






"""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,
)


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






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	str		,				UpperCamelCase     :	Any=100		,				UpperCamelCase     :	Union[str, Any]=" "    ):
  A__											=							text.split(UpperCamelCase    )
  return [character.join(text[i : i + n]    ).strip() for i in range(0		,				len(UpperCamelCase    )		,				UpperCamelCase    )]






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






def 						_SCREAMING_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
  A__											=							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__											=							dataset.map(UpperCamelCase		,				batched=UpperCamelCase		,				num_proc=processing_args.num_proc    )

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

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







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







@dataclass
class 					_UpperCamelCase		:
    __lowerCamelCase         =  field(
        default=__snake_case						, metadata={
            "help": "The number of processes to use to split the documents into passages. Default is single process."
        }						, )
    __lowerCamelCase         =  field(
        default=1_6						, metadata={
            "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
        }						, )







@dataclass
class 					_UpperCamelCase		:
    __lowerCamelCase         =  field(
        default=7_6_8						, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}						, )
    __lowerCamelCase         =  field(
        default=1_2_8						, 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)












"""simple docstring"""




from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
lowerCamelCase__			=   "\\n"

lowerCamelCase__			=   "\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n"

lowerCamelCase__			=   "\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.\n        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',\n        'tokenize', 'use_effective_order'.\n    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.\n        Keywords include 'concatenate_texts'.\nReturns: depending on the XTREME-S task, one or several of:\n    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'\n    \"f1\": F1 score - for 'minds14'\n    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'\n    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]\n    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'wer': 0.56, 'cer': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')\n    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]\n    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'bleu': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'accuracy': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'f1': 0.58, 'accuracy': 0.6}\n"

lowerCamelCase__			=   ["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
lowerCamelCase__			=   ""

try:
      from jiwer import transforms as tr

      lowerCamelCase__			=   True
except ImportError:
      lowerCamelCase__			=   False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):







      class 					_UpperCamelCase		( tr.AbstractTransform):






          def __init__(self						,      lowerCamelCase__ = " "   ):


            """simple docstring"""
            A__											=							sentence_delimiter






          def  A    (self						,      lowerCamelCase__   ):


            """simple docstring"""
            return list(lowerCamelCase__   )






          def  A    (self						,      lowerCamelCase__   ):


            """simple docstring"""
            A__											=							[]
            for sent_idx, sentence in enumerate(lowerCamelCase__   ):
              chars.extend(self.process_string(lowerCamelCase__   )   )
              if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCamelCase__   ) - 1:
                chars.append(self.sentence_delimiter   )
            return chars
      lowerCamelCase__			=   tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
elif _jiwer_available:
      lowerCamelCase__			=   tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )
else:
      lowerCamelCase__			=   None






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Tuple    ):
  return float((preds == labels).mean()    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	str    ):
  return {
      "f1": float(fa_score(y_true=UpperCamelCase		,				y_pred=UpperCamelCase		,				average="""macro"""    )    ),
      "accuracy": simple_accuracy(UpperCamelCase		,				UpperCamelCase    ),
  }






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Dict		,				UpperCamelCase     :	Any="exp"		,				UpperCamelCase     :	int=None		,				UpperCamelCase     :	Optional[int]=False		,				UpperCamelCase     :	str=False		,				UpperCamelCase     :	Optional[Any]=None		,				UpperCamelCase     :	Dict=False		,				):
  # xtreme-s can only have one label
  A__											=							[[label] for label in labels]
  A__											=							list(UpperCamelCase    )
  try:
    import sacrebleu as scb
  except ImportError:
    raise ValueError(
        """sacrebleu has to be installed in order to apply the bleu metric for covost2."""
        """You can install it via `pip install sacrebleu`."""    )

  if version.parse(scb.__version__    ) < version.parse("""1.4.12"""    ):
    raise ImportWarning(
        """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
        """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""    )

