code: 
  import os
  from functools import partial
  from glob import glob
  
  import faiss
  from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk
  
  from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[Any]   , _UpperCAmelCase     :      Union[str, Any]=100   , _UpperCAmelCase     :      Tuple=" "     )      -> str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  List[str]    			=      text.split(_UpperCAmelCase     )
  						return [character.join(text[i : i + n]     ).strip() for i in range(0   , len(_UpperCAmelCase     )   , _UpperCAmelCase     )]
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      List[str]     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase			,						_lowercase					:  Any    			=      [], []
  						for title, text in zip(documents["title"]   , documents["text"]     ):
  												if text is not None:
  																		for passage in split_text(_UpperCAmelCase     ):
  																								titles.append(title if title is not None else ""     )
  																								texts.append(_UpperCAmelCase     )
  						return {"title": titles, "text": texts}
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      List[Any]   , _UpperCAmelCase     :      Any   , _UpperCAmelCase     :      str   , _UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      List[Any]   , _UpperCAmelCase     :      List[Any]     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  str    			=      load_dataset(
  						    "csv"   , data_files=[csv_path]   , split="train"   , delimiter="\t"   , column_names=["title", "text"]     )
  						_lowercase					:  Optional[Any]    			=      kb_dataset.map(
  						    _UpperCAmelCase   , batched=_UpperCAmelCase   , num_proc=1     )  # if you want you can load already splitted csv.
  						_lowercase					:  List[Any]    			=      [kb_dataset.shard(_UpperCAmelCase   , _UpperCAmelCase   , contiguous=_UpperCAmelCase     ) for i in range(_UpperCAmelCase     )]
  						_lowercase					:  Tuple    			=      kb_list[process_num]
  
  						_lowercase					:  str    			=      "data_" + str(_UpperCAmelCase     )
  						_lowercase					:  List[Any]    			=      os.path.join(_UpperCAmelCase   , _UpperCAmelCase     )
  
  						_lowercase					:  Dict    			=      DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"     )
  						_lowercase					:  int    			=      ctx_encoder.to(device=_UpperCAmelCase     )
  
  						def embed(
  						    _UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      List[str]     ) -> dict:
  												_lowercase					:  Optional[int]    			=      ctx_tokenizer(
  												    documents["title"]   , documents["text"]   , truncation=_UpperCAmelCase   , padding="longest"   , return_tensors="pt"     )["input_ids"]
  												_lowercase					:  List[str]    			=      ctx_encoder(input_ids.to(device=_UpperCAmelCase     )   , return_dict=_UpperCAmelCase     ).pooler_output
  												return {"embeddings": embeddings.detach().cpu().numpy()}
  
  						_lowercase					:  Tuple    			=      Features(
  						    {"text": Value("string"     ), "title": Value("string"     ), "embeddings": Sequence(Value("float32"     )     )}     )  # optional, save as float32 instead of float64 to save space
  
  						_lowercase					:  Any    			=      data_shrad.map(
  						    partial(_UpperCAmelCase   , ctx_encoder=_UpperCAmelCase   , ctx_tokenizer=_UpperCAmelCase   , device=_UpperCAmelCase     )   , batched=_UpperCAmelCase   , batch_size=16   , features=_UpperCAmelCase   , )
  						dataset.save_to_disk(_UpperCAmelCase     )
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      int     )      -> Optional[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Any    			=      []
  
  						for shard_address in glob(str(_UpperCAmelCase     ) + "/*/"     ):
  												data_shard_list.append(load_from_disk(_UpperCAmelCase     )     )
  
  						_lowercase					:  str    			=      concatenate_datasets(_UpperCAmelCase     )
  						faiss.omp_set_num_threads(96     )
  
  						_lowercase					:  Optional[int]    			=      faiss.IndexHNSWFlat(768   , 128   , faiss.METRIC_INNER_PRODUCT     )
  						concat.add_faiss_index("embeddings"   , custom_index=_UpperCAmelCase     )
  						concat.get_index("embeddings"     ).save(
  						    _UpperCAmelCase     )  # since we load the index in to memory,we can directly update the index in the disk
  
  
  
  
  
style_context: 
  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."
  
  
  
  
  