






"""simple docstring"""




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


UpperCamelCase							       =    logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase							       =    """cuda""" if torch.cuda.is_available() else """cpu"""






def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							Optional[int]=100     , UpperCamelCase_:							Any=" "					)       ->							List[str]:







  '''simple docstring'''







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






def        lowerCAmelCase (      UpperCamelCase_:							dict					)       ->							dict:







  '''simple docstring'''







  _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        lowerCAmelCase (      UpperCamelCase_:							dict     , UpperCamelCase_:							DPRContextEncoder     , UpperCamelCase_:							DPRContextEncoderTokenizerFast					)       ->							dict:







  '''simple docstring'''







  _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()}






def        lowerCAmelCase (      UpperCamelCase_:							"RagExampleArguments"     , UpperCamelCase_:							"ProcessingArguments"     , UpperCamelCase_:							"IndexHnswArguments"     , )       ->							Dict:







  '''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
  _a							=	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
  _a							=	dataset.map(UpperCamelCase_     , batched=UpperCamelCase_     , num_proc=processing_args.num_proc					)

  # And compute the embeddings
  _a							=	DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCamelCase_					)
  _a							=	DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
  _a							=	Features(
      {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
  _a							=	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
  _a							=	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
  _a							=	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
  _a							=	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__	:		str       			=				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__	:		Optional[str]       			=				field(
          default=_UpperCAmelCase,    metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''},    )
      A__	:		str       			=				field(
          default='''facebook/rag-sequence-nq''',    metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''},    )
      A__	:		str       			=				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__	:		Optional[str]       			=				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__	:		Optional[int]       			=				field(
          default=_UpperCAmelCase,    metadata={
              '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
          },    )
      A__	:		int       			=				field(
          default=16,    metadata={
              '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
          },    )







@dataclass
class    lowercase_    :
      A__	:		int       			=				field(
          default=768,    metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''},    )
      A__	:		int       			=				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)

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










"""simple docstring"""




import math






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							list:







  '''simple docstring'''







  _a							=	[True] * n
  _a							=	False
  _a							=	False
  _a							=	True

  for i in range(3     , int(n**0.5 + 1					)     , 2					):
    _a							=	i * 2
    while index < n:
      _a							=	False
      _a							=	index + i

  _a							=	[2]

  for i in range(3     , UpperCamelCase_     , 2					):
    if is_prime[i]:
      primes.append(UpperCamelCase_					)

  return primes






def        lowerCAmelCase (      UpperCamelCase_:							int = 999966663333					)       ->							int:







  '''simple docstring'''







  _a							=	math.floor(math.sqrt(UpperCamelCase_					)					) + 100
  _a							=	prime_sieve(UpperCamelCase_					)

  _a							=	0
  _a							=	0
  _a							=	primes[prime_index]

  while (last_prime**2) <= limit:
    _a							=	primes[prime_index + 1]

    _a							=	last_prime**2
    _a							=	next_prime**2

    # Get numbers divisible by lps(current)
    _a							=	lower_bound + last_prime
    while upper_bound > current <= limit:
      matches_sum += current
      current += last_prime

    # Reset the upper_bound
    while (upper_bound - next_prime) > limit:
      upper_bound -= next_prime

    # Add the numbers divisible by ups(current)
    _a							=	upper_bound - next_prime
    while current > lower_bound:
      matches_sum += current
      current -= next_prime

    # Remove the numbers divisible by both ups and lps
    _a							=	0
    while upper_bound > current <= limit:
      if current <= lower_bound:
        # Increment the current number
        current += last_prime * next_prime
        continue

      if current > limit:
        break

      # Remove twice since it was added by both ups and lps
      matches_sum -= current * 2

      # Increment the current number
      current += last_prime * next_prime

    # Setup for next pair
    _a							=	next_prime
    prime_index += 1

  return matches_sum


if __name__ == "__main__":
     print(solution())


