

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 		_lowercase			(					lowercase__       ,     lowercase__=1_0_0       ,     lowercase__=" "						):
      __lowerCAmelCase :						str = text.split(lowercase__						)
      return [character.join(text[i : i + n]						).strip() for i in range(0       ,     len(lowercase__						)       ,     lowercase__						)]







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase,							__lowerCAmelCase :						List[Any] = [], []
      for title, text in zip(documents['''title''']       ,     documents['''text''']						):
            if text is not None:
                  for passage in split_text(lowercase__						):
                        titles.append(title if title is not None else ''''''						)
                        texts.append(lowercase__						)
      return {"title": titles, "text": texts}







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						str = load_dataset(
          '''csv'''       ,     data_files=[csv_path]       ,     split='''train'''       ,     delimiter='''\t'''       ,     column_names=['''title''', '''text''']						)
      __lowerCAmelCase :						str = kb_dataset.map(
          lowercase__       ,     batched=lowercase__       ,     num_proc=1						)  # if you want you can load already splitted csv.
      __lowerCAmelCase :						int = [kb_dataset.shard(lowercase__       ,     lowercase__       ,     contiguous=lowercase__						) for i in range(lowercase__						)]
      __lowerCAmelCase :						List[Any] = kb_list[process_num]

      __lowerCAmelCase :						int = '''data_''' + str(lowercase__						)
      __lowerCAmelCase :						int = os.path.join(lowercase__       ,     lowercase__						)

      __lowerCAmelCase :						int = DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''						)
      __lowerCAmelCase :						Union[str, Any] = ctx_encoder.to(device=lowercase__						)

      def embed(
          lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						) -> dict:
            __lowerCAmelCase :						Dict = ctx_tokenizer(
                documents['''title''']       ,     documents['''text''']       ,     truncation=lowercase__       ,     padding='''longest'''       ,     return_tensors='''pt'''						)['''input_ids''']
            __lowerCAmelCase :						str = ctx_encoder(input_ids.to(device=lowercase__						)       ,     return_dict=lowercase__						).pooler_output
            return {"embeddings": embeddings.detach().cpu().numpy()}

      __lowerCAmelCase :						Union[str, Any] = Features(
          {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space

      __lowerCAmelCase :						List[Any] = data_shrad.map(
          partial(lowercase__       ,     ctx_encoder=lowercase__       ,     ctx_tokenizer=lowercase__       ,     device=lowercase__						)       ,     batched=lowercase__       ,     batch_size=1_6       ,     features=lowercase__       ,     )
      dataset.save_to_disk(lowercase__						)







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Optional[int] = []

      for shard_address in glob(str(lowercase__						) + '''/*/'''						):
            data_shard_list.append(load_from_disk(lowercase__						)						)

      __lowerCAmelCase :						str = concatenate_datasets(lowercase__						)
      faiss.omp_set_num_threads(9_6						)

      __lowerCAmelCase :						List[Any] = faiss.IndexHNSWFlat(7_6_8       ,     1_2_8       ,     faiss.METRIC_INNER_PRODUCT						)
      concat.add_faiss_index('''embeddings'''       ,     custom_index=lowercase__						)
      concat.get_index('''embeddings'''						).save(
          lowercase__						)  # since we load the index in to memory,we can directly update the index in the disk





from dataclasses import dataclass

_UpperCamelCase			      =		"0.1"
_UpperCamelCase			      =		"Lucia Harcekova"







@dataclass
class   __lowercase				:
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 def __repr__(							self ) ->str:



       '''simple docstring'''





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







class   __lowercase				:
 def __init__(							self				,							A_ = 13				,							A_ = 6 ) ->None:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = window_size
       __lowerCAmelCase :						str = lookahead_buffer_size
       __lowerCAmelCase :						Dict = self.window_size - self.lookahead_buffer_size
 def      UpperCamelCase__  (							self				,							A_ ) ->list[Token]:



       '''simple docstring'''





       __lowerCAmelCase :						int = []
       __lowerCAmelCase :						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)
             __lowerCAmelCase :						Optional[int] = 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:
                   __lowerCAmelCase :						Dict = search_buffer[-self.search_buffer_size :]

             # update the text
             __lowerCAmelCase :						List[Any] = text[token.length + 1 :]

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

       return output
 def      UpperCamelCase__  (							self				,							A_ ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = ''''''

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

       return output
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Token:



       '''simple docstring'''





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

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

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

       for i, character in enumerate(A_ ):
             __lowerCAmelCase :						Dict = len(A_ ) - i
             if character == text[0]:
                   __lowerCAmelCase :						Dict = 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:
                         __lowerCAmelCase,							__lowerCAmelCase :						int = found_offset, found_length

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





 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_ ) ->int:



       '''simple docstring'''





       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
 _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."


