




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 	__lowerCAmelCase			(			unittest.TestCase				):
							def __init__(		self		:List[Any]	,   __magic_name__		:str	,   __magic_name__		:Union[str, Any]=7	,   __magic_name__		:int=3	,   __magic_name__		:List[Any]=18	,   __magic_name__		:List[str]=30	,   __magic_name__		:Any=400	,   __magic_name__		:Union[str, Any]=True	,   __magic_name__		:int=None	,   __magic_name__		:Optional[int]=0.875	,   __magic_name__		:str=True	,   __magic_name__		:Dict=[0.5, 0.5, 0.5]	,   __magic_name__		:Optional[int]=[0.5, 0.5, 0.5]	,   ):







								'''simple docstring'''







								a		       =							size if size is not None else {"""shortest_edge""": 20}
								a		       =							parent
								a		       =							batch_size
								a		       =							num_channels
								a		       =							image_size
								a		       =							min_resolution
								a		       =							max_resolution
								a		       =							do_resize
								a		       =							size
								a		       =							crop_pct
								a		       =							do_normalize
								a		       =							image_mean
								a		       =							image_std





							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''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 	__lowerCAmelCase			(			__magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						ConvNextImageProcessor if is_vision_available() else None
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							ConvNextImageProcessingTester(self       )
							@property
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								return self.image_processor_tester.prepare_image_processor_dict()
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(__magic_name__	,   """do_resize"""       )       )
								self.assertTrue(hasattr(__magic_name__	,   """size"""       )       )
								self.assertTrue(hasattr(__magic_name__	,   """crop_pct"""       )       )
								self.assertTrue(hasattr(__magic_name__	,   """do_normalize"""       )       )
								self.assertTrue(hasattr(__magic_name__	,   """image_mean"""       )       )
								self.assertTrue(hasattr(__magic_name__	,   """image_std"""       )       )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size	,   {"""shortest_edge""": 20}       )

								a		       =							self.image_processing_class.from_dict(self.image_processor_dict	,   size=42       )
								self.assertEqual(image_processor.size	,   {"""shortest_edge""": 42}       )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								pass
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								a		       =							prepare_image_inputs(self.image_processor_tester	,   equal_resolution=__magic_name__       )
								for image in image_inputs:
									self.assertIsInstance(__magic_name__	,   Image.Image       )

								# Test not batched input
								a		       =							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
								a		       =							image_processing(__magic_name__	,   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       ):







								'''simple docstring'''







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

								# Test not batched input
								a		       =							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
								a		       =							image_processing(__magic_name__	,   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		:List[str]       ):







								'''simple docstring'''







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

								# Test not batched input
								a		       =							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
								a		       =							image_processing(__magic_name__	,   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"""],
								    )	,   )







__UpperCamelCase      : Union[str, Any]					      =   {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}





def 	__A	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    char.upper()					)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
		import doctest

		doctest.testmod()  # Fist ensure that all our tests are passing...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

		print(F'Encrypted: {cipher_text}')
		print(F'Decrypted with key: {decrypt(key, cipher_text)}')

