

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase__  :      Optional[int]			=    '\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

UpperCamelCase__  :      Dict			=    '\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


UpperCamelCase__  :      List[Any]			=    '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    def remove_articles(_SCREAMING_SNAKE_CASE					:		Optional[Any]       ):
        SCREAMING_SNAKE_CASE_									=						re.compile(r'\b(a|an|the)\b' ,   re.UNICODE       )
        return re.sub(__lowerCAmelCase ,   ' ' ,   __lowerCAmelCase       )

    def white_space_fix(_SCREAMING_SNAKE_CASE					:		int       ):
        return " ".join(text.split()       )

    def remove_punc(_SCREAMING_SNAKE_CASE					:		Optional[Any]       ):
        SCREAMING_SNAKE_CASE_									=						set(string.punctuation       )
        return "".join(ch for ch in text if ch not in exclude       )

    def lower(_SCREAMING_SNAKE_CASE					:		str       ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase       )       )       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    return int(normalize_answer(__lowerCAmelCase       ) == normalize_answer(__lowerCAmelCase       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		List[str]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[any(compute_exact(__lowerCAmelCase ,   __lowerCAmelCase       ) for ref in refs       ) for pred, refs in zip(__lowerCAmelCase ,   __lowerCAmelCase       )]
    return (sum(__lowerCAmelCase       ) / len(__lowerCAmelCase       )) * 100


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE_									=						Counter(__lowerCAmelCase       )

    SCREAMING_SNAKE_CASE_									=						Counter(__lowerCAmelCase       )
    SCREAMING_SNAKE_CASE_									=						Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE_									=						scount * numref

    SCREAMING_SNAKE_CASE_									=						Counter(__lowerCAmelCase       )
    SCREAMING_SNAKE_CASE_									=						Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE_									=						ccount * numref

    # KEEP
    SCREAMING_SNAKE_CASE_									=						sgramcounter_rep & cgramcounter_rep
    SCREAMING_SNAKE_CASE_									=						keepgramcounter_rep & rgramcounter
    SCREAMING_SNAKE_CASE_									=						sgramcounter_rep & rgramcounter

    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    SCREAMING_SNAKE_CASE_									=						1
    SCREAMING_SNAKE_CASE_									=						1
    if len(__lowerCAmelCase       ) > 0:
        SCREAMING_SNAKE_CASE_									=						keeptmpscorea / len(__lowerCAmelCase       )
    if len(__lowerCAmelCase       ) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        SCREAMING_SNAKE_CASE_									=						keeptmpscorea / sum(keepgramcounterall_rep.values()       )
    SCREAMING_SNAKE_CASE_									=						0
    if keepscore_precision > 0 or keepscore_recall > 0:
        SCREAMING_SNAKE_CASE_									=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    SCREAMING_SNAKE_CASE_									=						sgramcounter_rep - cgramcounter_rep
    SCREAMING_SNAKE_CASE_									=						delgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE_									=						sgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE_									=						1
    if len(__lowerCAmelCase       ) > 0:
        SCREAMING_SNAKE_CASE_									=						deltmpscorea / len(__lowerCAmelCase       )

    # ADDITION
    SCREAMING_SNAKE_CASE_									=						set(__lowerCAmelCase       ) - set(__lowerCAmelCase       )
    SCREAMING_SNAKE_CASE_									=						set(__lowerCAmelCase       ) & set(__lowerCAmelCase       )
    SCREAMING_SNAKE_CASE_									=						set(__lowerCAmelCase       ) - set(__lowerCAmelCase       )

    SCREAMING_SNAKE_CASE_									=						0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE_									=						1
    SCREAMING_SNAKE_CASE_									=						1
    if len(__lowerCAmelCase       ) > 0:
        SCREAMING_SNAKE_CASE_									=						addtmpscore / len(__lowerCAmelCase       )
    if len(__lowerCAmelCase       ) > 0:
        SCREAMING_SNAKE_CASE_									=						addtmpscore / len(__lowerCAmelCase       )
    SCREAMING_SNAKE_CASE_									=						0
    if addscore_precision > 0 or addscore_recall > 0:
        SCREAMING_SNAKE_CASE_									=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						len(__lowerCAmelCase       )

