





import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def 				SCREAMING_SNAKE_CASE__							(	)   ->    List[Any]:
  _lowercase						    =			ArgumentParser(
      description=(
          'PyTorch TPU distributed training launch '
          'helper utility that will spawn up '
          'multiple distributed processes'
      )  )

  # Optional arguments for the launch helper
  parser.add_argument('--num_cores'      ,				type=snake_case__      ,				default=1      ,				help='Number of TPU cores to use (1 or 8).'  )

  # positional
  parser.add_argument(
      'training_script'      ,				type=snake_case__      ,				help=(
          'The full path to the single TPU training '
          'program/script to be launched in parallel, '
          'followed by all the arguments for the '
          'training script'
      )      ,				)

  # rest from the training program
  parser.add_argument('training_script_args'      ,				nargs=snake_case__  )

  return parser.parse_args()

def 				SCREAMING_SNAKE_CASE__							(	)   ->    List[str]:
  _lowercase						    =			parse_args()

  # Import training_script as a module.
  _lowercase						    =			Path(args.training_script  )
  sys.path.append(str(script_fpath.parent.resolve()  )  )
  _lowercase						    =			script_fpath.stem
  _lowercase						    =			importlib.import_module(snake_case__  )

  # Patch sys.argv
  _lowercase						    =			[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores  )]
  xmp.spawn(mod._mp_fn      ,				args=()      ,				nprocs=args.num_cores  )


if __name__ == "__main__":
      main()







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

import datasets


_lowerCAmelCase       =				"\\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"


_lowerCAmelCase       =				"\\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"


_lowerCAmelCase       =				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   SCREAMING_SNAKE_CASE	(					datasets.Metric    ):





	"""simple docstring"""




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

