


'''simple docstring'''




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 OwlViTImageProcessor






class 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			def __init__(							self					:			List[str]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			List[str]=7							,					lowerCAmelCase__					:			Optional[int]=3							,					lowerCAmelCase__					:			Optional[Any]=1_8							,					lowerCAmelCase__					:			Dict=3_0							,					lowerCAmelCase__					:			Union[str, Any]=4_0_0							,					lowerCAmelCase__					:			Optional[int]=True							,					lowerCAmelCase__					:			Optional[int]=None							,					lowerCAmelCase__					:			List[Any]=True							,					lowerCAmelCase__					:			List[Any]=None							,					lowerCAmelCase__					:			List[str]=True							,					lowerCAmelCase__					:			Any=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							,					lowerCAmelCase__					:			Dict=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							,					lowerCAmelCase__					:			List[str]=True							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		parent
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		Any   	=		num_channels
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		image_size
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		min_resolution
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		max_resolution
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		do_resize
					__SCREAMING_SNAKE_CASE	:		int   	=		size if size is not None else {"""height""": 1_8, """width""": 1_8}
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		do_center_crop
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
					__SCREAMING_SNAKE_CASE	:		Any   	=		do_normalize
					__SCREAMING_SNAKE_CASE	:		str   	=		image_mean
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		image_std
					__SCREAMING_SNAKE_CASE	:		Dict   	=		do_convert_rgb



			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					}






@require_torch
@require_vision
class 						_UpperCamelCase  (							_UpperCAmelCase   , unittest.TestCase   ):


			'''simple docstring'''

			_A				:		List[str]         =		OwlViTImageProcessor if is_vision_available() else None

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		OwlViTImageProcessingTester(self						)

			@property
			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""do_resize"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""size"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""do_center_crop"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""center_crop"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""do_normalize"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""image_mean"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""image_std"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,					"""do_convert_rgb"""						)						)

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size							,					{"""height""": 1_8, """width""": 1_8}						)
					self.assertEqual(image_processor.crop_size							,					{"""height""": 1_8, """width""": 1_8}						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.image_processing_class.from_dict(self.image_processor_dict							,					size=4_2							,					crop_size=8_4						)
					self.assertEqual(image_processor.size							,					{"""height""": 4_2, """width""": 4_2}						)
					self.assertEqual(image_processor.crop_size							,					{"""height""": 8_4, """width""": 8_4}						)

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					__SCREAMING_SNAKE_CASE	:		str   	=		prepare_image_inputs(self.image_processor_tester							,					equal_resolution=lowerCAmelCase__						)

					for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase__							,					Image.Image						)

					# Test not batched input
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )							,					)

					# Test batched
					__SCREAMING_SNAKE_CASE	:		int   	=		image_processing(lowerCAmelCase__							,					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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )							,					)

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






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

					# Test not batched input
					__SCREAMING_SNAKE_CASE	:		str   	=		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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )							,					)

					# Test batched
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		image_processing(lowerCAmelCase__							,					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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )							,					)



			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






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

					# Test not batched input
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )							,					)

					# Test batched
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		image_processing(lowerCAmelCase__							,					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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )							,					)




'''simple docstring'''




import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase__	:     Any    =			1_28
UpperCamelCase__	:     Optional[int]    =			2_56


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		message.encode("""ascii""" )

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


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
		for block_int in block_ints:
				__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
				for i in range(block_size - 1	,				-1	,				-1 ):
						if len(_lowerCamelCase ) + i < message_length:
								__SCREAMING_SNAKE_CASE	:		List[Any]   	=		block_int // (BYTE_SIZE**i)
								__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		block_int % (BYTE_SIZE**i)
								block_message.insert(0	,				chr(_lowerCamelCase ) )
				message.extend(_lowerCamelCase )
		return "".join(_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key

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


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase ) )
		return get_text_from_blocks(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str ):
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		content.split(""",""" )
		return (int(_lowerCamelCase ), int(_lowerCamelCase ), int(_lowerCamelCase ))


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		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	,				_lowerCamelCase ) )

		__SCREAMING_SNAKE_CASE	:		Dict   	=		[str(_lowerCamelCase ) for i in encrypt_message(_lowerCamelCase	,				(n, e)	,				_lowerCamelCase )]

		__SCREAMING_SNAKE_CASE	:		int   	=		""",""".join(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		F"{len(_lowerCamelCase )}_{block_size}_{encrypted_content}"
		with open(_lowerCamelCase	,				"""w""" ) as fo:
				fo.write(_lowerCamelCase )
		return encrypted_content


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str ):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Any   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		content.split("""_""" )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )

		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	,				_lowerCamelCase ) )

		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		for block in encrypted_message.split(""",""" ):
				encrypted_blocks.append(int(_lowerCamelCase ) )

		return decrypt_message(_lowerCamelCase	,				_lowerCamelCase	,				(n, d)	,				_lowerCamelCase )


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		"""encrypted_file.txt"""
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		input(r"""Encrypt\Decrypt [e\d]: """ )

		if response.lower().startswith("""e""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""encrypt"""
		elif response.lower().startswith("""d""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		"""decrypt"""

		if mode == "encrypt":
				if not os.path.exists("""rsa_pubkey.txt""" ):
						rkg.make_key_files("""rsa"""	,				10_24 )

				__SCREAMING_SNAKE_CASE	:		int   	=		input("""\nEnter message: """ )
				__SCREAMING_SNAKE_CASE	:		str   	=		"""rsa_pubkey.txt"""
				print(F"Encrypting and writing to {filename}..." )
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		encrypt_and_write_to_file(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )

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

		elif mode == "decrypt":
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		"""rsa_privkey.txt"""
				print(F"Reading from {filename} and decrypting..." )
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		read_from_file_and_decrypt(_lowerCamelCase	,				_lowerCamelCase )
				print("""writing decryption to rsa_decryption.txt...""" )
				with open("""rsa_decryption.txt"""	,				"""w""" ) as dec:
						dec.write(_lowerCamelCase )

				print("""\nDecryption:""" )
				print(_lowerCamelCase )


if __name__ == "__main__":
						main()