





'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)


__UpperCAmelCase            =       """▁"""

__UpperCAmelCase            =       {"""vocab_file""": """spiece.model"""}

__UpperCAmelCase            =       {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

__UpperCAmelCase            =       {
    """google/reformer-crime-and-punishment""": 524288,
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE__				=	['''input_ids''', '''attention_mask''']


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]      ,						lowerCamelCase_				:			Optional[Any]="</s>"      ,						lowerCamelCase_				:			List[Any]="<unk>"      ,						lowerCamelCase_				:			List[Any]=[]      ,						lowerCamelCase_				:			Optional[Dict[str, Any]] = None      ,						**lowerCamelCase_				:			Tuple      ,						):

							'''simple docstring'''



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

							super().__init__(
							    eos_token=lowerCamelCase_      ,						unk_token=lowerCamelCase_      ,						additional_special_tokens=lowerCamelCase_      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**lowerCamelCase_      ,						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_file
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(lowerCamelCase_						)


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

							'''simple docstring'''



							return self.sp_model.get_piece_size()


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			{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				:			Tuple						):

							'''simple docstring'''



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


					def __setstate__(						self				:			Tuple      ,						lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			d

							# for backward compatibility
							if not hasattr(self      ,						"""sp_model_kwargs"""						):
									SCREAMING_SNAKE_CASE :				Optional[int]		=			{}

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(self.vocab_file						)


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							return self.sp_model.encode(lowerCamelCase_      ,						out_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Optional[int]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							return self.sp_model.piece_to_id(lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							if index < self.sp_model.get_piece_size():
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.sp_model.IdToPiece(lowerCamelCase_						)
							return token


					def lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			""""""
							for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
											out_string += self.sp_model.decode(lowerCamelCase_						) + token
											SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
									else:
											current_sub_tokens.append(lowerCamelCase_						)
							out_string += self.sp_model.decode(lowerCamelCase_						)
							return out_string.strip()



					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						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
							SCREAMING_SNAKE_CASE :				Dict		=			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:
											SCREAMING_SNAKE_CASE :				Any		=			self.sp_model.serialized_model_proto()
											fi.write(lowerCamelCase_						)

							return (out_vocab_file,)










'''simple docstring'''




from __future__ import annotations

from statistics import mean



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			[0] * no_of_processes
		SCREAMING_SNAKE_CASE :				int		=			[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[int]		=			burst_time[i]
		SCREAMING_SNAKE_CASE :				list[int]		=			[]

		SCREAMING_SNAKE_CASE :				List[str]		=			0
		SCREAMING_SNAKE_CASE :				List[Any]		=			0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
				SCREAMING_SNAKE_CASE :				Any		=			-1

				for i in range(lowerCamelCase_	):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase_	)

				if len(lowerCamelCase_	) > 0:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										SCREAMING_SNAKE_CASE :				Any		=			i
						total_time += burst_time[target_process]
						completed += 1
						SCREAMING_SNAKE_CASE :				Any		=			0
						SCREAMING_SNAKE_CASE :				Optional[int]		=			(
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			[0] * no_of_processes
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			burst_time[i] + waiting_time[i]
		return turn_around_time


if __name__ == "__main__":
							print("""[TEST CASE 01]""")

							__UpperCAmelCase            =       4
							__UpperCAmelCase            =       [2, 5, 3, 7]
							__UpperCAmelCase            =       [0, 0, 0, 0]
							__UpperCAmelCase            =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
							__UpperCAmelCase            =       calculate_turnaroundtime(
							    burst_time, no_of_processes, waiting_time
							)

							# Printing the Result
							print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
							for i, process_id in enumerate(list(range(1, 5))):
														print(
														    f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
														    f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
														)
							print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
							print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')



