


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase						:				Any             =      "\\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"

UpperCamelCase						:				Optional[int]             =      "\\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"

UpperCamelCase						:				Any             =      "\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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		return float((preds == labels).mean()	)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__="binary"	)      ->		Union[str, Any]:







		"""simple docstring"""





		A					       = simple_accuracy(a__ ,			a__	)
		A					       = float(fa_score(y_true=a__ ,			y_pred=a__ ,			average=a__	)	)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		A					       = {}
		for id_pred, label in zip(a__ ,			a__	):
				A					       = f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
				A					       = id_pred['prediction']
				if question_id in question_map:
						question_map[question_id].append((pred, label)	)
				else:
						A					       = [(pred, label)]
		A       , A					       = [], []
		for question, preds_labels in question_map.items():
				A       , A					       = zip(*a__	)
				A					       = fa_score(y_true=a__ ,			y_pred=a__ ,			average='macro'	)
				fas.append(a__	)
				A					       = int(sum(pred == label for pred, label in preds_labels	) == len(a__	)	)
				ems.append(a__	)
		A					       = float(sum(a__	) / len(a__	)	)
		A					       = sum(a__	) / len(a__	)
		A					       = float(fa_score(y_true=a__ ,			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			):







						def __a						(							self   :    List[str]		):
								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 __a						(							self   :    str		):
								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 __a						(							self   :    Tuple			,			_lowercase   :    str			,			_lowercase   :    List[str]		):
								if self.config_name == "axb":
										return {"matthews_correlation": matthews_corrcoef(__A			,			__A		)}
								elif self.config_name == "cb":
										return acc_and_fa(__A			,			__A			,			fa_avg='macro'		)
								elif self.config_name == "record":
										A					       = [
										    {
										        'qas': [
										            {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
										            for ref in references
										        ]
										    }
										]
										A					       = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
										return evaluate_record(__A			,			__A		)[0]
								elif self.config_name == "multirc":
										return evaluate_multirc(__A			,			__A		)
								elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
										return {"accuracy": simple_accuracy(__A			,			__A		)}
								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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase						:				int             =      logging.get_logger(__name__)

UpperCamelCase						:				Optional[int]             =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}



class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):
						lowerCAmelCase       =     """bit"""
						lowerCAmelCase       =     ["""preactivation""", """bottleneck"""]
						lowerCAmelCase       =     ["""SAME""", """VALID"""]







						def __init__(							self   :    List[str]			,			_lowercase   :    int=3			,			_lowercase   :    List[str]=64			,			_lowercase   :    Optional[int]=[256, 512, 1_024, 2_048]			,			_lowercase   :    Optional[int]=[3, 4, 6, 3]			,			_lowercase   :    int="preactivation"			,			_lowercase   :    Union[str, Any]="relu"			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Tuple=32			,			_lowercase   :    Optional[int]=0.0			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Dict=32			,			_lowercase   :    Union[str, Any]=1			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Any=None			,			**_lowercase   :    str			,			):
								super().__init__(**_lowercase		)
								if layer_type not in self.layer_types:
										raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
								if global_padding is not None:
										if global_padding.upper() in self.supported_padding:
												A					       = global_padding.upper()
										else:
												raise ValueError(f'Padding strategy {global_padding} not supported'		)
								A					       = num_channels
								A					       = embedding_size
								A					       = hidden_sizes
								A					       = depths
								A					       = layer_type
								A					       = hidden_act
								A					       = global_padding
								A					       = num_groups
								A					       = drop_path_rate
								A					       = embedding_dynamic_padding
								A					       = output_stride
								A					       = width_factor

								A					       = ['stem'] + [f'stage{idx}' for idx in range(1			,			len(_lowercase		) + 1		)]
								A       , A					       = get_aligned_output_features_output_indices(
								    out_features=_lowercase			,			out_indices=_lowercase			,			stage_names=self.stage_names		)





