




'''simple docstring'''





from __future__ import annotations

import random
import string






class     lowercase  :






      def __init__(self			,	a__ = None       )							-> None:
         SCREAMING_SNAKE_CASE__      :					Any							    =			passcode or self.__passcode_creator()
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.__make_key_list()
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.__make_shift_key()






      def __str__(self       )							-> str:
         return "".join(self.__passcode       )






      def  A__(self			,	a__       )							-> list[int]:
         for i in range(1			,	len(a__       )			,	2       ):
            iterlist[i] *= -1
         return iterlist






      def  A__(self       )							-> list[str]:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			string.ascii_letters + string.digits
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[random.choice(a__       ) for _ in range(random.randint(1_0			,	2_0       )       )]
         return password






      def  A__(self       )							-> list[str]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			(
             string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
         )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]

         # creates points known as breakpoints to break the key_list_options at those
         # points and pivot each substring
         SCREAMING_SNAKE_CASE__      :					Dict							    =			sorted(set(self.__passcode       )       )
         SCREAMING_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(a__       )

            # 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       )							-> int:
         SCREAMING_SNAKE_CASE__      :					str							    =			sum(self.__neg_pos([ord(a__       ) for x in self.__passcode]       )       )
         return num if num > 0 else len(self.__passcode       )






      def  A__(self			,	a__       )							-> str:
         SCREAMING_SNAKE_CASE__      :					int							    =			''''''

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

         return decoded_message






      def  A__(self			,	a__       )							-> str:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			''''''

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

         return encoded_message


def 					UpperCamelCase						(	snake_case = "Hello, this is a modified Caesar cipher" ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			ShuffledShiftCipher()
   return cipa.decrypt(cipa.encrypt(snake_case ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()







'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''       )

         if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''       )

         # first, apply the image processor
         SCREAMING_SNAKE_CASE__      :					str							    =			self.image_processor(images=a__			,	return_tensors=a__       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(a__			,	a__       ):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(
             text=text if text is not None else features['''words''']			,	text_pair=text_pair if text_pair is not None else None			,	boxes=boxes if boxes is not None else features['''boxes''']			,	word_labels=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_token_type_ids=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)

         # add pixel values
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

         if len(a__       ) != len(a__       ):
            raise ValueError(
                '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                f""" {len(a__       )} and {len(a__       )}"""       )

         return images_with_overflow






      def  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,	a__			,	)
         return self.image_processor_class






      @property
      def  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

