


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       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=18       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=True       ,  lowercase_=32       ,  lowercase_=True       ,  )  -> str:
											a__      =parent
											a__      =batch_size
											a__      =num_channels
											a__      =image_size
											a__      =min_resolution
											a__      =max_resolution
											a__      =do_resize
											a__      =size_divisor
											a__      =do_rescale





				def 						__UpperCamelCase    (				self)  -> Dict:
											return {
											    "do_resize": self.do_resize,
											    "size_divisor": self.size_divisor,
											    "do_rescale": self.do_rescale,
											}







@require_torch
@require_vision
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =GLPNImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =GLPNImageProcessingTester(self)





				@property
				def 						__UpperCamelCase    (				self)  -> Tuple:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_       ,  'do_resize'))
											self.assertTrue(hasattr(lowercase_       ,  'size_divisor'))
											self.assertTrue(hasattr(lowercase_       ,  'resample'))
											self.assertTrue(hasattr(lowercase_       ,  'do_rescale'))





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											a__      =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 						__UpperCamelCase    (				self)  -> int:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  numpify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  np.ndarray)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											a__      =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 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  torch.Tensor)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											a__      =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)





_lowerCAmelCase:						List[str]   				=       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def   _lowercase(					):
							a__      =input('Enter message: '     )
							a__      =input('Enter key [alphanumeric]: '     )
							a__      =input('Encrypt/Decrypt [e/d]: '     )

							if mode.lower().startswith('e'     ):
														a__      ='encrypt'
														a__      =encrypt_message(__a    ,     __a     )
							elif mode.lower().startswith('d'     ):
														a__      ='decrypt'
														a__      =decrypt_message(__a    ,     __a     )

							print(f"""\n{mode.title()}ed message:"""     )
							print(__a     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'encrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'decrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str    ,     __a				:						str     ):
							a__      =[]
							a__      =0
							a__      =key.upper()

							for symbol in message:
														a__      =LETTERS.find(symbol.upper()     )
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]     )
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]     )

																					num %= len(__a     )

																					if symbol.isupper():
																												translated.append(LETTERS[num]     )
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()     )

																					key_index += 1
																					if key_index == len(__a     ):
																												a__      =0
														else:
																					translated.append(__a     )
							return "".join(__a     )


if __name__ == "__main__":
	main()

