





"""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    lowercase_ :

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=sys.maxsize   ):






        '''simple docstring'''

        snake_case__										= "bilinear"
        snake_case__										= max_size
        snake_case__										= short_edge_length

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= []
        for img in imgs:
         snake_case__							,      snake_case__										= img.shape[:2]
         # later: provide list and randomly choose index for resize
         snake_case__										= np.random.randint(self.short_edge_length[0]      ,    self.short_edge_length[1] + 1   )
         if size == 0:
          return img
         snake_case__										= size * 1.0 / min(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
         if h < w:
          snake_case__							,      snake_case__										= size, scale * w
         else:
          snake_case__							,      snake_case__										= scale * h, size
         if max(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) > self.max_size:
          snake_case__										= self.max_size * 1.0 / max(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
          snake_case__										= newh * scale
          snake_case__										= neww * scale
         snake_case__										= int(neww + 0.5   )
         snake_case__										= int(newh + 0.5   )

         if img.dtype == np.uinta:
          snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE   )
          snake_case__										= pil_image.resize((neww, newh)      ,    PILImageResampling.BILINEAR   )
          snake_case__										= np.asarray(__SCREAMING_SNAKE_CASE   )
         else:
          snake_case__										= img.permute(2      ,    0      ,    1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
          snake_case__										= nn.functional.interpolate(
              __SCREAMING_SNAKE_CASE      ,    (newh, neww)      ,    mode=self.interp_method      ,    align_corners=__SCREAMING_SNAKE_CASE   ).squeeze(0   )
         img_augs.append(__SCREAMING_SNAKE_CASE   )

        return img_augs



class    lowercase_ :

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,    cfg.INPUT.MAX_SIZE_TEST   )
        snake_case__										= cfg.INPUT.FORMAT
        snake_case__										= cfg.SIZE_DIVISIBILITY
        snake_case__										= cfg.PAD_VALUE
        snake_case__										= cfg.INPUT.MAX_SIZE_TEST
        snake_case__										= cfg.MODEL.DEVICE
        snake_case__										= torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )      ,    1      ,    1   )
        snake_case__										= torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )      ,    1      ,    1   )
        snake_case__										= lambda __SCREAMING_SNAKE_CASE   : (x - self.pixel_mean) / self.pixel_std

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= tuple(max(__SCREAMING_SNAKE_CASE   ) for s in zip(*[img.shape for img in images]   )   )
        snake_case__										= [im.shape[-2:] for im in images]
        snake_case__										= [
            nn.functional.pad(
                __SCREAMING_SNAKE_CASE      ,    [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,    value=self.pad_value      ,    )
            for size, im in zip(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        ]

        return torch.stack(__SCREAMING_SNAKE_CASE   ), torch.tensor(__SCREAMING_SNAKE_CASE   )

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=False   ):






        '''simple docstring'''

        with torch.no_grad():
         if not isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= [images]
         if single_image:
          assert len(__SCREAMING_SNAKE_CASE   ) == 1
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
          if isinstance(images[i]      ,    torch.Tensor   ):
           images.insert(__SCREAMING_SNAKE_CASE      ,    images.pop(__SCREAMING_SNAKE_CASE   ).to(self.device   ).float()   )
          elif not isinstance(images[i]      ,    torch.Tensor   ):
           images.insert(
               __SCREAMING_SNAKE_CASE      ,    torch.as_tensor(img_tensorize(images.pop(__SCREAMING_SNAKE_CASE   )      ,    input_format=self.input_format   )   )
               .to(self.device   )
               .float()      ,    )
            # resize smallest edge
         snake_case__										= torch.tensor([im.shape[:2] for im in images]   )
         snake_case__										= self.aug(__SCREAMING_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
         snake_case__										= [self.normalizer(__SCREAMING_SNAKE_CASE   ) for x in images]
         # now pad them to do the following operations
         snake_case__							,      snake_case__										= self.pad(__SCREAMING_SNAKE_CASE   )
         # Normalize

         if self.size_divisibility > 0:
          raise NotImplementedError()
         # pad
         snake_case__										= torch.true_divide(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
         if single_image:
          return images[0], sizes[0], scales_yx[0]
         else:
          return images, sizes, scales_yx





