


'''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,
)


a_  :    Optional[Any]      =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_  :    List[Any]      =			"""cuda""" if torch.cuda.is_available() else """cpu"""





def __snake_case (				UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						Optional[int]=100 ,    UpperCAmelCase_						:						List[str]=" "		):
 lowerCamelCase_								=  text.split(UpperCAmelCase_		)
 return [character.join(text[i : i + n]		).strip() for i in range(0 ,    len(UpperCAmelCase_		) ,    UpperCAmelCase_		)]





def __snake_case (				UpperCAmelCase_						:						dict		):
 lowerCamelCase_			,lowerCamelCase_								=  [], []
 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 __snake_case (				UpperCAmelCase_						:						dict ,    UpperCAmelCase_						:						DPRContextEncoder ,    UpperCAmelCase_						:						DPRContextEncoderTokenizerFast		):
 lowerCamelCase_								=  ctx_tokenizer(
     documents["title"] ,    documents["text"] ,    truncation=UpperCAmelCase_ ,    padding="longest" ,    return_tensors="pt"		)["input_ids"]
 lowerCamelCase_								=  ctx_encoder(input_ids.to(device=UpperCAmelCase_		) ,    return_dict=UpperCAmelCase_		).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}





def __snake_case (				UpperCAmelCase_						:						"RagExampleArguments" ,    UpperCAmelCase_						:						"ProcessingArguments" ,    UpperCAmelCase_						:						"IndexHnswArguments" ,    ):
 ######################################
 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
 lowerCamelCase_								=  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
 lowerCamelCase_								=  dataset.map(UpperCAmelCase_ ,    batched=UpperCAmelCase_ ,    num_proc=processing_args.num_proc		)

 # And compute the embeddings
 lowerCamelCase_								=  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		).to(device=UpperCAmelCase_		)
 lowerCamelCase_								=  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		)
 lowerCamelCase_								=  Features(
     {"text": Value("string"		), "title": Value("string"		), "embeddings": Sequence(Value("float32"		)		)}		)  # optional, save as float32 instead of float64 to save space
 lowerCamelCase_								=  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
 lowerCamelCase_								=  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
 lowerCamelCase_								=  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
 lowerCamelCase_								=  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
 lowerCamelCase_								=  RagRetriever.from_pretrained(
     rag_example_args.rag_model_name ,    index_name="custom" ,    indexed_dataset=UpperCAmelCase_		)
 lowerCamelCase_								=  RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,    retriever=UpperCAmelCase_		)
 lowerCamelCase_								=  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"		)
 ######################################

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





@dataclass
class 					snake_case   :





  """simple docstring"""




  _lowerCamelCase		   =  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		   =  field(
      default=lowercase      ,						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=lowercase      ,						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=lowercase      ,						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_  :    str      =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
     a_     ,       a_     ,       a_  :    str      =			parser.parse_args_into_dataclasses()
     with TemporaryDirectory() as tmp_dir:
          a_  :    List[Any]      =			rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_  :    Union[str, Any]      =			logging.get_logger(__name__)

a_  :    int      =			{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "nystromformer"






  def __init__(   self      ,  UpperCamelCase=3_0000      ,  UpperCamelCase=768      ,  UpperCamelCase=12      ,  UpperCamelCase=12      ,  UpperCamelCase=3072      ,  UpperCamelCase="gelu_new"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=510      ,  UpperCamelCase=2      ,  UpperCamelCase=64      ,  UpperCamelCase=64      ,  UpperCamelCase=65      ,  UpperCamelCase=False      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-5      ,  UpperCamelCase=1      ,  UpperCamelCase=0      ,  UpperCamelCase=2      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  segment_means_seq_len
   lowerCamelCase_								=  num_landmarks
   lowerCamelCase_								=  conv_kernel_size
   lowerCamelCase_								=  inv_coeff_init_option
   lowerCamelCase_								=  layer_norm_eps
   super().__init__(pad_token_id=UpperCamelCase      ,  bos_token_id=UpperCamelCase      ,  eos_token_id=UpperCamelCase      ,  **UpperCamelCase					)



