


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






def 		_A		(		_lowercase				,     _lowercase=1_00				,     _lowercase=" "   )				->   List[str]:


     """simple docstring"""


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






def 		_A		(		_lowercase   )				->   dict:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   dict:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     )				->   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   = 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   = dataset.map(_lowercase				,     batched=_lowercase				,     num_proc=processing_args.num_proc   )

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

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

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

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

     # Easy way to load the model
     __UpperCamelCase   = RagRetriever.from_pretrained(
         rag_example_args.rag_model_name				,     index_name='custom'				,     indexed_dataset=_lowercase   )
     __UpperCamelCase   = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name				,     retriever=_lowercase   )
     __UpperCamelCase   = 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   = rag_example_args.question or 'What does Moses\' rod turn into ?'
     __UpperCamelCase   = tokenizer.question_encoder(_lowercase				,     return_tensors='pt'   )['input_ids']
     __UpperCamelCase   = model.generate(_lowercase   )
     __UpperCamelCase   = tokenizer.batch_decode(_lowercase				,     skip_special_tokens=_lowercase   )[0]
     logger.info('Q: ' + question   )
     logger.info('A: ' + generated_string   )



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



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



@dataclass
class   __lowerCamelCase :
       _lowercase       							=    field(
           default=768							,			metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}							,			)
       _lowercase       							=    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             =	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    __snake_case       ,  __snake_case       ,  __snake_case             =	parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        __snake_case             =	rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)






def 		_A		(		_lowercase   )				->   bool:


     """simple docstring"""


     if not isinstance(_lowercase				,     _lowercase   ):
          raise ValueError('Input series is not valid, valid series - [1, 2/3, 2]'   )
     if len(_lowercase   ) == 0:
          raise ValueError('Input list must be a non empty list'   )
     if len(_lowercase   ) == 1 and series[0] != 0:
          return True
     __UpperCamelCase   = []
     __UpperCamelCase   = len(_lowercase   )
     for i in range(0				,     _lowercase   ):
          if series[i] == 0:
               raise ValueError('Input series cannot have 0 as an element'   )
          rec_series.append(1 / series[i]   )
     __UpperCamelCase   = rec_series[1] - rec_series[0]
     for index in range(2				,     _lowercase   ):
          if rec_series[index] - rec_series[index - 1] != common_diff:
               return False
     return True






def 		_A		(		_lowercase   )				->   float:


     """simple docstring"""


     if not isinstance(_lowercase				,     _lowercase   ):
          raise ValueError('Input series is not valid, valid series - [2, 4, 6]'   )
     if len(_lowercase   ) == 0:
          raise ValueError('Input list must be a non empty list'   )
     __UpperCamelCase   = 0
     for val in series:
          answer += 1 / val
     return len(_lowercase   ) / answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()