    SCREAMING_SNAKE_CASE_									=						ssent.split(' '       )
    SCREAMING_SNAKE_CASE_									=						csent.split(' '       )
    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]

    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    for rsent in rsents:
        SCREAMING_SNAKE_CASE_									=						rsent.split(' '       )
        SCREAMING_SNAKE_CASE_									=						[]
        SCREAMING_SNAKE_CASE_									=						[]
        SCREAMING_SNAKE_CASE_									=						[]
        ragramslist.append(__lowerCAmelCase       )
        for i in range(0 ,   len(__lowerCAmelCase       ) - 1       ):
            if i < len(__lowerCAmelCase       ) - 1:
                SCREAMING_SNAKE_CASE_									=						ragrams[i] + ' ' + ragrams[i + 1]
                ragrams.append(__lowerCAmelCase       )
            if i < len(__lowerCAmelCase       ) - 2:
                SCREAMING_SNAKE_CASE_									=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                ragrams.append(__lowerCAmelCase       )
            if i < len(__lowerCAmelCase       ) - 3:
                SCREAMING_SNAKE_CASE_									=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                ragrams.append(__lowerCAmelCase       )
        ragramslist.append(__lowerCAmelCase       )
        ragramslist.append(__lowerCAmelCase       )
        ragramslist.append(__lowerCAmelCase       )

    for i in range(0 ,   len(__lowerCAmelCase       ) - 1       ):
        if i < len(__lowerCAmelCase       ) - 1:
            SCREAMING_SNAKE_CASE_									=						sagrams[i] + ' ' + sagrams[i + 1]
            sagrams.append(__lowerCAmelCase       )
        if i < len(__lowerCAmelCase       ) - 2:
            SCREAMING_SNAKE_CASE_									=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
            sagrams.append(__lowerCAmelCase       )
        if i < len(__lowerCAmelCase       ) - 3:
            SCREAMING_SNAKE_CASE_									=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
            sagrams.append(__lowerCAmelCase       )

    for i in range(0 ,   len(__lowerCAmelCase       ) - 1       ):
        if i < len(__lowerCAmelCase       ) - 1:
            SCREAMING_SNAKE_CASE_									=						cagrams[i] + ' ' + cagrams[i + 1]
            cagrams.append(__lowerCAmelCase       )
        if i < len(__lowerCAmelCase       ) - 2:
            SCREAMING_SNAKE_CASE_									=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
            cagrams.append(__lowerCAmelCase       )
        if i < len(__lowerCAmelCase       ) - 3:
            SCREAMING_SNAKE_CASE_									=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
            cagrams.append(__lowerCAmelCase       )

    ((SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_))									=						SARIngram(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
    ((SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_))									=						SARIngram(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
    ((SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_))									=						SARIngram(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
    ((SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_))									=						SARIngram(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
    SCREAMING_SNAKE_CASE_									=						sum([keepascore, keepascore, keepascore, keepascore]       ) / 4
    SCREAMING_SNAKE_CASE_									=						sum([delascore, delascore, delascore, delascore]       ) / 4
    SCREAMING_SNAKE_CASE_									=						sum([addascore, addascore, addascore, addascore]       ) / 4
    SCREAMING_SNAKE_CASE_									=						(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		bool = True ,   _SCREAMING_SNAKE_CASE					:		str = "13a" ,   _SCREAMING_SNAKE_CASE					:		bool = True       ):





