





"""simple docstring"""




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase_									="""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

UpperCamelCase_									="""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

UpperCamelCase_									="""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""





def 	a_    (							_lowercase	,						_lowercase       ):
						return float((preds == labels).mean()       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase="binary"       ):
						_UpperCamelCase      : List[str]         =   simple_accuracy(_lowercase	,						_lowercase       )
						_UpperCamelCase      : Optional[int]         =   float(fa_score(y_true=_lowercase	,						y_pred=_lowercase	,						average=_lowercase       )       )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Any         =   {}
						for id_pred, label in zip(_lowercase	,						_lowercase       ):
												_UpperCamelCase      : Union[str, Any]         =   F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
												_UpperCamelCase      : int         =   id_pred['''prediction''']
												if question_id in question_map:
																		question_map[question_id].append((pred, label)       )
												else:
																		_UpperCamelCase      : Dict         =   [(pred, label)]
						_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   [], []
						for question, preds_labels in question_map.items():
												_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   zip(*_lowercase       )
												_UpperCamelCase      : Union[str, Any]         =   fa_score(y_true=_lowercase	,						y_pred=_lowercase	,						average='''macro'''       )
												fas.append(_lowercase       )
												_UpperCamelCase      : List[str]         =   int(sum(pred == label for pred, label in preds_labels       ) == len(_lowercase       )       )
												ems.append(_lowercase       )
						_UpperCamelCase      : List[Any]         =   float(sum(_lowercase       ) / len(_lowercase       )       )
						_UpperCamelCase      : Tuple         =   sum(_lowercase       ) / len(_lowercase       )
						_UpperCamelCase      : int         =   float(fa_score(y_true=_lowercase	,						y_pred=[id_pred['''prediction'''] for id_pred in ids_preds]       )       )
						return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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






				def     snake_case     (			self		:   Tuple							)     ->				int:




										'''simple docstring'''



										if self.config_name not in [
										    "boolq",
										    "cb",
										    "copa",
										    "multirc",
										    "record",
										    "rte",
										    "wic",
										    "wsc",
										    "wsc.fixed",
										    "axb",
										    "axg",
										]:
																raise KeyError(
																    '''You should supply a configuration name selected in '''
																    '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''							)
										return datasets.MetricInfo(
										    description=_DESCRIPTION,			citation=_CITATION,			inputs_description=_KWARGS_DESCRIPTION,			features=datasets.Features(self._get_feature_types()							),			codebase_urls=[],			reference_urls=[],			format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None,			)






				def     snake_case     (			self		:   int							)     ->				List[str]:




										'''simple docstring'''



										if self.config_name == "record":
																return {
																    "predictions": {
																        "idx": {
																            "passage": datasets.Value('''int64'''							),
																            "query": datasets.Value('''int64'''							),
																        },
																        "prediction_text": datasets.Value('''string'''							),
																    },
																    "references": {
																        "idx": {
																            "passage": datasets.Value('''int64'''							),
																            "query": datasets.Value('''int64'''							),
																        },
																        "answers": datasets.Sequence(datasets.Value('''string'''							)							),
																    },
																}
										elif self.config_name == "multirc":
																return {
																    "predictions": {
																        "idx": {
																            "answer": datasets.Value('''int64'''							),
																            "paragraph": datasets.Value('''int64'''							),
																            "question": datasets.Value('''int64'''							),
																        },
																        "prediction": datasets.Value('''int64'''							),
																    },
																    "references": datasets.Value('''int64'''							),
																}
										else:
																return {
																    "predictions": datasets.Value('''int64'''							),
																    "references": datasets.Value('''int64'''							),
																}






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										if self.config_name == "axb":
																return {"matthews_correlation": matthews_corrcoef(lowerCAmelCase__,			lowerCAmelCase__							)}
										elif self.config_name == "cb":
																return acc_and_fa(lowerCAmelCase__,			lowerCAmelCase__,			fa_avg='''macro'''							)
										elif self.config_name == "record":
																_UpperCamelCase      : Optional[Any]         =   [
																    {
																        '''qas''': [
																            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
																            for ref in references
																        ]
																    }
																]
																_UpperCamelCase      : List[str]         =   {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
																return evaluate_record(lowerCAmelCase__,			lowerCAmelCase__							)[0]
										elif self.config_name == "multirc":
																return evaluate_multirc(lowerCAmelCase__,			lowerCAmelCase__							)
										elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																return {"accuracy": simple_accuracy(lowerCAmelCase__,			lowerCAmelCase__							)}
										else:
																raise KeyError(
																    '''You should supply a configuration name selected in '''
																    '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''							)










"""simple docstring"""




import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase_									=logging.getLogger(__name__)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''masked_bert'''






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Dict=3_0_5_2_2,			lowerCAmelCase__		:   int=7_6_8,			lowerCAmelCase__		:   Tuple=1_2,			lowerCAmelCase__		:   Optional[Any]=1_2,			lowerCAmelCase__		:   Tuple=3_0_7_2,			lowerCAmelCase__		:   Optional[int]="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   Union[str, Any]=0,			lowerCAmelCase__		:   Dict="topK",			lowerCAmelCase__		:   Union[str, Any]="constant",			lowerCAmelCase__		:   Union[str, Any]=0.0,			**lowerCAmelCase__		:   Any,			)     ->				List[Any]:




										'''simple docstring'''



										super().__init__(pad_token_id=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Any         =   num_attention_heads
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : str         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : Optional[Any]         =   initializer_range
										_UpperCamelCase      : List[str]         =   layer_norm_eps
										_UpperCamelCase      : int         =   pruning_method
										_UpperCamelCase      : Union[str, Any]         =   mask_init
										_UpperCamelCase      : Any         =   mask_scale



