


'''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       _snake_case     (  A				,					A )		->					Tuple:
							return float((preds == labels).mean() )


def       _snake_case     (  A				,					A				,					A="binary" )		->					Tuple:
							lowerCAmelCase__      =			simple_accuracy(A				,					A )
							lowerCAmelCase__      =			float(fa_score(y_true=A				,					y_pred=A				,					average=A ) )
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


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





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple:
														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":
																					lowerCAmelCase__      =			[
																					    {
																					        '''qas''': [
																					            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
																					            for ref in references
																					        ]
																					    }
																					]
																					lowerCAmelCase__      =			{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'''






from typing import TYPE_CHECKING

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


__UpperCAmelCase					       =							{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							['''PoolFormerFeatureExtractor''']
		__UpperCAmelCase					       =							['''PoolFormerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							[
		    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PoolFormerForImageClassification''',
		    '''PoolFormerModel''',
		    '''PoolFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_poolformer import (
		    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    PoolFormerConfig,
		    PoolFormerOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_poolformer import PoolFormerFeatureExtractor
				from .image_processing_poolformer import PoolFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_poolformer import (
				    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PoolFormerForImageClassification,
				    PoolFormerModel,
				    PoolFormerPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase					       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)