



"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__a      =				"\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  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},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

__a      =				"\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

__a      =				"\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						return float((preds == labels).mean()	)
def    A_  (			_lowercase,					_lowercase,					_lowercase="binary"	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			simple_accuracy(_lowercase,					_lowercase	)
						snake_case_ :Tuple						=			float(fa_score(y_true=_lowercase,					y_pred=_lowercase,					average=_lowercase	)	)
						return {
						    "accuracy": acc,
						    "f1": fa,
						}
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			{}
						for id_pred, label in zip(_lowercase,					_lowercase	):
												snake_case_ :List[Any]						=			f"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
												snake_case_ :Dict						=			id_pred["""prediction"""]
												if question_id in question_map:
																		question_map[question_id].append((pred, label)	)
												else:
																		snake_case_ :Optional[int]						=			[(pred, label)]
						snake_case_,       snake_case_ :List[Any]						=			[], []
						for question, preds_labels in question_map.items():
												snake_case_,       snake_case_ :Union[str, Any]						=			zip(*_lowercase	)
												snake_case_ :Any						=			fa_score(y_true=_lowercase,					y_pred=_lowercase,					average="""macro"""	)
												fas.append(_lowercase	)
												snake_case_ :Tuple						=			int(sum(pred == label for pred, label in preds_labels	) == len(_lowercase	)	)
												ems.append(_lowercase	)
						snake_case_ :Optional[Any]						=			float(sum(_lowercase	) / len(_lowercase	)	)
						snake_case_ :List[Any]						=			sum(_lowercase	) / len(_lowercase	)
						snake_case_ :Dict						=			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 				lowerCamelCase  (			datasets.Metric				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Union[str, Any]:
											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    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Any:
											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    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							Union[str, Any]						,     snake_case:							Optional[Any]						)   ->	Optional[Any]:
											if self.config_name == "axb":
																	return {"matthews_correlation": matthews_corrcoef(snake_case						,     snake_case						)}
											elif self.config_name == "cb":
																	return acc_and_fa(snake_case						,     snake_case						,     fa_avg="""macro"""						)
											elif self.config_name == "record":
																	snake_case_ :List[str]						=			[
																	    {
																	        """qas""": [
																	            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
																	            for ref in references
																	        ]
																	    }
																	]
																	snake_case_ :List[str]						=			{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
																	return evaluate_record(snake_case						,     snake_case						)[0]
											elif self.config_name == "multirc":
																	return evaluate_multirc(snake_case						,     snake_case						)
											elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																	return {"accuracy": simple_accuracy(snake_case						,     snake_case						)}
											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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a      =				{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["MobileBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MobileBertForMaskedLM",
						    "MobileBertForMultipleChoice",
						    "MobileBertForNextSentencePrediction",
						    "MobileBertForPreTraining",
						    "MobileBertForQuestionAnswering",
						    "MobileBertForSequenceClassification",
						    "MobileBertForTokenClassification",
						    "MobileBertLayer",
						    "MobileBertModel",
						    "MobileBertPreTrainedModel",
						    "load_tf_weights_in_mobilebert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMobileBertForMaskedLM",
						    "TFMobileBertForMultipleChoice",
						    "TFMobileBertForNextSentencePrediction",
						    "TFMobileBertForPreTraining",
						    "TFMobileBertForQuestionAnswering",
						    "TFMobileBertForSequenceClassification",
						    "TFMobileBertForTokenClassification",
						    "TFMobileBertMainLayer",
						    "TFMobileBertModel",
						    "TFMobileBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mobilebert import (
						    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileBertConfig,
						    MobileBertOnnxConfig,
						)
						from .tokenization_mobilebert import MobileBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mobilebert_fast import MobileBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilebert import (
												    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileBertForMaskedLM,
												    MobileBertForMultipleChoice,
												    MobileBertForNextSentencePrediction,
												    MobileBertForPreTraining,
												    MobileBertForQuestionAnswering,
												    MobileBertForSequenceClassification,
												    MobileBertForTokenClassification,
												    MobileBertLayer,
												    MobileBertModel,
												    MobileBertPreTrainedModel,
												    load_tf_weights_in_mobilebert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilebert import (
												    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileBertForMaskedLM,
												    TFMobileBertForMultipleChoice,
												    TFMobileBertForNextSentencePrediction,
												    TFMobileBertForPreTraining,
												    TFMobileBertForQuestionAnswering,
												    TFMobileBertForSequenceClassification,
												    TFMobileBertForTokenClassification,
												    TFMobileBertMainLayer,
												    TFMobileBertModel,
												    TFMobileBertPreTrainedModel,
												)

else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
