code: 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						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					['image_processor', 'tokenizer']
  							lowerCAmelCase__       =					'LayoutLMv2ImageProcessor'
  							lowerCAmelCase__       =					('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
  
  
  
  							def __init__(    self     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	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     ,   )
  																					_lowercase				=	kwargs.pop("""feature_extractor"""  )
  
  														_lowercase				=	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     ,   ):
  
  
  
  
  
  
  
  														'''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
  														_lowercase				=	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  ):
  																												_lowercase				=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
  																					_lowercase				=	features["""words"""]
  
  														_lowercase				=	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
  														_lowercase				=	features.pop("""pixel_values"""  )
  														if return_overflowing_tokens is True:
  																					_lowercase				=	self.get_overflowing_images(UpperCAmelCase     ,   encoded_inputs["""overflow_to_sample_mapping"""]  )
  														_lowercase				=	images
  
  														return encoded_inputs
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	[]
  														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     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self.tokenizer.batch_decode(*UpperCAmelCase     ,   **UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self.tokenizer.decode(*UpperCAmelCase     ,   **UpperCAmelCase  )
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''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  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														warnings.warn(
  														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,   UpperCAmelCase     ,   )
  														return self.image_processor
  
  
  
style_context: import numpy as np
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	bin(int.from_bytes(text.encode(_A ,_A					) ,"""big"""					)					)[2:]
  							return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	int(_A ,2					)
  							return n.to_bytes((n.bit_length() + 7) // 8 ,"""big"""					).decode(_A ,_A					) or "\0"
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
  														raise ValueError("""size of parity don't match with size of data"""					)
  
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							# sorted information data for the size of the output data
  							_lowercase				=	[]
  							# data position template + parity
  							_lowercase				=	[]
  							# parity bit counter
  							_lowercase				=	0
  							# counter position of data bits
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par:
  																					if (np.log(_A					) / np.log(2					)).is_integer():
  																												data_out_gab.append("""P"""					)
  																												_lowercase				=	qtd_bp + 1
  																					else:
  																												data_out_gab.append("""D"""					)
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a given parity
  														_lowercase				=	0
  														# counter to control the loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(cont_bo % 2					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							return data_out
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  							# list of parity received
  							_lowercase				=	[]
  							_lowercase				=	[]
  
  							for x in range(1 ,len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														#  and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_output.append(data[cont_data]					)
  														else:
  																					parity_received.append(data[cont_data]					)
  														cont_data += 1
  
  							# -----------calculates the parity with the data
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							#  sorted information data for the size of the output data
  							_lowercase				=	[]
  							# Data position feedback + parity
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template position of bits - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data_output[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a certain parity
  														_lowercase				=	0
  														# Counter to control loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(str(cont_bo % 2					)					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # Parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							_lowercase				=	parity_received == parity
  							return data_output, ack
  
  
  