





"""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__							(     UpperCamelCase   ,    UpperCamelCase=100   ,    UpperCamelCase=" "			):
							A						=    text.split(UpperCamelCase			)
							return [character.join(text[i : i + n]			).strip() for i in range(0   ,    len(UpperCamelCase			)   ,    UpperCamelCase			)]






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






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

							A						=    "data_" + str(UpperCamelCase			)
							A						=    os.path.join(UpperCamelCase   ,    UpperCamelCase			)

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

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

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

							A						=    data_shrad.map(
							    partial(UpperCamelCase   ,    ctx_encoder=UpperCamelCase   ,    ctx_tokenizer=UpperCamelCase   ,    device=UpperCamelCase			)   ,    batched=UpperCamelCase   ,    batch_size=16   ,    features=UpperCamelCase   ,    )
							dataset.save_to_disk(UpperCamelCase			)






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    []

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

							A						=    concatenate_datasets(UpperCamelCase			)
							faiss.omp_set_num_threads(96			)

							A						=    faiss.IndexHNSWFlat(768   ,    128   ,    faiss.METRIC_INNER_PRODUCT			)
							concat.add_faiss_index("embeddings"   ,    custom_index=UpperCamelCase			)
							concat.get_index("embeddings"			).save(
							    UpperCamelCase			)  # 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__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


