





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       (    _UpperCAmelCase      ,   _UpperCAmelCase=1_00      ,   _UpperCAmelCase=" "					)    ->	List[str]:
				lowerCamelCase									=text.split(lowercase_					)
				return [character.join(text[i : i + n]					).strip() for i in range(0      ,   len(lowercase_					)      ,   lowercase_					)]





def   _lowercase       (    _UpperCAmelCase					)    ->	Union[str, Any]:
				lowerCamelCase			,	lowerCamelCase									=[], []
				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       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Optional[int]:
				lowerCamelCase									=load_dataset(
				    """csv"""      ,   data_files=[csv_path]      ,   split="""train"""      ,   delimiter="""\t"""      ,   column_names=["""title""", """text"""]					)
				lowerCamelCase									=kb_dataset.map(
				    lowercase_      ,   batched=lowercase_      ,   num_proc=1					)  # if you want you can load already splitted csv.
				lowerCamelCase									=[kb_dataset.shard(lowercase_      ,   lowercase_      ,   contiguous=lowercase_					) for i in range(lowercase_					)]
				lowerCamelCase									=kb_list[process_num]

				lowerCamelCase									="""data_""" + str(lowercase_					)
				lowerCamelCase									=os.path.join(lowercase_      ,   lowercase_					)

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

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

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

				lowerCamelCase									=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   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	int:
				lowerCamelCase									=[]

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

				lowerCamelCase									=concatenate_datasets(lowercase_					)
				faiss.omp_set_num_threads(96					)

				lowerCamelCase									=faiss.IndexHNSWFlat(7_68      ,   1_28      ,   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   UpperCamelCase(   lowercase_ = 100     ,   lowercase_ = 100				)      ->							int:





  '''simple docstring'''





  return max(
      sum(int(lowercase_				) for x in str(base**power				)				)
      for base in range(lowercase_				)
      for power in range(lowercase_				)				)


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()