





'''simple docstring'''

from __future__ import annotations

__lowerCAmelCase							:     List[Any]		     ="Muhammad Umer Farooq"
__lowerCAmelCase							:     List[Any]		     ="MIT"
__lowerCAmelCase							:     str		     ="1.0.0"
__lowerCAmelCase							:     Optional[Any]		     ="Muhammad Umer Farooq"
__lowerCAmelCase							:     int		     ="contact@muhammadumerfarooq.me"
__lowerCAmelCase							:     Tuple		     ="Alpha"

import re
from html.parser import HTMLParser
from urllib import parse

import requests






class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Dict						,	lowercase_				:str							)->							None:
     super().__init__()
     A__    =      []
     A__    =      domain



 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str						,	lowercase_				:list[tuple[str, str | None]]							)->							None:
     # Only parse the 'anchor' tag.
     if tag == "a":
         # Check the list of defined attributes.
         for name, value in attrs:
             # If href is defined, and not empty nor # print it.
             if name == "href" and value != "#" and value != "":
                 # If not already in urls.
                 if value not in self.urls:
                     A__    =      parse.urljoin(self.domain						,	lowercase_							)
                     self.urls.append(lowercase_							)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    return ".".join(get_sub_domain_name(_lowerCamelCase	).split("."	)[-2:]	)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    return parse.urlparse(_lowerCamelCase	).netloc





def 						UpperCamelCase    (							_lowerCamelCase : str = "https://github.com"	):
    A__    =      get_domain_name(_lowerCamelCase	)

    # Initialize the parser
    A__    =      Parser(_lowerCamelCase	)

    try:
        # Open URL
        A__    =      requests.get(_lowerCamelCase	)

        # pass the raw HTML to the parser to get links
        parser.feed(r.text	)

        # Get links and loop through
        A__    =      set()
        for link in parser.urls:
            # open URL.
            # read = requests.get(link)
            try:
                A__    =      requests.get(_lowerCamelCase	)
                # Get the valid email.
                A__    =      re.findall("[a-zA-Z0-9]+@" + domain						,	read.text	)
                # If not in list then append it.
                for email in emails:
                    valid_emails.add(_lowerCamelCase	)
            except ValueError:
                pass
    except ValueError:
        raise SystemExit(1	)

    # Finally return a sorted list of email addresses with no duplicates.
    return sorted(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     str		     =emails_from_url("https://github.com")
  print(f"""{len(emails)} emails found:""")
  print("\n".join(sorted(emails)))










'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase							:     Optional[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"

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


__lowerCAmelCase							:     str		     ="\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    (							_lowerCamelCase : List[Any]	):

    def remove_articles(_lowerCamelCase : Dict	):
        A__    =      re.compile(r"\b(a|an|the)\b"						,	re.UNICODE	)
        return re.sub(_lowerCamelCase						,	" "						,	_lowerCamelCase	)

    def white_space_fix(_lowerCamelCase : Tuple	):
        return " ".join(text.split()	)

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

    def lower(_lowerCamelCase : Optional[int]	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase	)	)	)	)





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]	):
    return int(normalize_answer(_lowerCamelCase	) == normalize_answer(_lowerCamelCase	)	)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : List[Any]	):
    A__    =      [any(compute_exact(_lowerCamelCase						,	_lowerCamelCase	) for ref in refs	) for pred, refs in zip(_lowerCamelCase						,	_lowerCamelCase	)]
    return (sum(_lowerCamelCase	) / len(_lowerCamelCase	)) * 1_00





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : str	):
    A__    =      [rgram for rgrams in rgramslist for rgram in rgrams]
    A__    =      Counter(_lowerCamelCase	)

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

    A__    =      Counter(_lowerCamelCase	)
    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(_lowerCamelCase	) > 0:
        A__    =      keeptmpscorea / len(_lowerCamelCase	)
    if len(_lowerCamelCase	) > 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(_lowerCamelCase	) > 0:
        A__    =      deltmpscorea / len(_lowerCamelCase	)

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

    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(_lowerCamelCase	) > 0:
        A__    =      addtmpscore / len(_lowerCamelCase	)
    if len(_lowerCamelCase	) > 0:
        A__    =      addtmpscore / len(_lowerCamelCase	)
    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 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]	):
    A__    =      len(_lowerCamelCase	)

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

    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    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 						UpperCamelCase    (							_lowerCamelCase : Tuple						,	_lowerCamelCase : bool = True						,	_lowerCamelCase : str = "13a"						,	_lowerCamelCase : bool = 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:
        A__    =      sentence.lower()

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

    if not return_str:
        A__    =      normalized_sent.split()

    return normalized_sent





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int						,	_lowerCamelCase : Union[str, Any]	):
    if not (len(_lowerCamelCase	) == len(_lowerCamelCase	) == len(_lowerCamelCase	)):
        raise ValueError("Sources length must match predictions and references lengths."	)
    A__    =      0
    for src, pred, refs in zip(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        sari_score += SARIsent(normalize(_lowerCamelCase	)						,	normalize(_lowerCamelCase	)						,	[normalize(_lowerCamelCase	) for sent in refs]	)
    A__    =      sari_score / len(_lowerCamelCase	)
    return 1_00 * sari_score





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Dict						,	_lowerCamelCase : List[Any]="exp"						,	_lowerCamelCase : int=None						,	_lowerCamelCase : str=False						,	_lowerCamelCase : List[str]=False						,	_lowerCamelCase : Dict=False						,	):
    A__    =      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"	)
    A__    =      [[refs[i] for refs in references] for i in range(_lowerCamelCase	)]
    A__    =      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        UpperCAmelCase     (	datasets.Metric    ):
 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     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 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Dict						,	lowercase_				:List[Any]						,	lowercase_				:int							)->							int:
     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



