
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__)






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."




