






'''simple docstring'''

import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print('Googling.....')
     SCREAMING_SNAKE_CASE__     =       'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
     SCREAMING_SNAKE_CASE__     =       requests.get(url, headers={'UserAgent': UserAgent().random})
     # res.raise_for_status()
     with open('project1a.html', 'wb') as out_file:  # only for knowing the class
          for data in res.iter_content(1_0_0_0_0):
               out_file.write(data)
     SCREAMING_SNAKE_CASE__     =       BeautifulSoup(res.text, 'html.parser')
     SCREAMING_SNAKE_CASE__     =       list(soup.select('.eZt8xd'))[:5]

     print(len(links))
     for link in links:
          if link.text == "Maps":
               webbrowser.open(link.get('href'))
          else:
               webbrowser.open(f'https://google.com{link.get("href")}')








'''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     a_						(      datasets.Metric    ):

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		)  ->				List[Any]:



        """simple docstring"""






        if regexes_to_ignore is not None:
              for s in regexes_to_ignore:
                    UpperCamelCase      = np.array([re.sub(_SCREAMING_SNAKE_CASE      ,		""""""      ,		_SCREAMING_SNAKE_CASE				) for x in predictions]				)
                    UpperCamelCase      = np.array([re.sub(_SCREAMING_SNAKE_CASE      ,		""""""      ,		_SCREAMING_SNAKE_CASE				) for x in references]				)
        else:
              UpperCamelCase      = np.asarray(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.asarray(_SCREAMING_SNAKE_CASE				)

        if ignore_case:
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)

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

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

        UpperCamelCase      = predictions == references

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