



"""simple docstring"""






import datasets

from .evaluate import evaluate


__snake_case				:  Any =       '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__snake_case				:  int =       '\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

__snake_case				:  Union[str, Any] =       '\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION				)
class      A__  (    datasets.Metric				):




	'''simple docstring'''





	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			int:

				"""simple docstring"""

				return datasets.MetricInfo(
				    description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
				        {
				            "predictions": {"id": datasets.Value("string"), "prediction_text": datasets.Value("string")},
				            "references": {
				                "id": datasets.Value("string"),
				                "answers": datasets.features.Sequence(
				                    {
				                        "text": datasets.Value("string"),
				                        "answer_start": datasets.Value("int32"),
				                    }),
				            },
				        })			,     codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,     reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,     )







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
				__lowerCAmelCase :    List[str]				=			[
				    {
				        "paragraphs": [
				            {
				                "qas": [
				                    {
				                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
				                        "id": ref["id"],
				                    }
				                    for ref in references
				                ]
				            }
				        ]
				    }
				]
				__lowerCAmelCase :    List[Any]				=			evaluate(dataset=A_			,     predictions=A_)
				return score



import pytest

import datasets


# Import fixture modules as plugins
__snake_case   							=						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit'] ):
               continue
          item.add_marker(pytest.mark.unit )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     config.addinivalue_line('markers'					,  'torchaudio_latest: mark test to run with torchaudio>=0.12' )






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			tmp_path_factory.getbasetemp() / 'cache'
     __UpperCamelCase =			test_hf_cache_home / 'datasets'
     __UpperCamelCase =			test_hf_cache_home / 'metrics'
     __UpperCamelCase =			test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'					,  str(_lowercase ) )






@pytest.fixture(autouse=_lowercase					,  scope='session' )
def 		_A		(		)					-> Dict:


     """simple docstring"""


     datasets.disable_progress_bar()






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'					,  _lowercase )






@pytest.fixture
def 		_A		(		_lowercase )					-> Any:


     """simple docstring"""


     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'					,  _lowercase )


