



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCAmelCase__   =       "\\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"

UpperCAmelCase__   =       "\\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"

UpperCAmelCase__   =       "\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	(     _UpperCAmelCase				:   Dict							,   _UpperCAmelCase				:   List[str]    )				-> Dict:







			'''simple docstring'''
			return float((preds == labels).mean()    )





def 							A	(     _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   Dict							,   _UpperCAmelCase				:   Union[str, Any]="binary"    )				-> str:







			'''simple docstring'''
			_UpperCAmelCase		       =   simple_accuracy(lowercase__							,   lowercase__    )
			_UpperCAmelCase		       =   float(fa_score(y_true=lowercase__							,   y_pred=lowercase__							,   average=lowercase__    )    )
			return {
			    "accuracy": acc,
			    "f1": fa,
			}





def 							A	(     _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   Any    )				-> Tuple:







			'''simple docstring'''
			_UpperCAmelCase		       =   {}
			for id_pred, label in zip(lowercase__							,   lowercase__    ):
						_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		       =   [], []
			for question, preds_labels in question_map.items():
						_UpperCAmelCase		       =   zip(*lowercase__    )
						_UpperCAmelCase		       =   fa_score(y_true=lowercase__							,   y_pred=lowercase__							,   average='macro'    )
						fas.append(lowercase__    )
						_UpperCAmelCase		       =   int(sum(pred == label for pred, label in preds_labels    ) == len(lowercase__    )    )
						ems.append(lowercase__    )
			_UpperCAmelCase		       =   float(sum(lowercase__    ) / len(lowercase__    )    )
			_UpperCAmelCase		       =   sum(lowercase__    ) / len(lowercase__    )
			_UpperCAmelCase		       =   float(fa_score(y_true=lowercase__							,   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						):



			def     _lowerCamelCase       (						self   :  List[Any])   ->    Union[str, Any]:



						"""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   :  Optional[int])   ->    List[Any]:



						"""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   :  Optional[int] ,		A   :  List[str] ,		A   :  int)   ->    Optional[int]:



						"""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":
									_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(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"""






import socket







def      _snake_case			(				):
				_lowerCamelCase     :			List[Any]    =    socket.socket(socket.AF_INET							,    socket.SOCK_STREAM		)
				_lowerCamelCase     :			Union[str, Any]    =    socket.gethostname()
				_lowerCamelCase     :			List[Any]    =    12312

				sock.connect((host, port)		)
				sock.send(B'Hello server!'		)

				with open('Received_file'							,    'wb'		) as out_file:
								print('File opened'		)
								print('Receiving data...'		)
								while True:
												_lowerCamelCase     :			int    =    sock.recv(1024		)
												if not data:
																break
												out_file.write(lowercase__		)

				print('Successfully received the file'		)
				sock.close()
				print('Connection closed'		)


if __name__ == "__main__":
			main()