






import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


a_							=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_							=    '''cuda''' if torch.cuda.is_available() else '''cpu'''


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Optional[int]=100    ,   UpperCamelCase_					:				Union[str, Any]=" "   ) ->   List[str]:
						"""simple docstring"""







						lowerCAmelCase__       =						text.split(UpperCamelCase_   )
						return [character.join(text[i : i + n]   ).strip() for i in range(0    ,   len(UpperCamelCase_   )    ,   UpperCamelCase_   )]


def        _a  (     UpperCamelCase_					:				dict   ) ->   dict:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__       =						[], []
						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_					:				dict    ,   UpperCamelCase_					:				DPRContextEncoder    ,   UpperCamelCase_					:				DPRContextEncoderTokenizerFast   ) ->   dict:
						"""simple docstring"""







						lowerCAmelCase__       =						ctx_tokenizer(
						    documents["title"]    ,   documents["text"]    ,   truncation=UpperCamelCase_    ,   padding="longest"    ,   return_tensors="pt"   )["input_ids"]
						lowerCAmelCase__       =						ctx_encoder(input_ids.to(device=UpperCamelCase_   )    ,   return_dict=UpperCamelCase_   ).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}


def        _a  (     UpperCamelCase_					:				"RagExampleArguments"    ,   UpperCamelCase_					:				"ProcessingArguments"    ,   UpperCamelCase_					:				"IndexHnswArguments"    ,   ) ->   Any:
						"""simple docstring"""







						logger.info("Step 1 - Create the dataset"   )
						######################################

						# The dataset needed for RAG must have three columns:
						# - title (string): title of the document
						# - text (string): text of a passage of the document
						# - embeddings (array of dimension d): DPR representation of the passage
						# Let's say you have documents in tab-separated csv files with columns "title" and "text"
						assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"

						# You can load a Dataset object this way
						lowerCAmelCase__       =						load_dataset(
						    "csv"    ,   data_files=[rag_example_args.csv_path]    ,   split="train"    ,   delimiter="\t"    ,   column_names=["title", "text"]   )

						# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

						# Then split the documents into passages of 100 words
						lowerCAmelCase__       =						dataset.map(UpperCamelCase_    ,   batched=UpperCamelCase_    ,   num_proc=processing_args.num_proc   )

						# And compute the embeddings
						lowerCAmelCase__       =						DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=UpperCamelCase_   )
						lowerCAmelCase__       =						DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
						lowerCAmelCase__       =						Features(
						    {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space
						lowerCAmelCase__       =						dataset.map(
						    partial(UpperCamelCase_    ,   ctx_encoder=UpperCamelCase_    ,   ctx_tokenizer=UpperCamelCase_   )    ,   batched=UpperCamelCase_    ,   batch_size=processing_args.batch_size    ,   features=UpperCamelCase_    ,   )

						# And finally save your dataset
						lowerCAmelCase__       =						os.path.join(rag_example_args.output_dir    ,   "my_knowledge_dataset"   )
						dataset.save_to_disk(UpperCamelCase_   )
						# from datasets import load_from_disk
						# dataset = load_from_disk(passages_path)  # to reload the dataset

						######################################
						logger.info("Step 2 - Index the dataset"   )
						######################################

						# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
						lowerCAmelCase__       =						faiss.IndexHNSWFlat(index_hnsw_args.d    ,   index_hnsw_args.m    ,   faiss.METRIC_INNER_PRODUCT   )
						dataset.add_faiss_index("embeddings"    ,   custom_index=UpperCamelCase_   )

						# And save the index
						lowerCAmelCase__       =						os.path.join(rag_example_args.output_dir    ,   "my_knowledge_dataset_hnsw_index.faiss"   )
						dataset.get_index("embeddings"   ).save(UpperCamelCase_   )
						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index


@dataclass
class      lowercase__     :
	a_         =field(
	    default=str(Path(_UpperCAmelCase	).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""	),					metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""},					)
	a_         =field(
	    default=_UpperCAmelCase,					metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""},					)
	a_         =field(
	    default="""facebook/rag-sequence-nq""",					metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""},					)
	a_         =field(
	    default="""facebook/dpr-ctx_encoder-multiset-base""",					metadata={
	        """help""": (
	            """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
	            """ 'facebook/dpr-ctx_encoder-multiset-base'"""
	        )
	    },					)
	a_         =field(
	    default=str(Path(_UpperCAmelCase	).parent / """test_run""" / """dummy-kb"""	),					metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""},					)





