


'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCamelCase__     						=  logging.get_logger(__name__)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):
			lowerCAmelCase			:						Any							 =  ["pixel_values"]




			def __init__(		self	:  int						,	lowerCamelCase__	:  bool = True						,	lowerCamelCase__	:  Dict[str, int] = None						,	lowerCamelCase__	:  bool = True						,	lowerCamelCase__	:  Dict[str, int] = None						,	lowerCamelCase__	:  PILImageResampling = PILImageResampling.BICUBIC						,	lowerCamelCase__	:  bool = True						,	lowerCamelCase__	:  Union[int, float] = 1 / 2_55						,	lowerCamelCase__	:  bool = True						,	lowerCamelCase__	:  Optional[Union[float, List[float]]] = None						,	lowerCamelCase__	:  Optional[Union[float, List[float]]] = None						,	**lowerCamelCase__	:  Any						,	) ->None:



					'''simple docstring'''







					super().__init__(**UpperCamelCase_			)
					_UpperCAmelCase		:							Optional[Any]             =   crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
					_UpperCAmelCase		:							List[str]             =   get_size_dict(UpperCamelCase_						,	param_name="crop_size"			)
					_UpperCAmelCase		:							str             =   size if size is not None else {'height': 2_24, 'width': 2_24}
					_UpperCAmelCase		:							List[Any]             =   get_size_dict(UpperCamelCase_			)

					_UpperCAmelCase		:							Tuple             =   do_center_crop
					_UpperCAmelCase		:							Tuple             =   crop_size
					_UpperCAmelCase		:							int             =   do_resize
					_UpperCAmelCase		:							str             =   size
					_UpperCAmelCase		:							List[str]             =   resample
					_UpperCAmelCase		:							Optional[int]             =   do_rescale
					_UpperCAmelCase		:							Tuple             =   rescale_factor
					_UpperCAmelCase		:							Dict             =   do_normalize
					_UpperCAmelCase		:							Union[str, Any]             =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					_UpperCAmelCase		:							List[Any]             =   image_std if image_std is not None else IMAGENET_DEFAULT_STD




			def 				lowerCAmelCase__     (		self	:  str						,	lowerCamelCase__	:  np.ndarray						,	lowerCamelCase__	:  Dict[str, int]						,	lowerCamelCase__	:  Optional[int] = None						,	lowerCamelCase__	:  Optional[Union[str, ChannelDimension]] = None						,	**lowerCamelCase__	:  Any						,	) ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   self.size if size is None else size
					_UpperCAmelCase		:							List[Any]             =   get_size_dict(UpperCamelCase_			)
					_UpperCAmelCase		:							List[Any]             =   get_size_dict(UpperCamelCase_						,	param_name="crop_size"			)

					_UpperCAmelCase		:							str             =   get_image_size(UpperCamelCase_			)
					_UpperCAmelCase		:							List[Any]             =   min(UpperCamelCase_						,	UpperCamelCase_			)
					_UpperCAmelCase		:							Union[str, Any]             =   (size['height'] / crop_size['height']) * min_dim
					_UpperCAmelCase		:							List[Any]             =   (size['width'] / crop_size['width']) * min_dim
					return center_crop(UpperCamelCase_						,	size=(cropped_height, cropped_width)						,	data_format=UpperCamelCase_						,	**UpperCamelCase_			)




			def 				lowerCAmelCase__     (		self	:  Any						,	lowerCamelCase__	:  np.ndarray						,	lowerCamelCase__	:  Dict[str, int]						,	lowerCamelCase__	:  PILImageResampling = PIL.Image.BICUBIC						,	lowerCamelCase__	:  Optional[Union[str, ChannelDimension]] = None						,	**lowerCamelCase__	:  Tuple						,	) ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   get_size_dict(UpperCamelCase_			)
					if "height" not in size or "width" not in size:
							raise ValueError(F"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}"""			)
					return resize(
					    UpperCamelCase_						,	size=(size["height"], size["width"])						,	resample=UpperCamelCase_						,	data_format=UpperCamelCase_						,	**UpperCamelCase_			)




			def 				lowerCAmelCase__     (		self	:  Optional[Any]						,	lowerCamelCase__	:  np.ndarray						,	lowerCamelCase__	:  Union[int, float]						,	lowerCamelCase__	:  Optional[Union[str, ChannelDimension]] = None						,	**lowerCamelCase__	:  Optional[Any]						,	) ->List[Any]:



