'''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 OwlViTImageProcessor




class    lowerCamelCase__  (   unittest.TestCase     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=7					,      lowerCAmelCase__=3					,      lowerCAmelCase__=18					,      lowerCAmelCase__=30					,      lowerCAmelCase__=400					,      lowerCAmelCase__=True					,      lowerCAmelCase__=None					,      lowerCAmelCase__=True					,      lowerCAmelCase__=None					,      lowerCAmelCase__=True					,      lowerCAmelCase__=[0.4814_5466, 0.457_8275, 0.4082_1073]					,      lowerCAmelCase__=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,      lowerCAmelCase__=True					,      )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =parent
        _UpperCamelCase  :Optional[Any]       =batch_size
        _UpperCamelCase  :Any       =num_channels
        _UpperCamelCase  :int       =image_size
        _UpperCamelCase  :Optional[Any]       =min_resolution
        _UpperCamelCase  :int       =max_resolution
        _UpperCamelCase  :List[Any]       =do_resize
        _UpperCamelCase  :Any       =size if size is not None else {"""height""": 18, """width""": 18}
        _UpperCamelCase  :Dict       =do_center_crop
        _UpperCamelCase  :List[str]       =crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        _UpperCamelCase  :Any       =do_normalize
        _UpperCamelCase  :Any       =image_mean
        _UpperCamelCase  :str       =image_std
        _UpperCamelCase  :str       =do_convert_rgb




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





        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }




@require_torch
@require_vision
class    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						OwlViTImageProcessor if is_vision_available() else None



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





        _UpperCamelCase  :Union[str, Any]       =OwlViTImageProcessingTester(self     )



   @property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        return self.image_processor_tester.prepare_image_processor_dict()



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_resize"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """size"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_center_crop"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """center_crop"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_normalize"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """image_mean"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """image_std"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_convert_rgb"""     )     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size					,      {"""height""": 18, """width""": 18}     )
        self.assertEqual(image_processor.crop_size					,      {"""height""": 18, """width""": 18}     )

        _UpperCamelCase  :str       =self.image_processing_class.from_dict(self.image_processor_dict					,      size=42					,      crop_size=84     )
        self.assertEqual(image_processor.size					,      {"""height""": 42, """width""": 42}     )
        self.assertEqual(image_processor.crop_size					,      {"""height""": 84, """width""": 84}     )



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





        _UpperCamelCase  :Union[str, Any]       =self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _UpperCamelCase  :List[str]       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__     )

        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      Image.Image     )

        # Test not batched input
        _UpperCamelCase  :str       =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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )					,      )

        # Test batched
        _UpperCamelCase  :List[Any]       =image_processing(lowerCAmelCase__					,      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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )					,      )



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





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

        # Test not batched input
        _UpperCamelCase  :Optional[int]       =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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )					,      )

        # Test batched
        _UpperCamelCase  :str       =image_processing(lowerCAmelCase__					,      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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )					,      )




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





        _UpperCamelCase  :List[str]       =self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _UpperCamelCase  :Dict       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__					,      torchify=lowerCAmelCase__     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      torch.Tensor     )

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

        # Test batched
        _UpperCamelCase  :Dict       =image_processing(lowerCAmelCase__					,      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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )					,      )

'''simple docstring'''
A__ :		Optional[Any]					=[chr(i) for i in range(32, 126)]
A__ :		Tuple					=list(range(len(alphabets)))
A__ :		int					=list(range(len(alphabets)))
A__ :		Optional[Any]					=list(range(len(alphabets)))
A__ :		Any					=list(reversed(range(len(alphabets))))
A__ :		Optional[int]					=[]
A__ :		Tuple					=0







def 							A_  (    )					->							Any:





  """simple docstring"""

  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __A		:  Any       				=		gear_one[0]
  gear_one.append(__SCREAMING_SNAKE_CASE			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
    __A		:  Any       				=		gear_two[0]
    gear_two.append(__SCREAMING_SNAKE_CASE			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
      __A		:  str       				=		gear_three[0]
      gear_three.append(__SCREAMING_SNAKE_CASE			)
      del gear_three[0]
      gear_three_pos += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		alphabets.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Dict       				=		gear_one[target]
  __A		:  int       				=		gear_two[target]
  __A		:  List[str]       				=		gear_three[target]
  __A		:  List[Any]       				=		reflector[target]
  __A		:  List[Any]       				=		gear_three.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		gear_two.index(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		gear_one.index(__SCREAMING_SNAKE_CASE			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     A__ :		int					=list(input('Type your message:\n'))
     while True:
          try:
               A__ :		Any					=int(input('Please set token:(must be only digits)\n'))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('\n' + ''.join(code))
     print(
         F"\nYour Token is {token} please write it down.\nIf you want to decode "
         'this message again you should input same digits as token!'
     )



