



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 GLPNImageProcessor



class 						UpperCAmelCase_					( unittest.TestCase):







							def __init__(     self,    __a,    __a=7,    __a=3,    __a=18,    __a=30,    __a=400,    __a=True,    __a=32,    __a=True,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					parent
														_lowerCAmelCase  :							List[str]				     =					batch_size
														_lowerCAmelCase  :							Union[str, Any]				     =					num_channels
														_lowerCAmelCase  :							int				     =					image_size
														_lowerCAmelCase  :							List[str]				     =					min_resolution
														_lowerCAmelCase  :							int				     =					max_resolution
														_lowerCAmelCase  :							int				     =					do_resize
														_lowerCAmelCase  :							Any				     =					size_divisor
														_lowerCAmelCase  :							int				     =					do_rescale







							def      snake_case__   (     self):

														'''simple docstring'''

														return {
														    "do_resize": self.do_resize,
														    "size_divisor": self.size_divisor,
														    "do_rescale": self.do_rescale,
														}



@require_torch
@require_vision
class 						UpperCAmelCase_					( a     ,      unittest.TestCase):
							lowerCamelCase__         =	GLPNImageProcessor if is_vision_available() else None







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					GLPNImageProcessingTester(self)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return self.image_processor_tester.prepare_image_processor_dict()







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.image_processing_class(**self.image_processor_dict)
														self.assertTrue(hasattr(__a,    "do_resize"))
														self.assertTrue(hasattr(__a,    "size_divisor"))
														self.assertTrue(hasattr(__a,    "resample"))
														self.assertTrue(hasattr(__a,    "do_rescale"))







							def      snake_case__   (     self):

														'''simple docstring'''

														pass







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.image_processing_class(**self.image_processor_dict)
														# create random PIL images
														_lowerCAmelCase  :							List[Any]				     =					prepare_image_inputs(self.image_processor_tester,    equal_resolution=__a)
														for image in image_inputs:
																					self.assertIsInstance(__a,    Image.Image)

														# Test not batched input (GLPNImageProcessor doesn't support batching)
														_lowerCAmelCase  :							Any				     =					image_processing(image_inputs[0],    return_tensors="pt").pixel_values
														self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
														self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.image_processing_class(**self.image_processor_dict)
														# create random numpy tensors
														_lowerCAmelCase  :							int				     =					prepare_image_inputs(self.image_processor_tester,    equal_resolution=__a,    numpify=__a)
														for image in image_inputs:
																					self.assertIsInstance(__a,    np.ndarray)

														# Test not batched input (GLPNImageProcessor doesn't support batching)
														_lowerCAmelCase  :							Any				     =					image_processing(image_inputs[0],    return_tensors="pt").pixel_values
														self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
														self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)







							def      snake_case__   (     self):

														'''simple docstring'''

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

														# Test not batched input (GLPNImageProcessor doesn't support batching)
														_lowerCAmelCase  :							Any				     =					image_processing(image_inputs[0],    return_tensors="pt").pixel_values
														self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
														self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)





import string






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							for key in range(len(string.ascii_uppercase						)						):
														_lowerCAmelCase  :							str				     =					""
														for symbol in message:
																					if symbol in string.ascii_uppercase:
																												_lowerCAmelCase  :							List[str]				     =					string.ascii_uppercase.find(_lowerCamelCase						)
																												_lowerCAmelCase  :							Dict				     =					num - key
																												if num < 0:
																																			_lowerCAmelCase  :							Dict				     =					num + len(string.ascii_uppercase						)
																												_lowerCAmelCase  :							Optional[Any]				     =					translated + string.ascii_uppercase[num]
																					else:
																												_lowerCAmelCase  :							int				     =					translated + symbol
														print(F"Decryption using Key #{key}: {translated}"						)






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					input("Encrypted message: "						)
							_lowerCAmelCase  :							Dict				     =					message.upper()
							decrypt(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()
