
def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if not isinstance(__magic_name__		,						__magic_name__	):
								raise TypeError("plaintext must be a string"	)
				if not isinstance(__magic_name__		,						__magic_name__	):
								raise TypeError("key must be a string"	)

				if not plaintext:
								raise ValueError("plaintext is empty"	)
				if not key:
								raise ValueError("key is empty"	)

				key += plaintext
				lowercase						    =			plaintext.lower()
				lowercase						    =			key.lower()
				lowercase						    =			0
				lowercase						    =			0
				lowercase						    =			""
				while plaintext_iterator < len(__magic_name__	):
								if (
								    ord(plaintext[plaintext_iterator]	) < 97
								    or ord(plaintext[plaintext_iterator]	) > 122
								):
												ciphertext += plaintext[plaintext_iterator]
												plaintext_iterator += 1
								elif ord(key[key_iterator]	) < 97 or ord(key[key_iterator]	) > 122:
												key_iterator += 1
								else:
												ciphertext += chr(
												    (
												        (ord(plaintext[plaintext_iterator]	) - 97 + ord(key[key_iterator]	))
												        - 97
												    )
												    % 26
												    + 97	)
												key_iterator += 1
												plaintext_iterator += 1
				return ciphertext

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if not isinstance(__magic_name__		,						__magic_name__	):
								raise TypeError("ciphertext must be a string"	)
				if not isinstance(__magic_name__		,						__magic_name__	):
								raise TypeError("key must be a string"	)

				if not ciphertext:
								raise ValueError("ciphertext is empty"	)
				if not key:
								raise ValueError("key is empty"	)

				lowercase						    =			key.lower()
				lowercase						    =			0
				lowercase						    =			0
				lowercase						    =			""
				while ciphertext_iterator < len(__magic_name__	):
								if (
								    ord(ciphertext[ciphertext_iterator]	) < 97
								    or ord(ciphertext[ciphertext_iterator]	) > 122
								):
												plaintext += ciphertext[ciphertext_iterator]
								else:
												plaintext += chr(
												    (ord(ciphertext[ciphertext_iterator]	) - ord(key[key_iterator]	)) % 26
												    + 97	)
												key += chr(
												    (ord(ciphertext[ciphertext_iterator]	) - ord(key[key_iterator]	)) % 26
												    + 97	)
												key_iterator += 1
								ciphertext_iterator += 1
				return plaintext


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						_snake_case			:       Optional[int]      =						int(input("Type 1 to encrypt or 2 to decrypt:"))
						if operation == 1:
												_snake_case			:       List[Any]      =						input("Typeplaintext to be encrypted:\n")
												_snake_case			:       Any      =						input("Type the key:\n")
												print(encrypt(plaintext, key))
						elif operation == 2:
												_snake_case			:       List[Any]      =						input("Type the ciphertext to be decrypted:\n")
												_snake_case			:       Union[str, Any]      =						input("Type the key:\n")
												print(decrypt(ciphertext, key))
						decrypt("jsqqs avvwo", "coffee")


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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor





class      UpperCamelCase_			(			unittest.TestCase		):







						'''simple docstring'''



						def __init__( self       :Any		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Optional[int]=7		,						lowerCAmelCase__       :Tuple=3		,						lowerCAmelCase__       :List[str]=10		,						lowerCAmelCase__       :Dict=18		,						lowerCAmelCase__       :int=30		,						lowerCAmelCase__       :List[str]=400		,						lowerCAmelCase__       :Union[str, Any]=True		,						lowerCAmelCase__       :List[Any]=None		,						lowerCAmelCase__       :List[Any]=True		,						lowerCAmelCase__       :int=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Optional[Any]=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Dict=None		,						)     ->Union[str, Any]:
										lowercase						    =			size if size is not None else {"shortest_edge": 18}
										lowercase						    =			crop_size if crop_size is not None else {"height": 18, "width": 18}

										lowercase						    =			parent
										lowercase						    =			batch_size
										lowercase						    =			num_channels
										lowercase						    =			num_frames
										lowercase						    =			image_size
										lowercase						    =			min_resolution
										lowercase						    =			max_resolution
										lowercase						    =			do_resize
										lowercase						    =			size
										lowercase						    =			do_normalize
										lowercase						    =			image_mean
										lowercase						    =			image_std
										lowercase						    =			crop_size





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->int:
										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_size": self.crop_size,
										}





@require_torch
@require_vision
class      UpperCamelCase_			(			__a		,						unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase			:     Union[str, Any]							      =				VideoMAEImageProcessor if is_vision_available() else None





						def 					SCREAMING_SNAKE_CASE( self       :List[str]    )     ->List[Any]:
										lowercase						    =			VideoMAEImageProcessingTester(self    )





						@property
						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->Any:
										return self.image_processor_tester.prepare_image_processor_dict()





						def 					SCREAMING_SNAKE_CASE( self       :Dict    )     ->str:
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"image_mean"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"image_std"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"do_normalize"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"do_resize"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"do_center_crop"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"size"    )    )





						def 					SCREAMING_SNAKE_CASE( self       :str    )     ->Tuple:
										lowercase						    =			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,						{"shortest_edge": 18}    )
										self.assertEqual(image_processor.crop_size		,						{"height": 18, "width": 18}    )

										lowercase						    =			self.image_processing_class.from_dict(self.image_processor_dict		,						size=42		,						crop_size=84    )
										self.assertEqual(image_processor.size		,						{"shortest_edge": 42}    )
										self.assertEqual(image_processor.crop_size		,						{"height": 84, "width": 84}    )





						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->Any:
										pass





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->List[Any]:
										# Initialize image_processing
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										# create random PIL videos
										lowercase						    =			prepare_video_inputs(self.image_processor_tester		,						equal_resolution=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														self.assertIsInstance(video[0]		,						Image.Image    )

										# Test not batched input
										lowercase						    =			image_processing(video_inputs[0]		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)

										# Test batched
										lowercase						    =			image_processing(lowerCAmelCase__		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)





						def 					SCREAMING_SNAKE_CASE( self       :Dict    )     ->Optional[int]:
										# Initialize image_processing
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										lowercase						    =			prepare_video_inputs(self.image_processor_tester		,						equal_resolution=lowerCAmelCase__		,						numpify=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														self.assertIsInstance(video[0]		,						np.ndarray    )

										# Test not batched input
										lowercase						    =			image_processing(video_inputs[0]		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)

										# Test batched
										lowercase						    =			image_processing(lowerCAmelCase__		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)





						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Dict:
										# Initialize image_processing
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										lowercase						    =			prepare_video_inputs(self.image_processor_tester		,						equal_resolution=lowerCAmelCase__		,						torchify=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														self.assertIsInstance(video[0]		,						torch.Tensor    )

										# Test not batched input
										lowercase						    =			image_processing(video_inputs[0]		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)

										# Test batched
										lowercase						    =			image_processing(lowerCAmelCase__		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)
