"""simple docstring"""






import datasets

from .evaluate import evaluate


lowerCAmelCase__  :  Optional[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__  :  List[str]						     =	'\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__  :  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 				snake_case       ( datasets.Metric				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	str			):
									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   :	int    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							{prediction['id']: prediction['prediction_text'] for prediction in predictions}
									UpperCAmelCase__             =							[
									    {
									        'paragraphs': [
									            {
									                'qas': [
									                    {
									                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
									                        'id': ref['id'],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									UpperCAmelCase__             =							evaluate(dataset=lowerCamelCase__    ,predictions=lowerCamelCase__			)
									return score




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__  :  str						     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						pass

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[str]						     =	[
						    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RoCBertForCausalLM',
						    'RoCBertForMaskedLM',
						    'RoCBertForMultipleChoice',
						    'RoCBertForPreTraining',
						    'RoCBertForQuestionAnswering',
						    'RoCBertForSequenceClassification',
						    'RoCBertForTokenClassification',
						    'RoCBertLayer',
						    'RoCBertModel',
						    'RoCBertPreTrainedModel',
						    'load_tf_weights_in_roc_bert',
						]

if TYPE_CHECKING:
						from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
						from .tokenization_roc_bert import RoCBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												raise OptionalDependencyNotAvailable()

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roc_bert import (
												    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoCBertForCausalLM,
												    RoCBertForMaskedLM,
												    RoCBertForMultipleChoice,
												    RoCBertForPreTraining,
												    RoCBertForQuestionAnswering,
												    RoCBertForSequenceClassification,
												    RoCBertForTokenClassification,
												    RoCBertLayer,
												    RoCBertModel,
												    RoCBertPreTrainedModel,
												    load_tf_weights_in_roc_bert,
												)


else:
						import sys

						lowerCAmelCase__  :  str						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



