


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   "\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"

_SCREAMING_SNAKE_CASE      :			Tuple      				=   "\\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"


_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   "\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__						(UpperCamelCase_    ):



     """simple docstring"""






     def remove_articles(UpperCamelCase_    ):
          snake_case					=    re.compile(r'''\b(a|an|the)\b'''							,re.UNICODE    )
          return re.sub(UpperCamelCase_							,''' '''							,UpperCamelCase_    )

     def white_space_fix(UpperCamelCase_    ):
          return " ".join(text.split()    )

     def remove_punc(UpperCamelCase_    ):
          snake_case					=    set(string.punctuation    )
          return "".join(ch for ch in text if ch not in exclude    )

     def lower(UpperCamelCase_    ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_    )    )    )    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     return int(normalize_answer(UpperCamelCase_    ) == normalize_answer(UpperCamelCase_    )    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    [any(compute_exact(UpperCamelCase_							,UpperCamelCase_    ) for ref in refs    ) for pred, refs in zip(UpperCamelCase_							,UpperCamelCase_    )]
     return (sum(UpperCamelCase_    ) / len(UpperCamelCase_    )) * 1_00
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    [rgram for rgrams in rgramslist for rgram in rgrams]
     snake_case					=    Counter(UpperCamelCase_    )

     snake_case					=    Counter(UpperCamelCase_    )
     snake_case					=    Counter()
     for sgram, scount in sgramcounter.items():
          snake_case					=    scount * numref

     snake_case					=    Counter(UpperCamelCase_    )
     snake_case					=    Counter()
     for cgram, ccount in cgramcounter.items():
          snake_case					=    ccount * numref

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

     snake_case					=    0
     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.
     snake_case					=    1
     snake_case					=    1
     if len(UpperCamelCase_    ) > 0:
          snake_case					=    keeptmpscorea / len(UpperCamelCase_    )
     if len(UpperCamelCase_    ) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          snake_case					=    keeptmpscorea / sum(keepgramcounterall_rep.values()    )
     snake_case					=    0
     if keepscore_precision > 0 or keepscore_recall > 0:
          snake_case					=    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

     # DELETION
     snake_case					=    sgramcounter_rep - cgramcounter_rep
     snake_case					=    delgramcounter_rep - rgramcounter
     snake_case					=    sgramcounter_rep - rgramcounter
     snake_case					=    0
     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.
     snake_case					=    1
     if len(UpperCamelCase_    ) > 0:
          snake_case					=    deltmpscorea / len(UpperCamelCase_    )

     # ADDITION
     snake_case					=    set(UpperCamelCase_    ) - set(UpperCamelCase_    )
     snake_case					=    set(UpperCamelCase_    ) & set(UpperCamelCase_    )
     snake_case					=    set(UpperCamelCase_    ) - set(UpperCamelCase_    )

     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.
     snake_case					=    1
     snake_case					=    1
     if len(UpperCamelCase_    ) > 0:
          snake_case					=    addtmpscore / len(UpperCamelCase_    )
     if len(UpperCamelCase_    ) > 0:
          snake_case					=    addtmpscore / len(UpperCamelCase_    )
     snake_case					=    0
     if addscore_precision > 0 or addscore_recall > 0:
          snake_case					=    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )

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

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

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

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

     ((snake_case)			,  (snake_case)			,  (snake_case))					=    SARIngram(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     ((snake_case)			,  (snake_case)			,  (snake_case))					=    SARIngram(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     ((snake_case)			,  (snake_case)			,  (snake_case))					=    SARIngram(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     ((snake_case)			,  (snake_case)			,  (snake_case))					=    SARIngram(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     snake_case					=    sum([keepascore, keepascore, keepascore, keepascore]    ) / 4
     snake_case					=    sum([delascore, delascore, delascore, delascore]    ) / 4
     snake_case					=    sum([addascore, addascore, addascore, addascore]    ) / 4
     snake_case					=    (avgkeepscore + avgdelscore + avgaddscore) / 3
     return finalscore
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_ = True							,UpperCamelCase_ = "13a"							,UpperCamelCase_ = True    ):



     """simple docstring"""






     if lowercase:
          snake_case					=    sentence.lower()

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

     if not return_str:
          snake_case					=    normalized_sent.split()

     return normalized_sent
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     if not (len(UpperCamelCase_    ) == len(UpperCamelCase_    ) == len(UpperCamelCase_    )):
          raise ValueError('''Sources length must match predictions and references lengths.'''    )
     snake_case					=    0
     for src, pred, refs in zip(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):
          sari_score += SARIsent(normalize(UpperCamelCase_    )							,normalize(UpperCamelCase_    )							,[normalize(UpperCamelCase_    ) for sent in refs]    )
     snake_case					=    sari_score / len(UpperCamelCase_    )
     return 1_00 * sari_score
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_="exp"							,UpperCamelCase_=None							,UpperCamelCase_=False							,UpperCamelCase_=False							,UpperCamelCase_=False							,):



     """simple docstring"""






     snake_case					=    len(references[0]    )
     if any(len(UpperCamelCase_    ) != references_per_prediction for refs in references    ):
          raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
     snake_case					=    [[refs[i] for refs in references] for i in range(UpperCamelCase_    )]
     snake_case					=    sacrebleu.corpus_bleu(
         UpperCamelCase_							,UpperCamelCase_							,smooth_method=UpperCamelCase_							,smooth_value=UpperCamelCase_							,force=UpperCamelCase_							,lowercase=UpperCamelCase_							,use_effective_order=UpperCamelCase_							,)
     return output.score

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







     """simple docstring"""

     def 		a_      (	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 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
          snake_case					=    {}
          result.update({'''sari''': compute_sari(sources=__snake_case      , predictions=__snake_case      , references=__snake_case   )}   )
          result.update({'''sacrebleu''': compute_sacrebleu(predictions=__snake_case      , references=__snake_case   )}   )
          result.update({'''exact''': compute_em(predictions=__snake_case      , references=__snake_case   )}   )
          return result








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(UpperCamelCase_    )

def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_terminal_summary_main

     snake_case					=    terminalreporter.config.getoption('''--make-reports'''    )
     if make_reports:
          pytest_terminal_summary_main(UpperCamelCase_							,id=UpperCamelCase_    )




