


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_	)







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.")



