code: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_	:    Optional[Any]  					=       {
      """auto_factory""": ["""get_values"""],
      """configuration_auto""": ["""ALL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CONFIG_MAPPING""", """MODEL_NAMES_MAPPING""", """AutoConfig"""],
      """feature_extraction_auto""": ["""FEATURE_EXTRACTOR_MAPPING""", """AutoFeatureExtractor"""],
      """image_processing_auto""": ["""IMAGE_PROCESSOR_MAPPING""", """AutoImageProcessor"""],
      """processing_auto""": ["""PROCESSOR_MAPPING""", """AutoProcessor"""],
      """tokenization_auto""": ["""TOKENIZER_MAPPING""", """AutoTokenizer"""],
  }
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							UpperCamelCase_	:    Optional[Any]  					=       [
  							    """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
  							    """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING""",
  							    """MODEL_FOR_AUDIO_XVECTOR_MAPPING""",
  							    """MODEL_FOR_BACKBONE_MAPPING""",
  							    """MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING""",
  							    """MODEL_FOR_CAUSAL_LM_MAPPING""",
  							    """MODEL_FOR_CTC_MAPPING""",
  							    """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
  							    """MODEL_FOR_DEPTH_ESTIMATION_MAPPING""",
  							    """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
  							    """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING""",
  							    """MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING""",
  							    """MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
  							    """MODEL_FOR_MASKED_LM_MAPPING""",
  							    """MODEL_FOR_MASK_GENERATION_MAPPING""",
  							    """MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
  							    """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
  							    """MODEL_FOR_OBJECT_DETECTION_MAPPING""",
  							    """MODEL_FOR_PRETRAINING_MAPPING""",
  							    """MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
  							    """MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
  							    """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
  							    """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
  							    """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
  							    """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
  							    """MODEL_FOR_TEXT_ENCODING_MAPPING""",
  							    """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
  							    """MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING""",
  							    """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING""",
  							    """MODEL_FOR_VISION_2_SEQ_MAPPING""",
  							    """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING""",
  							    """MODEL_MAPPING""",
  							    """MODEL_WITH_LM_HEAD_MAPPING""",
  							    """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
  							    """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING""",
  							    """AutoModel""",
  							    """AutoBackbone""",
  							    """AutoModelForAudioClassification""",
  							    """AutoModelForAudioFrameClassification""",
  							    """AutoModelForAudioXVector""",
  							    """AutoModelForCausalLM""",
  							    """AutoModelForCTC""",
  							    """AutoModelForDepthEstimation""",
  							    """AutoModelForImageClassification""",
  							    """AutoModelForImageSegmentation""",
  							    """AutoModelForInstanceSegmentation""",
  							    """AutoModelForMaskGeneration""",
  							    """AutoModelForTextEncoding""",
  							    """AutoModelForMaskedImageModeling""",
  							    """AutoModelForMaskedLM""",
  							    """AutoModelForMultipleChoice""",
  							    """AutoModelForNextSentencePrediction""",
  							    """AutoModelForObjectDetection""",
  							    """AutoModelForPreTraining""",
  							    """AutoModelForQuestionAnswering""",
  							    """AutoModelForSemanticSegmentation""",
  							    """AutoModelForSeq2SeqLM""",
  							    """AutoModelForSequenceClassification""",
  							    """AutoModelForSpeechSeq2Seq""",
  							    """AutoModelForTableQuestionAnswering""",
  							    """AutoModelForTokenClassification""",
  							    """AutoModelForUniversalSegmentation""",
  							    """AutoModelForVideoClassification""",
  							    """AutoModelForVision2Seq""",
  							    """AutoModelForVisualQuestionAnswering""",
  							    """AutoModelForDocumentQuestionAnswering""",
