


import os
import unicodedata
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 SPIECE_UNDERLINE, logging


A      :    str		=					logging.get_logger(__name__)

A      :    Union[str, Any]		=					{'''vocab_file''': '''spiece.model'''}

A      :    Any		=					{
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

A      :    List[str]		=					{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
A      :    Dict		=					0
A      :    Any		=					1
A      :    str		=					2
A      :    Optional[int]		=					3
A      :    str		=					4







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			VOCAB_FILES_NAMES
       A__											=			PRETRAINED_VOCAB_FILES_MAP
       A__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A__											=			'''left'''





       def __init__(   self							:			Optional[Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Optional[int]=False					,      __lowerCamelCase							:			int=True					,      __lowerCamelCase							:			List[str]=False					,      __lowerCamelCase							:			int="<s>"					,      __lowerCamelCase							:			Dict="</s>"					,      __lowerCamelCase							:			Optional[Any]="<unk>"					,      __lowerCamelCase							:			Union[str, Any]="<sep>"					,      __lowerCamelCase							:			Optional[Any]="<pad>"					,      __lowerCamelCase							:			Union[str, Any]="<cls>"					,      __lowerCamelCase							:			Optional[Any]="<mask>"					,      __lowerCamelCase							:			Dict=["<eop>", "<eod>"]					,      __lowerCamelCase							:			Optional[Dict[str, Any]] = None					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  AddedToken(__lowerCAmelCase					,      lstrip=__lowerCAmelCase					,      rstrip=__lowerCAmelCase       ) if isinstance(__lowerCAmelCase					,      __lowerCAmelCase       ) else mask_token

             lowerCamelCase__		:				str    =  {} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 do_lower_case=__lowerCAmelCase					,      remove_space=__lowerCAmelCase					,      keep_accents=__lowerCAmelCase					,      bos_token=__lowerCAmelCase					,      eos_token=__lowerCAmelCase					,      unk_token=__lowerCAmelCase					,      sep_token=__lowerCAmelCase					,      pad_token=__lowerCAmelCase					,      cls_token=__lowerCAmelCase					,      mask_token=__lowerCAmelCase					,      additional_special_tokens=__lowerCAmelCase					,      sp_model_kwargs=self.sp_model_kwargs					,      **__lowerCAmelCase					,      )

             lowerCamelCase__		:				Optional[Any]    =  3

             lowerCamelCase__		:				List[Any]    =  do_lower_case
             lowerCamelCase__		:				Optional[int]    =  remove_space
             lowerCamelCase__		:				Any    =  keep_accents
             lowerCamelCase__		:				Tuple    =  vocab_file

             lowerCamelCase__		:				Dict    =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
             self.sp_model.Load(__lowerCAmelCase       )





       @property
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             return len(self.sp_model       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  {self.convert_ids_to_tokens(__lowerCAmelCase       ): i for i in range(self.vocab_size       )}
             vocab.update(self.added_tokens_encoder       )
             return vocab





       def __getstate__(   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.__dict__.copy()
             lowerCamelCase__		:				Union[str, Any]    =  None
             return state





       def __setstate__(   self							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  d

             # for backward compatibility
             if not hasattr(self					,      "sp_model_kwargs"       ):
                   lowerCamelCase__		:				str    =  {}

             lowerCamelCase__		:				str    =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
             self.sp_model.Load(self.vocab_file       )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             if self.remove_space:
                   lowerCamelCase__		:				str    =  " ".join(inputs.strip().split()       )
             else:
                   lowerCamelCase__		:				int    =  inputs
             lowerCamelCase__		:				Dict    =  outputs.replace("``"					,      "\""       ).replace("''"					,      "\""       )

             if not self.keep_accents:
                   lowerCamelCase__		:				Dict    =  unicodedata.normalize("NFKD"					,      __lowerCAmelCase       )
                   lowerCamelCase__		:				List[Any]    =  "".join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase       )]       )
             if self.do_lower_case:
                   lowerCamelCase__		:				str    =  outputs.lower()

             return outputs





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.preprocess_text(__lowerCAmelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  self.sp_model.encode(__lowerCAmelCase					,      out_type=__lowerCAmelCase       )
             lowerCamelCase__		:				Tuple    =  []
             for piece in pieces:
                   if len(__lowerCAmelCase       ) > 1 and piece[-1] == str(","       ) and piece[-2].isdigit():
                         lowerCamelCase__		:				Any    =  self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase					,      ""       )       )
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                               if len(cur_pieces[0]       ) == 1:
                                     lowerCamelCase__		:				Union[str, Any]    =  cur_pieces[1:]
                               else:
                                     lowerCamelCase__		:				Optional[Any]    =  cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]       )
                         new_pieces.extend(__lowerCAmelCase       )
                   else:
                         new_pieces.append(__lowerCAmelCase       )

             return new_pieces





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             return self.sp_model.PieceToId(__lowerCAmelCase       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             return self.sp_model.IdToPiece(__lowerCAmelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  "".join(__lowerCAmelCase       ).replace(__lowerCAmelCase					,      " "       ).strip()
             return out_string





