





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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							List[Any]=100    ,      UpperCamelCase__:							Optional[Any]=" "			):
		SCREAMING_SNAKE_CASE__								=					text.split(UpperCamelCase__			)
		return [character.join(text[i : i + n]			).strip() for i in range(0    ,      len(UpperCamelCase__			)    ,      UpperCamelCase__			)]






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[Any]			):
		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(UpperCamelCase__			):
								titles.append(title if title is not None else """"""			)
								texts.append(UpperCamelCase__			)
		return {"title": titles, "text": texts}






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

		SCREAMING_SNAKE_CASE__								=					"""data_""" + str(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					os.path.join(UpperCamelCase__    ,      UpperCamelCase__			)

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

		def embed(
		    UpperCamelCase__:							dict    ,      UpperCamelCase__:							DPRContextEncoder    ,      UpperCamelCase__:							DPRContextEncoderTokenizerFast    ,      UpperCamelCase__:							Tuple			) -> dict:
				SCREAMING_SNAKE_CASE__								=					ctx_tokenizer(
				    documents["""title"""]    ,      documents["""text"""]    ,      truncation=UpperCamelCase__    ,      padding="""longest"""    ,      return_tensors="""pt"""			)["""input_ids"""]
				SCREAMING_SNAKE_CASE__								=					ctx_encoder(input_ids.to(device=UpperCamelCase__			)    ,      return_dict=UpperCamelCase__			).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(UpperCamelCase__    ,      ctx_encoder=UpperCamelCase__    ,      ctx_tokenizer=UpperCamelCase__    ,      device=UpperCamelCase__			)    ,      batched=UpperCamelCase__    ,      batch_size=16    ,      features=UpperCamelCase__    ,      )
		dataset.save_to_disk(UpperCamelCase__			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[int]    ,      UpperCamelCase__:							Tuple			):
		SCREAMING_SNAKE_CASE__								=					[]

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

		SCREAMING_SNAKE_CASE__								=					concatenate_datasets(UpperCamelCase__			)
		faiss.omp_set_num_threads(96			)

		SCREAMING_SNAKE_CASE__								=					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






from __future__ import annotations

import math
import random






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							int			):
		SCREAMING_SNAKE_CASE__								=					d * e - 1
		SCREAMING_SNAKE_CASE__								=					0
		SCREAMING_SNAKE_CASE__								=					0
		while p == 0:
				SCREAMING_SNAKE_CASE__								=					random.randint(2    ,      n - 1			)
				SCREAMING_SNAKE_CASE__								=					k
				while True:
						if t % 2 == 0:
								SCREAMING_SNAKE_CASE__								=					t // 2
								SCREAMING_SNAKE_CASE__								=					(g**t) % n
								SCREAMING_SNAKE_CASE__								=					math.gcd(x - 1    ,      UpperCamelCase__			)
								if x > 1 and y > 1:
										SCREAMING_SNAKE_CASE__								=					y
										SCREAMING_SNAKE_CASE__								=					n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()