

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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						str	,				_SCREAMING_SNAKE_CASE   :						Optional[Any]=100	,				_SCREAMING_SNAKE_CASE   :						Dict=" "    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						dict    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						dict	,				_SCREAMING_SNAKE_CASE   :						DPRContextEncoder	,				_SCREAMING_SNAKE_CASE   :						DPRContextEncoderTokenizerFast    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						"RagExampleArguments"	,				_SCREAMING_SNAKE_CASE   :						"ProcessingArguments"	,				_SCREAMING_SNAKE_CASE   :						"IndexHnswArguments"	,				):





    """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
    SCREAMING_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
    SCREAMING_SNAKE_CASE_													=  dataset.map(_SCREAMING_SNAKE_CASE	,				batched=_SCREAMING_SNAKE_CASE	,				num_proc=processing_args.num_proc    )

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

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

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

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

    # Easy way to load the model
    SCREAMING_SNAKE_CASE_													=  RagRetriever.from_pretrained(
        rag_example_args.rag_model_name	,				index_name='custom'	,				indexed_dataset=_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name	,				retriever=_SCREAMING_SNAKE_CASE    )
    SCREAMING_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'    )
    ######################################

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



@dataclass
class  __snake_case					:
  __lowerCAmelCase       :		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\''}       ,	)
  __lowerCAmelCase       :		Optional[str]  	=					field(
      default=lowerCAmelCase__       ,	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=lowerCAmelCase__       ,	metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}       ,	)






@dataclass
class  __snake_case					:
  __lowerCAmelCase       :		Optional[int]  	=					field(
      default=lowerCAmelCase__       ,	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  __snake_case					:
  __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)

       UpperCamelCase__					: str								=      HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__					: Optional[int]								=      parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              UpperCamelCase__					: Optional[Any]								=      rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)





from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase__					: Union[str, Any]								=      "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

UpperCamelCase__					: Optional[Any]								=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


UpperCamelCase__					: Tuple								=      "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION    )
class  __snake_case					(      datasets.Metric    ):

  def   lowerCAmelCase__       (   self):
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(self._get_feature_types())  ,						reference_urls=[
              'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
          ]  ,						)

  def   lowerCAmelCase__       (   self):
      if self.config_name == "multilist":
          return {
              "predictions": datasets.Sequence(datasets.Value('float')),
              "references": datasets.Sequence(datasets.Value('float')),
          }
      else:
          return {
              "predictions": datasets.Value('float'),
              "references": datasets.Value('float'),
          }


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=None  ,						_A="uniform_average"):
      SCREAMING_SNAKE_CASE_													=  mean_absolute_error(_A  ,						_A  ,						sample_weight=_A  ,						multioutput=_A)

      return {"mae": mae_score}


