






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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_=7    ,    lowercase_=3    ,    lowercase_=1_8    ,    lowercase_=3_0    ,    lowercase_=4_0_0    ,    lowercase_=True    ,    lowercase_=1 / 2_5_5    ,    lowercase_=True    ,    lowercase_=8    ,    )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  parent
														lowerCAmelCase_       		=  batch_size
														lowerCAmelCase_       		=  num_channels
														lowerCAmelCase_       		=  image_size
														lowerCAmelCase_       		=  min_resolution
														lowerCAmelCase_       		=  max_resolution
														lowerCAmelCase_       		=  do_rescale
														lowerCAmelCase_       		=  rescale_factor
														lowerCAmelCase_       		=  do_pad
														lowerCAmelCase_       		=  pad_size

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														return {
														    "do_rescale": self.do_rescale,
														    "rescale_factor": self.rescale_factor,
														    "do_pad": self.do_pad,
														    "pad_size": self.pad_size,
														}






							def   _lowercase (	self    ,    lowercase_=False    ,    lowercase_=False    ,    lowercase_=False     )      ->      int:
														'''simple docstring'''

														assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

														if equal_resolution:
																					lowerCAmelCase_       		=  []
																					for i in range(self.batch_size     ):
																												image_inputs.append(
																												    np.random.randint(
																												        2_5_5    ,    size=(self.num_channels, self.max_resolution, self.max_resolution)    ,    dtype=np.uinta     )     )
														else:
																					lowerCAmelCase_       		=  []
																					for i in range(self.batch_size     ):
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  np.random.choice(np.arange(self.min_resolution    ,    self.max_resolution     )    ,    2     )
																												image_inputs.append(np.random.randint(2_5_5    ,    size=(self.num_channels, width, height)    ,    dtype=np.uinta     )     )

														if not numpify and not torchify:
																					# PIL expects the channel dimension as last dimension
																					lowerCAmelCase_       		=  [Image.fromarray(np.moveaxis(lowercase_    ,    0    ,    -1     )     ) for x in image_inputs]

														if torchify:
																					lowerCAmelCase_       		=  [torch.from_numpy(lowercase_     ) for x in image_inputs]

														return image_inputs




@require_torch
@require_vision
class 							a_				(		_a			,			unittest.TestCase	):


							'''simple docstring'''






							__a:			List[Any]									=					SwinaSRImageProcessor if is_vision_available() else None

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

														lowerCAmelCase_       		=  SwinaSRImageProcessingTester(self     )

							@property
							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														return self.image_processor_tester.prepare_image_processor_dict()

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

														lowerCAmelCase_       		=  self.image_processing_class(**self.image_processor_dict     )
														self.assertTrue(hasattr(lowercase_    ,    'do_rescale'     )     )
														self.assertTrue(hasattr(lowercase_    ,    'rescale_factor'     )     )
														self.assertTrue(hasattr(lowercase_    ,    'do_pad'     )     )
														self.assertTrue(hasattr(lowercase_    ,    'pad_size'     )     )

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

														pass

							def   _lowercase (	self    ,    lowercase_     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_		,     lowerCAmelCase_       		=  get_image_size(lowercase_     )
														lowerCAmelCase_       		=  self.image_processor_tester.pad_size

														lowerCAmelCase_       		=  (old_height // size + 1) * size - old_height
														lowerCAmelCase_       		=  (old_width // size + 1) * size - old_width
														return old_height + pad_height, old_width + pad_width

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

														lowerCAmelCase_       		=  self.image_processing_class(**self.image_processor_dict     )
														# create random PIL images
														lowerCAmelCase_       		=  self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     )
														for image in image_inputs:
																					self.assertIsInstance(lowercase_    ,    Image.Image     )

														# Test not batched input
														lowerCAmelCase_       		=  image_processor(image_inputs[0]    ,    return_tensors='pt'     ).pixel_values
														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.calculate_expected_size(np.array(image_inputs[0]     )     )
														self.assertEqual(
														    encoded_images.shape    ,    (
														        1,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )    ,    )

							def   _lowercase (	self     )      ->      int:
														'''simple docstring'''

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

														# Test not batched input
														lowerCAmelCase_       		=  image_processor(image_inputs[0]    ,    return_tensors='pt'     ).pixel_values
														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.calculate_expected_size(image_inputs[0]     )
														self.assertEqual(
														    encoded_images.shape    ,    (
														        1,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )    ,    )






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

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

														# Test not batched input
														lowerCAmelCase_       		=  image_processor(image_inputs[0]    ,    return_tensors='pt'     ).pixel_values
														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.calculate_expected_size(image_inputs[0]     )
														self.assertEqual(
														    encoded_images.shape    ,    (
														        1,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )    ,    )











from __future__ import annotations

from string import ascii_letters
def   lowerCamelCase      (		a_				,		a_				,		a_ = None			)			->    str:
							lowerCAmelCase_       		=  alphabet or ascii_letters

							# The final result string
							lowerCAmelCase_       		=  ''

							for character in input_string:
														if character not in alpha:
																					# Append without encryption if character is not in the alphabet
																					result += character
														else:
																					# Get the index of the new key and make sure it isn't too large
																					lowerCAmelCase_       		=  (alpha.index(a_			) + key) % len(a_			)

																					# Append the encoded character to the alphabet
																					result += alpha[new_key]

							return result
def   lowerCamelCase      (		a_				,		a_				,		a_ = None			)			->    str:
							key *= -1

							return encrypt(a_				,		a_				,		a_			)
def   lowerCamelCase      (		a_				,		a_ = None			)			->    dict[int, str]:
							lowerCAmelCase_       		=  alphabet or ascii_letters

							# To store data on all the combinations
							lowerCAmelCase_       		=  {}

							# Cycle through each combination
							for key in range(1				,		len(a_			) + 1			):
														# Decrypt the message and store the result in the data
														lowerCAmelCase_       		=  decrypt(a_				,		a_				,		a_			)

							return brute_force_data


if __name__ == "__main__":
		while True:
				print(f'''\n{"-" * 1_0}\n Menu\n{"-" * 1_0}''')
				print(*["""1.Encrypt""", """2.Decrypt""", """3.BruteForce""", """4.Quit"""], sep="""\n""")

				# get user input
				lowerCamelCase_		  =						input("""\nWhat would you like to do?: """).strip() or """4"""

				# run functions based on what the user chose
				if choice not in ("1", "2", "3", "4"):
						print("""Invalid choice, please enter a valid choice""")
				elif choice == "1":
						lowerCamelCase_		  =						input("""Please enter the string to be encrypted: """)
						lowerCamelCase_		  =						int(input("""Please enter off-set: """).strip())

						print(encrypt(input_string, key))
				elif choice == "2":
						lowerCamelCase_		  =						input("""Please enter the string to be decrypted: """)
						lowerCamelCase_		  =						int(input("""Please enter off-set: """).strip())

						print(decrypt(input_string, key))
				elif choice == "3":
						lowerCamelCase_		  =						input("""Please enter the string to be decrypted: """)
						lowerCamelCase_		  =						brute_force(input_string)

						for key, value in brute_force_data.items():
								print(f'''Key: {key} | Message: {value}''')

				elif choice == "4":
						print("""Goodbye.""")
						break