  A__											=							len(labels[0]    )
  if any(len(UpperCamelCase    ) != references_per_prediction for refs in labels    ):
    raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
  A__											=							[[refs[i] for refs in labels] for i in range(UpperCamelCase    )]
  A__											=							scb.corpus_bleu(
      UpperCamelCase		,				UpperCamelCase		,				smooth_method=UpperCamelCase		,				smooth_value=UpperCamelCase		,				force=UpperCamelCase		,				lowercase=UpperCamelCase		,				use_effective_order=UpperCamelCase		,				**({"""tokenize""": tokenize} if tokenize else {})		,				)
  return {"bleu": output.score}






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Dict		,				UpperCamelCase     :	Any    ):
  try:
    from jiwer import compute_measures
  except ImportError:
    raise ValueError(
        F"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
        """You can install it via `pip install jiwer`."""    )

  if concatenate_texts:
    A__											=							compute_measures(UpperCamelCase		,				UpperCamelCase    )["""wer"""]

    A__											=							compute_measures(UpperCamelCase		,				UpperCamelCase		,				truth_transform=UpperCamelCase		,				hypothesis_transform=UpperCamelCase    )["""wer"""]
    return {"wer": wer, "cer": cer}
  else:

    def compute_score(UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Dict="wer"    ):
      A__											=							0
      A__											=							0
      for prediction, reference in zip(UpperCamelCase		,				UpperCamelCase    ):
        if score_type == "wer":
          A__											=							compute_measures(UpperCamelCase		,				UpperCamelCase    )
        elif score_type == "cer":
          A__											=							compute_measures(
              UpperCamelCase		,				UpperCamelCase		,				truth_transform=UpperCamelCase		,				hypothesis_transform=UpperCamelCase    )
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]
      return incorrect / total

    return {"wer": compute_score(UpperCamelCase		,				UpperCamelCase		,				"""wer"""    ), "cer": compute_score(UpperCamelCase		,				UpperCamelCase		,				"""cer"""    )}







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






    def  A    (self   ):


      """simple docstring"""
      if self.config_name not in _CONFIG_NAMES:
        raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""   )

      A__											=							"""int64""" if self.config_name in ["""fleurs-lang_id""", """minds14"""] else """string"""

      return datasets.MetricInfo(
          description=_DESCRIPTION						,      citation=_CITATION						,      inputs_description=_KWARGS_DESCRIPTION						,      features=datasets.Features(
              {"""predictions""": datasets.Value(lowerCamelCase__   ), """references""": datasets.Value(lowerCamelCase__   )}   )						,      codebase_urls=[]						,      reference_urls=[]						,      format="""numpy"""						,      )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None   ):


      """simple docstring"""
      A__											=							bleu_kwargs if bleu_kwargs is not None else {}
      A__											=							wer_kwargs if wer_kwargs is not None else {}

      if self.config_name == "fleurs-lang_id":
        return {"accuracy": simple_accuracy(lowerCamelCase__						,      lowerCamelCase__   )}
      elif self.config_name == "minds14":
        return fa_and_simple_accuracy(lowerCamelCase__						,      lowerCamelCase__   )
      elif self.config_name == "covost2":
        A__											=							bleu_kwargs.pop("""smooth_method"""						,      """exp"""   )
        A__											=							bleu_kwargs.pop("""smooth_value"""						,      lowerCamelCase__   )
        A__											=							bleu_kwargs.pop("""force"""						,      lowerCamelCase__   )
        A__											=							bleu_kwargs.pop("""lowercase"""						,      lowerCamelCase__   )
        A__											=							bleu_kwargs.pop("""tokenize"""						,      lowerCamelCase__   )
        A__											=							bleu_kwargs.pop("""use_effective_order"""						,      lowerCamelCase__   )
        return bleu(
            preds=lowerCamelCase__						,      labels=lowerCamelCase__						,      smooth_method=lowerCamelCase__						,      smooth_value=lowerCamelCase__						,      force=lowerCamelCase__						,      lowercase=lowerCamelCase__						,      tokenize=lowerCamelCase__						,      use_effective_order=lowerCamelCase__						,      )
      elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
        A__											=							wer_kwargs.pop("""concatenate_texts"""						,      lowerCamelCase__   )
        return wer_and_cer(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      self.config_name   )
      else:
        raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""   )