					'''simple docstring'''







					return rescale(UpperCamelCase_						,	scale=UpperCamelCase_						,	data_format=UpperCamelCase_						,	**UpperCamelCase_			)




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  np.ndarray						,	lowerCamelCase__	:  Union[float, List[float]]						,	lowerCamelCase__	:  Union[float, List[float]]						,	lowerCamelCase__	:  Optional[Union[str, ChannelDimension]] = None						,	**lowerCamelCase__	:  Optional[int]						,	) ->np.ndarray:



					'''simple docstring'''







					return normalize(UpperCamelCase_						,	mean=UpperCamelCase_						,	std=UpperCamelCase_						,	data_format=UpperCamelCase_						,	**UpperCamelCase_			)






			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  ImageInput						,	lowerCamelCase__	:  Optional[bool] = None						,	lowerCamelCase__	:  Optional[Dict[str, int]] = None						,	lowerCamelCase__	:  Optional[bool] = None						,	lowerCamelCase__	:  Optional[Dict[str, int]] = None						,	lowerCamelCase__	:  PILImageResampling = None						,	lowerCamelCase__	:  Optional[bool] = None						,	lowerCamelCase__	:  Optional[float] = None						,	lowerCamelCase__	:  Optional[bool] = None						,	lowerCamelCase__	:  Optional[Union[float, List[float]]] = None						,	lowerCamelCase__	:  Optional[Union[float, List[float]]] = None						,	lowerCamelCase__	:  Optional[Union[str, TensorType]] = None						,	lowerCamelCase__	:  ChannelDimension = ChannelDimension.FIRST						,	**lowerCamelCase__	:  Any						,	) ->PIL.Image.Image:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   do_center_crop if do_center_crop is not None else self.do_center_crop
					_UpperCAmelCase		:							Optional[int]             =   crop_size if crop_size is not None else self.crop_size
					_UpperCAmelCase		:							Dict             =   get_size_dict(UpperCamelCase_						,	param_name="crop_size"			)
					_UpperCAmelCase		:							str             =   do_resize if do_resize is not None else self.do_resize
					_UpperCAmelCase		:							int             =   size if size is not None else self.size
					_UpperCAmelCase		:							List[str]             =   get_size_dict(UpperCamelCase_			)
					_UpperCAmelCase		:							Tuple             =   resample if resample is not None else self.resample
					_UpperCAmelCase		:							Tuple             =   do_rescale if do_rescale is not None else self.do_rescale
					_UpperCAmelCase		:							Tuple             =   rescale_factor if rescale_factor is not None else self.rescale_factor
					_UpperCAmelCase		:							int             =   do_normalize if do_normalize is not None else self.do_normalize
					_UpperCAmelCase		:							Union[str, Any]             =   image_mean if image_mean is not None else self.image_mean
					_UpperCAmelCase		:							Tuple             =   image_std if image_std is not None else self.image_std

					_UpperCAmelCase		:							List[Any]             =   make_list_of_images(UpperCamelCase_			)

					if not valid_images(UpperCamelCase_			):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray."			)

					if do_center_crop and crop_size is None:
							raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."			)

					if do_resize and size is None:
							raise ValueError("Size must be specified if do_resize is True."			)

					if do_rescale and rescale_factor is None:
							raise ValueError("Rescale factor must be specified if do_rescale is True."			)

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."			)

					# All transformations expect numpy arrays.
					_UpperCAmelCase		:							Any             =   [to_numpy_array(UpperCamelCase_			) for image in images]

					if do_center_crop:
							_UpperCAmelCase		:							Optional[Any]             =   [self.center_crop(UpperCamelCase_						,	UpperCamelCase_						,	size=UpperCamelCase_			) for image in images]

					if do_resize:
							_UpperCAmelCase		:							Tuple             =   [self.resize(image=UpperCamelCase_						,	size=UpperCamelCase_						,	resample=UpperCamelCase_			) for image in images]

					if do_rescale:
							_UpperCAmelCase		:							List[Any]             =   [self.rescale(image=UpperCamelCase_						,	scale=UpperCamelCase_			) for image in images]

					if do_normalize:
							_UpperCAmelCase		:							Any             =   [self.normalize(image=UpperCamelCase_						,	mean=UpperCamelCase_						,	std=UpperCamelCase_			) for image in images]

