code: '''simple docstring'''
  
  
  
  
  
  from dataclasses import dataclass
  
  lowercase__    :       List[str]						=			'''0.1'''
  lowercase__    :       Dict						=			'''Lucia Harcekova'''
  
  
  
  
  @dataclass
  class      lowercase_						:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      int
  	UpperCAmelCase_						:      int
  	UpperCAmelCase_						:      str
  
  
  	def __repr__(							self       )		->str:
  								return F"({self.offset}, {self.length}, {self.indicator})"
  
  
  
  
  class      lowercase_						:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 13	,    __SCREAMING_SNAKE_CASE = 6       )		->None:
  								lowerCAmelCase				=	window_size
  								lowerCAmelCase				=	lookahead_buffer_size
  								lowerCAmelCase				=	self.window_size - self.lookahead_buffer_size
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->list[Token]:
  								lowerCAmelCase				=	[]
  								lowerCAmelCase				=	''''''
  
  								# 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)
  															lowerCAmelCase				=	self._find_encoding_token(__SCREAMING_SNAKE_CASE	,    __SCREAMING_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(__SCREAMING_SNAKE_CASE       ) > self.search_buffer_size:
  																						lowerCAmelCase				=	search_buffer[-self.search_buffer_size :]
  
  															# update the text
  															lowerCAmelCase				=	text[token.length + 1 :]
  
  															# append the token to output
  															output.append(__SCREAMING_SNAKE_CASE       )
  
  								return output
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->str:
  								lowerCAmelCase				=	''''''
  
  								for token in tokens:
  															for _ in range(token.length       ):
  																						output += output[-token.offset]
  															output += token.indicator
  
  								return output
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Token:
  
  								if not text:
  															raise ValueError('''We need some text to work with.'''       )
  
  								# Initialise result parameters to default values
  								lowerCAmelCase	,   lowerCAmelCase				=	0, 0
  
  								if not search_buffer:
  															return Token(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    text[length]       )
  
  								for i, character in enumerate(__SCREAMING_SNAKE_CASE       ):
  															lowerCAmelCase				=	len(__SCREAMING_SNAKE_CASE       ) - i
  															if character == text[0]:
  																						lowerCAmelCase				=	self._match_length_from_index(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    0	,    __SCREAMING_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:
  																													lowerCAmelCase	,   lowerCAmelCase				=	found_offset, found_length
  
  								return Token(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    text[length]       )
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->int:
  								if not text or text[text_index] != window[window_index]:
  															return 0
  								return 1 + self._match_length_from_index(
  								    __SCREAMING_SNAKE_CASE	,    window + text[text_index]	,    text_index + 1	,    window_index + 1       )
  
  
  if __name__ == "__main__":
  	from doctest import testmod
  
  	testmod()
  	# Initialize compressor class
  	lowercase__    :       Optional[int]						=			LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)
  
  	# Example
  	lowercase__    :       List[str]						=			'''cabracadabrarrarrad'''
  	lowercase__    :       List[str]						=			lzaa_compressor.compress(TEXT)
  	print(lzaa_compressor.compress('''ababcbababaa'''))
  	lowercase__    :       List[Any]						=			lzaa_compressor.decompress(compressed_text)
  	assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
style_context: import torch
  
  from accelerate import Accelerator, DistributedType
  
  
  
  
  class      lowercase_						:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __enter__(							self       )		->Dict:
  								if self.enabled:
  															lowerCAmelCase				=	self.model.no_sync()
  															self.model_sync_obj.__enter__()
  
  								return self
  
  
  	def __exit__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Any:
  								if self.enabled:
  															# Average all models on exit
  															self._sync_and_avg_model_params()
  															self.model_sync_obj.__exit__(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = True       )		->Tuple:
  								if accelerator.distributed_type not in [
  								    DistributedType.NO,
  								    DistributedType.MULTI_CPU,
  								    DistributedType.MULTI_GPU,
  								]:
  															raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
  								lowerCAmelCase				=	enabled and accelerator.distributed_type != DistributedType.NO
  								lowerCAmelCase				=	0
  								if self.enabled:
  															lowerCAmelCase				=	accelerator
  															lowerCAmelCase				=	model
  															lowerCAmelCase				=	local_sgd_steps
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
  								self.num_steps += 1
  								if not self.enabled:
  															return
  
  								if self.num_steps % self.local_sgd_steps == 0:
  															self._sync_and_avg_model_params()
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
  								self.accelerator.wait_for_everyone()
  								with self.accelerator.autocast():
  															for param in self.model.parameters():
  																						lowerCAmelCase				=	self.accelerator.reduce(param.data	,    reduction='''mean'''       )
  
  
  
  