
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowercase__							:							List[str]									=			"\\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"

lowercase__							:							Tuple									=			"\\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"

lowercase__							:							Dict									=			"\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_ (							snake_case							:   Union[str, Any] ,      snake_case							:   Tuple )      ->    int:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Tuple ,      snake_case							:   str ,      snake_case							:   Optional[Any]="binary" )      ->    int:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Dict ,      snake_case							:   str )      ->    int:



						'''simple docstring'''

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

							"""simple docstring"""






							def     A__		(  self			)->   Dict:





													'''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     A__		(  self			)->   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     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													if self.config_name == "axb":
																			return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)}
													elif self.config_name == "cb":
																			return acc_and_fa(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	fa_avg='''macro'''			)
													elif self.config_name == "record":
																			__UpperCamelCase       =			[
																			    {
																			        '''qas''': [
																			            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
																			            for ref in references
																			        ]
																			    }
																			]
																			__UpperCamelCase       =			{pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
																			return evaluate_record(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)[0]
													elif self.config_name == "multirc":
																			return evaluate_multirc(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)
													elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																			return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE_     ,	SCREAMING_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",]'''			)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__							:							int									=			{
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GitForCausalLM",
				    "GitModel",
				    "GitPreTrainedModel",
				    "GitVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
				from .processing_git import GitProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_git import (
								    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GitForCausalLM,
								    GitModel,
								    GitPreTrainedModel,
								    GitVisionModel,
								)

else:
				import sys

				lowercase__							:							Optional[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