					_UpperCAmelCase		:							Any             =   [to_channel_dimension_format(UpperCamelCase_						,	UpperCamelCase_			) for image in images]

					_UpperCAmelCase		:							Optional[Any]             =   {'pixel_values': images}
					return BatchFeature(data=UpperCamelCase_						,	tensor_type=UpperCamelCase_			)









import os
import sys

from . import rsa_key_generator as rkg

UpperCAmelCase__    :							str       		=     128
UpperCAmelCase__    :							int       		=     256


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			List[str]      =     message.encode('ascii'				)

  _lowercase:			str      =     []
  for block_start in range(0       ,  len(UpperCamelCase				)       ,  UpperCamelCase				):
    _lowercase:			Optional[int]      =     0
    for i in range(UpperCamelCase       ,  min(block_start + block_size       ,  len(UpperCamelCase				)				)				):
      block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
    block_ints.append(UpperCamelCase				)
  return block_ints


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		str:





  """simple docstring"""





  _lowercase:			list[str]      =     []
  for block_int in block_ints:
    _lowercase:			list[str]      =     []
    for i in range(block_size - 1       ,  -1       ,  -1				):
      if len(UpperCamelCase				) + i < message_length:
        _lowercase:			Optional[Any]      =     block_int // (BYTE_SIZE**i)
        _lowercase:			Dict      =     block_int % (BYTE_SIZE**i)
        block_message.insert(0       ,  chr(UpperCamelCase				)				)
    message.extend(UpperCamelCase				)
  return "".join(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key

  for block in get_blocks_from_text(UpperCamelCase       ,  UpperCamelCase				):
    encrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return encrypted_blocks


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key
  for block in encrypted_blocks:
    decrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return get_text_from_blocks(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		tuple[int, int, int]:





  """simple docstring"""





  with open(UpperCamelCase				) as fo:
    _lowercase:			Any      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Optional[Any]      =     content.split(','				)
  return (int(UpperCamelCase				), int(UpperCamelCase				), int(UpperCamelCase				))


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			int      =     read_key_file(UpperCamelCase				)
  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       ,  UpperCamelCase				)				)

  _lowercase:			int      =     [str(UpperCamelCase				) for i in encrypt_message(UpperCamelCase       ,  (n, e)       ,  UpperCamelCase				)]

  _lowercase:			List[Any]      =     ','.join(UpperCamelCase				)
  _lowercase:			Tuple      =     F'{len(UpperCamelCase				)}_{block_size}_{encrypted_content}'
  with open(UpperCamelCase       ,  'w'				) as fo:
    fo.write(UpperCamelCase				)
  return encrypted_content


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			Tuple      =     read_key_file(UpperCamelCase				)
  with open(UpperCamelCase				) as fo:
    _lowercase:			Optional[Any]      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Union[str, Any]      =     content.split('_'				)
  _lowercase:			List[Any]      =     int(UpperCamelCase				)
  _lowercase:			Dict      =     int(UpperCamelCase				)

  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       ,  UpperCamelCase				)				)

  _lowercase:			Union[str, Any]      =     []
  for block in encrypted_message.split(','				):
    encrypted_blocks.append(int(UpperCamelCase				)				)

  return decrypt_message(UpperCamelCase       ,  UpperCamelCase       ,  (n, d)       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





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

  if response.lower().startswith('e'				):
    _lowercase:			Optional[Any]      =     'encrypt'
  elif response.lower().startswith('d'				):
    _lowercase:			Union[str, Any]      =     'decrypt'

  if mode == "encrypt":
    if not os.path.exists('rsa_pubkey.txt'				):
      rkg.make_key_files('rsa'       ,  1_024				)

    _lowercase:			Union[str, Any]      =     input('\nEnter message: '				)
    _lowercase:			Tuple      =     'rsa_pubkey.txt'
    print(F'Encrypting and writing to {filename}...'				)
    _lowercase:			Optional[Any]      =     encrypt_and_write_to_file(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

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

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

    print('\nDecryption:'				)
    print(UpperCamelCase				)


if __name__ == "__main__":
   main()
