


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


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




def        snake_case_					(  snake_case      ,							snake_case=1_00      ,							snake_case=" "					)  ->    List[Any]:
       lowercase__:       Tuple        =       text.split(lowerCAmelCase__					)
       return [character.join(text[i : i + n]					).strip() for i in range(0      ,							len(lowerCAmelCase__					)      ,							lowerCAmelCase__					)]




def        snake_case_					(  snake_case					)  ->    Any:
       lowercase__      ,						lowercase__:       List[Any]        =       [], []
       for title, text in zip(documents['title']      ,							documents['text']					):
              if text is not None:
                     for passage in split_text(lowerCAmelCase__					):
                            titles.append(title if title is not None else ''					)
                            texts.append(lowerCAmelCase__					)
       return {"title": titles, "text": texts}




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    List[Any]:
       lowercase__:       int        =       ctx_tokenizer(
           documents['title']      ,							documents['text']      ,							truncation=lowerCAmelCase__      ,							padding='longest'      ,							return_tensors='pt'					)['input_ids']
       lowercase__:       str        =       ctx_encoder(input_ids.to(device=lowerCAmelCase__					)      ,							return_dict=lowerCAmelCase__					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							)  ->    int:
       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
       lowercase__:       Any        =       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
       lowercase__:       Optional[Any]        =       dataset.map(lowerCAmelCase__      ,							batched=lowerCAmelCase__      ,							num_proc=processing_args.num_proc					)

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

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

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





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





@dataclass
class   __a      :
 __lowercase			:							Optional[int]       					=			field(
     default=SCREAMING_SNAKE_CASE__	,	metadata={
         'help': 'The number of processes to use to split the documents into passages. Default is single process.'
     }	,	)
 __lowercase			:							int       					=			field(
     default=16	,	metadata={
         'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
     }	,	)





@dataclass
class   __a      :
 __lowercase			:							int       					=			field(
     default=768	,	metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}	,	)
 __lowercase			:							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			=							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)








import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCAmelCase			=							'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__lowerCAmelCase			=							'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

__lowerCAmelCase			=							'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        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"`.'			)
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,			citation=_CITATION    ,			homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf'    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
                {
                    'predictions': datasets.Value('string'    ,			id='sequence'			),
                    'references': datasets.Sequence(datasets.Value('string'    ,			id='sequence'			)    ,			id='references'			),
                }			)    ,			codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf']    ,			reference_urls=[
                'https://github.com/m-popovic/chrF',
            ]    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = CHRF.CHAR_ORDER    ,			lowerCAmelCase__ = CHRF.WORD_ORDER    ,			lowerCAmelCase__ = CHRF.BETA    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       len(references[0]			)
        if any(len(lowerCAmelCase__			) != references_per_prediction for refs in references			):
               raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
        lowercase__:       List[str]        =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__			)]

        lowercase__:       Union[str, Any]        =       CHRF(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       str        =       sb_chrf.corpus_score(lowerCAmelCase__    ,			lowerCAmelCase__			)

        return {
            "score": output.score,
            "char_order": output.char_order,
            "word_order": output.word_order,
            "beta": output.beta,
        }


