




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase								=					'\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								=					'\\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								=					'\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 							_A       (						lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     def remove_articles(lowerCAmelCase_					: Tuple      ):
          lowerCAmelCase__          =			re.compile(r"\b(a|an|the)\b"       ,    re.UNICODE      )
          return re.sub(lowerCAmelCase_       ,    " "       ,    lowerCAmelCase_      )

     def white_space_fix(lowerCAmelCase_					: Any      ):
          return " ".join(text.split()      )

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

     def lower(lowerCAmelCase_					: int      ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_      )      )      )      )



def 							_A       (						lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     return int(normalize_answer(lowerCAmelCase_      ) == normalize_answer(lowerCAmelCase_      )      )



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			[rgram for rgrams in rgramslist for rgram in rgrams]
     lowerCAmelCase__          =			Counter(lowerCAmelCase_      )

     lowerCAmelCase__          =			Counter(lowerCAmelCase_      )
     lowerCAmelCase__          =			Counter()
     for sgram, scount in sgramcounter.items():
          lowerCAmelCase__          =			scount * numref

     lowerCAmelCase__          =			Counter(lowerCAmelCase_      )
     lowerCAmelCase__          =			Counter()
     for cgram, ccount in cgramcounter.items():
          lowerCAmelCase__          =			ccount * numref

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

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

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

     # ADDITION
     lowerCAmelCase__          =			set(lowerCAmelCase_      ) - set(lowerCAmelCase_      )
     lowerCAmelCase__          =			set(lowerCAmelCase_      ) & set(lowerCAmelCase_      )
     lowerCAmelCase__          =			set(lowerCAmelCase_      ) - set(lowerCAmelCase_      )

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

     return (keepscore, delscore_precision, addscore)



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			len(lowerCAmelCase_      )

     lowerCAmelCase__          =			ssent.split(" "      )
     lowerCAmelCase__          =			csent.split(" "      )
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]

     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     for rsent in rsents:
          lowerCAmelCase__          =			rsent.split(" "      )
          lowerCAmelCase__          =			[]
          lowerCAmelCase__          =			[]
          lowerCAmelCase__          =			[]
          ragramslist.append(lowerCAmelCase_      )
          for i in range(0       ,    len(lowerCAmelCase_      ) - 1      ):
               if i < len(lowerCAmelCase_      ) - 1:
                    lowerCAmelCase__          =			ragrams[i] + " " + ragrams[i + 1]
                    ragrams.append(lowerCAmelCase_      )
               if i < len(lowerCAmelCase_      ) - 2:
                    lowerCAmelCase__          =			ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                    ragrams.append(lowerCAmelCase_      )
               if i < len(lowerCAmelCase_      ) - 3:
                    lowerCAmelCase__          =			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:
               lowerCAmelCase__          =			sagrams[i] + " " + sagrams[i + 1]
               sagrams.append(lowerCAmelCase_      )
          if i < len(lowerCAmelCase_      ) - 2:
               lowerCAmelCase__          =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
               sagrams.append(lowerCAmelCase_      )
          if i < len(lowerCAmelCase_      ) - 3:
               lowerCAmelCase__          =			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:
               lowerCAmelCase__          =			cagrams[i] + " " + cagrams[i + 1]
               cagrams.append(lowerCAmelCase_      )
          if i < len(lowerCAmelCase_      ) - 2:
               lowerCAmelCase__          =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
               cagrams.append(lowerCAmelCase_      )
          if i < len(lowerCAmelCase_      ) - 3:
               lowerCAmelCase__          =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
               cagrams.append(lowerCAmelCase_      )

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



def 							_A       (						lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: bool = True       ,    lowerCAmelCase_					: str = "13a"       ,    lowerCAmelCase_					: bool = True      ):
     """simple docstring"""

     if lowercase:
          lowerCAmelCase__          =			sentence.lower()

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

     if not return_str:
          lowerCAmelCase__          =			normalized_sent.split()

     return normalized_sent



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: str="exp"       ,    lowerCAmelCase_					: Optional[int]=None       ,    lowerCAmelCase_					: Optional[int]=False       ,    lowerCAmelCase_					: str=False       ,    lowerCAmelCase_					: int=False       ,    ):
     """simple docstring"""

     lowerCAmelCase__          =			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"      )
     lowerCAmelCase__          =			[[refs[i] for refs in references] for i in range(lowerCAmelCase_      )]
     lowerCAmelCase__          =			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 			__lowerCamelCase	(   datasets.Metric   ):




    """simple docstring"""

    def        a				(	self					:					List[str]       )				->	str:
         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					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Union[str, Any]:
         lowerCAmelCase__          =			{}
         result.update({"sari": compute_sari(sources=SCREAMING_SNAKE_CASE__       ,     predictions=SCREAMING_SNAKE_CASE__       ,     references=SCREAMING_SNAKE_CASE__       )}       )
         result.update({"sacrebleu": compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE__       ,     references=SCREAMING_SNAKE_CASE__       )}       )
         result.update({"exact": compute_em(predictions=SCREAMING_SNAKE_CASE__       ,     references=SCREAMING_SNAKE_CASE__       )}       )
         return result












from math import pi, sqrt



def 							_A       (						lowerCAmelCase_					: float      ):
     """simple docstring"""

     if num <= 0:
          raise ValueError("math domain error"      )
     if num > 171.5:
          raise OverflowError("math range error"      )
     elif num - int(lowerCAmelCase_      ) not in (0, 0.5):
          raise NotImplementedError("num must be an integer or a half-integer"      )
     elif num == 0.5:
          return sqrt(lowerCAmelCase_      )
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1      )



def 							_A       (						):
     """simple docstring"""

     assert gamma(0.5      ) == sqrt(lowerCAmelCase_      )
     assert gamma(1      ) == 1.0
     assert gamma(2      ) == 1.0


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     UpperCamelCase								=					1.0
     while num:
          UpperCamelCase								=					float(input('Gamma of: '))
          print(F"""gamma({num}) = {gamma(num)}""")
          print('\nEnter 0 to exit...')