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     int:

 '''simple docstring'''






 assert torch.isfinite(_UpperCAmelCase		).all(), "Box tensor contains infinite or NaN!"
 snake_case__							,      snake_case__										= box_size
 tensor[:, 0].clamp_(min=0		,   max=_UpperCAmelCase		)
 tensor[:, 1].clamp_(min=0		,   max=_UpperCAmelCase		)
 tensor[:, 2].clamp_(min=0		,   max=_UpperCAmelCase		)
 tensor[:, 3].clamp_(min=0		,   max=_UpperCAmelCase		)








"""simple docstring"""


import random
import sys

from . import cryptomath_module as cryptomath

a__    :   int    						= (
    r""" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"""
    r"""abcdefghijklmnopqrstuvwxyz{|}~"""
)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     None:

 '''simple docstring'''






 if mode == "encrypt":
  if key_a == 1:
   sys.exit(
       "The affine cipher becomes weak when key "
       "A is set to 1. Choose different key"		)
  if key_b == 0:
   sys.exit(
       "The affine cipher becomes weak when key "
       "B is set to 0. Choose different key"		)
 if key_a < 0 or key_b < 0 or key_b > len(_UpperCAmelCase		) - 1:
  sys.exit(
      "Key A must be greater than 0 and key B must "
      f"be between 0 and {len(_UpperCAmelCase		) - 1}."		)
 if cryptomath.gcd(_UpperCAmelCase		,   len(_UpperCAmelCase		)		) != 1:
  sys.exit(
      f"Key A {key_a} and the symbol set size {len(_UpperCAmelCase		)} "
      "are not relatively prime. Choose a different key."		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     str:

 '''simple docstring'''






 snake_case__							,      snake_case__										= divmod(_UpperCAmelCase		,   len(_UpperCAmelCase		)		)
 check_keys(_UpperCAmelCase		,   _UpperCAmelCase		,   "encrypt"		)
 snake_case__										= ""
 for symbol in message:
  if symbol in SYMBOLS:
   snake_case__										= SYMBOLS.find(_UpperCAmelCase		)
   cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_UpperCAmelCase		)]
  else:
   cipher_text += symbol
 return cipher_text





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     str:

 '''simple docstring'''






 snake_case__							,      snake_case__										= divmod(_UpperCAmelCase		,   len(_UpperCAmelCase		)		)
 check_keys(_UpperCAmelCase		,   _UpperCAmelCase		,   "decrypt"		)
 snake_case__										= ""
 snake_case__										= cryptomath.find_mod_inverse(_UpperCAmelCase		,   len(_UpperCAmelCase		)		)
 for symbol in message:
  if symbol in SYMBOLS:
   snake_case__										= SYMBOLS.find(_UpperCAmelCase		)
   plain_text += SYMBOLS[
       (sym_index - key_b) * mod_inverse_of_key_a % len(_UpperCAmelCase		)
   ]
  else:
   plain_text += symbol
 return plain_text





def 							a__	(     )			->     int:

 '''simple docstring'''






 while True:
  snake_case__										= random.randint(2		,   len(_UpperCAmelCase		)		)
  snake_case__										= random.randint(2		,   len(_UpperCAmelCase		)		)
  if cryptomath.gcd(_UpperCAmelCase		,   len(_UpperCAmelCase		)		) == 1 and key_b % len(_UpperCAmelCase		) != 0:
   return key_b * len(_UpperCAmelCase		) + key_b





def 							a__	(     )			->     None:

 '''simple docstring'''






 snake_case__										= input("Enter message: "		).strip()
 snake_case__										= int(input("Enter key [2000 - 9000]: "		).strip()		)
 snake_case__										= input("Encrypt/Decrypt [E/D]: "		).strip().lower()

 if mode.startswith("e"		):
  snake_case__										= "encrypt"
  snake_case__										= encrypt_message(_UpperCAmelCase		,   _UpperCAmelCase		)
 elif mode.startswith("d"		):
  snake_case__										= "decrypt"
  snake_case__										= decrypt_message(_UpperCAmelCase		,   _UpperCAmelCase		)
 print(f"\n{mode.title()}ed text: \n{translated}"		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # main()

