


'''simple docstring'''
from collections import deque




class 			lowerCamelCase_   :






      def __init__(					self				:    str						,						_A				:    str						,						_A				:    int						,						_A				:    int ):


          '''simple docstring'''


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




class 			lowerCamelCase_   :






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    list[int]						,						_A				:    deque[Process]						,						_A				:    int						,						):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    List[str]						,						_A				:    list[Process] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				[]
          for i in range(len(_A ) ):
              waiting_times.append(queue[i].waiting_time )
          return waiting_times






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    list[Process] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				[]
          for i in range(len(_A ) ):
              turnaround_times.append(queue[i].turnaround_time )
          return turnaround_times






      def lowercase_							(					self				:    List[str]						,						_A				:    list[Process] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				[]
          for i in range(len(_A ) ):
              completion_times.append(queue[i].stop_time )
          return completion_times






      def lowercase_							(					self				:    int						,						_A				:    deque[Process] ):


          '''simple docstring'''


          return [q.burst_time for q in queue]






      def lowercase_							(					self				:    List[Any]						,						_A				:    Process ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    int						,						_A				:    deque[Process] ):


          '''simple docstring'''


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

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






      def lowercase_							(					self				:    Dict						,						_A				:    deque[Process]						,						_A				:    int ):


          '''simple docstring'''


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

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






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          for i in range(self.number_of_queues - 1 ):
              UpperCAmelCase__						,						UpperCAmelCase__			:		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

  UpperCamelCase__    					=      Process('''P1''', 0, 5_3)
  UpperCamelCase__    					=      Process('''P2''', 0, 1_7)
  UpperCamelCase__    					=      Process('''P3''', 0, 6_8)
  UpperCamelCase__    					=      Process('''P4''', 0, 2_4)
  UpperCamelCase__    					=      3
  UpperCamelCase__    					=      [1_7, 2_5]
  UpperCamelCase__    					=      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])})

  UpperCamelCase__    					=      Process('''P1''', 0, 5_3)
  UpperCamelCase__    					=      Process('''P2''', 0, 1_7)
  UpperCamelCase__    					=      Process('''P3''', 0, 6_8)
  UpperCamelCase__    					=      Process('''P4''', 0, 2_4)
  UpperCamelCase__    					=      3
  UpperCamelCase__    					=      [1_7, 2_5]
  UpperCamelCase__    					=      deque([Pa, Pa, Pa, Pa])
  UpperCamelCase__    					=      MLFQ(number_of_queues, time_slices, queue, 0)
  UpperCamelCase__    					=      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 multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[str]						,						_A				:    NestedDataStructureLike[PathLike]						,						_A				:    Optional[NamedSplit] = None						,						_A				:    Optional[Features] = None						,						_A				:    str = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    Optional[str] = None						,						_A				:    Optional[int] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          super().__init__(
              _A						,						split=_A						,						features=_A						,						cache_dir=_A						,						keep_in_memory=_A						,						streaming=_A						,						num_proc=_A						,						**_A						,						)
          UpperCAmelCase__			:		List[str]													=				field
          UpperCAmelCase__			:		Optional[Any]													=				path_or_paths if isinstance(_A						,						_A ) else {self.split: path_or_paths}
          UpperCAmelCase__			:		Any													=				Json(
              cache_dir=_A						,						data_files=_A						,						features=_A						,						field=_A						,						**_A						,						)






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if self.streaming:
              UpperCAmelCase__			:		int													=				self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__			:		List[str]													=				None
              UpperCAmelCase__			:		Dict													=				None
              UpperCAmelCase__			:		Optional[int]													=				None
              UpperCAmelCase__			:		List[Any]													=				None

              self.builder.download_and_prepare(
                  download_config=_A						,						download_mode=_A						,						verification_mode=_A						,						base_path=_A						,						num_proc=self.num_proc						,						)
              UpperCAmelCase__			:		str													=				self.builder.as_dataset(
                  split=self.split						,						verification_mode=_A						,						in_memory=self.keep_in_memory )
          return dataset




class 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Dataset						,						_A				:    Union[PathLike, BinaryIO]						,						_A				:    Optional[int] = None						,						_A				:    Optional[int] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
              raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

          UpperCAmelCase__			:		Dict													=				dataset
          UpperCAmelCase__			:		Any													=				path_or_buf
          UpperCAmelCase__			:		List[str]													=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__			:		Tuple													=				num_proc
          UpperCAmelCase__			:		Any													=				'''utf-8'''
          UpperCAmelCase__			:		Optional[int]													=				to_json_kwargs






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.to_json_kwargs.pop('''path_or_buf'''						,						_A )
          UpperCAmelCase__			:		Optional[int]													=				self.to_json_kwargs.pop('''orient'''						,						'''records''' )
          UpperCAmelCase__			:		Tuple													=				self.to_json_kwargs.pop('''lines'''						,						True if orient == '''records''' else False )
          UpperCAmelCase__			:		str													=				self.to_json_kwargs.pop('''index'''						,						False if orient in ['''split''', '''table'''] else True )
          UpperCAmelCase__			:		Optional[Any]													=				self.to_json_kwargs.pop('''compression'''						,						_A )

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(f"""`datasets` currently does not support {compression} compression""" )

          if isinstance(self.path_or_buf						,						(str, bytes, os.PathLike) ):
              with fsspec.open(self.path_or_buf						,						'''wb'''						,						compression=_A ) as buffer:
                  UpperCAmelCase__			:		Union[str, Any]													=				self._write(file_obj=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          else:
              if compression:
                  raise NotImplementedError(
                      f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                      ''' was passed. Please provide a local path instead.''' )
              UpperCAmelCase__			:		Union[str, Any]													=				self._write(
                  file_obj=self.path_or_buf						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          return written






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				args

          UpperCAmelCase__			:		Dict													=				query_table(
              table=self.dataset.data						,						key=slice(_A						,						offset + self.batch_size )						,						indices=self.dataset._indices						,						)
          UpperCAmelCase__			:		str													=				batch.to_pandas().to_json(
              path_or_buf=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**_A )
          if not json_str.endswith('''\n''' ):
              json_str += "\n"
          return json_str.encode(self.encoding )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    BinaryIO						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    Any						,						**_A				:    Any						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				0

          if self.num_proc is None or self.num_proc == 1:
              for offset in logging.tqdm(
                  range(0						,						len(self.dataset )						,						self.batch_size )						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                  UpperCAmelCase__			:		Optional[int]													=				self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                  written += file_obj.write(_A )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				len(self.dataset ), self.batch_size
              with multiprocessing.Pool(self.num_proc ) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json						,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,						_A						,						_A )]						,						)						,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                      written += file_obj.write(_A )

          return written
