


from collections import deque

class      lowerCAmelCase							:
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   int			)    ->			None:
     lowerCamelCase__					:   Optional[int]      				=	process_name  # process name
     lowerCamelCase__					:   Union[str, Any]      				=	arrival_time  # arrival time of the process
     # completion time of finished process or last interrupted time
     lowerCamelCase__					:   Any      				=	arrival_time
     lowerCamelCase__					:   Dict      				=	burst_time  # remaining burst time
     lowerCamelCase__					:   Any      				=	0  # total time of the process wait in ready queue
     lowerCamelCase__					:   List[Any]      				=	0  # time from arrival time to completion time


class      lowerCAmelCase							:
 def __init__(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   list[int]    ,  UpperCAmelCase						:   deque[Process]    ,  UpperCAmelCase						:   int    ,  )    ->			None:
     # total number of mlfq's queues
     lowerCamelCase__					:   List[Any]      				=	number_of_queues
     # time slice of queues that round robin algorithm applied
     lowerCamelCase__					:   Dict      				=	time_slices
     # unfinished process is in this ready_queue
     lowerCamelCase__					:   Optional[int]      				=	queue
     # current time
     lowerCamelCase__					:   str      				=	current_time
     # finished process is in this sequence queue
     lowerCamelCase__					:   deque[Process]      				=	deque()
 def   A_			(   self						:   Union[str, Any]			)    ->			list[str]:
     lowerCamelCase__					:   Optional[Any]      				=	[]
     for i in range(len(self.finish_queue			)			):
         sequence.append(self.finish_queue[i].process_name			)
     return sequence
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   list[Process]			)    ->			list[int]:
     lowerCamelCase__					:   str      				=	[]
     for i in range(len(UpperCAmelCase			)			):
         waiting_times.append(queue[i].waiting_time			)
     return waiting_times
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   list[Process]			)    ->			list[int]:
     lowerCamelCase__					:   Dict      				=	[]
     for i in range(len(UpperCAmelCase			)			):
         turnaround_times.append(queue[i].turnaround_time			)
     return turnaround_times
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   list[Process]			)    ->			list[int]:
     lowerCamelCase__					:   str      				=	[]
     for i in range(len(UpperCAmelCase			)			):
         completion_times.append(queue[i].stop_time			)
     return completion_times
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   deque[Process]			)    ->			list[int]:
     return [q.burst_time for q in queue]
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   Process			)    ->			int:
     process.waiting_time += self.current_time - process.stop_time
     return process.waiting_time
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   deque[Process]			)    ->			deque[Process]:
     lowerCamelCase__					:   deque[Process]      				=	deque()  # sequence deque of finished process
     while len(UpperCAmelCase			) != 0:
         lowerCamelCase__					:   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(UpperCAmelCase			)
         # 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__					:   Any      				=	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(UpperCAmelCase			)

     self.finish_queue.extend(UpperCAmelCase			)  # add finished process to finish queue
     # FCFS will finish all remaining processes
     return finished
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   deque[Process]    ,  UpperCAmelCase						:   int			)    ->			tuple[deque[Process], deque[Process]]:
     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(UpperCAmelCase			)			):
         lowerCamelCase__					:   Dict      				=	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
             lowerCamelCase__					:   int      				=	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
             lowerCamelCase__					:   List[Any]      				=	0
             # set the finish time
             lowerCamelCase__					:   int      				=	self.current_time
             # update the process' turnaround time because it is finished
             lowerCamelCase__					:   Dict      				=	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   A_			(   self						:   Tuple			)    ->			deque[Process]:

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












import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Optional[int]      				=	'laion/clap-htsat-unfused'
     lowerCamelCase__					:   List[Any]      				=	tempfile.mkdtemp()
 def   A_			(   self						:   Optional[int]    ,  **UpperCAmelCase						:   int			)    ->			Optional[Any]:
     return RobertaTokenizer.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			List[str]:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   List[str]			)    ->			Dict:
     shutil.rmtree(self.tmpdirname			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Tuple      				=	self.get_tokenizer()
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     processor.save_pretrained(self.tmpdirname			)
     lowerCamelCase__					:   List[Any]      				=	ClapProcessor.from_pretrained(self.tmpdirname			)

     self.assertEqual(processor.tokenizer.get_vocab()    ,  tokenizer.get_vocab()			)
     self.assertIsInstance(processor.tokenizer    ,  UpperCAmelCase			)

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			int:
     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=self.get_tokenizer()    ,  feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase__					:   Dict      				=	self.get_tokenizer(bos_token='(BOS)'    ,  eos_token='(EOS)'			)
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor(do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

     lowerCamelCase__					:   str      				=	ClapProcessor.from_pretrained(
         self.tmpdirname    ,  bos_token='(BOS)'    ,  eos_token='(EOS)'    ,  do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

     self.assertEqual(processor.tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab()			)
     self.assertIsInstance(processor.tokenizer    ,  UpperCAmelCase			)

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   List[Any]			)    ->			int:
     lowerCamelCase__					:   Any      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	floats_list((3, 1000)			)

     lowerCamelCase__					:   Tuple      				=	feature_extractor(UpperCAmelCase    ,  return_tensors='np'			)
     lowerCamelCase__					:   Tuple      				=	processor(audios=UpperCAmelCase    ,  return_tensors='np'			)

     for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()    ,  input_processor[key].sum()    ,  delta=1e-2			)
 def   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[Any]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Any      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Union[str, Any]      				=	'This is a test string'

     lowerCamelCase__					:   Union[str, Any]      				=	processor(text=UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	tokenizer(UpperCAmelCase			)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]			)
 def   A_			(   self						:   Optional[int]			)    ->			Tuple:
     lowerCamelCase__					:   Tuple      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[str]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[int]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__					:   List[Any]      				=	processor.batch_decode(UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	tokenizer.batch_decode(UpperCAmelCase			)

     self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Optional[Any]			)    ->			int:
     lowerCamelCase__					:   str      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     self.assertListEqual(
         processor.model_input_names[2:]    ,  feature_extractor.model_input_names    ,  msg='`processor` and `feature_extractor` model input names do not match'    ,  )





