



"""simple docstring"""






from collections import deque



class     A__     :




	'''simple docstring'''





	def __init__(  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	int)       -> None:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= process_name  # process name
				__lowerCAmelCase   :						List[str]				= arrival_time  # arrival time of the process
				# completion time of finished process or last interrupted time
				__lowerCAmelCase   :						Any				= arrival_time
				__lowerCAmelCase   :						List[str]				= burst_time  # remaining burst time
				__lowerCAmelCase   :						Union[str, Any]				= 0  # total time of the process wait in ready queue
				__lowerCAmelCase   :						Optional[int]				= 0  # time from arrival time to completion time






class     A__     :




	'''simple docstring'''





	def __init__(  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	list[int] ,    _SCREAMING_SNAKE_CASE:	deque[Process] ,    _SCREAMING_SNAKE_CASE:	int ,    )       -> None:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= number_of_queues
				# time slice of queues that round robin algorithm applied
				__lowerCAmelCase   :						Dict				= time_slices
				# unfinished process is in this ready_queue
				__lowerCAmelCase   :						Dict				= queue
				# current time
				__lowerCAmelCase   :						Union[str, Any]				= current_time
				# finished process is in this sequence queue
				__lowerCAmelCase   :						deque[Process]				= deque()

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> list[str]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	list[Process])       -> list[int]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	list[Process])       -> list[int]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	list[Process])       -> list[int]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	deque[Process])       -> list[int]:

				"""simple docstring"""

				return [q.burst_time for q in queue]

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Process)       -> int:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	deque[Process])       -> deque[Process]:

				"""simple docstring"""

				__lowerCAmelCase   :						deque[Process]				= deque()  # sequence deque of finished process
				while len(_SCREAMING_SNAKE_CASE) != 0:
							__lowerCAmelCase   :						Union[str, Any]				= 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(_SCREAMING_SNAKE_CASE)
							# update current time
							self.current_time += cp.burst_time
							# finish the process and set the process's burst-time 0
							__lowerCAmelCase   :						Optional[int]				= 0
							# set the process's turnaround time because it is finished
							__lowerCAmelCase   :						List[str]				= self.current_time - cp.arrival_time
							# set the completion time
							__lowerCAmelCase   :						Any				= self.current_time
							# add the process to queue that has finished queue
							finished.append(_SCREAMING_SNAKE_CASE)

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	deque[Process] ,    _SCREAMING_SNAKE_CASE:	int)       -> tuple[deque[Process], deque[Process]]:

				"""simple docstring"""

				__lowerCAmelCase   :						deque[Process]				= deque()  # sequence deque of terminated process
				# just for 1 cycle and unfinished processes will go back to queue
				for _ in range(len(_SCREAMING_SNAKE_CASE)):
							__lowerCAmelCase   :						Tuple				= 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(_SCREAMING_SNAKE_CASE)
							# 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
										__lowerCAmelCase   :						Dict				= self.current_time
										# locate the process behind the queue because it is not finished
										ready_queue.append(_SCREAMING_SNAKE_CASE)
							else:
										# use CPU for remaining burst time
										self.current_time += cp.burst_time
										# set burst time 0 because the process is finished
										__lowerCAmelCase   :						Optional[Any]				= 0
										# set the finish time
										__lowerCAmelCase   :						Optional[int]				= self.current_time
										# update the process' turnaround time because it is finished
										__lowerCAmelCase   :						str				= self.current_time - cp.arrival_time
										# add the process to queue that has finished queue
										finished.append(_SCREAMING_SNAKE_CASE)

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> deque[Process]:

				"""simple docstring"""

				for i in range(self.number_of_queues - 1):
							__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= 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

	__snake_case							:     List[Any]   =					Process('P1', 0, 53)
	__snake_case							:     str   =					Process('P2', 0, 17)
	__snake_case							:     Optional[Any]   =					Process('P3', 0, 68)
	__snake_case							:     Tuple   =					Process('P4', 0, 24)
	__snake_case							:     Tuple   =					3
	__snake_case							:     List[Any]   =					[17, 25]
	__snake_case							:     List[str]   =					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])})

