


from dataclasses import dataclass

snake_case__     							= '0.1'
snake_case__     							= 'Lucia Harcekova'



@dataclass
class     snake_case__  :
				__lowerCAmelCase		=      42
				__lowerCAmelCase		=      42
				__lowerCAmelCase		=      42
				def __repr__(  self      ):




										"""simple docstring"""




										return F"({self.offset}, {self.length}, {self.indicator})"






class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase = 13     ,      _UpperCAmelCase = 6      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      window_size
										__magic_name__				:							Optional[int]				=      lookahead_buffer_size
										__magic_name__				:							Tuple				=      self.window_size - self.lookahead_buffer_size
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      []
										__magic_name__				:							List[Any]				=      ''

										# 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)
																__magic_name__				:							Dict				=      self._find_encoding_token(_UpperCAmelCase     ,      _UpperCAmelCase      )

																# 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(_UpperCAmelCase      ) > self.search_buffer_size:
																						__magic_name__				:							List[Any]				=      search_buffer[-self.search_buffer_size :]

																# update the text
																__magic_name__				:							List[str]				=      text[token.length + 1 :]

																# append the token to output
																output.append(_UpperCAmelCase      )

										return output
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      ''

										for token in tokens:
																for _ in range(token.length      ):
																						output += output[-token.offset]
																output += token.indicator

										return output
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if not text:
																raise ValueError('We need some text to work with.'      )

										# Initialise result parameters to default values
										__magic_name__		, __magic_name__				:							Union[str, Any]				=      0, 0

										if not search_buffer:
																return Token(_UpperCAmelCase     ,      _UpperCAmelCase     ,      text[length]      )

										for i, character in enumerate(_UpperCAmelCase      ):
																__magic_name__				:							List[Any]				=      len(_UpperCAmelCase      ) - i
																if character == text[0]:
																						__magic_name__				:							Any				=      self._match_length_from_index(_UpperCAmelCase     ,      _UpperCAmelCase     ,      0     ,      _UpperCAmelCase      )
																						# 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:
																												__magic_name__		, __magic_name__				:							Optional[Any]				=      found_offset, found_length

										return Token(_UpperCAmelCase     ,      _UpperCAmelCase     ,      text[length]      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if not text or text[text_index] != window[window_index]:
																return 0
										return 1 + self._match_length_from_index(
										    _UpperCAmelCase     ,      window + text[text_index]     ,      text_index + 1     ,      window_index + 1      )


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			# Initialize compressor class
			snake_case__     							= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

			# Example
			snake_case__     							= 'cabracadabrarrarrad'
			snake_case__     							= lzaa_compressor.compress(TEXT)
			print(lzaa_compressor.compress('ababcbababaa'))
			snake_case__     							= lzaa_compressor.decompress(compressed_text)
			assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

			snake_case__     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


