






"""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,
)


UpperCamelCase_						:					Union[str, Any]      				=			logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_						:					Optional[int]      				=			'cuda' if torch.cuda.is_available() else 'cpu'



def      __lowercase       (     a			:					int							,		a			:					str=100							,		a			:					Any=" "			)       ->       List[str]:
      __snake_case   :     Dict	      =text.split(a			)
      return [character.join(text[i : i + n]			).strip() for i in range(0							,		len(a			)							,		a			)]



def      __lowercase       (     a			:					List[Any]			)       ->       dict:
      __snake_case   :     int	      =[], []
      for title, text in zip(documents['''title''']							,		documents['''text''']			):
            if text is not None:
                  for passage in split_text(a			):
                        titles.append(title if title is not None else ''''''			)
                        texts.append(a			)
      return {"title": titles, "text": texts}



def      __lowercase       (     a			:					Dict							,		a			:					Tuple							,		a			:					Dict			)       ->       dict:
      __snake_case   :     List[Any]	      =ctx_tokenizer(
          documents['''title''']							,		documents['''text''']							,		truncation=a							,		padding='''longest'''							,		return_tensors='''pt'''			)['input_ids']
      __snake_case   :     List[Any]	      =ctx_encoder(input_ids.to(device=a			)							,		return_dict=a			).pooler_output
      return {"embeddings": embeddings.detach().cpu().numpy()}



def      __lowercase       (     a			:					Optional[int]							,		a			:					List[str]							,		a			:					List[str]							,		)       ->       Union[str, Any]:
      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
      __snake_case   :     Optional[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
      __snake_case   :     Union[str, Any]	      =dataset.map(a							,		batched=a							,		num_proc=processing_args.num_proc			)

      # And compute the embeddings
      __snake_case   :     List[Any]	      =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			).to(device=a			)
      __snake_case   :     List[str]	      =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			)
      __snake_case   :     List[str]	      =Features(
          {'''text''': Value('''string'''			), '''title''': Value('''string'''			), '''embeddings''': Sequence(Value('''float32'''			)			)}			)  # optional, save as float32 instead of float64 to save space
      __snake_case   :     Optional[Any]	      =dataset.map(
          partial(a							,		ctx_encoder=a							,		ctx_tokenizer=a			)							,		batched=a							,		batch_size=processing_args.batch_size							,		features=a							,		)

      # And finally save your dataset
      __snake_case   :     Dict	      =os.path.join(rag_example_args.output_dir							,		'''my_knowledge_dataset'''			)
      dataset.save_to_disk(a			)
      # 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
      __snake_case   :     Optional[int]	      =faiss.IndexHNSWFlat(index_hnsw_args.d							,		index_hnsw_args.m							,		faiss.METRIC_INNER_PRODUCT			)
      dataset.add_faiss_index('''embeddings'''							,		custom_index=a			)

      # And save the index
      __snake_case   :     List[Any]	      =os.path.join(rag_example_args.output_dir							,		'''my_knowledge_dataset_hnsw_index.faiss'''			)
      dataset.get_index('''embeddings'''			).save(a			)
      # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

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

      # Easy way to load the model
      __snake_case   :     Dict	      =RagRetriever.from_pretrained(
          rag_example_args.rag_model_name							,		index_name='''custom'''							,		indexed_dataset=a			)
      __snake_case   :     Union[str, Any]	      =RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,		retriever=a			)
      __snake_case   :     str	      =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'''			)
      ######################################

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


@dataclass
class   _lowercase			:
      _a :  str										= field(
          default=str(Path(lowerCAmelCase    ).parent / '''test_data''' / '''my_knowledge_dataset.csv'''    )		,	metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}		,	)
      _a :  Optional[str]										= field(
          default=lowerCAmelCase		,	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=lowerCAmelCase		,	metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}		,	)



@dataclass
class   _lowercase			:
      _a :  Optional[int]										= field(
          default=lowerCAmelCase		,	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_						:					Optional[int]      				=			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    UpperCamelCase_						:					Any      				=			parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        UpperCamelCase_						:					List[str]      				=			rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)








import os
import sys

from . import rsa_key_generator as rkg

UpperCAmelCase__    :							str       		=     128
UpperCAmelCase__    :							int       		=     256


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			List[str]      =     message.encode('ascii'				)

