






"""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 						lowercase__(A					,		A=100					,		A=" "						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      List[Any]=       text.split(A						)
						return [character.join(text[i : i + n]						).strip() for i in range(0					,		len(A						)					,		A						)]





def 						lowercase__(A						)				->Any:




						"""simple docstring"""



						lowercase__,	lowercase__				:      Tuple=       [], []
						for title, text in zip(documents["title"]					,		documents["text"]						):
												if text is not None:
																		for passage in split_text(A						):
																								titles.append(title if title is not None else ""						)
																								texts.append(A						)
						return {"title": titles, "text": texts}





def 						lowercase__(A					,		A					,		A					,		A					,		A					,		A						)				->Tuple:




						"""simple docstring"""



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

						lowercase__				:      List[Any]=       "data_" + str(A						)
						lowercase__				:      Any=       os.path.join(A					,		A						)

						lowercase__				:      Tuple=       DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"						)
						lowercase__				:      Union[str, Any]=       ctx_encoder.to(device=A						)

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

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

						lowercase__				:      Optional[Any]=       data_shrad.map(
						    partial(A					,		ctx_encoder=A					,		ctx_tokenizer=A					,		device=A						)					,		batched=A					,		batch_size=16					,		features=A					,		)
						dataset.save_to_disk(A						)







def 						lowercase__(A					,		A						)				->Optional[Any]:




						"""simple docstring"""



						lowercase__				:      List[Any]=       []

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

						lowercase__				:      Optional[Any]=       concatenate_datasets(A						)
						faiss.omp_set_num_threads(96						)

						lowercase__				:      Any=       faiss.IndexHNSWFlat(768					,		128					,		faiss.METRIC_INNER_PRODUCT						)
						concat.add_faiss_index("embeddings"					,		custom_index=A						)
						concat.get_index("embeddings"						).save(
						    A						)  # since we load the index in to memory,we can directly update the index in the disk












def 					snake_case					(							A			)							->							int:




	'''simple docstring'''


	if not isinstance(A						,   A			):
		raise ValueError('double_factorial() only accepts integral values'			)
	if n < 0:
		raise ValueError('double_factorial() not defined for negative values'			)
	return 1 if n <= 1 else n * double_factorial(n - 2			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
