





from __future__ import annotations

import random
import string



class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    Tuple					, UpperCamelCase					:    str | None = None							):


    '''simple docstring'''





    _snake_case      : Dict														=		passcode or self.__passcode_creator()
    _snake_case      : int														=		self.__make_key_list()
    _snake_case      : Any														=		self.__make_shift_key()







  def __str__(					self					:    Optional[Any]							):


    '''simple docstring'''





    return "".join(self.__passcode							)







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    list[int]							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		string.ascii_letters + string.digits
    _snake_case      : Optional[Any]														=		[random.choice(UpperCamelCase							) for _ in range(random.randint(10					, 20							)							)]
    return password







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : List[str]														=		(
        string.ascii_letters + string.digits + string.punctuation + ' \t\n'
    )

    _snake_case      : int														=		[]

    # creates points known as breakpoints to break the key_list_options at those
    # points and pivot each substring
    _snake_case      : 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(UpperCamelCase							)

      # 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 UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    str							):


    '''simple docstring'''





    _snake_case      : Dict														=		''

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

    return decoded_message







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    str							):


    '''simple docstring'''





    _snake_case      : Tuple														=		''

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

    return encoded_message




def 			lowerCamelCase_		(					lowerCAmelCase:		str = "Hello, this is a modified Caesar cipher" )->						str:
  _snake_case      : Optional[Any]														=		ShuffledShiftCipher()
  return cipa.decrypt(cipa.encrypt(lowerCAmelCase ) )


if __name__ == "__main__":
      import doctest

      doctest.testmod()











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 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[str]					    =["""image_processor""", """tokenizer"""]
  a_							:				Optional[Any]					    ="""LayoutLMv2ImageProcessor"""
  a_							:				Any					    =("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(					self					:    Union[str, Any]					, UpperCamelCase					:    str=None					, UpperCamelCase					:    List[Any]=None					, **UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Dict														=		None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'					, UpperCamelCase					, )
      _snake_case      : Dict														=		kwargs.pop('feature_extractor'							)

    _snake_case      : Optional[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__(UpperCamelCase					, UpperCamelCase							)







  def __call__(					self					:    int					, UpperCamelCase					:    Tuple					, UpperCamelCase					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					, UpperCamelCase					:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					, UpperCamelCase					:    Union[List[List[int]], List[List[List[int]]]] = None					, UpperCamelCase					:    Optional[Union[List[int], List[List[int]]]] = None					, UpperCamelCase					:    bool = True					, UpperCamelCase					:    Union[bool, str, PaddingStrategy] = False					, UpperCamelCase					:    Union[bool, str, TruncationStrategy] = False					, UpperCamelCase					:    Optional[int] = None					, UpperCamelCase					:    int = 0					, UpperCamelCase					:    Optional[int] = None					, UpperCamelCase					:    Optional[bool] = None					, UpperCamelCase					:    Optional[bool] = None					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = True					, UpperCamelCase					:    Optional[Union[str, TensorType]] = None					, **UpperCamelCase					:    Optional[Any]					, ):


    '''simple docstring'''





    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
    _snake_case      : Optional[Any]														=		self.image_processor(images=UpperCamelCase					, return_tensors=UpperCamelCase							)

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

    _snake_case      : Optional[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=UpperCamelCase					, add_special_tokens=UpperCamelCase					, padding=UpperCamelCase					, truncation=UpperCamelCase					, max_length=UpperCamelCase					, stride=UpperCamelCase					, pad_to_multiple_of=UpperCamelCase					, return_token_type_ids=UpperCamelCase					, return_attention_mask=UpperCamelCase					, return_overflowing_tokens=UpperCamelCase					, return_special_tokens_mask=UpperCamelCase					, return_offsets_mapping=UpperCamelCase					, return_length=UpperCamelCase					, verbose=UpperCamelCase					, return_tensors=UpperCamelCase					, **UpperCamelCase					, )

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

    return encoded_inputs







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]							)

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

    return images_with_overflow







  def UpperCamelCase_       (					self					:    List[Any]					, *UpperCamelCase					:    Any					, **UpperCamelCase					:    int							):


    '''simple docstring'''





    return self.tokenizer.batch_decode(*UpperCamelCase					, **UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Any					, *UpperCamelCase					:    Optional[int]					, **UpperCamelCase					:    Any							):


    '''simple docstring'''





    return self.tokenizer.decode(*UpperCamelCase					, **UpperCamelCase							)







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    warnings.warn(
        '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					, UpperCamelCase					, )
    return self.image_processor_class







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					, UpperCamelCase					, )
    return self.image_processor