	__snake_case							:     str   =					Process('P1', 0, 53)
	__snake_case							:     Any   =					Process('P2', 0, 17)
	__snake_case							:     str   =					Process('P3', 0, 68)
	__snake_case							:     Dict   =					Process('P4', 0, 24)
	__snake_case							:     Optional[int]   =					3
	__snake_case							:     Union[str, Any]   =					[17, 25]
	__snake_case							:     str   =					deque([Pa, Pa, Pa, Pa])
	__snake_case							:     int   =					MLFQ(number_of_queues, time_slices, queue, 0)
	__snake_case							:     Optional[int]   =					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()}"""
	)




"""simple docstring"""






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			assert isinstance(__snake_case      ,__snake_case			)
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"      ,[False, True]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Dict				= tmp_path / "cache"
			__lowerCAmelCase   :						Union[str, Any]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__lowerCAmelCase   :						Optional[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


@pytest.mark.parametrize(
    "features"      ,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Tuple				= tmp_path / "cache"
			__lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= features.copy() if features else default_expected_features
			__lowerCAmelCase   :						Dict				= (
			    Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
			)
			__lowerCAmelCase   :						Optional[int]				= ParquetDatasetReader(__snake_case      ,features=__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"			), "train", "test"]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,split=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)
			assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"      ,[str, list]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
			if issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= parquet_path
			elif issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= [parquet_path]
			__lowerCAmelCase   :						str				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						Tuple				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=("train",)			)		->     int:
			assert isinstance(__snake_case      ,__snake_case			)
			for split in splits:
						__lowerCAmelCase   :						str				= dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"      ,[False, True]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Any				= tmp_path / "cache"
			__lowerCAmelCase   :						int				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__lowerCAmelCase   :						str				= ParquetDatasetReader(
						    {"train": parquet_path}      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__snake_case			)


@pytest.mark.parametrize(
    "features"      ,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						List[Any]				= tmp_path / "cache"
			__lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						str				= features.copy() if features else default_expected_features
			__lowerCAmelCase   :						str				= (
			    Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
			)
			__lowerCAmelCase   :						str				= ParquetDatasetReader({"train": parquet_path}      ,features=__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__snake_case			)


@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"			), "train", "test"]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			if split:
						__lowerCAmelCase   :						Optional[int]				= {split: parquet_path}
			else:
						__lowerCAmelCase   :						str				= "train"
						__lowerCAmelCase   :						Optional[Any]				= {"train": parquet_path, "test": parquet_path}
			__lowerCAmelCase   :						Tuple				= tmp_path / "cache"
			__lowerCAmelCase   :						int				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						Union[str, Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__snake_case      ,splits=list(path.keys()			)			)
			assert all(dataset[split].split == split for split in path.keys()			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						int				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
			assert writer.write() > 0
			__lowerCAmelCase   :						str				= pq.ParquetFile(tmp_path / "foo.parquet"			)
			__lowerCAmelCase   :						int				= pf.read()
			assert dataset.data.table == output_table


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						int				= str(shared_datadir / "test_image_rgb.jpg"			)
			__lowerCAmelCase   :						Any				= {"image": [image_path]}
			__lowerCAmelCase   :						List[Any]				= Features({"image": Image()}			)
			__lowerCAmelCase   :						Tuple				= Dataset.from_dict(__snake_case      ,features=__snake_case			)
			__lowerCAmelCase   :						Dict				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
			assert writer.write() > 0

			__lowerCAmelCase   :						List[Any]				= Dataset.from_parquet(str(tmp_path / "foo.parquet"			)			)
			assert dataset.features == reloaded_dataset.features

			__lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(str(tmp_path / "foo.parquet"			)      ,streaming=__snake_case			).read()
			assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    "feature, expected"      ,[
        (Features({"foo": Value("int32"			)}			), None),
        (Features({"image": Image(), "foo": Value("int32"			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     List[Any]:
			assert get_writer_batch_size(__snake_case			) == expected