


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase	: Optional[Any]  				=5_0_0_0_0_0

__lowerCAmelCase				,__lowerCAmelCase	: List[Any]  				=os.path.split(__file__)
__lowerCAmelCase	: Tuple  				=os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def        _UpperCamelCase							(      lowercase__    ,       **lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		dataset.map(**lowercase__      )


@get_duration
def        _UpperCamelCase							(      lowercase__    ,       **lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		dataset.filter(**lowercase__      )


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		datasets.Features({'''text''': datasets.Value('''string'''      ), '''numbers''': datasets.Value('''float32'''      )}      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		generate_example_dataset(
												    os.path.join(lowercase__    ,       '''dataset.arrow'''      )    ,       lowercase__    ,       num_examples=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     Any					=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''    ,       use_fast=lowercase__      )

												def tokenize(lowercase__      ):
																		return tokenizer(examples['''text''']      )

												__SCREAMING_SNAKE_CASE						:     List[str]					=		map(lowercase__      )

												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		map(lowercase__    ,       batched=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     str					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''numpy'''      ):
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''pandas'''      ):
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''torch'''    ,       columns='''numbers'''      ):
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''tensorflow'''    ,       columns='''numbers'''      ):
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     Tuple					=		map(lowercase__    ,       function=lowercase__    ,       batched=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     int					=		filter(lowercase__      )

												# Activate later when tokenizer support batched inputs
												# with dataset.formatted_as(type='numpy'):
												#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

						with open(lowercase__    ,       '''wb'''      ) as f:
												f.write(json.dumps(lowercase__      ).encode('''utf-8'''      )      )


if __name__ == "__main__":  # useful to run the profiler
						benchmark_map_filter()




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

import datasets


__lowerCAmelCase	: Optional[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'


__lowerCAmelCase	: 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'


__lowerCAmelCase	: Optional[Any]  				=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        _lowercase      (			datasets.Metric       ):






				'''simple docstring'''



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