  							    """AutoModelWithLMHead""",
  							    """AutoModelForZeroShotImageClassification""",
  							    """AutoModelForZeroShotObjectDetection""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							UpperCamelCase_	:    Tuple  					=       [
  							    """TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
  							    """TF_MODEL_FOR_CAUSAL_LM_MAPPING""",
  							    """TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
  							    """TF_MODEL_FOR_MASK_GENERATION_MAPPING""",
  							    """TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
  							    """TF_MODEL_FOR_MASKED_LM_MAPPING""",
  							    """TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
  							    """TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
  							    """TF_MODEL_FOR_PRETRAINING_MAPPING""",
  							    """TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
  							    """TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
  							    """TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
  							    """TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
  							    """TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
  							    """TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
  							    """TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
  							    """TF_MODEL_FOR_TEXT_ENCODING_MAPPING""",
  							    """TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
  							    """TF_MODEL_FOR_VISION_2_SEQ_MAPPING""",
  							    """TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
  							    """TF_MODEL_MAPPING""",
  							    """TF_MODEL_WITH_LM_HEAD_MAPPING""",
  							    """TFAutoModel""",
  							    """TFAutoModelForAudioClassification""",
  							    """TFAutoModelForCausalLM""",
  							    """TFAutoModelForImageClassification""",
  							    """TFAutoModelForMaskedImageModeling""",
  							    """TFAutoModelForMaskedLM""",
  							    """TFAutoModelForMaskGeneration""",
  							    """TFAutoModelForMultipleChoice""",
  							    """TFAutoModelForNextSentencePrediction""",
  							    """TFAutoModelForPreTraining""",
  							    """TFAutoModelForDocumentQuestionAnswering""",
  							    """TFAutoModelForQuestionAnswering""",
  							    """TFAutoModelForSemanticSegmentation""",
  							    """TFAutoModelForSeq2SeqLM""",
  							    """TFAutoModelForSequenceClassification""",
  							    """TFAutoModelForSpeechSeq2Seq""",
  							    """TFAutoModelForTableQuestionAnswering""",
  							    """TFAutoModelForTextEncoding""",
  							    """TFAutoModelForTokenClassification""",
  							    """TFAutoModelForVision2Seq""",
  							    """TFAutoModelForZeroShotImageClassification""",
  							    """TFAutoModelWithLMHead""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							UpperCamelCase_	:    Optional[int]  					=       [
  							    """FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
  							    """FLAX_MODEL_FOR_CAUSAL_LM_MAPPING""",
  							    """FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
  							    """FLAX_MODEL_FOR_MASKED_LM_MAPPING""",
  							    """FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
  							    """FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
  							    """FLAX_MODEL_FOR_PRETRAINING_MAPPING""",
  							    """FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
  							    """FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
  							    """FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
  							    """FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
  							    """FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
  							    """FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING""",
  							    """FLAX_MODEL_MAPPING""",
  							    """FlaxAutoModel""",
  							    """FlaxAutoModelForCausalLM""",
  							    """FlaxAutoModelForImageClassification""",
  							    """FlaxAutoModelForMaskedLM""",
  							    """FlaxAutoModelForMultipleChoice""",
  							    """FlaxAutoModelForNextSentencePrediction""",
  							    """FlaxAutoModelForPreTraining""",
  							    """FlaxAutoModelForQuestionAnswering""",
  							    """FlaxAutoModelForSeq2SeqLM""",
  							    """FlaxAutoModelForSequenceClassification""",
  							    """FlaxAutoModelForSpeechSeq2Seq""",
  							    """FlaxAutoModelForTokenClassification""",
  							    """FlaxAutoModelForVision2Seq""",
  							]
  
