





"""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 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							int=100 ,      lowercase_   :							int=" "		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       text.split(lowercase_		)
 return [character.join(text[i : i + n]		).strip() for i in range(0 ,      len(lowercase_		) ,      lowercase_		)]




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			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 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							int ,      lowercase_   :							Tuple ,      lowercase_   :							Tuple ,      lowercase_   :							Optional[int] ,      lowercase_   :							List[str]		):

 '''simple docstring'''






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

 __SCREAMING_SNAKE_CASE :			Dict												=       '''data_''' + str(lowercase_		)
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       os.path.join(lowercase_ ,      lowercase_		)

 __SCREAMING_SNAKE_CASE :			int												=       DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       ctx_encoder.to(device=lowercase_		)

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

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

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       []

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

 __SCREAMING_SNAKE_CASE :			List[Any]												=       concatenate_datasets(lowercase_		)
 faiss.omp_set_num_threads(96		)

 __SCREAMING_SNAKE_CASE :			int												=       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







def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  0
    for ele in arr:
        total_sum += product_sum(lowercase_    ,   depth + 1					) if isinstance(lowercase_    ,   lowercase_					) else ele
    return total_sum * depth




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return product_sum(lowercase_    ,   1					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






