



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		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase=1_3 ,       __lowercase=3 ,       __lowercase=2_2_4 ,       __lowercase=3_0 ,       __lowercase=4_0_0 ,       __lowercase=True ,       __lowercase=None ,       __lowercase=True ,       __lowercase=[0.5, 0.5, 0.5] ,       __lowercase=[0.5, 0.5, 0.5] ,       ) ->				int:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				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__    (A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					ViTImageProcessor if is_vision_available() else None

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						str							=  EfficientFormerImageProcessorTester(self  )

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									return self.image_proc_tester.prepare_image_processor_dict()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[str]							=  self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(__lowercase ,       """image_mean"""  )  )
									self.assertTrue(hasattr(__lowercase ,       """image_std"""  )  )
									self.assertTrue(hasattr(__lowercase ,       """do_normalize"""  )  )
									self.assertTrue(hasattr(__lowercase ,       """do_resize"""  )  )
									self.assertTrue(hasattr(__lowercase ,       """size"""  )  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.image_processing_class(**self.image_processor_dict  )
									# create random PIL images
									a__       :						int							=  prepare_image_inputs(self.image_proc_tester ,       equal_resolution=__lowercase  )
									for image in image_inputs:
												self.assertIsInstance(__lowercase ,       Image.Image  )

									# Test not batched input
									a__       :						Optional[int]							=  image_processor(image_inputs[0] ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    ) ,       )

									# Test batched
									a__       :						Optional[Any]							=  image_processor(__lowercase ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    ) ,       )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						List[Any]							=  self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									a__       :						Union[str, Any]							=  prepare_image_inputs(self.image_proc_tester ,       equal_resolution=__lowercase ,       numpify=__lowercase  )
									for image in image_inputs:
												self.assertIsInstance(__lowercase ,       np.ndarray  )

									# Test not batched input
									a__       :						Union[str, Any]							=  image_processor(image_inputs[0] ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    ) ,       )

									# Test batched
									a__       :						Any							=  image_processor(__lowercase ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    ) ,       )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						int							=  self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									a__       :						int							=  prepare_image_inputs(self.image_proc_tester ,       equal_resolution=__lowercase ,       torchify=__lowercase  )
									for image in image_inputs:
												self.assertIsInstance(__lowercase ,       torch.Tensor  )

									# Test not batched input
									a__       :						Tuple							=  image_processor(image_inputs[0] ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    ) ,       )

									# Test batched
									a__       :						Optional[Any]							=  image_processor(__lowercase ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    ) ,       )









from string import ascii_uppercase

_lowercase		:				str      		={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase		:				Dict      		=dict(enumerate(ascii_uppercase))

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Any							=  len(_lowercase)
			a__       :						Optional[int]							=  0
			while True:
						if x == i:
									a__       :						Optional[Any]							=  0
						if len(_lowercase) == len(_lowercase):
									break
						key += key[i]
						i += 1
			return key

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Tuple							=  """"""
			a__       :						str							=  0
			for letter in message:
						if letter == " ":
									cipher_text += " "
						else:
									a__       :						List[str]							=  (dicta[letter] - dicta[key_new[i]]) % 26
									i += 1
									cipher_text += dicta[x]
			return cipher_text

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						int							=  """"""
			a__       :						int							=  0
			for letter in cipher_text:
						if letter == " ":
									or_txt += " "
						else:
									a__       :						Dict							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
									i += 1
									or_txt += dicta[x]
			return or_txt

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						List[Any]							=  """THE GERMAN ATTACK"""
			a__       :						List[Any]							=  """SECRET"""
			a__       :						Tuple							=  generate_key(_lowercase						,       _lowercase)
			a__       :						str							=  cipher_text(_lowercase						,       _lowercase)
			print(F'''Encrypted Text = {s}''')
			print(F'''Original Text = {original_text(_lowercase						,       _lowercase)}''')


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




