



"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    word.split()

				def justify(_SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Any				) -> str:
								_UpperCAmelCase					=    max_width - width
								_UpperCAmelCase					=    len(_lowercase				)
								if len(_lowercase				) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												_UpperCAmelCase					=    words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												_UpperCAmelCase					=    spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												_UpperCAmelCase					=    (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(_lowercase				):
																num_spaces_between_words_list[i] += 1
												_UpperCAmelCase					=    []
												for i in range(_lowercase				):
																# add the word
																aligned_words_list.append(line[i]				)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ''' '''				)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]				)
												# join the aligned words list to form a justified line
												return "".join(_lowercase				)

				_UpperCAmelCase					=    []
				_UpperCAmelCase					=    []
				_UpperCAmelCase					=    0
				for word in words:
								if width + len(_lowercase				) + len(_lowercase				) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(_lowercase				)
												width += len(_lowercase				)
								else:
												# justify the line and add it to result
												answer.append(justify(_lowercase					,     _lowercase					,     _lowercase				)				)
												# reset new line and new width
												_UpperCAmelCase					=    [word], len(_lowercase				)
				_UpperCAmelCase					=    max_width - width - len(_lowercase				)
				answer.append(''' '''.join(_lowercase				) + (remaining_spaces + 1) * ''' '''				)
				return answer


if __name__ == "__main__":
						from doctest import testmod

						testmod()








"""simple docstring"""


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

import datasets


__A   :							Any										=   "\\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"


__A   :							Optional[Any]										=   "\\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"


__A   :							Tuple										=   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  lowercase__						(		self     :    List[Any]   )->Dict:
							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  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    0.0
							for i, j in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											n_correct += 1.0 if math_equivalence.is_equiv(__UpperCamelCase				,   __UpperCamelCase   ) else 0.0
							_UpperCAmelCase					=    n_correct / len(__UpperCamelCase   )
							return {
							    "accuracy": accuracy,
							}



