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

import datasets

from .record_evaluation import evaluate as evaluate_record


SCREAMING_SNAKE_CASE__      						=  "\\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"

SCREAMING_SNAKE_CASE__      						=  "\\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"

SCREAMING_SNAKE_CASE__      						=  "\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    lowerCAmelCase__    (			_UpperCamelCase       :				Any ,			_UpperCamelCase       :				int					)					->				int:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				List[Any]="binary"					)					->				List[str]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				str					)					->				int:





						"""simple docstring"""






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



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""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 		snake_case (				self    ):






										"""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 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										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":
																snake_case								=			[
																    {
																        """qas""": [
																            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
																            for ref in references
																        ]
																    }
																]
																snake_case								=			{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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
					from .camera import create_pan_cameras
					from .pipeline_shap_e import ShapEPipeline
					from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
					from .renderer import (
					    BoundingBoxVolume,
					    ImportanceRaySampler,
					    MLPNeRFModelOutput,
					    MLPNeRSTFModel,
					    ShapEParamsProjModel,
					    ShapERenderer,
					    StratifiedRaySampler,
					    VoidNeRFModel,
					)


