
"""simple docstring"""






import datasets

from .evaluate import evaluate


lowerCamelCase__ :       List[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"

lowerCamelCase__ :       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"

lowerCamelCase__ :       Tuple						     =			"\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 				lowercase__(				datasets.Metric   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :str					)							->			Dict:







			'''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   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :List[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
			SCREAMING_SNAKE_CASE       :     str				= [
			    {
			        '''paragraphs''': [
			            {
			                '''qas''': [
			                    {
			                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
			                        '''id''': ref['''id'''],
			                    }
			                    for ref in references
			                ]
			            }
			        ]
			    }
			]
			SCREAMING_SNAKE_CASE       :     List[Any]				= evaluate(dataset=lowerCamelCase_		, predictions=lowerCamelCase_					)
			return score







"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__ :       Tuple						     =			{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Union[str, Any]						     =			[
					    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MCTCTForCTC",
					    "MCTCTModel",
					    "MCTCTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
					from .feature_extraction_mctct import MCTCTFeatureExtractor
					from .processing_mctct import MCTCTProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
					import sys

					lowerCamelCase__ :       Tuple						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