    """simple docstring"""


    if lowercase:
        SCREAMING_SNAKE_CASE_									=						sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__       ).major >= 2:
            SCREAMING_SNAKE_CASE_									=						sacrebleu.metrics.bleu._get_tokenizer(__lowerCAmelCase       )()(__lowerCAmelCase       )
        else:
            SCREAMING_SNAKE_CASE_									=						sacrebleu.TOKENIZERS[tokenizer]()(__lowerCAmelCase       )
    elif tokenizer == "moses":
        SCREAMING_SNAKE_CASE_									=						sacremoses.MosesTokenizer().tokenize(__lowerCAmelCase ,   return_str=__lowerCAmelCase ,   escape=__lowerCAmelCase       )
    elif tokenizer == "penn":
        SCREAMING_SNAKE_CASE_									=						sacremoses.MosesTokenizer().penn_tokenize(__lowerCAmelCase ,   return_str=__lowerCAmelCase       )
    else:
        SCREAMING_SNAKE_CASE_									=						sentence

    if not return_str:
        SCREAMING_SNAKE_CASE_									=						normalized_sent.split()

    return normalized_sent


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    if not (len(__lowerCAmelCase       ) == len(__lowerCAmelCase       ) == len(__lowerCAmelCase       )):
        raise ValueError('Sources length must match predictions and references lengths.'       )
    SCREAMING_SNAKE_CASE_									=						0
    for src, pred, refs in zip(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       ):
        sari_score += SARIsent(normalize(__lowerCAmelCase       ) ,   normalize(__lowerCAmelCase       ) ,   [normalize(__lowerCAmelCase       ) for sent in refs]       )
    SCREAMING_SNAKE_CASE_									=						sari_score / len(__lowerCAmelCase       )
    return 100 * sari_score


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		List[str]="exp" ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=None ,   _SCREAMING_SNAKE_CASE					:		Dict=False ,   _SCREAMING_SNAKE_CASE					:		List[str]=False ,   _SCREAMING_SNAKE_CASE					:		str=False ,   ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						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'       )
    SCREAMING_SNAKE_CASE_									=						[[refs[i] for refs in references] for i in range(__lowerCAmelCase       )]
    SCREAMING_SNAKE_CASE_									=						sacrebleu.corpus_bleu(
        __lowerCAmelCase ,   __lowerCAmelCase ,   smooth_method=__lowerCAmelCase ,   smooth_value=__lowerCAmelCase ,   force=__lowerCAmelCase ,   lowercase=__lowerCAmelCase ,   use_effective_order=__lowerCAmelCase ,   )
    return output.score





@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(
              {
                  'predictions': datasets.Value('string'						,		id='sequence'),
                  'references': datasets.Sequence(datasets.Value('string'						,		id='sequence')						,		id='references'),
              })						,		codebase_urls=[
              'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
              'https://github.com/cocoxu/simplification/blob/master/SARI.py',
              'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
              'https://github.com/mjpost/sacreBLEU',
          ]						,		reference_urls=[
              'https://www.aclweb.org/anthology/Q16-1029.pdf',
              'https://github.com/mjpost/sacreBLEU',
              'https://en.wikipedia.org/wiki/BLEU',
              'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
          ]						,		)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						{}
      result.update({'sari': compute_sari(sources=_lowerCAmelCase						,		predictions=_lowerCAmelCase						,		references=_lowerCAmelCase)})
      result.update({'sacrebleu': compute_sacrebleu(predictions=_lowerCAmelCase						,		references=_lowerCAmelCase)})
      result.update({'exact': compute_em(predictions=_lowerCAmelCase						,		references=_lowerCAmelCase)})
      return result






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase__  :      int			=    "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"



class       __snake_case  (    unittest.TestCase     , lowerCAmelCase__       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						load_tool('text-question-answering')
      self.tool.setup()
      SCREAMING_SNAKE_CASE_									=						load_tool('text-question-answering'						,		remote=_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.tool(_A						,		'What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.remote_tool(_A						,		'What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.tool(text=_A						,		question='What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.remote_tool(text=_A						,		question='What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')


