





'''simple docstring'''

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


lowercase__		:     Tuple			=							[
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]







def    a__							(							lowercase   :	Tuple=True )						->						List[Any]:





     """simple docstring"""

     if with_config:
          return [
              {
                  "testcase_name": d["dataset"] + "/" + d["config_name"],
                  "dataset": d["dataset"],
                  "config_name": d["config_name"],
              }
              for d in DATASETS_ON_HF_GCP
          ]
     else:
          return [
              {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
          ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__magic_name__					)					)
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Union[str, Any]									=       None
       _snake_case      :	Dict									=       None





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Any	)       ->	Dict:

            '''simple docstring'''




            with TemporaryDirectory() as tmp_dir:
                 _UpperCamelCase							       =			dataset_module_factory(lowerCAmelCase__			,					cache_dir=lowerCAmelCase__	)

                 _UpperCamelCase							       =			import_main_class(dataset_module.module_path			,					dataset=lowerCAmelCase__	)

                 _UpperCamelCase							       =			builder_cls(
                     cache_dir=lowerCAmelCase__			,					config_name=lowerCAmelCase__			,					hash=dataset_module.hash			,					)

                 _UpperCamelCase							       =			'''/'''.join(
                     [
                         HF_GCP_BASE_URL,
                         builder_instance._relative_data_dir(with_hash=lowerCAmelCase__	).replace(os.sep			,					'''/'''	),
                         config.DATASET_INFO_FILENAME,
                     ]	)
                 _UpperCamelCase							       =			cached_path(lowerCAmelCase__			,					cache_dir=lowerCAmelCase__	)
                 self.assertTrue(os.path.exists(lowerCAmelCase__	)	)







@pytest.mark.integration
def    a__							(							lowercase   :	Any )						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
     _UpperCamelCase							       =			dataset_module_factory('''wikipedia''',   cache_dir=lowercase )
     _UpperCamelCase							       =			import_main_class(dataset_module.module_path )
     _UpperCamelCase							       =			builder_cls(
         cache_dir=lowercase,   config_name='''20220301.frr''',   hash=dataset_module.hash,   )
     # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
     _UpperCamelCase							       =			None
     builder_instance.download_and_prepare()
     _UpperCamelCase							       =			builder_instance.as_dataset()
     assert ds







@pytest.mark.integration
def    a__							(							lowercase   :	Any )						->						int:





     """simple docstring"""

     _UpperCamelCase							       =			dataset_module_factory('''wikipedia''',   cache_dir=lowercase )
     _UpperCamelCase							       =			import_main_class(dataset_module.module_path,   dataset=lowercase )
     _UpperCamelCase							       =			builder_cls(
         cache_dir=lowercase,   config_name='''20220301.frr''',   hash=dataset_module.hash,   )
     _UpperCamelCase							       =			builder_instance.as_streaming_dataset()
     assert ds
     assert isinstance(lowercase,   lowercase )
     assert "train" in ds
     assert isinstance(ds['''train'''],   lowercase )
     assert next(iter(ds['''train'''] ) )










'''simple docstring'''

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

import datasets


lowercase__		:     str			=							'\\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'


lowercase__		:     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'


lowercase__		:     Dict			=							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 							__lowerCAmelCase	(       datasets.Metric					):






       """simple docstring"""





       def snake_case__		(				self  :						Dict	)       ->	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  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						int	)       ->	List[str]:

            '''simple docstring'''




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



