code: 
  
  
  """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__							(				A_							):
     __UpperCAmelCase       					=				['''image_processor''', '''tokenizer''']
     __UpperCAmelCase       					=				'''LayoutLMv2ImageProcessor'''
     __UpperCAmelCase       					=				('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      **SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        _lowerCamelCase  :				Union[str, 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."""			,      SCREAMING_SNAKE_CASE			,      )
           _lowerCamelCase  :				Optional[Any]      =		kwargs.pop("""feature_extractor""")
  
        _lowerCamelCase  :				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__(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def __call__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = 0			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = None			,      **SCREAMING_SNAKE_CASE			,      )     ->     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
        _lowerCamelCase  :				Tuple      =		self.image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors=SCREAMING_SNAKE_CASE)
  
        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				Any      =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
           _lowerCamelCase  :				List[str]      =		features["""words"""]
  
        _lowerCamelCase  :				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=SCREAMING_SNAKE_CASE			,      add_special_tokens=SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE			,      truncation=SCREAMING_SNAKE_CASE			,      max_length=SCREAMING_SNAKE_CASE			,      stride=SCREAMING_SNAKE_CASE			,      pad_to_multiple_of=SCREAMING_SNAKE_CASE			,      return_token_type_ids=SCREAMING_SNAKE_CASE			,      return_attention_mask=SCREAMING_SNAKE_CASE			,      return_overflowing_tokens=SCREAMING_SNAKE_CASE			,      return_special_tokens_mask=SCREAMING_SNAKE_CASE			,      return_offsets_mapping=SCREAMING_SNAKE_CASE			,      return_length=SCREAMING_SNAKE_CASE			,      verbose=SCREAMING_SNAKE_CASE			,      return_tensors=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )
  
        # add pixel values
        _lowerCamelCase  :				List[Any]      =		features.pop("""pixel_values""")
        if return_overflowing_tokens is True:
           _lowerCamelCase  :				List[Any]      =		self.get_overflowing_images(SCREAMING_SNAKE_CASE			,      encoded_inputs["""overflow_to_sample_mapping"""])
        _lowerCamelCase  :				Dict      =		images
  
        return encoded_inputs
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     str:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        _lowerCamelCase  :				Dict      =		[]
        for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx])
  
        if len(SCREAMING_SNAKE_CASE) != len(SCREAMING_SNAKE_CASE):
           raise ValueError(
               """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
               F' {len(SCREAMING_SNAKE_CASE)} and {len(SCREAMING_SNAKE_CASE)}')
  
        return images_with_overflow
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     Dict:
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     int:
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     Dict:
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,      SCREAMING_SNAKE_CASE			,      )
        return self.image_processor_class
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,      SCREAMING_SNAKE_CASE			,      )
        return self.image_processor
  
  
style_context: 
  
  
  """simple docstring"""
  from dataclasses import dataclass
  
  UpperCAmelCase   =	"""0.1"""
  UpperCAmelCase   =	"""Lucia Harcekova"""
  
  
  
  
  @dataclass
  class   lowercase__							:
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
  
  
  
  
  
     def __repr__(       self)     ->     str:
        return F'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 13			,      SCREAMING_SNAKE_CASE = 6)     ->     None:
        _lowerCamelCase  :				Optional[Any]      =		window_size
        _lowerCamelCase  :				List[str]      =		lookahead_buffer_size
        _lowerCamelCase  :				List[Any]      =		self.window_size - self.lookahead_buffer_size
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     list[Token]:
        _lowerCamelCase  :				Union[str, Any]      =		[]
        _lowerCamelCase  :				Tuple      =		""""""
  
        # while there are still characters in text to compress
        while text:
           # find the next encoding phrase
           # - triplet with offset, length, indicator (the next encoding character)
           _lowerCamelCase  :				List[Any]      =		self._find_encoding_token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
           # update the search buffer:
           # - add new characters from text into it
           # - check if size exceed the max search buffer size, if so, drop the
           #   oldest elements
           search_buffer += text[: token.length + 1]
           if len(SCREAMING_SNAKE_CASE) > self.search_buffer_size:
              _lowerCamelCase  :				int      =		search_buffer[-self.search_buffer_size :]
  
           # update the text
           _lowerCamelCase  :				Tuple      =		text[token.length + 1 :]
  
           # append the token to output
           output.append(SCREAMING_SNAKE_CASE)
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				str      =		""""""
  
        for token in tokens:
           for _ in range(token.length):
              output += output[-token.offset]
           output += token.indicator
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Token:
  
        if not text:
           raise ValueError("""We need some text to work with.""")
  
        # Initialise result parameters to default values
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		0, 0
  
        if not search_buffer:
           return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
        for i, character in enumerate(SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Tuple      =		len(SCREAMING_SNAKE_CASE) - i
           if character == text[0]:
              _lowerCamelCase  :				Any      =		self._match_length_from_index(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      0			,      SCREAMING_SNAKE_CASE)
              # if the found length is bigger than the current or if it's equal,
              # which means it's offset is smaller: update offset and length
              if found_length >= length:
                 _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		found_offset, found_length
  
        return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
        if not text or text[text_index] != window[window_index]:
           return 0
        return 1 + self._match_length_from_index(
            SCREAMING_SNAKE_CASE			,      window + text[text_index]			,      text_index + 1			,      window_index + 1)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   # Initialize compressor class
   UpperCAmelCase   =	LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
   # Example
   UpperCAmelCase   =	"""cabracadabrarrarrad"""
   UpperCAmelCase   =	lzaa_compressor.compress(TEXT)
   print(lzaa_compressor.compress("""ababcbababaa"""))
   UpperCAmelCase   =	lzaa_compressor.decompress(compressed_text)
   assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  