  
  if TYPE_CHECKING:
  							from .auto_factory import get_values
  							from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
  							from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
  							from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
  							from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
  							from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_auto import (
  														    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
  														    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
  														    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
  														    MODEL_FOR_BACKBONE_MAPPING,
  														    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
  														    MODEL_FOR_CAUSAL_LM_MAPPING,
  														    MODEL_FOR_CTC_MAPPING,
  														    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
  														    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
  														    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
  														    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
  														    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
  														    MODEL_FOR_MASK_GENERATION_MAPPING,
  														    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
  														    MODEL_FOR_MASKED_LM_MAPPING,
  														    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
  														    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
  														    MODEL_FOR_OBJECT_DETECTION_MAPPING,
  														    MODEL_FOR_PRETRAINING_MAPPING,
  														    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
  														    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
  														    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  														    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
  														    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
  														    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  														    MODEL_FOR_TEXT_ENCODING_MAPPING,
  														    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
  														    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
  														    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
  														    MODEL_FOR_VISION_2_SEQ_MAPPING,
  														    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
  														    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
  														    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
  														    MODEL_MAPPING,
  														    MODEL_WITH_LM_HEAD_MAPPING,
  														    AutoBackbone,
  														    AutoModel,
  														    AutoModelForAudioClassification,
  														    AutoModelForAudioFrameClassification,
  														    AutoModelForAudioXVector,
  														    AutoModelForCausalLM,
  														    AutoModelForCTC,
  														    AutoModelForDepthEstimation,
  														    AutoModelForDocumentQuestionAnswering,
  														    AutoModelForImageClassification,
  														    AutoModelForImageSegmentation,
  														    AutoModelForInstanceSegmentation,
  														    AutoModelForMaskedImageModeling,
  														    AutoModelForMaskedLM,
  														    AutoModelForMaskGeneration,
  														    AutoModelForMultipleChoice,
  														    AutoModelForNextSentencePrediction,
  														    AutoModelForObjectDetection,
  														    AutoModelForPreTraining,
  														    AutoModelForQuestionAnswering,
  														    AutoModelForSemanticSegmentation,
  														    AutoModelForSeqaSeqLM,
  														    AutoModelForSequenceClassification,
  														    AutoModelForSpeechSeqaSeq,
  														    AutoModelForTableQuestionAnswering,
  														    AutoModelForTextEncoding,
  														    AutoModelForTokenClassification,
  														    AutoModelForUniversalSegmentation,
  														    AutoModelForVideoClassification,
  														    AutoModelForVisionaSeq,
  														    AutoModelForVisualQuestionAnswering,
  														    AutoModelForZeroShotImageClassification,
  														    AutoModelForZeroShotObjectDetection,
  														    AutoModelWithLMHead,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_auto import (
  														    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
  														    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
  														    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
  														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
  														    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
  														    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
  														    TF_MODEL_FOR_MASKED_LM_MAPPING,
  														    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
  														    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
  														    TF_MODEL_FOR_PRETRAINING_MAPPING,
  														    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
  														    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
  														    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  														    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
  														    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
  														    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  														    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
  														    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
  														    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
  														    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
  														    TF_MODEL_MAPPING,
  														    TF_MODEL_WITH_LM_HEAD_MAPPING,
  														    TFAutoModel,
  														    TFAutoModelForAudioClassification,
  														    TFAutoModelForCausalLM,
  														    TFAutoModelForDocumentQuestionAnswering,
  														    TFAutoModelForImageClassification,
  														    TFAutoModelForMaskedImageModeling,
  														    TFAutoModelForMaskedLM,
  														    TFAutoModelForMaskGeneration,
  														    TFAutoModelForMultipleChoice,
  														    TFAutoModelForNextSentencePrediction,
  														    TFAutoModelForPreTraining,
  														    TFAutoModelForQuestionAnswering,
  														    TFAutoModelForSemanticSegmentation,
  														    TFAutoModelForSeqaSeqLM,
  														    TFAutoModelForSequenceClassification,
  														    TFAutoModelForSpeechSeqaSeq,
  														    TFAutoModelForTableQuestionAnswering,
  														    TFAutoModelForTextEncoding,
  														    TFAutoModelForTokenClassification,
  														    TFAutoModelForVisionaSeq,
  														    TFAutoModelForZeroShotImageClassification,
  														    TFAutoModelWithLMHead,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_auto import (
  														    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
  														    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
  														    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
  														    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
  														    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
  														    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
  														    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
  														    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
  														    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  														    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
  														    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
  														    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
  														    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
  														    FLAX_MODEL_MAPPING,
  														    FlaxAutoModel,
  														    FlaxAutoModelForCausalLM,
  														    FlaxAutoModelForImageClassification,
  														    FlaxAutoModelForMaskedLM,
  														    FlaxAutoModelForMultipleChoice,
  														    FlaxAutoModelForNextSentencePrediction,
  														    FlaxAutoModelForPreTraining,
  														    FlaxAutoModelForQuestionAnswering,
  														    FlaxAutoModelForSeqaSeqLM,
  														    FlaxAutoModelForSequenceClassification,
  														    FlaxAutoModelForSpeechSeqaSeq,
  														    FlaxAutoModelForTokenClassification,
  														    FlaxAutoModelForVisionaSeq,
  														)
  
