
from collections import deque

class 						lowercase_				:

 def __init__(					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				process_name  # process name
        UpperCamelCase_											=				arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        UpperCamelCase_											=				arrival_time
        UpperCamelCase_											=				burst_time  # remaining burst time
        UpperCamelCase_											=				0  # total time of the process wait in ready queue
        UpperCamelCase_											=				0  # time from arrival time to completion time



class 						lowercase_				:

 def __init__(					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		):




        """simple docstring"""


        UpperCamelCase_											=				number_of_queues
        # time slice of queues that round robin algorithm applied
        UpperCamelCase_											=				time_slices
        # unfinished process is in this ready_queue
        UpperCamelCase_											=				queue
        # current time
        UpperCamelCase_											=				current_time
        # finished process is in this sequence queue
        UpperCamelCase_											=				deque()

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				[]
        for i in range(len(self.finish_queue   )   ):
               sequence.append(self.finish_queue[i].process_name   )
        return sequence

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				[]
        for i in range(len(__lowerCAmelCase   )   ):
               waiting_times.append(queue[i].waiting_time   )
        return waiting_times

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				[]
        for i in range(len(__lowerCAmelCase   )   ):
               turnaround_times.append(queue[i].turnaround_time   )
        return turnaround_times

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				[]
        for i in range(len(__lowerCAmelCase   )   ):
               completion_times.append(queue[i].stop_time   )
        return completion_times

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        return [q.burst_time for q in queue]

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				deque()  # sequence deque of finished process
        while len(__lowerCAmelCase   ) != 0:
               UpperCamelCase_											=				ready_queue.popleft()  # current process

               # if process's arrival time is later than current time, update current time
               if self.current_time < cp.arrival_time:
                      self.current_time += cp.arrival_time

               # update waiting time of current process
               self.update_waiting_time(__lowerCAmelCase   )
               # update current time
               self.current_time += cp.burst_time
               # finish the process and set the process's burst-time 0
               UpperCamelCase_											=				0
               # set the process's turnaround time because it is finished
               UpperCamelCase_											=				self.current_time - cp.arrival_time
               # set the completion time
               UpperCamelCase_											=				self.current_time
               # add the process to queue that has finished queue
               finished.append(__lowerCAmelCase   )

        self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
        # FCFS will finish all remaining processes
        return finished

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(__lowerCAmelCase   )   ):
               UpperCamelCase_											=				ready_queue.popleft()  # current process

               # if process's arrival time is later than current time, update current time
               if self.current_time < cp.arrival_time:
                      self.current_time += cp.arrival_time

               # update waiting time of unfinished processes
               self.update_waiting_time(__lowerCAmelCase   )
               # if the burst time of process is bigger than time-slice
               if cp.burst_time > time_slice:
                      # use CPU for only time-slice
                      self.current_time += time_slice
                      # update remaining burst time
                      cp.burst_time -= time_slice
                      # update end point time
                      UpperCamelCase_											=				self.current_time
                      # locate the process behind the queue because it is not finished
                      ready_queue.append(__lowerCAmelCase   )
               else:
                      # use CPU for remaining burst time
                      self.current_time += cp.burst_time
                      # set burst time 0 because the process is finished
                      UpperCamelCase_											=				0
                      # set the finish time
                      UpperCamelCase_											=				self.current_time
                      # update the process' turnaround time because it is finished
                      UpperCamelCase_											=				self.current_time - cp.arrival_time
                      # add the process to queue that has finished queue
                      finished.append(__lowerCAmelCase   )

        self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
        # return finished processes queue and remaining processes queue
        return finished, ready_queue

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        for i in range(self.number_of_queues - 1   ):
               UpperCamelCase_				, UpperCamelCase_											=				self.round_robin(
                   self.ready_queue   ,		self.time_slices[i]   )
        #  the last queue has first_come_first_served algorithm
        self.first_come_first_served(self.ready_queue   )

        return self.finish_queue


if __name__ == "__main__":
      import doctest

      _A		     =    Process('''P1''', 0, 53)
      _A		     =    Process('''P2''', 0, 17)
      _A		     =    Process('''P3''', 0, 68)
      _A		     =    Process('''P4''', 0, 24)
      _A		     =    3
      _A		     =    [17, 25]
      _A		     =    deque([Pa, Pa, Pa, Pa])

      if len(time_slices) != number_of_queues - 1:
            raise SystemExit(0)

      doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

      _A		     =    Process('''P1''', 0, 53)
      _A		     =    Process('''P2''', 0, 17)
      _A		     =    Process('''P3''', 0, 68)
      _A		     =    Process('''P4''', 0, 24)
      _A		     =    3
      _A		     =    [17, 25]
      _A		     =    deque([Pa, Pa, Pa, Pa])
      _A		     =    MLFQ(number_of_queues, time_slices, queue, 0)
      _A		     =    mlfq.multi_level_feedback_queue()

      # print total waiting times of processes(P1, P2, P3, P4)
      print(
          F'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print completion times of processes(P1, P2, P3, P4)
      print(
          F'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print total turnaround times of processes(P1, P2, P3, P4)
      print(
          F'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print sequence of finished processes
      print(
          F'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
      )










from sklearn.metrics import fa_score

import datasets


A    :					Any =      '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


A    :					List[Any] =      '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


A    :					List[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}
}
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,     _KWARGS_DESCRIPTION    )
class   A			(datasets.Metric    ):
			'''simple docstring'''




			def    a_       (			self   :    Optional[int]   )	->	List[Any]:







					"""simple docstring"""






					return datasets.MetricInfo(
					    description=_DESCRIPTION							,	citation=_CITATION							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
					        {
					            """predictions""": datasets.Sequence(datasets.Value("""int32"""   )   ),
					            """references""": datasets.Sequence(datasets.Value("""int32"""   )   ),
					        }
					        if self.config_name == """multilabel"""
					        else {
					            """predictions""": datasets.Value("""int32"""   ),
					            """references""": datasets.Value("""int32"""   ),
					        }   )							,	reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""]							,	)







			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Dict=None							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    Any="binary"							,	__lowerCAmelCase   :    Optional[int]=None   )	->	List[Any]:







					"""simple docstring"""






					A__        =   fa_score(
					    __lowerCAmelCase							,	__lowerCAmelCase							,	labels=__lowerCAmelCase							,	pos_label=__lowerCAmelCase							,	average=__lowerCAmelCase							,	sample_weight=__lowerCAmelCase   )
					return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



