'''simple docstring'''






from collections import deque







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Tuple, UpperCamelCase__      :			str, UpperCamelCase__      :			int, UpperCamelCase__      :			int     )  ->			None:
        _A    		=  process_name  # process name
        _A    		=  arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        _A    		=  arrival_time
        _A    		=  burst_time  # remaining burst time
        _A    		=  0  # total time of the process wait in ready queue
        _A    		=  0  # time from arrival time to completion time







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			list[int], UpperCamelCase__      :			deque[Process], UpperCamelCase__      :			int, )  ->			None:
        # total number of mlfq's queues
        _A    		=  number_of_queues
        # time slice of queues that round robin algorithm applied
        _A    		=  time_slices
        # unfinished process is in this ready_queue
        _A    		=  queue
        # current time
        _A    		=  current_time
        # finished process is in this sequence queue
        _A    		=  deque()
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			list[str]:
        _A    		=  []
        for i in range(len(self.finish_queue     )     ):
             sequence.append(self.finish_queue[i].process_name     )
        return sequence
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			list[Process]     )  ->			list[int]:
        _A    		=  []
        for i in range(len(UpperCamelCase__     )     ):
             waiting_times.append(queue[i].waiting_time     )
        return waiting_times
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			list[Process]     )  ->			list[int]:
        _A    		=  []
        for i in range(len(UpperCamelCase__     )     ):
             turnaround_times.append(queue[i].turnaround_time     )
        return turnaround_times
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			list[Process]     )  ->			list[int]:
        _A    		=  []
        for i in range(len(UpperCamelCase__     )     ):
             completion_times.append(queue[i].stop_time     )
        return completion_times
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			deque[Process]     )  ->			list[int]:
        return [q.burst_time for q in queue]
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			Process     )  ->			int:
        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			deque[Process]     )  ->			deque[Process]:
        _A    		=  deque()  # sequence deque of finished process
        while len(UpperCamelCase__     ) != 0:
             _A    		=  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(UpperCamelCase__     )
             # update current time
             self.current_time += cp.burst_time
             # finish the process and set the process's burst-time 0
             _A    		=  0
             # set the process's turnaround time because it is finished
             _A    		=  self.current_time - cp.arrival_time
             # set the completion time
             _A    		=  self.current_time
             # add the process to queue that has finished queue
             finished.append(UpperCamelCase__     )

        self.finish_queue.extend(UpperCamelCase__     )  # add finished process to finish queue
        # FCFS will finish all remaining processes
        return finished
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			deque[Process], UpperCamelCase__      :			int     )  ->			tuple[deque[Process], deque[Process]]:
        _A    		=  deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(UpperCamelCase__     )     ):
             _A    		=  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(UpperCamelCase__     )
             # 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
                  _A    		=  self.current_time
                  # locate the process behind the queue because it is not finished
                  ready_queue.append(UpperCamelCase__     )
             else:
                  # use CPU for remaining burst time
                  self.current_time += cp.burst_time
                  # set burst time 0 because the process is finished
                  _A    		=  0
                  # set the finish time
                  _A    		=  self.current_time
                  # update the process' turnaround time because it is finished
                  _A    		=  self.current_time - cp.arrival_time
                  # add the process to queue that has finished queue
                  finished.append(UpperCamelCase__     )

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

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

        #  all queues except last one have round_robin algorithm
        for i in range(self.number_of_queues - 1     ):
             _A		,       _A    		=  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    :     List[Any]         =       Process('''P1''', 0, 53)
   _UpperCAmelCase    :     Any         =       Process('''P2''', 0, 17)
   _UpperCAmelCase    :     Any         =       Process('''P3''', 0, 68)
   _UpperCAmelCase    :     Union[str, Any]         =       Process('''P4''', 0, 24)
   _UpperCAmelCase    :     Optional[Any]         =       3
   _UpperCAmelCase    :     Dict         =       [17, 25]
   _UpperCAmelCase    :     Dict         =       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    :     Optional[int]         =       Process('''P1''', 0, 53)
   _UpperCAmelCase    :     Tuple         =       Process('''P2''', 0, 17)
   _UpperCAmelCase    :     Tuple         =       Process('''P3''', 0, 68)
   _UpperCAmelCase    :     str         =       Process('''P4''', 0, 24)
   _UpperCAmelCase    :     Union[str, Any]         =       3
   _UpperCAmelCase    :     int         =       [17, 25]
   _UpperCAmelCase    :     Optional[int]         =       deque([Pa, Pa, Pa, Pa])
   _UpperCAmelCase    :     int         =       MLFQ(number_of_queues, time_slices, queue, 0)
   _UpperCAmelCase    :     Tuple         =       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 unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
   import torch







@is_pipeline_test
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

        _A    		=  generator(['This is great !', 'Something else'], num_return_sequences=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        _A    		=  generator(
            ['This is great !', 'Something else'], num_return_sequences=2, batch_size=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': ''},
        ]
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  generator('This is a test', do_sample=UpperCamelCase__, num_return_sequences=2, return_tensors=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                {'generated_token_ids': ANY(torch.Tensor     )},
                {'generated_token_ids': ANY(torch.Tensor     )},
            ], )
        _A    		=  generator.model.config.eos_token_id
        _A    		=  '<pad>'
        _A    		=  generator(
            ['This is a test', 'This is a second test'], do_sample=UpperCamelCase__, num_return_sequences=2, batch_size=2, return_tensors=UpperCamelCase__, )
        self.assertEqual(
            UpperCamelCase__, [
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
            ], )

   @require_tf
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='tf'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )



