

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ConvNextImageProcessor





class 			__lowerCAmelCase					( unittest.TestCase							):




  def __init__(    self,       lowerCAmelCase,       lowerCAmelCase=7,       lowerCAmelCase=3,       lowerCAmelCase=18,       lowerCAmelCase=30,       lowerCAmelCase=400,       lowerCAmelCase=True,       lowerCAmelCase=None,       lowerCAmelCase=0.875,       lowerCAmelCase=True,       lowerCAmelCase=[0.5, 0.5, 0.5],       lowerCAmelCase=[0.5, 0.5, 0.5],       )  ->			int:
        '''simple docstring'''






        _lowercase     	=size if size is not None else {"""shortest_edge""": 20}
        _lowercase     	=parent
        _lowercase     	=batch_size
        _lowercase     	=num_channels
        _lowercase     	=image_size
        _lowercase     	=min_resolution
        _lowercase     	=max_resolution
        _lowercase     	=do_resize
        _lowercase     	=size
        _lowercase     	=crop_pct
        _lowercase     	=do_normalize
        _lowercase     	=image_mean
        _lowercase     	=image_std




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_pct": self.crop_pct,
        }





@require_torch
@require_vision
class 			__lowerCAmelCase					( _a      ,      unittest.TestCase							):
  _a						      =  ConvNextImageProcessor if is_vision_available() else None




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=ConvNextImageProcessingTester(self )




  @property
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(snake_case_,       'do_resize' ) )
        self.assertTrue(hasattr(snake_case_,       'size' ) )
        self.assertTrue(hasattr(snake_case_,       'crop_pct' ) )
        self.assertTrue(hasattr(snake_case_,       'do_normalize' ) )
        self.assertTrue(hasattr(snake_case_,       'image_mean' ) )
        self.assertTrue(hasattr(snake_case_,       'image_std' ) )




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class.from_dict(self.image_processor_dict )
        self.assertEqual(image_processor.size,       {'shortest_edge': 20} )

        _lowercase     	=self.image_processing_class.from_dict(self.image_processor_dict,       size=42 )
        self.assertEqual(image_processor.size,       {'shortest_edge': 42} )




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        pass




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        _lowercase     	=prepare_image_inputs(self.image_processor_tester,       equal_resolution=snake_case_ )
        for image in image_inputs:
              self.assertIsInstance(snake_case_,       Image.Image )

        # Test not batched input
        _lowercase     	=image_processing(image_inputs[0],       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape,       (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),       )

        # Test batched
        _lowercase     	=image_processing(snake_case_,       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),       )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        _lowercase     	=prepare_image_inputs(self.image_processor_tester,       equal_resolution=snake_case_,       numpify=snake_case_ )
        for image in image_inputs:
              self.assertIsInstance(snake_case_,       np.ndarray )

        # Test not batched input
        _lowercase     	=image_processing(image_inputs[0],       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape,       (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),       )

        # Test batched
        _lowercase     	=image_processing(snake_case_,       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),       )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        _lowercase     	=prepare_image_inputs(self.image_processor_tester,       equal_resolution=snake_case_,       torchify=snake_case_ )
        for image in image_inputs:
              self.assertIsInstance(snake_case_,       torch.Tensor )

        # Test not batched input
        _lowercase     	=image_processing(image_inputs[0],       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape,       (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),       )

        # Test batched
        _lowercase     	=image_processing(snake_case_,       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),       )





import os
import sys

from . import rsa_key_generator as rkg

lowercase_     =       1_2_8
lowercase_     =       2_5_6
def 					a				(      A__     :       str				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				list[int]:





      """simple docstring"""



      _lowercase     	=message.encode('ascii'					)

      _lowercase     	=[]
      for block_start in range(0				,       len(A__					)				,       A__					):
            _lowercase     	=0
            for i in range(A__				,       min(block_start + block_size				,       len(A__					)					)					):
                  block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
            block_ints.append(A__					)
      return block_ints
def 					a				(      A__     :       list[int]				,       A__     :       int				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				str:





      """simple docstring"""



      _lowercase     	=[]
      for block_int in block_ints:
            _lowercase     	=[]
            for i in range(block_size - 1				,       -1				,       -1					):
                  if len(A__					) + i < message_length:
                        _lowercase     	=block_int // (BYTE_SIZE**i)
                        _lowercase     	=block_int % (BYTE_SIZE**i)
                        block_message.insert(0				,       chr(A__					)					)
            message.extend(A__					)
      return "".join(A__					)
def 					a				(      A__     :       str				,       A__     :       tuple[int, int]				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				list[int]:





      """simple docstring"""



      _lowercase     	=[]
      _lowercase	,	_lowercase     	=key

      for block in get_blocks_from_text(A__				,       A__					):
            encrypted_blocks.append(pow(A__				,       A__				,       A__					)					)
      return encrypted_blocks
def 					a				(      A__     :       list[int]				,       A__     :       int				,       A__     :       tuple[int, int]				,       A__     :       int = DEFAULT_BLOCK_SIZE				,       )   ->				str:





      """simple docstring"""



      _lowercase     	=[]
      _lowercase	,	_lowercase     	=key
      for block in encrypted_blocks:
            decrypted_blocks.append(pow(A__				,       A__				,       A__					)					)
      return get_text_from_blocks(A__				,       A__				,       A__					)
def 					a				(      A__     :       str					)   ->				tuple[int, int, int]:





      """simple docstring"""



      with open(A__					) as fo:
            _lowercase     	=fo.read()
      _lowercase	,	_lowercase	,	_lowercase     	=content.split(','					)
      return (int(A__					), int(A__					), int(A__					))
def 					a				(      A__     :       str				,       A__     :       str				,       A__     :       str				,       A__     :       int = DEFAULT_BLOCK_SIZE				,       )   ->				str:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=read_key_file(A__					)
      if key_size < block_size * 8:
            sys.exit(
                'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
                'requires the block size to be equal to or greater than the key size. '
                'Either decrease the block size or use different keys.'.format(
                    block_size * 8				,       A__					)					)

      _lowercase     	=[str(A__					) for i in encrypt_message(A__				,       (n, e)				,       A__					)]

      _lowercase     	=','.join(A__					)
      _lowercase     	=F'''{len(A__					)}_{block_size}_{encrypted_content}'''
      with open(A__				,       'w'					) as fo:
            fo.write(A__					)
      return encrypted_content
def 					a				(      A__     :       str				,       A__     :       str					)   ->				str:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=read_key_file(A__					)
      with open(A__					) as fo:
            _lowercase     	=fo.read()
      _lowercase	,	_lowercase	,	_lowercase     	=content.split('_'					)
      _lowercase     	=int(A__					)
      _lowercase     	=int(A__					)

      if key_size < block_size * 8:
            sys.exit(
                'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
                'requires the block size to be equal to or greater than the key size. '
                'Did you specify the correct key file and encrypted file?'.format(
                    block_size * 8				,       A__					)					)

      _lowercase     	=[]
      for block in encrypted_message.split(','					):
            encrypted_blocks.append(int(A__					)					)

      return decrypt_message(A__				,       A__				,       (n, d)				,       A__					)
def 					a				(      )   ->				None:





      """simple docstring"""



      _lowercase     	='encrypted_file.txt'
      _lowercase     	=input(r'Encrypt\Decrypt [e\d]: '					)

      if response.lower().startswith('e'					):
            _lowercase     	='encrypt'
      elif response.lower().startswith('d'					):
            _lowercase     	='decrypt'

      if mode == "encrypt":
            if not os.path.exists('rsa_pubkey.txt'					):
                  rkg.make_key_files('rsa'				,       1024					)

            _lowercase     	=input('\nEnter message: '					)
            _lowercase     	='rsa_pubkey.txt'
            print(F'''Encrypting and writing to {filename}...'''					)
            _lowercase     	=encrypt_and_write_to_file(A__				,       A__				,       A__					)

            print('\nEncrypted text:'					)
            print(A__					)

      elif mode == "decrypt":
            _lowercase     	='rsa_privkey.txt'
            print(F'''Reading from {filename} and decrypting...'''					)
            _lowercase     	=read_from_file_and_decrypt(A__				,       A__					)
            print('writing decryption to rsa_decryption.txt...'					)
            with open('rsa_decryption.txt'				,       'w'					) as dec:
                  dec.write(A__					)

            print('\nDecryption:'					)
            print(A__					)


if __name__ == "__main__":
  main()


