

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,
)


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



def    UpperCamelCase(					lowercase_			,					lowercase_=100			,					lowercase_=" "							)	->				List[str]:





  '''simple docstring'''





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



def    UpperCamelCase(					lowercase_							)	->				dict:





  '''simple docstring'''





  snake_case_						,				snake_case_       						=      [], []
  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    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_							)	->				dict:





  '''simple docstring'''





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



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					)	->				List[str]:





  '''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
  snake_case_       						=      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_       						=      dataset.map(lowercase_			,					batched=lowercase_			,					num_proc=processing_args.num_proc							)

  # And compute the embeddings
  snake_case_       						=      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							).to(device=lowercase_							)
  snake_case_       						=      DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							)
  snake_case_       						=      Features(
      {"""text""": Value("""string"""							), """title""": Value("""string"""							), """embeddings""": Sequence(Value("""float32"""							)							)}							)  # optional, save as float32 instead of float64 to save space
  snake_case_       						=      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
  snake_case_       						=      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
  snake_case_       						=      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
  snake_case_       						=      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
  snake_case_       						=      RagRetriever.from_pretrained(
      rag_example_args.rag_model_name			,					index_name="""custom"""			,					indexed_dataset=lowercase_							)
  snake_case_       						=      RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,					retriever=lowercase_							)
  snake_case_       						=      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_       						=      rag_example_args.question or """What does Moses' rod turn into ?"""
  snake_case_       						=      tokenizer.question_encoder(lowercase_			,					return_tensors="""pt"""							)["""input_ids"""]
  snake_case_       						=      model.generate(lowercase_							)
  snake_case_       						=      tokenizer.batch_decode(lowercase_			,					skip_special_tokens=lowercase_							)[0]
  logger.info("""Q: """ + question							)
  logger.info("""A: """ + generated_string							)







@dataclass
class  __lowerCamelCase				:
      lowerCamelCase_	:      str          =					field(
          default=str(Path(__snake_case	).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=__snake_case  ,							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=__snake_case  ,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}  ,							)







@dataclass
class  __lowerCamelCase				:
      lowerCamelCase_	:      Optional[int]          =					field(
          default=__snake_case  ,							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  __lowerCamelCase				:
      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)

    lowerCamelCase_    =					HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_    =					parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        lowerCamelCase_    =					rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					'rembert'







      def __init__(     self							,  lowerCamelCase=250300							,  lowerCamelCase=1152							,  lowerCamelCase=32							,  lowerCamelCase=18							,  lowerCamelCase=256							,  lowerCamelCase=1664							,  lowerCamelCase=4608							,  lowerCamelCase="gelu"							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.1							,  lowerCamelCase=512							,  lowerCamelCase=2							,  lowerCamelCase=0.02							,  lowerCamelCase=1e-12							,  lowerCamelCase=True							,  lowerCamelCase=0							,  lowerCamelCase=312							,  lowerCamelCase=313							,  **lowerCamelCase							,  )						->       Union[str, Any]:
        super().__init__(pad_token_id=lowerCamelCase							,  bos_token_id=lowerCamelCase							,  eos_token_id=lowerCamelCase							,  **lowerCamelCase   )

        snake_case_       						=      vocab_size
        snake_case_       						=      input_embedding_size
        snake_case_       						=      output_embedding_size
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      hidden_size
        snake_case_       						=      num_hidden_layers
        snake_case_       						=      num_attention_heads
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      classifier_dropout_prob
        snake_case_       						=      initializer_range
        snake_case_       						=      type_vocab_size
        snake_case_       						=      layer_norm_eps
        snake_case_       						=      use_cache
        snake_case_       						=      False







class  __lowerCamelCase				(	__snake_case	):







      @property
      def 	lowerCAmelCase_    (     self   )						->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          snake_case_       						=      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
          snake_case_       						=      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]   )







      @property
      def 	lowerCAmelCase_    (     self   )						->       float:
        return 1e-4