'''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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


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


def    _lowerCamelCase       ( lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				str=100      ,	lowerCamelCase_   :				Tuple=" "  ):



     """simple docstring"""





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


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple  ):



     """simple docstring"""





     UpperCAmelCase_  ,     UpperCAmelCase_				:  str  = [], []
     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       ( lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Tuple  ):



     """simple docstring"""





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


def    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				List[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
     UpperCAmelCase_				:  List[Any]  = 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
     UpperCAmelCase_				:  int  = dataset.map(_UpperCamelCase      ,	batched=_UpperCamelCase      ,	num_proc=processing_args.num_proc  )

     # And compute the embeddings
     UpperCAmelCase_				:  List[Any]  = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  ).to(device=_UpperCamelCase  )
     UpperCAmelCase_				:  Tuple  = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  )
     UpperCAmelCase_				:  Tuple  = Features(
         {'text': Value('string'  ), 'title': Value('string'  ), 'embeddings': Sequence(Value('float32'  )  )}  )  # optional, save as float32 instead of float64 to save space
     UpperCAmelCase_				:  str  = 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
     UpperCAmelCase_				:  Optional[Any]  = 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
     UpperCAmelCase_				:  Tuple  = 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
     UpperCAmelCase_				:  int  = 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

     ######################################
     logger.info('Step 3 - Load RAG'  )
     ######################################

     # Easy way to load the model
     UpperCAmelCase_				:  Optional[Any]  = RagRetriever.from_pretrained(
         rag_example_args.rag_model_name      ,	index_name='custom'      ,	indexed_dataset=_UpperCamelCase  )
     UpperCAmelCase_				:  Optional[Any]  = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,	retriever=_UpperCamelCase  )
     UpperCAmelCase_				:  List[Any]  = RagTokenizer.from_pretrained(rag_example_args.rag_model_name  )

     # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
     # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

     ######################################
     logger.info('Step 4 - Have fun'  )
     ######################################

     UpperCAmelCase_				:  Any  = rag_example_args.question or 'What does Moses\' rod turn into ?'
     UpperCAmelCase_				:  Tuple  = tokenizer.question_encoder(_UpperCamelCase      ,	return_tensors='pt'  )['input_ids']
     UpperCAmelCase_				:  str  = model.generate(_UpperCamelCase  )
     UpperCAmelCase_				:  Any  = tokenizer.batch_decode(_UpperCamelCase      ,	skip_special_tokens=_UpperCamelCase  )[0]
     logger.info('Q: ' + question  )
     logger.info('A: ' + generated_string  )






@dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  lowerCamelCase_						:str		       =						field(
      default=str(Path(__lowercase			).parent / '''test_data''' / '''my_knowledge_dataset.csv'''			)		,      metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}		,      )
  lowerCamelCase_						:Optional[str]		       =						field(
      default=__lowercase		,      metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}		,      )
  lowerCamelCase_						:str		       =						field(
      default='''facebook/rag-sequence-nq'''		,      metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}		,      )
  lowerCamelCase_						: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\''''
          )
      }		,      )
  lowerCamelCase_						:Optional[str]		       =						field(
      default=__lowercase		,      metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}		,      )







@dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  lowerCamelCase_						:Optional[int]		       =						field(
      default=__lowercase		,      metadata={
          '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
      }		,      )
  lowerCamelCase_						:int		       =						field(
      default=16		,      metadata={
          '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
      }		,      )







@dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






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

 snake_case__	:     Tuple      =    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 snake_case__     , snake_case__     , snake_case__	:     int      =    parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  snake_case__	:     Union[str, Any]      =    rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)









from __future__ import annotations

from collections import Counter



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  _snake_case		=				[True] * n
  _snake_case		=				False
  _snake_case		=				False
  _snake_case		=				True

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

  _snake_case		=				[2]

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

  return primes



def  snake_case_(_UpperCamelCase	)				->      list[list[int]]:



  """simple docstring"""






  _snake_case		=				str(_UpperCamelCase	)
  _snake_case		=				[]
  _snake_case		=				['''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''']

  for duplicate in Counter(_UpperCamelCase	) - Counter(set(_UpperCamelCase	)	):
    _snake_case		=				[int(number_str.replace(_UpperCamelCase					,			_UpperCamelCase	)	) for digit in digits]
    replacements.append(_UpperCamelCase	)

  return replacements



def  snake_case_(_UpperCamelCase = 8	)				->      int:



  """simple docstring"""






  _snake_case		=				set()

  # Filter primes with less than 3 replaceable digits
  _snake_case		=				{
      x for x in set(prime_sieve(1_000_000	)	) if len(str(_UpperCamelCase	)	) - len(set(str(_UpperCamelCase	)	)	) >= 3
  }

  for prime in primes:
    if prime in numbers_checked:
      continue

    _snake_case		=				digit_replacements(_UpperCamelCase	)

    for family in replacements:
      numbers_checked.update(_UpperCamelCase	)
      _snake_case		=				primes.intersection(_UpperCamelCase	)

      if len(_UpperCamelCase	) != family_length:
        continue

      return min(_UpperCamelCase	)

  return -1


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

