




'''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 ViTImageProcessor




class      UpperCamelCase       (	unittest.TestCase       ):



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=7		,     __lowerCAmelCase=3		,     __lowerCAmelCase=18		,     __lowerCAmelCase=30		,     __lowerCAmelCase=400		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=True		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     ):







      '''simple docstring'''


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



  def     a_							(			self ):







      '''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      UpperCamelCase       (	snake_case__      ,		unittest.TestCase       ):
  a     :List[str]          =					ViTImageProcessor if is_vision_available() else None



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      ViTImageProcessingTester(self )



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return self.image_processor_tester.prepare_image_processor_dict()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_mean""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_std""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_normalize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_resize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """size""" ) )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size		,     {"""height""": 18, """width""": 18} )

      _a				:							Dict							       =      self.image_processing_class.from_dict(self.image_processor_dict		,     size=42 )
      self.assertEqual(image_processor.size		,     {"""height""": 42, """width""": 42} )



  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


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

      # Test not batched input
      _a				:							List[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.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )		,     )

      # Test batched
      _a				:							int							       =      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.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a				:							Dict							       =      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
      _a				:							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.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )		,     )

      # Test batched
      _a				:							Optional[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.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a				:							Tuple							       =      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
      _a				:							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.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )		,     )

      # Test batched
      _a				:							Optional[int]							       =      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.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )		,     )








'''simple docstring'''





from __future__ import annotations

import random
import string




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      _a				:							List[Any]							       =      passcode or self.__passcode_creator()
      _a				:							List[str]							       =      self.__make_key_list()
      _a				:							Optional[int]							       =      self.__make_shift_key()



  def __str__(			self ):







      '''simple docstring'''


      return "".join(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      for i in range(1		,     len(__lowerCAmelCase )		,     2 ):
          iterlist[i] *= -1
      return iterlist



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      string.ascii_letters + string.digits
      _a				:							str							       =      [random.choice(__lowerCAmelCase ) for _ in range(random.randint(10		,     20 ) )]
      return password



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      (
          string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
      )

      _a				:							str							       =      []

      # creates points known as breakpoints to break the key_list_options at those
      # points and pivot each substring
      _a				:							Dict							       =      sorted(set(self.__passcode ) )
      _a				:							list[str]							       =      []

      # algorithm for creating a new shuffled list, keys_l, out of key_list_options
      for i in key_list_options:
          temp_list.extend(__lowerCAmelCase )

          # checking breakpoints at which to pivot temporary sublist and add it into
          # keys_l
          if i in breakpoints or i == key_list_options[-1]:
              keys_l.extend(temp_list[::-1] )
              temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
      return keys_l



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      sum(self.__neg_pos([ord(__lowerCAmelCase ) for x in self.__passcode] ) )
      return num if num > 0 else len(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Tuple							       =      """"""

      # decoding shift like Caesar cipher algorithm implementing negative shift or
      # reverse shift or left shift
      for i in encoded_message:
          _a				:							List[str]							       =      self.__key_list.index(__lowerCAmelCase )
          decoded_message += self.__key_list[
              (position - self.__shift_key) % -len(self.__key_list )
          ]

      return decoded_message



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[str]							       =      """"""

      # encoding shift like Caesar cipher algorithm implementing positive shift or
      # forward shift or right shift
      for i in plaintext:
          _a				:							Any							       =      self.__key_list.index(__lowerCAmelCase )
          encoded_message += self.__key_list[
              (position + self.__shift_key) % len(self.__key_list )
          ]

      return encoded_message






def UpperCAmelCase    (       UpperCAmelCase_ = "Hello, this is a modified Caesar cipher"						):
    _a				:							Union[str, Any]							       =      ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


