






from __future__ import annotations

import requests

SCREAMING_SNAKE_CASE 							=						set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)

def      _lowerCamelCase	(						__A    :							str		,		__A    :							int = 1		,		__A    :							str = "new"		,		__A    :							list | None = None )      ->					dict:
    _UpperCAmelCase				:      Any			  =				wanted_data or []
    if invalid_search_terms := ", ".join(sorted(set(__A ) - valid_terms ) ):
        _UpperCAmelCase				:      str			  =				f'''Invalid search term: {invalid_search_terms}'''
        raise ValueError(__A )
    _UpperCAmelCase				:      Any			  =				requests.get(
        f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''		,		headers={'''User-agent''': '''A random string'''}		,		)
    if response.status_code == 429:
        raise requests.HTTPError

    _UpperCAmelCase				:      Optional[Any]			  =				response.json()
    if not wanted_data:
        return {id_: data["data"]["children"][id_] for id_ in range(__A )}

    _UpperCAmelCase				:      Dict			  =				{}
    for id_ in range(__A ):
        _UpperCAmelCase				:      Tuple			  =				{
            item: data['''data''']['''children'''][id_]['''data'''][item] for item in wanted_data
        }
    return data_dict


if __name__ == "__main__":
  # If you get Error 429, that means you are rate limited.Try after some time
  print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))












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

import datasets


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


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


SCREAMING_SNAKE_CASE 							=						R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION			)
class 							A_    (						datasets.Metric			):






 '''simple docstring'''




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

     """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  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				0.0
     for i, j in zip(_A							,  _A):
         n_correct += 1.0 if math_equivalence.is_equiv(_A							,  _A) else 0.0
     _UpperCAmelCase				:      Tuple			  =				n_correct / len(_A)
     return {
         "accuracy": accuracy,
     }




