






from sklearn.metrics import mean_squared_error

import datasets


__snake_case    :    Optional[Any]         =     """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

__snake_case    :    str         =     """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


__snake_case    :    Dict         =     """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION)
class     __SCREAMING_SNAKE_CASE					(   datasets.Metric):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(self._get_feature_types()   )     ,						reference_urls=[
												        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
												    ]     ,						)






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												if self.config_name == "multilist":
																		return {
																		    "predictions": datasets.Sequence(datasets.Value('float'   )   ),
																		    "references": datasets.Sequence(datasets.Value('float'   )   ),
																		}
												else:
																		return {
																		    "predictions": datasets.Value('float'   ),
																		    "references": datasets.Value('float'   ),
																		}







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=None     ,						_UpperCamelCase="uniform_average"     ,						_UpperCamelCase=True   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						mean_squared_error(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						sample_weight=_UpperCamelCase     ,						multioutput=_UpperCamelCase     ,						squared=_UpperCamelCase   )

												return {"mse": mse}













from __future__ import annotations

import pandas as pd







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   list[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * no_of_processes
						lowerCAmelCase__    						=						[0] * no_of_processes
						# Copy the burst time into remaining_time[]
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						burst_time[i]

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						9_9999_9999
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						False

						# Process until all processes are completed
						while complete != no_of_processes:
												for j in range(UpperCamelCase_		):
																		if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																								if remaining_time[j] < minm:
																														lowerCAmelCase__    						=						remaining_time[j]
																														lowerCAmelCase__    						=						j
																														lowerCAmelCase__    						=						True

												if not check:
																		increment_time += 1
																		continue
												remaining_time[short] -= 1

												lowerCAmelCase__    						=						remaining_time[short]
												if minm == 0:
																		lowerCAmelCase__    						=						9_9999_9999

												if remaining_time[short] == 0:
																		complete += 1
																		lowerCAmelCase__    						=						False

																		# Find finish time of current process
																		lowerCAmelCase__    						=						increment_time + 1

																		# Calculate waiting time
																		lowerCAmelCase__    						=						finish_time - arrival_time[short]
																		lowerCAmelCase__    						=						finar - burst_time[short]

																		if waiting_time[short] < 0:
																								lowerCAmelCase__    						=						0

        # Increment time
												increment_time += 1
						return waiting_time







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					list[int]		)  ->   list[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * no_of_processes
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						burst_time[i] + waiting_time[i]
						return turn_around_time







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   None:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						total_waiting_time + waiting_time[i]
												lowerCAmelCase__    						=						total_turn_around_time + turn_around_time[i]
						print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}"		)
						print('Average turn around time ='    ,       total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
					print("""Enter how many process you want to analyze""")
					__snake_case    :    Dict         =     int(input())
					__snake_case    :    List[Any]         =     [0] * no_of_processes
					__snake_case    :    str         =     [0] * no_of_processes
					__snake_case    :    Optional[int]         =     list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
										__snake_case     , __snake_case    :    Any         =     map(int, input().split())

					__snake_case    :    str         =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					__snake_case    :    Dict         =     burst_time
					__snake_case    :    Optional[int]         =     no_of_processes
					__snake_case    :    str         =     waiting_time
					__snake_case    :    int         =     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					__snake_case    :    Dict         =     pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        """Process""",
					        """BurstTime""",
					        """ArrivalTime""",
					        """WaitingTime""",
					        """TurnAroundTime""",
					    ],
					)

					# Printing the dataFrame
					pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
					print(fcfs)





