
from dataclasses import dataclass

SCREAMING_SNAKE_CASE							:Optional[Any]											=       '0.1'
SCREAMING_SNAKE_CASE							:List[str]											=       'Lucia Harcekova'





@dataclass
class 							UpperCAmelCase   :




	'''simple docstring'''





	snake_case_      					=  42
	snake_case_      					=  42
	snake_case_      					=  42






	def __repr__( self      :				Dict				):
					return f'''({self.offset}, {self.length}, {self.indicator})'''





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				str							,A      :				int = 13							,A      :				int = 6				):
					__A         =				window_size
					__A         =				lookahead_buffer_size
					__A         =				self.window_size - self.lookahead_buffer_size






	def        UpperCamelCase_						( self      :				Dict							,A      :				str				):
					__A         =				[]
					__A         =				""

					# 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         =				self._find_encoding_token(A							,A				)

									# 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(A				) > self.search_buffer_size:
													__A         =				search_buffer[-self.search_buffer_size :]

									# update the text
									__A         =				text[token.length + 1 :]

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

					return output






	def        UpperCamelCase_						( self      :				str							,A      :				list[Token]				):
					__A         =				""

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

					return output






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				str							,A      :				str				):

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

					# Initialise result parameters to default values
					__A    ,							__A         =				0, 0

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

					for i, character in enumerate(A				):
									__A         =				len(A				) - i
									if character == text[0]:
													__A         =				self._match_length_from_index(A							,A							,0							,A				)
													# 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         =				found_offset, found_length

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







	def        UpperCamelCase_						( self      :				List[Any]							,A      :				str							,A      :				str							,A      :				int							,A      :				int				):
					if not text or text[text_index] != window[window_index]:
									return 0
					return 1 + self._match_length_from_index(
					    A							,window + text[text_index]							,text_index + 1							,window_index + 1				)


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

					testmod()
					# Initialize compressor class
					SCREAMING_SNAKE_CASE							:List[Any]											=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)

					# Example
					SCREAMING_SNAKE_CASE							:List[Any]											=       'cabracadabrarrarrad'
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       lzaa_compressor.compress(TEXT)
					print(lzaa_compressor.compress('ababcbababaa'))
					SCREAMING_SNAKE_CASE							:List[str]											=       lzaa_compressor.decompress(compressed_text)
					assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."


from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
					from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
