




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE 						="""
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@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\",
}
"""

__SCREAMING_SNAKE_CASE 						="""\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__SCREAMING_SNAKE_CASE 						="""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""

def 			a   (_lowerCAmelCase     ):

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

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase     )     )     )     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							return int(normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    [any(compute_exact(_lowerCAmelCase  ,    _lowerCAmelCase     ) for ref in refs     ) for pred, refs in zip(_lowerCAmelCase  ,    _lowerCAmelCase     )]
							return (sum(_lowerCAmelCase     ) / len(_lowerCAmelCase     )) * 1_0_0

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase = True  ,    _lowerCAmelCase = "13a"  ,    _lowerCAmelCase = True     ):
							# Normalization is requried for the ASSET dataset (one of the primary
							# datasets in sentence simplification) to allow using space
							# to split the sentence. Even though Wiki-Auto and TURK datasets,
							# do not require normalization, we do it for consistency.
							# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
							# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

							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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							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 1_0_0 * sari_score

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase="exp"  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=False  ,    _lowerCAmelCase=False  ,    _lowerCAmelCase=False  ,    ):
							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  __magic_name__						(  datasets.Metric):
				'''simple docstring'''







				def     _A							(							self:   Tuple		):
											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:   Tuple		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Any		):
											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 webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						="""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

			print("""Googling.....""")

			__SCREAMING_SNAKE_CASE 						=f"""https://www.google.com/search?q={query}&num=100"""

			__SCREAMING_SNAKE_CASE 						=requests.get(
			    url,
			    headers={"""User-Agent""": str(UserAgent().random)},
			)

			try:
						__SCREAMING_SNAKE_CASE 						=(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """yuRUbf"""})
						    .find("""a""")
						    .get("""href""")
						)

			except AttributeError:
						__SCREAMING_SNAKE_CASE 						=parse_qs(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """kCrYT"""})
						    .find("""a""")
						    .get("""href""")
						)["""url"""][0]

			webbrowser.open(link)


