


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\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			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		return float((preds == labels).mean()					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="binary"					):


		"""simple docstring"""




		lowercase__						=				simple_accuracy(lowerCamelCase__						,    lowerCamelCase__					)
		lowercase__						=				float(fa_score(y_true=lowerCamelCase__						,    y_pred=lowerCamelCase__						,    average=lowerCamelCase__					)					)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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





									"""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 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict							)      ->       Any:





									"""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":
											lowercase__						=				[
											    {
											        '''qas''': [
											            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
											            for ref in references
											        ]
											    }
											]
											lowercase__						=				{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",]'''							)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase	=							{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LukeForEntityClassification',
			    'LukeForEntityPairClassification',
			    'LukeForEntitySpanClassification',
			    'LukeForMultipleChoice',
			    'LukeForQuestionAnswering',
			    'LukeForSequenceClassification',
			    'LukeForTokenClassification',
			    'LukeForMaskedLM',
			    'LukeModel',
			    'LukePreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase	=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


