





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


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



def A_	(		A__		,      A__=100		,      A__=" "				)  ->		List[str]:
       a__						:	List[Any] =				text.split(A__				)
       return [character.join(text[i : i + n]				).strip() for i in range(0		,      len(A__				)		,      A__				)]



def A_	(		A__				)  ->		dict:
       a__ ,							a__						:	Tuple =				[], []
       for title, text in zip(documents['title']		,      documents['text']				):
              if text is not None:
                     for passage in split_text(A__				):
                            titles.append(title if title is not None else ''				)
                            texts.append(A__				)
       return {"title": titles, "text": texts}



def A_	(		A__		,      A__		,      A__				)  ->		dict:
       a__						:	Optional[int] =				ctx_tokenizer(
           documents['title']		,      documents['text']		,      truncation=A__		,      padding='longest'		,      return_tensors='pt'				)['input_ids']
       a__						:	List[Any] =				ctx_encoder(input_ids.to(device=A__				)		,      return_dict=A__				).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def A_	(		A__		,      A__		,      A__		,      )  ->		Dict:
       ######################################
       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__						:	Optional[int] =				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__						:	Any =				dataset.map(A__		,      batched=A__		,      num_proc=processing_args.num_proc				)

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

       # And finally save your dataset
       a__						:	Any =				os.path.join(rag_example_args.output_dir		,      'my_knowledge_dataset'				)
       dataset.save_to_disk(A__				)
       # 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__						:	Optional[Any] =				faiss.IndexHNSWFlat(index_hnsw_args.d		,      index_hnsw_args.m		,      faiss.METRIC_INNER_PRODUCT				)
       dataset.add_faiss_index('embeddings'		,      custom_index=A__				)

       # And save the index
       a__						:	Optional[int] =				os.path.join(rag_example_args.output_dir		,      'my_knowledge_dataset_hnsw_index.faiss'				)
       dataset.get_index('embeddings'				).save(A__				)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index





@dataclass
class  A__    :




   """simple docstring"""



   __A   :				str         =  field(
       default=str(Path(__UpperCAmelCase	).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''	) ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''} ,  )
   __A   :				Optional[str]         =  field(
       default=__UpperCAmelCase ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''} ,  )
   __A   :				str         =  field(
       default='''facebook/rag-sequence-nq''' ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''} ,  )
   __A   :				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\''''
           )
       } ,  )
   __A   :				Optional[str]         =  field(
       default=str(Path(__UpperCAmelCase	).parent / '''test_run''' / '''dummy-kb'''	) ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''} ,  )





@dataclass
class  A__    :




   """simple docstring"""



   __A   :				Optional[int]         =  field(
       default=__UpperCAmelCase ,  metadata={
           '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
       } ,  )
   __A   :				int         =  field(
       default=1_6 ,  metadata={
           '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
       } ,  )





@dataclass
class  A__    :




   """simple docstring"""



   __A   :				int         =  field(
       default=7_6_8 ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''} ,  )
   __A   :				int         =  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)

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









from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

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


lowercase      :       Tuple									=  """"""


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





    class  A__    (							tr.AbstractTransform	):




       """simple docstring"""



       def __init__(					self					,  lowercase = " ")			->						Tuple:







              '''simple docstring'''

              a__						:	Tuple =				sentence_delimiter

       def 							__lowercase		(					self					,  lowercase)			->						Optional[int]:







              '''simple docstring'''

              return list(lowercase)

       def 							__lowercase		(					self					,  lowercase)			->						Dict:







              '''simple docstring'''

              a__						:	Tuple =				[]
              for sent_idx, sentence in enumerate(lowercase):
                     chars.extend(self.process_string(lowercase))
                     if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase) - 1:
                            chars.append(self.sentence_delimiter)
              return chars

    lowercase      :       Union[str, Any]									=  tr.Compose(
        [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
    )
else:
    lowercase      :       List[str]									=  tr.Compose(
        [
            tr.RemoveMultipleSpaces(),
            tr.Strip(),
            tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
            tr.ReduceToListOfListOfChars(),
        ]
    )


lowercase      :       List[Any]									=  """\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

lowercase      :       Optional[int]									=  """\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""

lowercase      :       Optional[Any]									=  """
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""





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




   """simple docstring"""



   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          return datasets.MetricInfo(
              description=_DESCRIPTION					,  citation=_CITATION					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
                  {
                      'predictions': datasets.Value('string'					,  id='sequence'),
                      'references': datasets.Value('string'					,  id='sequence'),
                  })					,  codebase_urls=['https://github.com/jitsi/jiwer/']					,  reference_urls=[
                  'https://en.wikipedia.org/wiki/Word_error_rate',
                  'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
              ]					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase=False)			->						Any:







          '''simple docstring'''

          if concatenate_texts:
                 return jiwer.compute_measures(
                     lowercase					,  lowercase					,  truth_transform=lowercase					,  hypothesis_transform=lowercase					,  )["wer"]

          a__						:	Optional[int] =				0
          a__						:	str =				0
          for prediction, reference in zip(lowercase					,  lowercase):
                 a__						:	Optional[int] =				jiwer.compute_measures(
                     lowercase					,  lowercase					,  truth_transform=lowercase					,  hypothesis_transform=lowercase					,  )
                 incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                 total += measures["substitutions"] + measures["deletions"] + measures["hits"]

          return incorrect / total


