


from collections import deque





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    None:




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





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			) ->    None:




        '''simple docstring'''
        # total number of mlfq's queues
        lowercase__:       Tuple        =       number_of_queues
        # time slice of queues that round robin algorithm applied
        lowercase__:       Union[str, Any]        =       time_slices
        # unfinished process is in this ready_queue
        lowercase__:       Optional[int]        =       queue
        # current time
        lowercase__:       Optional[Any]        =       current_time
        # finished process is in this sequence queue
        lowercase__:       deque[Process]        =       deque()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    list[str]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       []
        for i in range(len(self.finish_queue			)			):
               sequence.append(self.finish_queue[i].process_name			)
        return sequence
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    list[int]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       []
        for i in range(len(lowerCAmelCase__			)			):
               waiting_times.append(queue[i].waiting_time			)
        return waiting_times
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    list[int]:




        '''simple docstring'''
        lowercase__:       Any        =       []
        for i in range(len(lowerCAmelCase__			)			):
               turnaround_times.append(queue[i].turnaround_time			)
        return turnaround_times
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    list[int]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       []
        for i in range(len(lowerCAmelCase__			)			):
               completion_times.append(queue[i].stop_time			)
        return completion_times
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    list[int]:




        '''simple docstring'''
        return [q.burst_time for q in queue]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    deque[Process]:




        '''simple docstring'''
        lowercase__:       deque[Process]        =       deque()  # sequence deque of finished process
        while len(lowerCAmelCase__			) != 0:
               lowercase__:       str        =       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
               lowercase__:       Any        =       0
               # set the process's turnaround time because it is finished
               lowercase__:       int        =       self.current_time - cp.arrival_time
               # set the completion time
               lowercase__:       Optional[Any]        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    tuple[deque[Process], deque[Process]]:




        '''simple docstring'''
        lowercase__:       deque[Process]        =       deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(lowerCAmelCase__			)			):
               lowercase__:       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(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
                      lowercase__:       List[str]        =       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
                      lowercase__:       Optional[Any]        =       0
                      # set the finish time
                      lowercase__:       int        =       self.current_time
                      # update the process' turnaround time because it is finished
                      lowercase__:       Any        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    deque[Process]:




        '''simple docstring'''

        #  all queues except last one have round_robin algorithm
        for i in range(self.number_of_queues - 1			):
               lowercase__      ,						lowercase__:       str        =       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

 __lowerCAmelCase			=							Process('''P1''', 0, 53)
 __lowerCAmelCase			=							Process('''P2''', 0, 17)
 __lowerCAmelCase			=							Process('''P3''', 0, 68)
 __lowerCAmelCase			=							Process('''P4''', 0, 24)
 __lowerCAmelCase			=							3
 __lowerCAmelCase			=							[17, 25]
 __lowerCAmelCase			=							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])})

 __lowerCAmelCase			=							Process('''P1''', 0, 53)
 __lowerCAmelCase			=							Process('''P2''', 0, 17)
 __lowerCAmelCase			=							Process('''P3''', 0, 68)
 __lowerCAmelCase			=							Process('''P4''', 0, 24)
 __lowerCAmelCase			=							3
 __lowerCAmelCase			=							[17, 25]
 __lowerCAmelCase			=							deque([Pa, Pa, Pa, Pa])
 __lowerCAmelCase			=							MLFQ(number_of_queues, time_slices, queue, 0)
 __lowerCAmelCase			=							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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							'''▁'''

__lowerCAmelCase			=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__lowerCAmelCase			=							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__lowerCAmelCase			=							{'''vinai/bartpho-syllable''': 10_24}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							int       					=			VOCAB_FILES_NAMES
 __lowercase			:							str       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							str       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase			:							Optional[int]       					=			['input_ids', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="<unk>"    ,			lowerCAmelCase__="<pad>"    ,			lowerCAmelCase__="<mask>"    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    None:




        '''simple docstring'''
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase__:       List[str]        =       AddedToken(lowerCAmelCase__    ,			lstrip=lowerCAmelCase__    ,			rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			) else mask_token

        lowercase__:       Any        =       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCAmelCase__    ,			eos_token=lowerCAmelCase__    ,			unk_token=lowerCAmelCase__    ,			sep_token=lowerCAmelCase__    ,			cls_token=lowerCAmelCase__    ,			pad_token=lowerCAmelCase__    ,			mask_token=lowerCAmelCase__    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**lowerCAmelCase__    ,			)

        lowercase__:       Dict        =       vocab_file
        lowercase__:       str        =       monolingual_vocab_file
        lowercase__:       int        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(lowerCAmelCase__			)			)

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        lowercase__:       List[Any]        =       {}
        lowercase__:       Optional[int]        =       0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
               if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
                      lowercase__:       str        =       cnt
                      cnt += 1
        with open(lowerCAmelCase__    ,			'r'    ,			encoding='utf-8'			) as f:
               for line in f.readlines():
                      lowercase__:       Optional[Any]        =       line.strip().split()[0]
                      lowercase__:       Optional[Any]        =       len(self.fairseq_tokens_to_ids			)
        if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
               lowercase__:       Optional[int]        =       len(self.fairseq_tokens_to_ids			)

        lowercase__:       Optional[Any]        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.__dict__.copy()
        lowercase__:       Tuple        =       None
        lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
        return state
 def __setstate__(       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       d

        # for backward compatibility
        if not hasattr(self    ,			'sp_model_kwargs'			):
               lowercase__:       Union[str, Any]        =       {}

        lowercase__:       List[Any]        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase__:       Optional[int]        =       [self.cls_token_id]
        lowercase__:       Union[str, Any]        =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False			) ->    List[int]:




        '''simple docstring'''

        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase__    ,			token_ids_a=lowerCAmelCase__    ,			already_has_special_tokens=lowerCAmelCase__			)

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCAmelCase__			)) + [1]
        return [1] + ([0] * len(lowerCAmelCase__			)) + [1, 1] + ([0] * len(lowerCAmelCase__			)) + [1]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''
        lowercase__:       Dict        =       [self.sep_token_id]
        lowercase__:       Dict        =       [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep			) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return len(self.fairseq_ids_to_tokens			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        return self.sp_model.encode(lowerCAmelCase__    ,			out_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        else:
               return self.unk_token_id
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        return self.fairseq_ids_to_tokens[index]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       ''.join(lowerCAmelCase__			).replace(lowerCAmelCase__    ,			' '			).strip()
        return out_string
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__			):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
               return
        lowercase__:       int        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       List[str]        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']    ,			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.vocab_file			):
               with open(lowerCAmelCase__    ,			'wb'			) as fi:
                      lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__			)

        if os.path.abspath(self.monolingual_vocab_file			) != os.path.abspath(
            lowerCAmelCase__			) and os.path.isfile(self.monolingual_vocab_file			):
               copyfile(self.monolingual_vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.monolingual_vocab_file			):
               with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as fp:
                      for token in self.fairseq_tokens_to_ids:
                             if token not in self.all_special_tokens:
                                    fp.write(F'{str(lowerCAmelCase__			)} \n'			)

        return out_vocab_file, out_monolingual_vocab_file


