





'''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						(       lowerCamelCase_	,	lowerCamelCase_=1_00	,	lowerCamelCase_=" "					):


		"""simple docstring"""



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



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



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

		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	"""data_""" + str(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	os.path.join(lowerCamelCase_	,	lowerCamelCase_					)

		SCREAMING_SNAKE_CASE				:							List[str]						=	DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""					)
		SCREAMING_SNAKE_CASE				:							Dict						=	ctx_encoder.to(device=lowerCamelCase_					)

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

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

		SCREAMING_SNAKE_CASE				:							Any						=	data_shrad.map(
		    partial(lowerCamelCase_	,	ctx_encoder=lowerCamelCase_	,	ctx_tokenizer=lowerCamelCase_	,	device=lowerCamelCase_					)	,	batched=lowerCamelCase_	,	batch_size=16	,	features=lowerCamelCase_	,	)
		dataset.save_to_disk(lowerCamelCase_					)



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							str						=	[]

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

		SCREAMING_SNAKE_CASE				:							Dict						=	concatenate_datasets(lowerCamelCase_					)
		faiss.omp_set_num_threads(96					)

		SCREAMING_SNAKE_CASE				:							Dict						=	faiss.IndexHNSWFlat(7_68	,	1_28	,	faiss.METRIC_INNER_PRODUCT					)
		concat.add_faiss_index("""embeddings"""	,	custom_index=lowerCamelCase_					)
		concat.get_index("""embeddings"""					).save(
		    lowerCamelCase_					)  # since we load the index in to memory,we can directly update the index in the disk









'''simple docstring'''




from __future__ import annotations



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		if len(lowerCamelCase_					) == 0:
				raise ValueError("""find_min() arg is an empty sequence"""					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	nums[0]
		for num in nums:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	min(lowerCamelCase_	,	lowerCamelCase_					)
		return min_num


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)



