





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_fnet import FNetTokenizer
else:
							_SCREAMING_SNAKE_CASE      	=		None

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE      	=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE      	=		{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_SCREAMING_SNAKE_CASE      	=		{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_SCREAMING_SNAKE_CASE      	=		"""▁"""
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Union[str, Any]       =       VOCAB_FILES_NAMES
						__magic_name__:			Any       =       PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:			Optional[Any]       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:			List[Any]       =       ["input_ids", "token_type_ids"]
						__magic_name__:			Optional[Any]       =       FNetTokenizer






						def __init__(      self      :      Dict		,   _A      :      Union[str, Any]=None		,   _A      :      Tuple=None		,   _A      :      Dict=False		,   _A      :      List[str]=True		,   _A      :      Union[str, Any]=True		,   _A      :      Optional[int]="<unk>"		,   _A      :      List[Any]="[SEP]"		,   _A      :      Dict="<pad>"		,   _A      :      Tuple="[CLS]"		,   _A      :      List[str]="[MASK]"		,   **_A      :      str		,   )							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	(
							    AddedToken(_A		,   lstrip=_A		,   rstrip=_A		,   normalized=_A	)
							    if isinstance(_A		,   _A	)
							    else mask_token
							)

							super().__init__(
							    _A		,   tokenizer_file=_A		,   do_lower_case=_A		,   remove_space=_A		,   keep_accents=_A		,   unk_token=_A		,   sep_token=_A		,   pad_token=_A		,   cls_token=_A		,   mask_token=_A		,   **_A		,   )

							snake_case_		:			Tuple											=	do_lower_case
							snake_case_		:			str											=	remove_space
							snake_case_		:			int											=	keep_accents
							snake_case_		:			Optional[Any]											=	vocab_file
							snake_case_		:			List[Any]											=	False if not self.vocab_file else True






						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	[self.sep_token_id]
							snake_case_		:			Union[str, Any]											=	[self.cls_token_id]
							if token_ids_a is None:
								return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	[self.sep_token_id]
							snake_case_		:			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	) * [0] + len(token_ids_a + sep	) * [1]






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      str		,   _A      :      Optional[str] = None	)							->    Tuple[str]:







							"""simple docstring"""


							if not os.path.isdir(_A	):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
								return
							snake_case_		:			List[str]											=	os.path.join(
							    _A		,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

							if os.path.abspath(self.vocab_file	) != os.path.abspath(_A	):
								copyfile(self.vocab_file		,   _A	)

							return (out_vocab_file,)








from statistics import mean

import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	0
	# Number of processes finished
	snake_case_		:			List[str]											=	0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	snake_case_		:			str											=	[0] * no_of_process
	# List to include calculation results
	snake_case_		:			Optional[int]											=	[0] * no_of_process

	# Sort by arrival time.
	snake_case_		:			str											=	[burst_time[i] for i in np.argsort(__a   )]
	snake_case_		:			str											=	[process_name[i] for i in np.argsort(__a   )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		snake_case_		:			int											=	0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			snake_case_		:			str											=	arrival_time[i]

		snake_case_		:			Optional[Any]											=	0
		# Index showing the location of the process being performed
		snake_case_		:			Tuple											=	0
		# Saves the current response ratio.
		snake_case_		:			List[Any]											=	0
		for i in range(0   , __a   ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				snake_case_		:			Optional[Any]											=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				snake_case_		:			Optional[int]											=	temp
				snake_case_		:			Optional[Any]											=	i

        # Calculate the turn around time
		snake_case_		:			Any											=	current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		snake_case_		:			Optional[Any]											=	1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	[0] * no_of_process
	for i in range(0   , __a   ):
		snake_case_		:			Optional[int]											=	turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		5
						_SCREAMING_SNAKE_CASE      	=		["""A""", """B""", """C""", """D""", """E"""]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]

						_SCREAMING_SNAKE_CASE      	=		calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						_SCREAMING_SNAKE_CASE      	=		calculate_waiting_time(
						    process_name, turn_around_time, burst_time, no_of_process
						)

						print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
						for i in range(0, no_of_process):
												print(
												    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
												    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
												)

						print(F'''average waiting time : {mean(waiting_time):.5f}''')
						print(F'''average turn around time : {mean(turn_around_time):.5f}''')