@dataclass
class      lowercase__     :
	a_         =field(
	    default=_UpperCAmelCase,					metadata={
	        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
	    },					)
	a_         =field(
	    default=16,					metadata={
	        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
	    },					)





@dataclass
class      lowercase__     :
	a_         =field(
	    default=768,					metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""},					)
	a_         =field(
	    default=128,					metadata={
	        """help""": (
	            """The number of bi-directional links created for every new element during the HNSW index construction."""
	        )
	    },					)


if __name__ == "__main__":
	logging.basicConfig(level=logging.WARNING)
	logger.setLevel(logging.INFO)

	a_							=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	a_,						a_,						a_							=    parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		a_							=    rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)














import random


def        _a  (     UpperCamelCase_					:				int   ) ->   bool:
						"""simple docstring"""







						lowerCAmelCase__       =						num - 1
						lowerCAmelCase__       =						0

						while s % 2 == 0:
												lowerCAmelCase__       =						s // 2
												t += 1

						for _ in range(5   ):
												lowerCAmelCase__       =						random.randrange(2    ,   num - 1   )
												lowerCAmelCase__       =						pow(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
												if v != 1:
																		lowerCAmelCase__       =						0
																		while v != (num - 1):
																								if i == t - 1:
																														return False
																								else:
																														lowerCAmelCase__       =						i + 1
																														lowerCAmelCase__       =						(v**2) % num
						return True


def        _a  (     UpperCamelCase_					:				int   ) ->   bool:
						"""simple docstring"""







						if num < 2:
												return False

						lowerCAmelCase__       =						[
						    2,
						    3,
						    5,
						    7,
						    11,
						    13,
						    17,
						    19,
						    23,
						    29,
						    31,
						    37,
						    41,
						    43,
						    47,
						    53,
						    59,
						    61,
						    67,
						    71,
						    73,
						    79,
						    83,
						    89,
						    97,
						    101,
						    103,
						    107,
						    109,
						    113,
						    127,
						    131,
						    137,
						    139,
						    149,
						    151,
						    157,
						    163,
						    167,
						    173,
						    179,
						    181,
						    191,
						    193,
						    197,
						    199,
						    211,
						    223,
						    227,
						    229,
						    233,
						    239,
						    241,
						    251,
						    257,
						    263,
						    269,
						    271,
						    277,
						    281,
						    283,
						    293,
						    307,
						    311,
						    313,
						    317,
						    331,
						    337,
						    347,
						    349,
						    353,
						    359,
						    367,
						    373,
						    379,
						    383,
						    389,
						    397,
						    401,
						    409,
						    419,
						    421,
						    431,
						    433,
						    439,
						    443,
						    449,
						    457,
						    461,
						    463,
						    467,
						    479,
						    487,
						    491,
						    499,
						    503,
						    509,
						    521,
						    523,
						    541,
						    547,
						    557,
						    563,
						    569,
						    571,
						    577,
						    587,
						    593,
						    599,
						    601,
						    607,
						    613,
						    617,
						    619,
						    631,
						    641,
						    643,
						    647,
						    653,
						    659,
						    661,
						    673,
						    677,
						    683,
						    691,
						    701,
						    709,
						    719,
						    727,
						    733,
						    739,
						    743,
						    751,
						    757,
						    761,
						    769,
						    773,
						    787,
						    797,
						    809,
						    811,
						    821,
						    823,
						    827,
						    829,
						    839,
						    853,
						    857,
						    859,
						    863,
						    877,
						    881,
						    883,
						    887,
						    907,
						    911,
						    919,
						    929,
						    937,
						    941,
						    947,
						    953,
						    967,
						    971,
						    977,
						    983,
						    991,
						    997,
						]

						if num in low_primes:
												return True

						for prime in low_primes:
												if (num % prime) == 0:
																		return False

						return rabin_miller(UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				int = 1_024   ) ->   int:
						"""simple docstring"""







						while True:
												lowerCAmelCase__       =						random.randrange(2 ** (keysize - 1)    ,   2 ** (keysize)   )
												if is_prime_low_num(UpperCamelCase_   ):
																		return num


if __name__ == "__main__":
	a_							=    generate_large_prime()
	print(('''Prime number:''', num))
	print(('''is_prime_low_num:''', is_prime_low_num(num)))






