"""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 ViTImageProcessor





class 					snake_case			(       unittest.TestCase						):

				def __init__(			self							:       List[str]  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Union[str, Any]=7  ,						UpperCamelCase__							:       List[str]=3  ,						UpperCamelCase__							:       Dict=1_8  ,						UpperCamelCase__							:       Any=3_0  ,						UpperCamelCase__							:       List[str]=4_0_0  ,						UpperCamelCase__							:       Any=True  ,						UpperCamelCase__							:       str=None  ,						UpperCamelCase__							:       str=True  ,						UpperCamelCase__							:       List[Any]=[0.5, 0.5, 0.5]  ,						UpperCamelCase__							:       int=[0.5, 0.5, 0.5]  ,						)->     Tuple:



						'''simple docstring'''


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




				def 							lowercase_  (			self							:       Any)->     List[Any]:



						'''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,
						}





@require_torch
@require_vision
class 					snake_case			(       __snake_case,							unittest.TestCase						):
				SCREAMING_SNAKE_CASE_     :   Union[str, Any]        =						ViTImageProcessor if is_vision_available() else None

				def 							lowercase_  (			self							:       str)->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    ViTImageProcessingTester(self)

				@property
				def 							lowercase_  (			self							:       Any)->     Optional[Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_  (			self							:       List[Any])->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Dict					   =    self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__  ,						"image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__  ,						"image_std"))
						self.assertTrue(hasattr(UpperCamelCase__  ,						"do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__  ,						"do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__  ,						"size"))

				def 							lowercase_  (			self							:       int)->     List[Any]:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size  ,						{"height": 1_8, "width": 1_8})

						__lowerCAmelCase: Union[str, Any]					   =    self.image_processing_class.from_dict(self.image_processor_dict  ,						size=4_2)
						self.assertEqual(image_processor.size  ,						{"height": 4_2, "width": 4_2})

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



						'''simple docstring'''


						pass

				def 							lowercase_  (			self							:       List[Any])->     str:



						'''simple docstring'''


						__lowerCAmelCase: int					   =    self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase: Union[str, Any]					   =    prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__  ,						Image.Image)

						# Test not batched input
						__lowerCAmelCase: 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.size["height"],
						        self.image_processor_tester.size["width"],
						    )  ,						)

						# Test batched
						__lowerCAmelCase: str					   =    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["height"],
						        self.image_processor_tester.size["width"],
						    )  ,						)

				def 							lowercase_  (			self							:       int)->     Any:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase: Any					   =    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
						__lowerCAmelCase: List[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.size["height"],
						        self.image_processor_tester.size["width"],
						    )  ,						)

						# Test batched
						__lowerCAmelCase: Optional[Any]					   =    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["height"],
						        self.image_processor_tester.size["width"],
						    )  ,						)




				def 							lowercase_  (			self							:       Optional[Any])->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase: Dict					   =    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
						__lowerCAmelCase: Union[str, 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.size["height"],
						        self.image_processor_tester.size["width"],
						    )  ,						)

						# Test batched
						__lowerCAmelCase: int					   =    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["height"],
						        self.image_processor_tester.size["width"],
						    )  ,						)





"""simple docstring"""


import random
import sys

__A		     =    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def 					a__					(					)			->			None:
		__lowerCAmelCase: int					   =    input("Enter message: "				)
		__lowerCAmelCase: Any					   =    "LFWOAYUISVKMNXPBDCRJTQEGHZ"
		__lowerCAmelCase: int					   =    input("Encrypt/Decrypt [e/d]: "				)

		check_valid_key(__SCREAMING_SNAKE_CASE				)

		if resp.lower().startswith("e"				):
				__lowerCAmelCase: Tuple					   =    "encrypt"
				__lowerCAmelCase: int					   =    encrypt_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		elif resp.lower().startswith("d"				):
				__lowerCAmelCase: str					   =    "decrypt"
				__lowerCAmelCase: List[str]					   =    decrypt_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

		print(F"\n{mode.title()}ion: \n{translated}"				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			None:
		__lowerCAmelCase: List[Any]					   =    list(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[int]					   =    list(__SCREAMING_SNAKE_CASE				)
		key_list.sort()
		letters_list.sort()

		if key_list != letters_list:
				sys.exit("Error in the key or symbol set."				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		return translate_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				"encrypt"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		return translate_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				"decrypt"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase: Union[str, Any]					   =    ""
		__lowerCAmelCase: List[str]					   =    LETTERS
		__lowerCAmelCase: Optional[int]					   =    key

		if mode == "decrypt":
				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    chars_b, chars_a

		for symbol in message:
				if symbol.upper() in chars_a:
						__lowerCAmelCase: Union[str, Any]					   =    chars_a.find(symbol.upper()				)
						if symbol.isupper():
								translated += chars_b[sym_index].upper()
						else:
								translated += chars_b[sym_index].lower()
				else:
						translated += symbol

		return translated






def 					a__					(					)			->			str:
		__lowerCAmelCase: int					   =    list(__SCREAMING_SNAKE_CASE				)
		random.shuffle(__SCREAMING_SNAKE_CASE				)
		return "".join(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			main()


