



'''simple docstring'''





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=False					,						lowerCamelCase_=False					,						lowerCamelCase_=False							):
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   snake_case   :							Optional[int]						  =[]
   for i in range(image_processor_tester.batch_size							):
      if equal_resolution:
         snake_case   :							List[Any]						  =image_processor_tester.max_resolution
      else:
         # To avoid getting image width/height 0
         snake_case   :							List[str]						  =image_processor_tester.min_resolution
         if getattr(lowerCamelCase_					,						'''size_divisor'''					,						lowerCamelCase_							):
            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
            snake_case   :							Optional[Any]						  =max(image_processor_tester.size_divisor					,						lowerCamelCase_							)
         snake_case  ,    snake_case   :							Union[str, Any]						  =np.random.choice(np.arange(lowerCamelCase_					,						image_processor_tester.max_resolution							)					,						2							)
      image_inputs.append(
          np.random.randint(2_55					,						size=(image_processor_tester.num_channels, width, height)					,						dtype=np.uinta							)							)

   if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      snake_case   :							List[Any]						  =[Image.fromarray(np.moveaxis(lowerCamelCase_					,						0					,						-1							)							) for image in image_inputs]

   if torchify:
      snake_case   :							List[str]						  =[torch.from_numpy(lowerCamelCase_							) for image in image_inputs]

   return image_inputs
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=10					,						lowerCamelCase_=10					,						lowerCamelCase_=False					,						lowerCamelCase_=False							):
   snake_case   :							List[str]						  =[]
   for i in range(image_processor_tester.num_frames							):
      video.append(np.random.randint(2_55					,						size=(image_processor_tester.num_channels, width, height)					,						dtype=np.uinta							)							)

   if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      snake_case   :							List[str]						  =[Image.fromarray(np.moveaxis(lowerCamelCase_					,						0					,						-1							)							) for frame in video]

   if torchify:
      snake_case   :							Union[str, Any]						  =[torch.from_numpy(lowerCamelCase_							) for frame in video]

   return video
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=False					,						lowerCamelCase_=False					,						lowerCamelCase_=False							):
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   snake_case   :							int						  =[]
   for i in range(image_processor_tester.batch_size							):
      if equal_resolution:
         snake_case   :							Any						  =image_processor_tester.max_resolution
      else:
         snake_case  ,    snake_case   :							List[Any]						  =np.random.choice(
             np.arange(image_processor_tester.min_resolution					,						image_processor_tester.max_resolution							)					,						2							)
         snake_case   :							str						  =prepare_video(
             image_processor_tester=lowerCamelCase_					,						width=lowerCamelCase_					,						height=lowerCamelCase_					,						numpify=lowerCamelCase_					,						torchify=lowerCamelCase_					,						)
      video_inputs.append(lowerCamelCase_							)

   return video_inputs





class 							lowerCAmelCase_		:
    __UpperCAmelCase							=	None





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  =self.image_processing_class(**self.image_processor_dict     )
       snake_case   :							Optional[int]						  =json.loads(image_processor.to_json_string()     )
       for key, value in self.image_processor_dict.items():
          self.assertEqual(obj[key],    _snake_case     )





    def 					__snake_case       (    self    :    int     ):



       '''simple docstring'''







       snake_case   :							Dict						  =self.image_processing_class(**self.image_processor_dict     )

       with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case   :							str						  =os.path.join(_snake_case,    '''image_processor.json'''     )
          image_processor_first.to_json_file(_snake_case     )
          snake_case   :							Any						  =self.image_processing_class.from_json_file(_snake_case     )

       self.assertEqual(image_processor_second.to_dict(),    image_processor_first.to_dict()     )





    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							Dict						  =self.image_processing_class(**self.image_processor_dict     )

       with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case   :							Tuple						  =image_processor_first.save_pretrained(_snake_case     )[0]
          check_json_file_has_correct_format(_snake_case     )
          snake_case   :							Optional[int]						  =self.image_processing_class.from_pretrained(_snake_case     )

       self.assertEqual(image_processor_second.to_dict(),    image_processor_first.to_dict()     )





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							List[str]						  =self.image_processing_class()
       self.assertIsNotNone(_snake_case     )






    @require_torch
    @require_vision
    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       if self.test_cast_dtype is not None:
          # Initialize image_processor
          snake_case   :							Union[str, Any]						  =self.image_processing_class(**self.image_processor_dict     )

          # create random PyTorch tensors
          snake_case   :							int						  =prepare_image_inputs(self.image_processor_tester,    equal_resolution=_snake_case,    torchify=_snake_case     )

          snake_case   :							List[str]						  =image_processor(_snake_case,    return_tensors='''pt'''     )
          # for layoutLM compatiblity
          self.assertEqual(encoding.pixel_values.device,    torch.device('''cpu'''     )     )
          self.assertEqual(encoding.pixel_values.dtype,    torch.floataa     )

          snake_case   :							List[Any]						  =image_processor(_snake_case,    return_tensors='''pt'''     ).to(torch.floataa     )
          self.assertEqual(encoding.pixel_values.device,    torch.device('''cpu'''     )     )
          self.assertEqual(encoding.pixel_values.dtype,    torch.floataa     )

          snake_case   :							Dict						  =image_processor(_snake_case,    return_tensors='''pt'''     ).to('''cpu''',    torch.bfloataa     )
          self.assertEqual(encoding.pixel_values.device,    torch.device('''cpu'''     )     )
          self.assertEqual(encoding.pixel_values.dtype,    torch.bfloataa     )

          with self.assertRaises(_snake_case     ):
             snake_case   :							Optional[Any]						  =image_processor(_snake_case,    return_tensors='''pt'''     ).to(torch.bfloataa,    '''cpu'''     )

          # Try with text + image feature
          snake_case   :							List[Any]						  =image_processor(_snake_case,    return_tensors='''pt'''     )
          encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]     )}     )
          snake_case   :							Dict						  =encoding.to(torch.floataa     )

          self.assertEqual(encoding.pixel_values.device,    torch.device('''cpu'''     )     )
          self.assertEqual(encoding.pixel_values.dtype,    torch.floataa     )
          self.assertEqual(encoding.input_ids.dtype,    torch.long     )










'''simple docstring'''





from __future__ import annotations

import random
import string





class 							lowerCAmelCase_		:





    def __init__(    self    :    Union[str, Any],    _snake_case    :    str | None = None     ):



       '''simple docstring'''







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





    def __str__(    self    :    List[Any]     ):



       '''simple docstring'''







       return "".join(self.__passcode     )





    def 					__snake_case       (    self    :    int,    _snake_case    :    list[int]     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							int						  =string.ascii_letters + string.digits
       snake_case   :							Union[str, Any]						  =[random.choice(_snake_case     ) for _ in range(random.randint(10,    20     )     )]
       return password





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Dict						  =(
           string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
       )

       snake_case   :							Any						  =[]

       # creates points known as breakpoints to break the key_list_options at those
       # points and pivot each substring
       snake_case   :							Optional[int]						  =sorted(set(self.__passcode     )     )
       snake_case   :							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(_snake_case     )

          # 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 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =sum(self.__neg_pos([ord(_snake_case     ) for x in self.__passcode]     )     )
       return num if num > 0 else len(self.__passcode     )





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =''''''

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

       return decoded_message






    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							int						  =''''''

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

       return encoded_message




def 		_a   (  lowerCamelCase_ = "Hello, this is a modified Caesar cipher"							):
   snake_case   :							List[Any]						  =ShuffledShiftCipher()
   return cipa.decrypt(cipa.encrypt(lowerCamelCase_							)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





