

from collections import deque






class 	UpperCAmelCase__		:






  """simple docstring"""


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






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		int     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		list[int]     ,       __lowerCamelCase						:		deque[Process]     ,       __lowerCamelCase						:		int     ,       )		->							None:
       # total number of mlfq's queues
       SCREAMING_SNAKE_CASE__   =   number_of_queues
       # time slice of queues that round robin algorithm applied
       SCREAMING_SNAKE_CASE__   =   time_slices
       # unfinished process is in this ready_queue
       SCREAMING_SNAKE_CASE__   =   queue
       # current time
       SCREAMING_SNAKE_CASE__   =   current_time
       # finished process is in this sequence queue
       SCREAMING_SNAKE_CASE__   =   deque()






  def 				lowercase_		(							self						:		Dict )		->							list[str]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(self.finish_queue ) ):
            sequence.append(self.finish_queue[i].process_name )
       return sequence






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		list[Process] )		->							list[int]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(__lowerCamelCase ) ):
            waiting_times.append(queue[i].waiting_time )
       return waiting_times






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		list[Process] )		->							list[int]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(__lowerCamelCase ) ):
            turnaround_times.append(queue[i].turnaround_time )
       return turnaround_times






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		list[Process] )		->							list[int]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(__lowerCamelCase ) ):
            completion_times.append(queue[i].stop_time )
       return completion_times






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		deque[Process] )		->							list[int]:
       return [q.burst_time for q in queue]






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Process )		->							int:
       process.waiting_time += self.current_time - process.stop_time
       return process.waiting_time






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		deque[Process] )		->							deque[Process]:
       SCREAMING_SNAKE_CASE__   =   deque()  # sequence deque of finished process
       while len(__lowerCamelCase ) != 0:
            SCREAMING_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
            SCREAMING_SNAKE_CASE__   =   0
            # set the process's turnaround time because it is finished
            SCREAMING_SNAKE_CASE__   =   self.current_time - cp.arrival_time
            # set the completion time
            SCREAMING_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 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		deque[Process]     ,       __lowerCamelCase						:		int )		->							tuple[deque[Process], deque[Process]]:
       SCREAMING_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 ) ):
            SCREAMING_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
                 SCREAMING_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
                 SCREAMING_SNAKE_CASE__   =   0
                 # set the finish time
                 SCREAMING_SNAKE_CASE__   =   self.current_time
                 # update the process' turnaround time because it is finished
                 SCREAMING_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 				lowercase_		(							self						:		int )		->							deque[Process]:

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

       _SCREAMING_SNAKE_CASE					:  str             =      Process('''P1''', 0, 53)
       _SCREAMING_SNAKE_CASE					:  Optional[int]             =      Process('''P2''', 0, 17)
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      Process('''P3''', 0, 68)
       _SCREAMING_SNAKE_CASE					:  Any             =      Process('''P4''', 0, 24)
       _SCREAMING_SNAKE_CASE					:  str             =      3
       _SCREAMING_SNAKE_CASE					:  Dict             =      [17, 25]
       _SCREAMING_SNAKE_CASE					:  Optional[int]             =      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])})

       _SCREAMING_SNAKE_CASE					:  List[Any]             =      Process('''P1''', 0, 53)
       _SCREAMING_SNAKE_CASE					:  Dict             =      Process('''P2''', 0, 17)
       _SCREAMING_SNAKE_CASE					:  Optional[int]             =      Process('''P3''', 0, 68)
       _SCREAMING_SNAKE_CASE					:  Dict             =      Process('''P4''', 0, 24)
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      3
       _SCREAMING_SNAKE_CASE					:  Any             =      [17, 25]
       _SCREAMING_SNAKE_CASE					:  Any             =      deque([Pa, Pa, Pa, Pa])
       _SCREAMING_SNAKE_CASE					:  Dict             =      MLFQ(number_of_queues, time_slices, queue, 0)
       _SCREAMING_SNAKE_CASE					:  str             =      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 argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_SCREAMING_SNAKE_CASE					:  Tuple             =      data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE					:  Dict             =      data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      data_utils
_SCREAMING_SNAKE_CASE					:  Any             =      data_utils
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(_A				,		'''rb'''    ) as fp:
               SCREAMING_SNAKE_CASE__   =   pickle.load(_A				,		encoding='''latin1'''    )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''    )
          SCREAMING_SNAKE_CASE__   =   corpus.vocab.__dict__
          torch.save(_A				,		_A    )

          SCREAMING_SNAKE_CASE__   =   corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''				,		_A    )
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F'''Save dataset to {pytorch_dataset_dump_path}'''    )
          torch.save(_A				,		_A    )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )

          print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig()
          else:
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig.from_json_file(_A    )
          print(F'''Building PyTorch model from configuration: {config}'''    )
          SCREAMING_SNAKE_CASE__   =   TransfoXLLMHeadModel(_A    )

          SCREAMING_SNAKE_CASE__   =   load_tf_weights_in_transfo_xl(_A				,		_A				,		_A    )
          # Save pytorch-model
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          print(F'''Save PyTorch model to {os.path.abspath(_A    )}'''    )
          torch.save(model.state_dict()				,		_A    )
          print(F'''Save configuration file to {os.path.abspath(_A    )}'''    )
          with open(_A				,		'''w'''				,		encoding='''utf-8'''    ) as f:
               f.write(config.to_json_string()    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Any             =      argparse.ArgumentParser()
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
       )
       parser.add_argument(
           '''--tf_checkpoint_path''',
           default='''''',
           type=str,
           help='''An optional path to a TensorFlow checkpoint path to be converted.''',
       )
       parser.add_argument(
           '''--transfo_xl_config_file''',
           default='''''',
           type=str,
           help=(
               '''An optional config json file corresponding to the pre-trained BERT model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--transfo_xl_dataset_file''',
           default='''''',
           type=str,
           help='''An optional dataset file to be converted in a vocabulary.''',
       )
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      parser.parse_args()
       convert_transfo_xl_checkpoint_to_pytorch(
           args.tf_checkpoint_path,
           args.transfo_xl_config_file,
           args.pytorch_dump_folder_path,
           args.transfo_xl_dataset_file,
       )


