"""simple docstring"""



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Union[str, Any]				    =							['image_processor', 'tokenizer']
			_UpperCamelCase     :	Optional[int]				    =							'AutoImageProcessor'
			_UpperCamelCase     :	Optional[Any]				    =							'AutoTokenizer'





			def __init__( self      ,			snake_case_=None      ,			snake_case_=None      ,			**snake_case_       ):







										'''simple docstring'''


										A__					:    List[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."""      ,			snake_case_      ,			)
																	A__					:    Any														= kwargs.pop("""feature_extractor"""       )

										A__					:    Optional[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__(snake_case_      ,			snake_case_       )
										A__					:    str														= self.image_processor
										A__					:    Optional[Any]														= False





			def __call__( self      ,			*snake_case_      ,			**snake_case_       ):







										'''simple docstring'''


										if self._in_target_context_manager:
																	return self.current_processor(*snake_case_      ,			**snake_case_       )

										A__					:    Optional[Any]														= kwargs.pop("""images"""      ,			snake_case_       )
										A__					:    Optional[Any]														= kwargs.pop("""text"""      ,			snake_case_       )
										if len(snake_case_       ) > 0:
																	A__					:    str														= args[0]
																	A__					:    Optional[int]														= args[1:]

										if images is None and text is None:
																	raise ValueError("""You need to specify either an `images` or `text` input to process."""       )

										if images is not None:
																	A__					:    Dict														= self.image_processor(snake_case_      ,			*snake_case_      ,			**snake_case_       )
										if text is not None:
																	A__					:    Optional[int]														= self.tokenizer(snake_case_      ,			**snake_case_       )

										if text is None:
																	return inputs
										elif images is None:
																	return encodings
										else:
																	A__					:    Optional[int]														= encodings["""input_ids"""]
																	return inputs





			def 		lowerCamelCase				( self      ,			*snake_case_      ,			**snake_case_       ):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*snake_case_      ,			**snake_case_       )





			def 		lowerCamelCase				( self      ,			*snake_case_      ,			**snake_case_       ):







										'''simple docstring'''


										return self.tokenizer.decode(*snake_case_      ,			**snake_case_       )





			@contextmanager
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										warnings.warn(
										    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
										    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
										    """your images inputs, or in a separate call."""       )
										A__					:    int														= True
										A__					:    List[str]														= self.tokenizer
										yield
										A__					:    Optional[Any]														= self.image_processor
										A__					:    List[str]														= False





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,			snake_case_      ,			)
										return self.image_processor_class





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,			snake_case_      ,			)
										return self.image_processor






"""simple docstring"""



from dataclasses import dataclass

_UpperCamelCase         =		"0.1"
_UpperCamelCase         =		"Lucia Harcekova"
@dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			_UpperCamelCase     :	int
			_UpperCamelCase     :	int
			_UpperCamelCase     :	str





			def __repr__( self       ):







										'''simple docstring'''


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





class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = 13      ,			snake_case_ = 6       ):







										'''simple docstring'''


										A__					:    str														= window_size
										A__					:    Optional[int]														= lookahead_buffer_size
										A__					:    Optional[Any]														= self.window_size - self.lookahead_buffer_size





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    str														= []
										A__					:    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)
																	A__					:    Optional[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:
																								A__					:    Any														= search_buffer[-self.search_buffer_size :]

																	# update the text
																	A__					:    Optional[Any]														= text[token.length + 1 :]

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

										return output





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= """"""

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

										return output





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


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

										# Initialise result parameters to default values
										A__							,       A__					:    List[Any]														= 0, 0

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

										for i, character in enumerate(snake_case_       ):
																	A__					:    Tuple														= len(snake_case_       ) - i
																	if character == text[0]:
																								A__					:    Tuple														= 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:
																															A__							,       A__					:    List[str]														= found_offset, found_length

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										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         =		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."