  _lowercase:			str      =     []
  for block_start in range(0       ,  len(UpperCamelCase				)       ,  UpperCamelCase				):
    _lowercase:			Optional[int]      =     0
    for i in range(UpperCamelCase       ,  min(block_start + block_size       ,  len(UpperCamelCase				)				)				):
      block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
    block_ints.append(UpperCamelCase				)
  return block_ints


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		str:





  """simple docstring"""





  _lowercase:			list[str]      =     []
  for block_int in block_ints:
    _lowercase:			list[str]      =     []
    for i in range(block_size - 1       ,  -1       ,  -1				):
      if len(UpperCamelCase				) + i < message_length:
        _lowercase:			Optional[Any]      =     block_int // (BYTE_SIZE**i)
        _lowercase:			Dict      =     block_int % (BYTE_SIZE**i)
        block_message.insert(0       ,  chr(UpperCamelCase				)				)
    message.extend(UpperCamelCase				)
  return "".join(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key

  for block in get_blocks_from_text(UpperCamelCase       ,  UpperCamelCase				):
    encrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return encrypted_blocks


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key
  for block in encrypted_blocks:
    decrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return get_text_from_blocks(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		tuple[int, int, int]:





  """simple docstring"""





  with open(UpperCamelCase				) as fo:
    _lowercase:			Any      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Optional[Any]      =     content.split(','				)
  return (int(UpperCamelCase				), int(UpperCamelCase				), int(UpperCamelCase				))


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			int      =     read_key_file(UpperCamelCase				)
  if key_size < block_size * 8:
    sys.exit(
        'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
        'requires the block size to be equal to or greater than the key size. '
        'Either decrease the block size or use different keys.'.format(
            block_size * 8       ,  UpperCamelCase				)				)

  _lowercase:			int      =     [str(UpperCamelCase				) for i in encrypt_message(UpperCamelCase       ,  (n, e)       ,  UpperCamelCase				)]

  _lowercase:			List[Any]      =     ','.join(UpperCamelCase				)
  _lowercase:			Tuple      =     F'{len(UpperCamelCase				)}_{block_size}_{encrypted_content}'
  with open(UpperCamelCase       ,  'w'				) as fo:
    fo.write(UpperCamelCase				)
  return encrypted_content


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			Tuple      =     read_key_file(UpperCamelCase				)
  with open(UpperCamelCase				) as fo:
    _lowercase:			Optional[Any]      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Union[str, Any]      =     content.split('_'				)
  _lowercase:			List[Any]      =     int(UpperCamelCase				)
  _lowercase:			Dict      =     int(UpperCamelCase				)

  if key_size < block_size * 8:
    sys.exit(
        'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
        'requires the block size to be equal to or greater than the key size. '
        'Did you specify the correct key file and encrypted file?'.format(
            block_size * 8       ,  UpperCamelCase				)				)

  _lowercase:			Union[str, Any]      =     []
  for block in encrypted_message.split(','				):
    encrypted_blocks.append(int(UpperCamelCase				)				)

  return decrypt_message(UpperCamelCase       ,  UpperCamelCase       ,  (n, d)       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Tuple      =     'encrypted_file.txt'
  _lowercase:			Optional[Any]      =     input(r'Encrypt\Decrypt [e\d]: '				)

  if response.lower().startswith('e'				):
    _lowercase:			Optional[Any]      =     'encrypt'
  elif response.lower().startswith('d'				):
    _lowercase:			Union[str, Any]      =     'decrypt'

  if mode == "encrypt":
    if not os.path.exists('rsa_pubkey.txt'				):
      rkg.make_key_files('rsa'       ,  1_024				)

    _lowercase:			Union[str, Any]      =     input('\nEnter message: '				)
    _lowercase:			Tuple      =     'rsa_pubkey.txt'
    print(F'Encrypting and writing to {filename}...'				)
    _lowercase:			Optional[Any]      =     encrypt_and_write_to_file(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

    print('\nEncrypted text:'				)
    print(UpperCamelCase				)

  elif mode == "decrypt":
    _lowercase:			Optional[Any]      =     'rsa_privkey.txt'
    print(F'Reading from {filename} and decrypting...'				)
    _lowercase:			int      =     read_from_file_and_decrypt(UpperCamelCase       ,  UpperCamelCase				)
    print('writing decryption to rsa_decryption.txt...'				)
    with open('rsa_decryption.txt'       ,  'w'				) as dec:
      dec.write(UpperCamelCase				)

    print('\nDecryption:'				)
    print(UpperCamelCase				)


if __name__ == "__main__":
   main()
