

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_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''image_processor''', '''tokenizer''']
 _lowerCAmelCase									=	'''LayoutLMv2ImageProcessor'''
 _lowerCAmelCase									=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')




 def __init__(			self  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  **_UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     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  ,  )
     __UpperCAmelCase	:  Optional[int]		=     kwargs.pop('feature_extractor'							)

   __UpperCAmelCase	:  Dict		=     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  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = 0  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   # 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
   __UpperCAmelCase	:  List[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							):
       __UpperCAmelCase	:  Dict		=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
     __UpperCAmelCase	:  Optional[int]		=     features['words']

   __UpperCAmelCase	:  Union[str, Any]		=     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
   __UpperCAmelCase	:  int		=     features.pop('pixel_values'							)
   if return_overflowing_tokens is True:
     __UpperCAmelCase	:  str		=     self.get_overflowing_images(_UpperCAmelCase  ,  encoded_inputs['overflow_to_sample_mapping']							)
   __UpperCAmelCase	:  Dict		=     images

   return encoded_inputs




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   __UpperCAmelCase	:  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     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.batch_decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 @property
 def     a       (			self							):
   return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




 @property
 def     a       (			self							):
   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     a       (			self							):
   warnings.warn(
       '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,  _UpperCAmelCase  ,  )
   return self.image_processor







'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      [chr(i) for i in range(32, 126)]
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(reversed(range(len(alphabets))))
__SCREAMING_SNAKE_CASE						=      []
__SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      0
def 					__magic_name__(       ):
						global gear_one_pos
						global gear_two_pos
						global gear_three_pos
						UpperCamelCase_     :	List[Any]					     =     gear_one[0]
						gear_one.append(UpperCAmelCase__	)
						del gear_one[0]
						gear_one_pos += 1
						if gear_one_pos % int(len(UpperCAmelCase__	)	) == 0:
												UpperCamelCase_     :	List[str]					     =     gear_two[0]
												gear_two.append(UpperCAmelCase__	)
												del gear_two[0]
												gear_two_pos += 1
												if gear_two_pos % int(len(UpperCAmelCase__	)	) == 0:
																		UpperCamelCase_     :	List[Any]					     =     gear_three[0]
																		gear_three.append(UpperCAmelCase__	)
																		del gear_three[0]
																		gear_three_pos += 1
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Any					     =     alphabets.index(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     gear_one[target]
						UpperCamelCase_     :	str					     =     gear_two[target]
						UpperCamelCase_     :	int					     =     gear_three[target]
						UpperCamelCase_     :	Union[str, Any]					     =     reflector[target]
						UpperCamelCase_     :	Dict					     =     gear_three.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_two.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_one.index(UpperCAmelCase__	)
						code.append(alphabets[target]	)
						rotator()


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      list(input("Type your message:\n"))
	while True:
		try:
			__SCREAMING_SNAKE_CASE						=      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!"
	)




