



'''simple docstring'''






from statistics import mean

import numpy as np


def 				snake_case_  (      _lowerCAmelCase						:		list     ,   _lowerCAmelCase						:		list     ,   _lowerCAmelCase						:		list     ,   _lowerCAmelCase						:		int						)   ->     list:
							UpperCAmelCase				: List[str]											=	0
							# Number of processes finished
							UpperCAmelCase				: int											=	0
							# Displays the finished process.
							# If it is 0, the performance is completed if it is 1, before the performance.
							UpperCAmelCase				: Dict											=	[0] * no_of_process
							# List to include calculation results
							UpperCAmelCase				: Dict											=	[0] * no_of_process

							# Sort by arrival time.
							UpperCAmelCase				: List[str]											=	[burst_time[i] for i in np.argsort(_lowerCAmelCase						)]
							UpperCAmelCase				: Tuple											=	[process_name[i] for i in np.argsort(_lowerCAmelCase						)]
							arrival_time.sort()

							while no_of_process > finished_process_count:
														UpperCAmelCase				: Any											=	0
														while finished_process[i] == 1:
																					i += 1
														if current_time < arrival_time[i]:
																					UpperCAmelCase				: Dict											=	arrival_time[i]

														UpperCAmelCase				: List[str]											=	0
														# Index showing the location of the process being performed
														UpperCAmelCase				: Optional[int]											=	0
														# Saves the current response ratio.
														UpperCAmelCase				: Dict											=	0
														for i in range(0     ,   _lowerCAmelCase						):
																					if finished_process[i] == 0 and arrival_time[i] <= current_time:
																												UpperCAmelCase				: Tuple											=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																												    i
																												]
																					if response_ratio < temp:
																												UpperCAmelCase				: Optional[int]											=	temp
																												UpperCAmelCase				: Tuple											=	i

        # Calculate the turn around time
														UpperCAmelCase				: Any											=	current_time + burst_time[loc] - arrival_time[loc]
														current_time += burst_time[loc]
														# Indicates that the process has been performed.
														UpperCAmelCase				: Any											=	1
														# Increase finished_process_count by 1
														finished_process_count += 1

							return turn_around_time


def 				snake_case_  (      _lowerCAmelCase						:		list     ,   _lowerCAmelCase						:		list     ,   _lowerCAmelCase						:		list     ,   _lowerCAmelCase						:		int						)   ->     list:
							UpperCAmelCase				: str											=	[0] * no_of_process
							for i in range(0     ,   _lowerCAmelCase						):
														UpperCAmelCase				: Dict											=	turn_around_time[i] - burst_time[i]
							return waiting_time


if __name__ == "__main__":
						UpperCamelCase__:     int										=    5
						UpperCamelCase__:     Optional[int]										=    ["A", "B", "C", "D", "E"]
						UpperCamelCase__:     int										=    [1, 2, 3, 4, 5]
						UpperCamelCase__:     int										=    [1, 2, 3, 4, 5]

						UpperCamelCase__:     Optional[int]										=    calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						UpperCamelCase__:     List[Any]										=    calculate_waiting_time(
						    process_name, turn_around_time, burst_time, no_of_process
						)

						print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
						for i in range(0, no_of_process):
												print(
												    F"{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"
												    F"{turn_around_time[i]}\t\t\t{waiting_time[i]}"
												)

						print(F"average waiting time : {mean(waiting_time):.5f}")
						print(F"average turn around time : {mean(turn_around_time):.5f}")










'''simple docstring'''






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase__:     int										=    "\\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__:     Dict										=    "\\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__:     str										=    "\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 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Tuple						)   ->     Dict:
							return float((preds == labels).mean()						)


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Dict="binary"						)   ->     Any:
							UpperCAmelCase				: str											=	simple_accuracy(_lowerCAmelCase     ,   _lowerCAmelCase						)
							UpperCAmelCase				: str											=	float(fa_score(y_true=_lowerCAmelCase     ,   y_pred=_lowerCAmelCase     ,   average=_lowerCAmelCase						)						)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		str						)   ->     List[str]:
							UpperCAmelCase				: Optional[int]											=	{}
							for id_pred, label in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														UpperCAmelCase				: Union[str, Any]											=	f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
														UpperCAmelCase				: List[str]											=	id_pred['''prediction''']
														if question_id in question_map:
																					question_map[question_id].append((pred, label)						)
														else:
																					UpperCAmelCase				: Union[str, Any]											=	[(pred, label)]
							UpperCAmelCase							,	UpperCAmelCase				: Dict											=	[], []
							for question, preds_labels in question_map.items():
														UpperCAmelCase							,	UpperCAmelCase				: List[Any]											=	zip(*_lowerCAmelCase						)
														UpperCAmelCase				: int											=	fa_score(y_true=_lowerCAmelCase     ,   y_pred=_lowerCAmelCase     ,   average='''macro'''						)
														fas.append(_lowerCAmelCase						)
														UpperCAmelCase				: Dict											=	int(sum(pred == label for pred, label in preds_labels						) == len(_lowerCAmelCase						)						)
														ems.append(_lowerCAmelCase						)
							UpperCAmelCase				: int											=	float(sum(_lowerCAmelCase						) / len(_lowerCAmelCase						)						)
							UpperCAmelCase				: Optional[int]											=	sum(_lowerCAmelCase						) / len(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	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        SCREAMING_SNAKE_CASE(							datasets.Metric    ):







	"""simple docstring"""






	def   A			(		self    :  Optional[Any]	)    ->	Dict:
								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    :  Union[str, Any]	)    ->	Dict:
								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    :  Dict	,    __snake_case    :  str	,    __snake_case    :  str	)    ->	Optional[int]:
								if self.config_name == "axb":
															return {"matthews_correlation": matthews_corrcoef(__snake_case	,    __snake_case	)}
								elif self.config_name == "cb":
															return acc_and_fa(__snake_case	,    __snake_case	,    fa_avg='''macro'''	)
								elif self.config_name == "record":
															UpperCAmelCase				: Tuple											=	[
															    {
															        '''qas''': [
															            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
															            for ref in references
															        ]
															    }
															]
															UpperCAmelCase				: int											=	{pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
															return evaluate_record(__snake_case	,    __snake_case	)[0]
								elif self.config_name == "multirc":
															return evaluate_multirc(__snake_case	,    __snake_case	)
								elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
															return {"accuracy": simple_accuracy(__snake_case	,    __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",]'''	)





