_decoder=snake_case__   ,       decoder_start_token_id=snake_case__   ,       **snake_case__   ,       )
  