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 			a      :
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=sys.maxsize							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				='''bilinear'''
  						A__     :							Dict   				=max_size
  						A__     :							Tuple   				=short_edge_length
  
  
  
  
  
  
  		def __call__(  self	,     __UpperCamelCase							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=[]
  						for img in imgs:
  										A__				,  A__     :							str   				=img.shape[:2]
  										# later: provide list and randomly choose index for resize
  										A__     :							Any   				=np.random.randint(self.short_edge_length[0]	,     self.short_edge_length[1] + 1							)
  										if size == 0:
  														return img
  										A__     :							Optional[int]   				=size * 1.0 / min(__UpperCamelCase	,     __UpperCamelCase							)
  										if h < w:
  														A__				,  A__     :							Any   				=size, scale * w
  										else:
  														A__				,  A__     :							Dict   				=scale * h, size
  										if max(__UpperCamelCase	,     __UpperCamelCase							) > self.max_size:
  														A__     :							Any   				=self.max_size * 1.0 / max(__UpperCamelCase	,     __UpperCamelCase							)
  														A__     :							int   				=newh * scale
  														A__     :							str   				=neww * scale
  										A__     :							int   				=int(neww + 0.5							)
  										A__     :							List[str]   				=int(newh + 0.5							)
  
  										if img.dtype == np.uinta:
  														A__     :							Optional[Any]   				=Image.fromarray(__UpperCamelCase							)
  														A__     :							Optional[int]   				=pil_image.resize((neww, newh)	,     PILImageResampling.BILINEAR							)
  														A__     :							List[str]   				=np.asarray(__UpperCamelCase							)
  										else:
  														A__     :							List[Any]   				=img.permute(2	,     0	,     1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
  														A__     :							List[Any]   				=nn.functional.interpolate(
  														    __UpperCamelCase	,     (newh, neww)	,     mode=self.interp_method	,     align_corners=__UpperCamelCase							).squeeze(0							)
  										img_augs.append(__UpperCamelCase							)
  
  						return img_augs
  
  
  
  
  
  
  
  class 			a      :
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							int   				=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,     cfg.INPUT.MAX_SIZE_TEST							)
  						A__     :							Optional[int]   				=cfg.INPUT.FORMAT
  						A__     :							List[Any]   				=cfg.SIZE_DIVISIBILITY
  						A__     :							Tuple   				=cfg.PAD_VALUE
  						A__     :							str   				=cfg.INPUT.MAX_SIZE_TEST
  						A__     :							List[str]   				=cfg.MODEL.DEVICE
  						A__     :							Union[str, Any]   				=torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)	,     1	,     1							)
  						A__     :							Dict   				=torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)	,     1	,     1							)
  						A__     :							Optional[Any]   				=lambda __UpperCamelCase							: (x - self.pixel_mean) / self.pixel_std
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=tuple(max(__UpperCamelCase							) for s in zip(*[img.shape for img in images]							)							)
  						A__     :							int   				=[im.shape[-2:] for im in images]
  						A__     :							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							)->			str:
  						'''simple docstring'''
  
  
  						with torch.no_grad():
  										if not isinstance(__UpperCamelCase	,     __UpperCamelCase							):
  														A__     :							Optional[int]   				=[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
  										A__     :							Dict   				=torch.tensor([im.shape[:2] for im in images]							)
  										A__     :							Union[str, Any]   				=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
  										A__     :							str   				=[self.normalizer(__UpperCamelCase							) for x in images]
  										# now pad them to do the following operations
  										A__				,  A__     :							Any   				=self.pad(__UpperCamelCase							)
  										# Normalize
  
  										if self.size_divisibility > 0:
  														raise NotImplementedError()
  										# pad
  										A__     :							Any   				=torch.true_divide(__UpperCamelCase	,     __UpperCamelCase							)
  										if single_image:
  														return images[0], sizes[0], scales_yx[0]
  										else:
  														return images, sizes, scales_yx
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							List[Any]:
  				boxes[:, 0::2] *= scale_yx[:, 1]
  				boxes[:, 1::2] *= scale_yx[:, 0]
  				return boxes
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							int:
  				assert torch.isfinite(snake_case_	).all(), "Box tensor contains infinite or NaN!"
  				A__				,  A__     :							Union[str, Any]   				=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: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				barcode //= 1_0  # exclude the last digit
  				A__     :							List[str]   				=False
  				A__     :							Dict   				=0
  
  				# extract and check each digit
  				while barcode != 0:
  								A__     :							Union[str, Any]   				=1 if checker else 3
  								s += mult * (barcode % 1_0)
  								barcode //= 1_0
  								A__     :							Union[str, Any]   				=not checker
  
  				return (1_0 - (s % 1_0)) % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							bool:
  				return len(str(snake_case_	)	) == 1_3 and get_check_digit(snake_case_	) == barcode % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if str(snake_case_	).isalpha():
  								A__     :							Tuple   				=f'Barcode \'{barcode}\' has alphabetic characters.'
  								raise ValueError(snake_case_	)
  				elif int(snake_case_	) < 0:
  								raise ValueError('''The entered barcode has a negative value. Try again.'''	)
  				else:
  								return int(snake_case_	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  						__lowerCamelCase       :       List[Any]				   =			get_barcode(input("Barcode: ").strip())
  
  						if is_valid(barcode):
  												print(F"'{barcode}' is a valid barcode.")
  						else:
  												print(F"'{barcode}' is NOT a valid barcode.")
  
  
  
  