

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase_			=  "\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"

lowercase_			=  "\\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"


lowercase_			=  "\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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Tuple				)							->				Union[str, Any]:





    '''simple docstring'''






    def remove_articles(SCREAMING_SNAKE_CASE__      :     int				):
        A__   				=       re.compile(R'\b(a|an|the)\b'   ,      re.UNICODE				)
        return re.sub(SCREAMING_SNAKE_CASE__   ,      ' '   ,      SCREAMING_SNAKE_CASE__				)

    def white_space_fix(SCREAMING_SNAKE_CASE__      :     List[Any]				):
        return " ".join(text.split()				)

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

    def lower(SCREAMING_SNAKE_CASE__      :     Dict				):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__				)				)				)				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]				)							->				Optional[int]:





    '''simple docstring'''






    return int(normalize_answer(SCREAMING_SNAKE_CASE__				) == normalize_answer(SCREAMING_SNAKE_CASE__				)				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Tuple				)							->				List[str]:





    '''simple docstring'''






    A__   				=       [any(compute_exact(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				) for ref in refs				) for pred, refs in zip(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)]
    return (sum(SCREAMING_SNAKE_CASE__				) / len(SCREAMING_SNAKE_CASE__				)) * 100
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]				)							->				str:





    '''simple docstring'''






    A__   				=       [rgram for rgrams in rgramslist for rgram in rgrams]
    A__   				=       Counter(SCREAMING_SNAKE_CASE__				)

    A__   				=       Counter(SCREAMING_SNAKE_CASE__				)
    A__   				=       Counter()
    for sgram, scount in sgramcounter.items():
        A__   				=       scount * numref

    A__   				=       Counter(SCREAMING_SNAKE_CASE__				)
    A__   				=       Counter()
    for cgram, ccount in cgramcounter.items():
        A__   				=       ccount * numref

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

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

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

    # ADDITION
    A__   				=       set(SCREAMING_SNAKE_CASE__				) - set(SCREAMING_SNAKE_CASE__				)
    A__   				=       set(SCREAMING_SNAKE_CASE__				) & set(SCREAMING_SNAKE_CASE__				)
    A__   				=       set(SCREAMING_SNAKE_CASE__				) - set(SCREAMING_SNAKE_CASE__				)

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

    return (keepscore, delscore_precision, addscore)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Optional[int]:





    '''simple docstring'''






    A__   				=       len(SCREAMING_SNAKE_CASE__				)

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

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

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

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

    ((A__)				,						(A__)				,						(A__))   				=       SARIngram(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    ((A__)				,						(A__)				,						(A__))   				=       SARIngram(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    ((A__)				,						(A__)				,						(A__))   				=       SARIngram(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    ((A__)				,						(A__)				,						(A__))   				=       SARIngram(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    A__   				=       sum([keepascore, keepascore, keepascore, keepascore]				) / 4
    A__   				=       sum([delascore, delascore, delascore, delascore]				) / 4
    A__   				=       sum([addascore, addascore, addascore, addascore]				) / 4
    A__   				=       (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     bool = True   ,      SCREAMING_SNAKE_CASE__      :     str = "13a"   ,      SCREAMING_SNAKE_CASE__      :     bool = True				)							->				Union[str, Any]:





    '''simple docstring'''






    if lowercase:
        A__   				=       sentence.lower()

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

    if not return_str:
        A__   				=       normalized_sent.split()

    return normalized_sent
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				List[str]:





    '''simple docstring'''






    if not (len(SCREAMING_SNAKE_CASE__				) == len(SCREAMING_SNAKE_CASE__				) == len(SCREAMING_SNAKE_CASE__				)):
        raise ValueError('Sources length must match predictions and references lengths.'				)
    A__   				=       0
    for src, pred, refs in zip(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				):
        sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE__				)   ,      normalize(SCREAMING_SNAKE_CASE__				)   ,      [normalize(SCREAMING_SNAKE_CASE__				) for sent in refs]				)
    A__   				=       sari_score / len(SCREAMING_SNAKE_CASE__				)
    return 100 * sari_score
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Tuple="exp"   ,      SCREAMING_SNAKE_CASE__      :     int=None   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]=False   ,      )							->				Optional[Any]:





    '''simple docstring'''






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


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







    """simple docstring"""





    def 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







        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 						snake_case__					(				self			:	Tuple,lowercase_			:	str,lowercase_			:	List[Any],lowercase_			:	Union[str, Any]		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       {}
        result.update({'sari': compute_sari(sources=lowercase_,predictions=lowercase_,references=lowercase_		)}		)
        result.update({'sacrebleu': compute_sacrebleu(predictions=lowercase_,references=lowercase_		)}		)
        result.update({'exact': compute_em(predictions=lowercase_,references=lowercase_		)}		)
        return result




from collections.abc import Sequence
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Sequence[int] | None = None				)							->				int:





    '''simple docstring'''






    if nums is None or not nums:
        raise ValueError('Input sequence should not be empty'				)

    A__   				=       nums[0]
    for i in range(1   ,      len(SCREAMING_SNAKE_CASE__				)				):
        A__   				=       nums[i]
        A__   				=       max(SCREAMING_SNAKE_CASE__   ,      ans + num   ,      SCREAMING_SNAKE_CASE__				)

    return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     lowercase_			=  int(input("Enter number of elements : ").strip())
     lowercase_			=  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
     print(max_subsequence_sum(array))

