






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_	,  lowercase_=1_0_0	,  lowercase_=" "			)				->List[str]:







 """simple docstring"""

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





def 						A_					(						lowercase_			)				->Optional[int]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		[], []
 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_	,  lowercase_	,  lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE				    =		'data_' + str(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		os.path.join(lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'			)
 SCREAMING_SNAKE_CASE				    =		ctx_encoder.to(device=lowercase_			)

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

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

 SCREAMING_SNAKE_CASE				    =		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 						A_					(						lowercase_	,  lowercase_			)				->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		[]

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

 SCREAMING_SNAKE_CASE				    =		concatenate_datasets(lowercase_			)
 faiss.omp_set_num_threads(9_6			)

 SCREAMING_SNAKE_CASE				    =		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






"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    str(lowerCAmelCase		).strip()
     if not oct_string:
          raise ValueError("Empty string was passed to the function"		)
     UpperCAmelCase__				:			Tuple							=    oct_string[0] == "-"
     if is_negative:
          UpperCAmelCase__				:			List[Any]							=    oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCAmelCase		) <= 7 for char in oct_string		):
          raise ValueError("Non-octal value was passed to the function"		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for char in oct_string:
          UpperCAmelCase__				:			Tuple							=    8 * decimal_number + int(lowerCAmelCase		)
     if is_negative:
          UpperCAmelCase__				:			int							=    -decimal_number
     return decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()



