code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  __lowerCAmelCase     =			{
      "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
  }
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowerCAmelCase     =			["SegformerFeatureExtractor"]
  				__lowerCAmelCase     =			["SegformerImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowerCAmelCase     =			[
  				    "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "SegformerDecodeHead",
  				    "SegformerForImageClassification",
  				    "SegformerForSemanticSegmentation",
  				    "SegformerLayer",
  				    "SegformerModel",
  				    "SegformerPreTrainedModel",
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowerCAmelCase     =			[
  				    "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "TFSegformerDecodeHead",
  				    "TFSegformerForImageClassification",
  				    "TFSegformerForSemanticSegmentation",
  				    "TFSegformerModel",
  				    "TFSegformerPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_segformer import SegformerFeatureExtractor
  								from .image_processing_segformer import SegformerImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_segformer import (
  								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    SegformerDecodeHead,
  								    SegformerForImageClassification,
  								    SegformerForSemanticSegmentation,
  								    SegformerLayer,
  								    SegformerModel,
  								    SegformerPreTrainedModel,
  								)
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_segformer import (
  								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    TFSegformerDecodeHead,
  								    TFSegformerForImageClassification,
  								    TFSegformerForSemanticSegmentation,
  								    TFSegformerModel,
  								    TFSegformerPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__lowerCAmelCase     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from dataclasses import dataclass
  
  __lowerCAmelCase     =			"0.1"
  __lowerCAmelCase     =			"Lucia Harcekova"
  
  
  
  
  @dataclass
  class   _UpperCAmelCase	:
  					__A          =   42
  					__A          =   42
  					__A          =   42
  
  
  
  
  					def __repr__(						self       )						->  str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase = 13    ,      __lowerCAmelCase = 6       )						->  None:
  							UpperCamelCase__         =	window_size
  							UpperCamelCase__         =	lookahead_buffer_size
  							UpperCamelCase__         =	self.window_size - self.lookahead_buffer_size
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  list[Token]:
  							UpperCamelCase__         =	[]
  							UpperCamelCase__         =	""""""
  
  							# 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)
  									UpperCamelCase__         =	self._find_encoding_token(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  									# 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(__lowerCAmelCase       ) > self.search_buffer_size:
  											UpperCamelCase__         =	search_buffer[-self.search_buffer_size :]
  
  									# update the text
  									UpperCamelCase__         =	text[token.length + 1 :]
  
  									# append the token to output
  									output.append(__lowerCAmelCase       )
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  str:
  							UpperCamelCase__         =	""""""
  
  							for token in tokens:
  									for _ in range(token.length       ):
  											output += output[-token.offset]
  									output += token.indicator
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Token:
  
  							if not text:
  									raise ValueError("""We need some text to work with."""       )
  
  							# Initialise result parameters to default values
  							UpperCamelCase__  ,			UpperCamelCase__         =	0, 0
  
  							if not search_buffer:
  									return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  							for i, character in enumerate(__lowerCAmelCase       ):
  									UpperCamelCase__         =	len(__lowerCAmelCase       ) - i
  									if character == text[0]:
  											UpperCamelCase__         =	self._match_length_from_index(__lowerCAmelCase    ,      __lowerCAmelCase    ,      0    ,      __lowerCAmelCase       )
  											# 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:
  													UpperCamelCase__  ,			UpperCamelCase__         =	found_offset, found_length
  
  							return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  							if not text or text[text_index] != window[window_index]:
  									return 0
  							return 1 + self._match_length_from_index(
  							    __lowerCAmelCase    ,      window + text[text_index]    ,      text_index + 1    ,      window_index + 1       )
  
  
  if __name__ == "__main__":
  				from doctest import testmod
  
  				testmod()
  				# Initialize compressor class
  				__lowerCAmelCase     =			LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  				# Example
  				__lowerCAmelCase     =			"cabracadabrarrarrad"
  				__lowerCAmelCase     =			lzaa_compressor.compress(TEXT)
  				print(lzaa_compressor.compress("ababcbababaa"))
  				__lowerCAmelCase     =			lzaa_compressor.decompress(compressed_text)
  				assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  
  