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			List[int]					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = None					,      __lowerCamelCase							:			bool = True					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  kwargs.pop("use_source_tokenizer"					,      __lowerCAmelCase       )

             lowerCamelCase__		:				Any    =  self.convert_ids_to_tokens(__lowerCAmelCase					,      skip_special_tokens=__lowerCAmelCase       )

             # To avoid mixing byte-level and unicode for byte-level BPT
             # we need to build string separately for added tokens and byte-level tokens
             # cf. https://github.com/huggingface/transformers/issues/1133
             lowerCamelCase__		:				Tuple    =  []
             lowerCamelCase__		:				str    =  []
             for token in filtered_tokens:
                   if skip_special_tokens and token in self.all_special_ids:
                         continue
                   if token in self.added_tokens_encoder:
                         if current_sub_text:
                               sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase       )       )
                               lowerCamelCase__		:				List[str]    =  []
                         sub_texts.append(__lowerCAmelCase       )
                   else:
                         current_sub_text.append(__lowerCAmelCase       )
             if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase       )       )

             # Mimic the behavior of the Rust tokenizer:
             # By default, there are no spaces between special tokens
             lowerCamelCase__		:				str    =  "".join(__lowerCAmelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  (
                 clean_up_tokenization_spaces
                 if clean_up_tokenization_spaces is not None
                 else self.clean_up_tokenization_spaces
             )
             if clean_up_tokenization_spaces:
                   lowerCamelCase__		:				Tuple    =  self.clean_up_tokenization(__lowerCAmelCase       )
                   return clean_text
             else:
                   return text





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			List[int]					,      __lowerCamelCase							:			Optional[List[int]] = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  [self.sep_token_id]
             lowerCamelCase__		:				int    =  [self.cls_token_id]
             if token_ids_a is None:
                   return token_ids_a + sep + cls
             return token_ids_a + sep + token_ids_a + sep + cls





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			List[int]					,      __lowerCamelCase							:			Optional[List[int]] = None					,      __lowerCamelCase							:			bool = False       ):


             '''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 not None:
                   return ([0] * len(__lowerCAmelCase       )) + [1] + ([0] * len(__lowerCAmelCase       )) + [1, 1]
             return ([0] * len(__lowerCAmelCase       )) + [1, 1]





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			List[int]					,      __lowerCamelCase							:			Optional[List[int]] = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  [self.sep_token_id]
             lowerCamelCase__		:				List[Any]    =  [2]

             if token_ids_a is None:
                   return len(token_ids_a + sep       ) * [0] + cls_segment_id
             return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[str] = None       ):


             '''simple docstring'''



             if not os.path.isdir(__lowerCAmelCase       ):
                   logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
                   return
             lowerCamelCase__		:				Optional[int]    =  os.path.join(
                 __lowerCAmelCase					,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
                         lowerCamelCase__		:				Tuple    =  self.sp_model.serialized_model_proto()
                         fi.write(__lowerCAmelCase       )

             return (out_vocab_file,)









from collections import deque


class   A			:
			'''simple docstring'''




			def __init__(			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					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   A			:
			'''simple docstring'''




			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    list[int]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int							,	)	->	None:







					"""simple docstring"""






					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    a_       (			self   :    Dict   )	->	list[str]:







					"""simple docstring"""






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






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






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






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






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






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






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






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    deque[Process]   )	->	list[int]:







					"""simple docstring"""






					return [q.burst_time for q in queue]






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Process   )	->	int:







					"""simple docstring"""






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






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    deque[Process]   )	->	deque[Process]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of finished process
					while len(__lowerCAmelCase   ) != 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(__lowerCAmelCase   )
							# 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(__lowerCAmelCase   )

					self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
					# FCFS will finish all remaining processes
					return finished






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int   )	->	tuple[deque[Process], deque[Process]]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of terminated process
					# just for 1 cycle and unfinished processes will go back to queue
					for _ in range(len(__lowerCAmelCase   )   ):
							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(__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
									A__        =   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
									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(__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   :    List[Any]   )	->	deque[Process]:







					"""simple docstring"""






					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

					A    :					Union[str, Any] =      Process('''P1''', 0, 5_3)
					A    :					Optional[Any] =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					int =      Process('''P4''', 0, 2_4)
					A    :					Any =      3
					A    :					List[Any] =      [1_7, 2_5]
					A    :					Optional[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])})

					A    :					Optional[Any] =      Process('''P1''', 0, 5_3)
					A    :					int =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					Tuple =      Process('''P4''', 0, 2_4)
					A    :					Union[str, Any] =      3
					A    :					Optional[Any] =      [1_7, 2_5]
					A    :					Tuple =      deque([Pa, Pa, Pa, Pa])
					A    :					Optional[int] =      MLFQ(number_of_queues, time_slices, queue, 0)
					A    :					Dict =      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()}'''
					)