  else:
  							import sys
  
  							UpperCamelCase_	:    Optional[int]  					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  from dataclasses import dataclass
  
  UpperCamelCase_	:    List[str]  					=       """0.1"""
  UpperCamelCase_	:    Optional[Any]  					=       """Lucia Harcekova"""
  
  
  
  
  @dataclass
  class      __lowercase	:
  	_A   			=      42
  	_A   			=      42
  	_A   			=      42
  
  
  
  
  
  
  	def __repr__(self							:   List[str]						)   -> str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   Union[str, Any]			,  snake_case							:   int = 13			,  snake_case							:   int = 6						)   -> None:
  							_lowercase					:  int    			=      window_size
  							_lowercase					:  List[Any]    			=      lookahead_buffer_size
  							_lowercase					:  str    			=      self.window_size - self.lookahead_buffer_size
  
  
  
  
  
  
  	def 			_a(self							:   List[str]			,  snake_case							:   str						)   -> list[Token]:
  							_lowercase					:  List[Any]    			=      []
  							_lowercase					:  str    			=      ""
  
  							# 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)
  													_lowercase					:  Union[str, Any]    			=      self._find_encoding_token(snake_case			,  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(snake_case						) > self.search_buffer_size:
  																			_lowercase					:  Optional[int]    			=      search_buffer[-self.search_buffer_size :]
  
  													# update the text
  													_lowercase					:  str    			=      text[token.length + 1 :]
  
  													# append the token to output
  													output.append(snake_case						)
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   list[Token]						)   -> str:
  							_lowercase					:  Optional[int]    			=      ""
  
  							for token in tokens:
  													for _ in range(token.length						):
  																			output += output[-token.offset]
  													output += token.indicator
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   str						)   -> Token:
  
  							if not text:
  													raise ValueError("We need some text to work with."						)
  
  							# Initialise result parameters to default values
  							_lowercase			,						_lowercase					:  Tuple    			=      0, 0
  
  							if not search_buffer:
  													return Token(snake_case			,  snake_case			,  text[length]						)
  
  							for i, character in enumerate(snake_case						):
  													_lowercase					:  Tuple    			=      len(snake_case						) - i
  													if character == text[0]:
  																			_lowercase					:  List[str]    			=      self._match_length_from_index(snake_case			,  snake_case			,  0			,  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:
  																									_lowercase			,						_lowercase					:  Optional[Any]    			=      found_offset, found_length
  
  							return Token(snake_case			,  snake_case			,  text[length]						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   int			,  snake_case							:   int						)   -> int:
  							if not text or text[text_index] != window[window_index]:
  													return 0
  							return 1 + self._match_length_from_index(
  							    snake_case			,  window + text[text_index]			,  text_index + 1			,  window_index + 1						)
  
  
  if __name__ == "__main__":
  							from doctest import testmod
  
  							testmod()
  							# Initialize compressor class
  							UpperCamelCase_	:    Optional[int]  					=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  							# Example
  							UpperCamelCase_	:    Optional[int]  					=       """cabracadabrarrarrad"""
  							UpperCamelCase_	:    Tuple  					=       lzaa_compressor.compress(TEXT)
  							print(lzaa_compressor.compress("""ababcbababaa"""))
  							UpperCamelCase_	:    Union[str, Any]  					=       lzaa_compressor.decompress(compressed_text)
  							assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  