


'''simple docstring'''
import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__snake_case							   =  '''\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
'''


__snake_case							   =  '''\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.
'''


__snake_case							   =  R'''
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting "1/2" to "\\frac{1}{2}")

Examples:
    >>> metric = datasets.load_metric("competition_math")
    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])
    >>> print(results)
    {\'accuracy\': 1.0}
'''
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION			,	_KWARGS_DESCRIPTION      )
class      lowercase			(							datasets.Metric      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION					,     citation=_CITATION					,     inputs_description=_KWARGS_DESCRIPTION					,     features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''							),
							            '''references''': datasets.Value('''string'''							),
							        }							)					,     homepage='''https://github.com/hendrycks/math'''					,     codebase_urls=['''https://github.com/hendrycks/math''']					,     )




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				0.0
							for i, j in zip(UpperCamelCase_					,     UpperCamelCase_							):
												n_correct += 1.0 if math_equivalence.is_equiv(UpperCamelCase_					,     UpperCamelCase_							) else 0.0
							UpperCamelCase__     :List[Any]							      =				n_correct / len(UpperCamelCase_							)
							return {
							    "accuracy": accuracy,
							}



'''simple docstring'''
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()