


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 							A					( unittest.TestCase							):
					'''simple docstring'''






					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Tuple   ,      _UpperCAmelCase					:		int=7   ,      _UpperCAmelCase					:		List[Any]=3   ,      _UpperCAmelCase					:		Tuple=18   ,      _UpperCAmelCase					:		str=30   ,      _UpperCAmelCase					:		Optional[Any]=400   ,      _UpperCAmelCase					:		List[Any]=True   ,      _UpperCAmelCase					:		Optional[Any]=None   ,      _UpperCAmelCase					:		List[str]=0.875   ,      _UpperCAmelCase					:		List[Any]=True   ,      _UpperCAmelCase					:		Dict=[0.5, 0.5, 0.5]   ,      _UpperCAmelCase					:		Any=[0.5, 0.5, 0.5]   ,      )		->     List[Any]:




							"""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 						lowerCamelCase__  (self					:		Any	)		->     List[str]:




							"""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 							A					( UpperCAmelCase__				,						unittest.TestCase							):
					'''simple docstring'''






					A__    =				ConvNextImageProcessor if is_vision_available() else None

					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Any:




							"""simple docstring"""


							lowercase__      =	ConvNextImageProcessingTester(self	)

					@property
					def 						lowerCamelCase__  (self					:		str	)		->     str:




							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()

					def 						lowerCamelCase__  (self					:		List[Any]	)		->     Dict:




							"""simple docstring"""


							lowercase__      =	self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCAmelCase   ,      """do_resize"""	)	)
							self.assertTrue(hasattr(_UpperCAmelCase   ,      """size"""	)	)
							self.assertTrue(hasattr(_UpperCAmelCase   ,      """crop_pct"""	)	)
							self.assertTrue(hasattr(_UpperCAmelCase   ,      """do_normalize"""	)	)
							self.assertTrue(hasattr(_UpperCAmelCase   ,      """image_mean"""	)	)
							self.assertTrue(hasattr(_UpperCAmelCase   ,      """image_std"""	)	)

					def 						lowerCamelCase__  (self					:		Dict	)		->     Union[str, Any]:




							"""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 						lowerCamelCase__  (self					:		int	)		->     Dict:




							"""simple docstring"""


							pass

					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     List[str]:




							"""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=_UpperCAmelCase	)
							for image in image_inputs:
									self.assertIsInstance(_UpperCAmelCase   ,      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(_UpperCAmelCase   ,      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 						lowerCamelCase__  (self					:		Dict	)		->     Optional[Any]:




							"""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=_UpperCAmelCase   ,      numpify=_UpperCAmelCase	)
							for image in image_inputs:
									self.assertIsInstance(_UpperCAmelCase   ,      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(_UpperCAmelCase   ,      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 						lowerCamelCase__  (self					:		str	)		->     Dict:




							"""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=_UpperCAmelCase   ,      torchify=_UpperCAmelCase	)
							for image in image_inputs:
									self.assertIsInstance(_UpperCAmelCase   ,      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(_UpperCAmelCase   ,      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

A :	List[str]												=     1_2_8
A :	Optional[Any]												=     2_5_6






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						list[int]:
		"""simple docstring"""




		lowercase__      =	message.encode("""ascii"""  )

		lowercase__      =	[]
		for block_start in range(0					,					len(__magic_name__  )					,					__magic_name__  ):
				lowercase__      =	0
				for i in range(__magic_name__					,					min(block_start + block_size					,					len(__magic_name__  )  )  ):
						block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
				block_ints.append(__magic_name__  )
		return block_ints






def 				UpperCamelCase      (			__magic_name__  :							list[int]					,					__magic_name__  :							int					,					__magic_name__  :							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(__magic_name__  ) + i < message_length:
								lowercase__      =	block_int // (BYTE_SIZE**i)
								lowercase__      =	block_int % (BYTE_SIZE**i)
								block_message.insert(0					,					chr(__magic_name__  )  )
				message.extend(__magic_name__  )
		return "".join(__magic_name__  )






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							tuple[int, int]					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						list[int]:
		"""simple docstring"""




		lowercase__      =	[]
		lowercase__    ,			lowercase__      =	key

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






def 				UpperCamelCase      (			__magic_name__  :							list[int]					,					__magic_name__  :							int					,					__magic_name__  :							tuple[int, int]					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE					,					)     ->						str:
		"""simple docstring"""




		lowercase__      =	[]
		lowercase__    ,			lowercase__      =	key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(__magic_name__					,					__magic_name__					,					__magic_name__  )  )
		return get_text_from_blocks(__magic_name__					,					__magic_name__					,					__magic_name__  )






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						tuple[int, int, int]:
		"""simple docstring"""




		with open(__magic_name__  ) as fo:
				lowercase__      =	fo.read()
		lowercase__    ,			lowercase__    ,			lowercase__      =	content.split(""","""  )
		return (int(__magic_name__  ), int(__magic_name__  ), int(__magic_name__  ))






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							str					,					__magic_name__  :							str					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE					,					)     ->						str:
		"""simple docstring"""




		lowercase__    ,			lowercase__    ,			lowercase__      =	read_key_file(__magic_name__  )
		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					,					__magic_name__  )  )

		lowercase__      =	[str(__magic_name__  ) for i in encrypt_message(__magic_name__					,					(n, e)					,					__magic_name__  )]

		lowercase__      =	""",""".join(__magic_name__  )
		lowercase__      =	f'''{len(__magic_name__  )}_{block_size}_{encrypted_content}'''
		with open(__magic_name__					,					"""w"""  ) as fo:
				fo.write(__magic_name__  )
		return encrypted_content






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							str  )     ->						str:
		"""simple docstring"""




		lowercase__    ,			lowercase__    ,			lowercase__      =	read_key_file(__magic_name__  )
		with open(__magic_name__  ) as fo:
				lowercase__      =	fo.read()
		lowercase__    ,			lowercase__    ,			lowercase__      =	content.split("""_"""  )
		lowercase__      =	int(__magic_name__  )
		lowercase__      =	int(__magic_name__  )

		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					,					__magic_name__  )  )

		lowercase__      =	[]
		for block in encrypted_message.split(""","""  ):
				encrypted_blocks.append(int(__magic_name__  )  )

		return decrypt_message(__magic_name__					,					__magic_name__					,					(n, d)					,					__magic_name__  )






def 				UpperCamelCase      (			)     ->						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(__magic_name__					,					__magic_name__					,					__magic_name__  )

				print("""\nEncrypted text:"""  )
				print(__magic_name__  )

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

				print("""\nDecryption:"""  )
				print(__magic_name__  )


if __name__ == "__main__":
	main()

