


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

import string
from itertools import cycle, product
from pathlib import Path

UpperCamelCase__    					=      (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase__    					=      [ord(letter) for letter in string.ascii_lowercase]
UpperCamelCase__    					=      {ord(char) for char in VALID_CHARS}

UpperCamelCase__    					=      ["the", "be", "to", "of", "and", "in", "that", "have"]





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				''''''
    UpperCAmelCase__			:		Union[str, Any]													=				42
    UpperCAmelCase__			:		Any													=				42
    UpperCAmelCase__			:		Optional[int]													=				42

    for keychar, cipherchar in zip(cycle(lowerCAmelCase_						)  ,					lowerCAmelCase_						):
        UpperCAmelCase__			:		Tuple													=				cipherchar ^ keychar
        if decodedchar not in VALID_INTS:
            return None
        decoded += chr(lowerCAmelCase_						)

    return decoded





def 					a__       (					lowerCAmelCase__						)    ->	Any:
    UpperCAmelCase__			:		Dict													=				[]
    for key in product(lowerCAmelCase_  ,					repeat=3						):
        UpperCAmelCase__			:		Dict													=				try_key(lowerCAmelCase_  ,					lowerCAmelCase_						)
        if encoded is not None:
            possibles.append(lowerCAmelCase_						)
    return possibles





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    return [possible for possible in possibles if common_word in possible.lower()]





def 					a__       (					lowerCAmelCase__ = "p059_cipher.txt"						)    ->	Optional[int]:
    UpperCAmelCase__			:		int													=				42
    UpperCAmelCase__			:		Optional[Any]													=				42
    UpperCAmelCase__			:		Optional[int]													=				42
    UpperCAmelCase__			:		List[str]													=				42
    UpperCAmelCase__			:		List[str]													=				Path(lowerCAmelCase_						).parent.joinpath(lowerCAmelCase_						).read_text(encoding='''utf-8'''						)

    UpperCAmelCase__			:		Optional[int]													=				[int(lowerCAmelCase_						) for number in data.strip().split(''','''						)]

    UpperCAmelCase__			:		Tuple													=				filter_valid_chars(lowerCAmelCase_						)
    for common_word in COMMON_WORDS:
        UpperCAmelCase__			:		int													=				filter_common_word(lowerCAmelCase_  ,					lowerCAmelCase_						)
        if len(lowerCAmelCase_						) == 1:
            break

    UpperCAmelCase__			:		List[Any]													=				possibles[0]
    return sum(ord(lowerCAmelCase_						) for char in decoded_text						)


if __name__ == "__main__":
  print(F"""{solution() = }""")







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

import datasets


_snake_case  :    Tuple						    =  '\\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'


_snake_case  :    str						    =  '\\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'


_snake_case  :    List[str]						    =  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 				_UpperCAmelCase    (    datasets.Metric    ):

  """simple docstring"""


  def 				lowercase		(				self :		str     )							->						int:
     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 :		str					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Optional[int]     )							->						List[Any]:
     __lowerCAmelCase								=	0.0
     for i, j in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_					,  lowerCAmelCase_     ) else 0.0
     __lowerCAmelCase								=	n_correct / len(lowerCAmelCase_     )
     return {
         "accuracy": accuracy,
     }

