





"""simple docstring"""



import math







def 		__lowercase     (   )   ->None:



    '''simple docstring'''







    __A    :						Tuple						 =     input('''Enter message: '''						)
    __A    :						Union[str, Any]						 =     int(input(F"""Enter key [2-{len(snake_case_						) - 1}]: """						)						)
    __A    :						Any						 =     input('''Encryption/Decryption [e/d]: '''						)

    if mode.lower().startswith('''e'''						):
        __A    :						Union[str, Any]						 =     encrypt_message(snake_case_				,snake_case_						)
    elif mode.lower().startswith('''d'''						):
        __A    :						int						 =     decrypt_message(snake_case_				,snake_case_						)

    # Append pipe symbol (vertical bar) to identify spaces at the end.
    print(F"""Output:\n{text + '|'}"""						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  str						)   ->str:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     [''''''] * key
    for col in range(snake_case_						):
        __A    :						str						 =     col
        while pointer < len(snake_case_						):
            cipher_text[col] += message[pointer]
            pointer += key
    return "".join(snake_case_						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  str						)   ->str:



    '''simple docstring'''







    __A    :						Tuple						 =     math.ceil(len(snake_case_						) / key						)
    __A    :						Dict						 =     key
    __A    :						Tuple						 =     (num_cols * num_rows) - len(snake_case_						)
    __A    :						Tuple						 =     [''''''] * num_cols
    __A    :						List[str]						 =     0
    __A    :						Dict						 =     0

    for symbol in message:
        plain_text[col] += symbol
        col += 1

        if (
            (col == num_cols)
            or (col == num_cols - 1)
            and (row >= num_rows - num_shaded_boxes)
        ):
            __A    :						Dict						 =     0
            row += 1

    return "".join(snake_case_						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()











"""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


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







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Tuple=100				,snake_case_  :  int=" "						)   ->List[str]:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  dict						)   ->dict:



    '''simple docstring'''







    __A ,       __A    :						Optional[int]						 =     [], []
    for title, text in zip(documents['''title''']				,documents['''text''']						):
        if text is not None:
            for passage in split_text(snake_case_						):
                titles.append(title if title is not None else ''''''						)
                texts.append(snake_case_						)
    return {"title": titles, "text": texts}







def 		__lowercase     (   snake_case_  :  dict				,snake_case_  :  DPRContextEncoder				,snake_case_  :  DPRContextEncoderTokenizerFast						)   ->dict:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  "RagExampleArguments"				,snake_case_  :  "ProcessingArguments"				,snake_case_  :  "IndexHnswArguments"				,)   ->Tuple:



    '''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    :						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
    __A    :						str						 =     dataset.map(snake_case_				,batched=snake_case_				,num_proc=processing_args.num_proc						)

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

    # And finally save your dataset
    __A    :						int						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset'''						)
    dataset.save_to_disk(snake_case_						)
    # 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    :						Tuple						 =     faiss.IndexHNSWFlat(index_hnsw_args.d				,index_hnsw_args.m				,faiss.METRIC_INNER_PRODUCT						)
    dataset.add_faiss_index('''embeddings'''				,custom_index=snake_case_						)

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






@dataclass
class 		__snake_case					:





 """simple docstring"""


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






@dataclass
class 		__snake_case					:





 """simple docstring"""


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






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=7_68							,							metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}							,							)
 _lowerCamelCase        =     field(
     default=1_28							,							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)




