"""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   )   ->			Dict:





						"""simple docstring"""






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





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





						"""simple docstring"""






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





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





						"""simple docstring"""






						snake_case        =     {}
						for id_pred, label in zip(_UpperCamelCase	,     _UpperCamelCase   ):
												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   ,snake_case        =     [], []
						for question, preds_labels in question_map.items():
												snake_case   ,snake_case        =     zip(*_UpperCamelCase   )
												snake_case        =     fa_score(y_true=_UpperCamelCase	,     y_pred=_UpperCamelCase	,     average='macro'   )
												fas.append(_UpperCamelCase   )
												snake_case        =     int(sum(pred == label for pred, label in preds_labels   ) == len(_UpperCamelCase   )   )
												ems.append(_UpperCamelCase   )
						snake_case        =     float(sum(_UpperCamelCase   ) / len(_UpperCamelCase   )   )
						snake_case        =     sum(_UpperCamelCase   ) / len(_UpperCamelCase   )
						snake_case        =     float(fa_score(y_true=_UpperCamelCase	,     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(lowerCAmelCase						,      lowerCAmelCase      )}
										elif self.config_name == "cb":
																return acc_and_fa(lowerCAmelCase						,      lowerCAmelCase						,      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(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 __future__ import annotations

from typing import Any



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass





class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     data
										snake_case        =     None

				def __iter__(			self      ):






										"""simple docstring"""

										snake_case        =     self
										snake_case        =     []
										while node:
																if node in visited:
																						raise ContainsLoopError
																visited.append(lowerCAmelCase      )
																yield node.data
																snake_case        =     node.next_node

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										try:
																list(self      )
																return False
										except ContainsLoopError:
																return True


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					Node(1)
					SCREAMING_SNAKE_CASE__   =					Node(2)
					SCREAMING_SNAKE_CASE__   =					Node(3)
					SCREAMING_SNAKE_CASE__   =					Node(4)
					print(root_node.has_loop)  # False
					SCREAMING_SNAKE_CASE__   =					root_node.next_node
					print(root_node.has_loop)  # True

					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					print(root_node.has_loop)  # False

					SCREAMING_SNAKE_CASE__   =					Node(1)
					print(root_node.has_loop)  # False


