
import requests
from bsa import BeautifulSoup






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE					,				params=__SCREAMING_SNAKE_CASE				).content					,				'html.parser'				)
	lowercase	    =       soup.find('div'					,				attrs={'class': 'gs_ri'}				)
	lowercase	    =       div.find('div'					,				attrs={'class': 'gs_fl'}				).find_all('a'				)
	return anchors[2].get_text()


if __name__ == "__main__":
	UpperCAmelCase       =					{
	    '''title''': (
	        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
	        '''capacitance, volumetric capacitance, and energy density'''
	    ),
	    '''journal''': '''Chem. Mater.''',
	    '''volume''': 30,
	    '''pages''': '''3979-3990''',
	    '''year''': 2018,
	    '''hl''': '''en''',
	}
	print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))








'''simple docstring'''


import re
import string

import numpy as np

import datasets


_SCREAMING_SNAKE_CASE									=				"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

_SCREAMING_SNAKE_CASE									=				"\n"

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




 def 				_snake_case (						self		)     ->				List[str]:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
             {
                 "predictions": datasets.Value("string"      ,  id="sequence"		),
                 "references": datasets.Value("string"      ,  id="sequence"		),
             }		)      ,  reference_urls=[]      ,  )





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  )     ->				str:
     if regexes_to_ignore is not None:
         for s in regexes_to_ignore:
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in predictions]		)
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in references]		)
     else:
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)

     if ignore_case:
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)

     if ignore_punctuation:
         _lowerCAmelCase						      =   string.punctuation.maketrans(""      ,  ""      ,  string.punctuation		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     if ignore_numbers:
         _lowerCAmelCase						      =   string.digits.maketrans(""      ,  ""      ,  string.digits		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     _lowerCAmelCase						      =   predictions == references

     return {"exact_match": np.mean(_lowerCAmelCase		) * 100}


