





'''simple docstring'''




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor






class UpperCAmelCase							(		unittest.TestCase							):



    def __init__(  self     ,			__A     ,			__A=7     ,			__A=3     ,			__A=18     ,			__A=30     ,			__A=400     ,			__A=True     ,			__A=None     ,			__A=True     ,			__A=[0.5, 0.5, 0.5]     ,			__A=[0.5, 0.5, 0.5]     ,			):
     __UpperCAmelCase					=   size if size is not None else {'height': 18, 'width': 18}
     __UpperCAmelCase					=   parent
     __UpperCAmelCase					=   batch_size
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   image_size
     __UpperCAmelCase					=   min_resolution
     __UpperCAmelCase					=   max_resolution
     __UpperCAmelCase					=   do_resize
     __UpperCAmelCase					=   size
     __UpperCAmelCase					=   do_normalize
     __UpperCAmelCase					=   image_mean
     __UpperCAmelCase					=   image_std




    def 					__lowerCamelCase						(  self					):
     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 UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			Tuple						   =       DPTImageProcessor if is_vision_available() else None



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   DPTImageProcessingTester(self					)



    @property
    def 					__lowerCamelCase						(  self					):
     return self.image_processor_tester.prepare_image_processor_dict()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     self.assertTrue(hasattr(__A     ,			'image_mean'					)					)
     self.assertTrue(hasattr(__A     ,			'image_std'					)					)
     self.assertTrue(hasattr(__A     ,			'do_normalize'					)					)
     self.assertTrue(hasattr(__A     ,			'do_resize'					)					)
     self.assertTrue(hasattr(__A     ,			'size'					)					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.image_processing_class.from_dict(self.image_processor_dict					)
     self.assertEqual(image_processor.size     ,			{'height': 18, 'width': 18}					)

     __UpperCAmelCase					=   self.image_processing_class.from_dict(self.image_processor_dict     ,			size=42					)
     self.assertEqual(image_processor.size     ,			{'height': 42, 'width': 42}					)



    def 					__lowerCamelCase						(  self					):
     # Initialize image_processing
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     # create random PIL images
     __UpperCAmelCase					=   prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__A					)
     for image in image_inputs:
      self.assertIsInstance(__A     ,			Image.Image					)

     # Test not batched input
     __UpperCAmelCase					=   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
     __UpperCAmelCase					=   image_processing(__A     ,			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 					__lowerCamelCase						(  self					):
     # Initialize image_processing
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     # create random numpy tensors
     __UpperCAmelCase					=   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
     __UpperCAmelCase					=   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
     __UpperCAmelCase					=   image_processing(__A     ,			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 					__lowerCamelCase						(  self					):
     # Initialize image_processing
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     # create random PyTorch tensors
     __UpperCAmelCase					=   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
     __UpperCAmelCase					=   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
     __UpperCAmelCase					=   image_processing(__A     ,			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'''




from string import ascii_uppercase

_A:					Union[str, Any]							=				{char: i for i, char in enumerate(ascii_uppercase)}
_A:					str							=				dict(enumerate(ascii_uppercase))







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   len(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 while True:
  if x == i:
   __UpperCAmelCase					=   0
  if len(_lowerCAmelCase    ) == len(_lowerCAmelCase    ):
   break
  key += key[i]
  i += 1
 return key







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in message:
  if letter == " ":
   cipher_text += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] - dicta[key_new[i]]) % 26
   i += 1
   cipher_text += dicta[x]
 return cipher_text







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in cipher_text:
  if letter == " ":
   or_txt += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] + dicta[key_new[i]] + 26) % 26
   i += 1
   or_txt += dicta[x]
 return or_txt







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   'THE GERMAN ATTACK'
 __UpperCAmelCase					=   'SECRET'
 __UpperCAmelCase					=   generate_key(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   cipher_text(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print(F'Encrypted Text = {s}'    )
 print(F'Original Text = {original_text(_lowerCAmelCase       ,    _lowerCAmelCase    )}'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





