


'''simple docstring'''



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       _a      (		_lowercase   :      Tuple     ,   _lowercase   :      List[str]=100     ,   _lowercase   :      Optional[Any]=" "    ):





     '''simple docstring'''






     __UpperCAmelCase      :					int    				=     text.split(_lowercase    )
     return [character.join(text[i : i + n]    ).strip() for i in range(0     ,   len(_lowercase    )     ,   _lowercase    )]




def       _a      (		_lowercase   :      Dict    ):





     '''simple docstring'''






     __UpperCAmelCase				,  __UpperCAmelCase      :					List[str]    				=     [], []
     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       _a      (		_lowercase   :      Any     ,   _lowercase   :      int     ,   _lowercase   :      List[str]     ,   _lowercase   :      int     ,   _lowercase   :      Dict     ,   _lowercase   :      Any    ):





     '''simple docstring'''






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

     __UpperCAmelCase      :					Any    				=     '''data_''' + str(_lowercase    )
     __UpperCAmelCase      :					Any    				=     os.path.join(_lowercase     ,   _lowercase    )

     __UpperCAmelCase      :					Union[str, Any]    				=     DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''    )
     __UpperCAmelCase      :					List[Any]    				=     ctx_encoder.to(device=_lowercase    )

     def embed(
         _lowercase   :      dict     ,   _lowercase   :      DPRContextEncoder     ,   _lowercase   :      DPRContextEncoderTokenizerFast     ,   _lowercase   :      Optional[int]    ) -> dict:
          __UpperCAmelCase      :					int    				=     ctx_tokenizer(
              documents['''title''']     ,   documents['''text''']     ,   truncation=_lowercase     ,   padding='''longest'''     ,   return_tensors='''pt'''    )['''input_ids''']
          __UpperCAmelCase      :					List[str]    				=     ctx_encoder(input_ids.to(device=_lowercase    )     ,   return_dict=_lowercase    ).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}

     __UpperCAmelCase      :					Tuple    				=     Features(
         {'''text''': Value('''string'''    ), '''title''': Value('''string'''    ), '''embeddings''': Sequence(Value('''float32'''    )    )}    )  # optional, save as float32 instead of float64 to save space

     __UpperCAmelCase      :					List[str]    				=     data_shrad.map(
         partial(_lowercase     ,   ctx_encoder=_lowercase     ,   ctx_tokenizer=_lowercase     ,   device=_lowercase    )     ,   batched=_lowercase     ,   batch_size=16     ,   features=_lowercase     ,   )
     dataset.save_to_disk(_lowercase    )




def       _a      (		_lowercase   :      Tuple     ,   _lowercase   :      List[str]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     []

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

     __UpperCAmelCase      :					Tuple    				=     concatenate_datasets(_lowercase    )
     faiss.omp_set_num_threads(96    )

     __UpperCAmelCase      :					Union[str, Any]    				=     faiss.IndexHNSWFlat(768     ,   128     ,   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



'''simple docstring'''



def       _a      (		_lowercase   :      list    ):





     '''simple docstring'''






     if len(_lowercase    ) <= 1:
          return data
     else:
          return [
              *quick_sort([e for e in data[1:] if e <= data[0]]    ),
              data[0],
              *quick_sort([e for e in data[1:] if e > data[0]]    ),
          ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()