





from collections import deque

class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				Union[str, Any]    ,      a__	:				Optional[Any]    ,      a__	:				Optional[Any]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       process_name  # process name
         __snake_case								=       arrival_time  # arrival time of the process
         # completion time of finished process or last interrupted time
         __snake_case								=       arrival_time
         __snake_case								=       burst_time  # remaining burst time
         __snake_case								=       0  # total time of the process wait in ready queue
         __snake_case								=       0  # time from arrival time to completion time






class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				str    ,      a__	:				Optional[int]    ,      a__	:				str    ,      a__	:				Dict    ,      a__	:				List[Any]    ,      ):





         """simple docstring"""



         __snake_case								=       number_of_queues
         # time slice of queues that round robin algorithm applied
         __snake_case								=       time_slices
         # unfinished process is in this ready_queue
         __snake_case								=       queue
         # current time
         __snake_case								=       current_time
         # finished process is in this sequence queue
         __snake_case								=       deque()


      def        a			(self	:				str				):





         """simple docstring"""



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


      def        a			(self	:				str    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       []
         for i in range(len(lowerCAmelCase__				)				):
            waiting_times.append(queue[i].waiting_time				)
         return waiting_times


      def        a			(self	:				Optional[Any]    ,      a__	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       []
         for i in range(len(lowerCAmelCase__				)				):
            turnaround_times.append(queue[i].turnaround_time				)
         return turnaround_times


      def        a			(self	:				Union[str, Any]    ,      a__	:				int				):





         """simple docstring"""



         __snake_case								=       []
         for i in range(len(lowerCAmelCase__				)				):
            completion_times.append(queue[i].stop_time				)
         return completion_times


      def        a			(self	:				Dict    ,      a__	:				int				):





         """simple docstring"""



         return [q.burst_time for q in queue]


      def        a			(self	:				Union[str, Any]    ,      a__	:				Dict				):





         """simple docstring"""



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


      def        a			(self	:				Tuple    ,      a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       deque()  # sequence deque of finished process
         while len(lowerCAmelCase__				) != 0:
            __snake_case								=       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
            __snake_case								=       0
            # set the process's turnaround time because it is finished
            __snake_case								=       self.current_time - cp.arrival_time
            # set the completion time
            __snake_case								=       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        a			(self	:				List[Any]    ,      a__	:				int    ,      a__	:				Dict				):





         """simple docstring"""



         __snake_case								=       deque()  # sequence deque of terminated process
         # just for 1 cycle and unfinished processes will go back to queue
         for _ in range(len(lowerCAmelCase__				)				):
            __snake_case								=       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
               __snake_case								=       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
               __snake_case								=       0
               # set the finish time
               __snake_case								=       self.current_time
               # update the process' turnaround time because it is finished
               __snake_case								=       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        a			(self	:				Dict				):





         """simple docstring"""



         for i in range(self.number_of_queues - 1				):
            __snake_case								=       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_           =      Process('P1', 0, 53)
    snake_case_           =      Process('P2', 0, 17)
    snake_case_           =      Process('P3', 0, 68)
    snake_case_           =      Process('P4', 0, 24)
    snake_case_           =      3
    snake_case_           =      [17, 25]
    snake_case_           =      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_           =      Process('P1', 0, 53)
    snake_case_           =      Process('P2', 0, 17)
    snake_case_           =      Process('P3', 0, 68)
    snake_case_           =      Process('P4', 0, 24)
    snake_case_           =      3
    snake_case_           =      [17, 25]
    snake_case_           =      deque([Pa, Pa, Pa, Pa])
    snake_case_           =      MLFQ(number_of_queues, time_slices, queue, 0)
    snake_case_           =      mlfq.multi_level_feedback_queue()

    # print total waiting times of processes(P1, P2, P3, P4)
    print(
        F'waiting time:\\n        \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:\\n        \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:\\n        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'
    )
    # print sequence of finished processes
    print(
        F'sequence of finished processes:\\n        {mlfq.calculate_sequence_of_finish_queue()}'
    )










import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {'tokenizer_file': 'tokenizer.json'}

snake_case_           =      {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    List[str]							=   VOCAB_FILES_NAMES
      A_						:    Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    str							=   ['input_ids', 'attention_mask']
      A_						:    Optional[Any]							=   None


      def __init__(self	:				Optional[int]    ,      a__	:				int=None    ,      a__	:				str=None    ,      a__	:				Any=None    ,      a__	:				List[Any]="<unk>"    ,      a__	:				List[Any]="<s>"    ,      a__	:				Optional[int]="</s>"    ,      a__	:				List[str]="<pad>"    ,      a__	:				Union[str, Any]=False    ,      a__	:				str=False    ,      **a__	:				Optional[Any]    ,      ):





         """simple docstring"""



         super().__init__(
             a__    ,      a__    ,      tokenizer_file=a__    ,      unk_token=a__    ,      bos_token=a__    ,      eos_token=a__    ,      pad_token=a__    ,      add_prefix_space=a__    ,      clean_up_tokenization_spaces=a__    ,      **a__    ,      )
         __snake_case								=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
         if pre_tok_state.get('''add_prefix_space'''    ,      a__				) != add_prefix_space:
            __snake_case								=       getattr(a__    ,      pre_tok_state.pop('''type'''				)				)
            __snake_case								=       add_prefix_space
            __snake_case								=       pre_tok_class(**a__				)

         __snake_case								=       add_prefix_space


      def        a			(self	:				int    ,      *a__	:				Tuple    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       kwargs.get('''is_split_into_words'''    ,      a__				)
         if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.'''				)

         return super()._batch_encode_plus(*a__    ,      **a__				)


      def        a			(self	:				List[str]    ,      *a__	:				List[str]    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       kwargs.get('''is_split_into_words'''    ,      a__				)

         if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.'''				)

         return super()._encode_plus(*a__    ,      **a__				)


      def        a			(self	:				List[Any]    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         __snake_case								=       self._tokenizer.model.save(a__    ,      name=a__				)
         return tuple(a__				)


      def        a			(self	:				Tuple    ,      a__	:				"Conversation"				):





         """simple docstring"""



         __snake_case								=       []
         for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(a__    ,      add_special_tokens=a__				) + [self.eos_token_id]				)

         if len(a__				) > self.model_max_length:
            __snake_case								=       input_ids[-self.model_max_length :]
         return input_ids

