

"""simple docstring"""




import os
import re
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 logging


__A	:       Union[str, Any]        =						logging.get_logger(__name__)
__A	:       List[str]        =						{'vocab_file': 'spiece.model'}

__A	:       Tuple        =						{
    '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',
    },
}

__A	:       int        =						{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

__A	:       List[str]        =						'▁'





class      __UpperCamelCase					(  _A		):
		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", "token_type_ids"]


		def __init__(self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Any=False	,	__SCREAMING_SNAKE_CASE					:						List[Any]=True	,	__SCREAMING_SNAKE_CASE					:						int=True	,	__SCREAMING_SNAKE_CASE					:						Tuple="<unk>"	,	__SCREAMING_SNAKE_CASE					:						List[str]="[SEP]"	,	__SCREAMING_SNAKE_CASE					:						int="<pad>"	,	__SCREAMING_SNAKE_CASE					:						int="[CLS]"	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]="[MASK]"	,	__SCREAMING_SNAKE_CASE					:						Optional[Dict[str, Any]] = None	,	**__SCREAMING_SNAKE_CASE					:						Dict	,	):
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							A     =   (
							    AddedToken(__SCREAMING_SNAKE_CASE	,	lstrip=__SCREAMING_SNAKE_CASE	,	rstrip=__SCREAMING_SNAKE_CASE	,	normalized=__SCREAMING_SNAKE_CASE)
							    if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							    else mask_token
							)

							A     =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=__SCREAMING_SNAKE_CASE	,	remove_space=__SCREAMING_SNAKE_CASE	,	keep_accents=__SCREAMING_SNAKE_CASE	,	unk_token=__SCREAMING_SNAKE_CASE	,	sep_token=__SCREAMING_SNAKE_CASE	,	pad_token=__SCREAMING_SNAKE_CASE	,	cls_token=__SCREAMING_SNAKE_CASE	,	mask_token=__SCREAMING_SNAKE_CASE	,	sp_model_kwargs=self.sp_model_kwargs	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   do_lower_case
							A     =   remove_space
							A     =   keep_accents
							A     =   vocab_file

							A     =   spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							return len(self.sp_model)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
							vocab.update(self.added_tokens_encoder)
							return vocab


		def __getstate__(self					:						int):
							A     =   self.__dict__.copy()
							A     =   None
							return state


		def __setstate__(self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[str]):
							A     =   d

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

							A     =   spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(self.vocab_file)


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						Any):
							if self.remove_space:
												A     =   " ".join(inputs.strip().split())
							else:
												A     =   inputs
							A     =   outputs.replace("``"	,	"\"").replace("''"	,	"\"")

							if not self.keep_accents:
												A     =   unicodedata.normalize("NFKD"	,	__SCREAMING_SNAKE_CASE)
												A     =   "".join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE)])
							if self.do_lower_case:
												A     =   outputs.lower()

							return outputs


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						str):
							A     =   self.preprocess_text(__SCREAMING_SNAKE_CASE)
							A     =   self.sp_model.encode(__SCREAMING_SNAKE_CASE	,	out_type=__SCREAMING_SNAKE_CASE)
							A     =   []
							for piece in pieces:
												if len(__SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
																	A     =   self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE	,	""))
																	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																						if len(cur_pieces[0]) == 1:
																											A     =   cur_pieces[1:]
																						else:
																											A     =   cur_pieces[0][1:]
																	cur_pieces.append(piece[-1])
																	new_pieces.extend(__SCREAMING_SNAKE_CASE)
												else:
																	new_pieces.append(__SCREAMING_SNAKE_CASE)

							return new_pieces


		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	__SCREAMING_SNAKE_CASE					:						List[Any]):
							return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						int):
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						List[Any]):
							A     =   []
							A     =   ""
							A     =   False
							for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																	if not prev_is_special:
																						out_string += " "
																	out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE) + token
																	A     =   True
																	A     =   []
												else:
																	current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
																	A     =   False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
							return out_string.strip()


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						bool = False	,	__SCREAMING_SNAKE_CASE					:						bool = None	,	__SCREAMING_SNAKE_CASE					:						bool = True	,	**__SCREAMING_SNAKE_CASE					:						Tuple	,	):
							A     =   kwargs.pop("use_source_tokenizer"	,	__SCREAMING_SNAKE_CASE)

							A     =   self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE	,	skip_special_tokens=__SCREAMING_SNAKE_CASE)

							# 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
							A     =   []
							A     =   []
							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(__SCREAMING_SNAKE_CASE))
																						A     =   []
																	sub_texts.append(__SCREAMING_SNAKE_CASE)
												else:
																	current_sub_text.append(__SCREAMING_SNAKE_CASE)
							if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(__SCREAMING_SNAKE_CASE))

							# Mimic the behavior of the Rust tokenizer:
							# No space after <unk>
							if spaces_between_special_tokens:
												A     =   re.sub(R"(<unk>) "	,	R"\1"	,	" ".join(__SCREAMING_SNAKE_CASE))
							else:
												A     =   "".join(__SCREAMING_SNAKE_CASE)

							A     =   (
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
												A     =   self.clean_up_tokenization(__SCREAMING_SNAKE_CASE)
												return clean_text
							else:
												return text


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[List[int]] = None):
							A     =   [self.sep_token_id]
							A     =   [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    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[List[int]] = None	,	__SCREAMING_SNAKE_CASE					:						bool = False):

							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__SCREAMING_SNAKE_CASE	,	token_ids_a=__SCREAMING_SNAKE_CASE	,	already_has_special_tokens=__SCREAMING_SNAKE_CASE)

							if token_ids_a is not None:
												return [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[List[int]] = None):
							A     =   [self.sep_token_id]
							A     =   [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    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Optional[str] = None):
							if not os.path.isdir(__SCREAMING_SNAKE_CASE):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
												return
							A     =   os.path.join(
							    __SCREAMING_SNAKE_CASE	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

							if os.path.abspath(self.vocab_file) != os.path.abspath(__SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
												copyfile(self.vocab_file	,	__SCREAMING_SNAKE_CASE)
							elif not os.path.isfile(self.vocab_file):
												with open(__SCREAMING_SNAKE_CASE	,	"wb") as fi:
																	A     =   self.sp_model.serialized_model_proto()
																	fi.write(__SCREAMING_SNAKE_CASE)

							return (out_vocab_file,)






from __future__ import annotations

from statistics import mean

def   a_					(_lowerCAmelCase  :   list[int]       )->				list[int]:
    snake_case:						Tuple         =   2
    snake_case:						str         =   list(_lowerCAmelCase       )
    snake_case:						Union[str, Any]         =   [0] * len(_lowerCAmelCase       )
    snake_case:						List[Any]         =   0
    while True:
        snake_case:						Optional[int]         =   True
        for i, burst_time in enumerate(_lowerCAmelCase       ):
            if rem_burst_times[i] > 0:
                snake_case:						Dict         =   False
                if rem_burst_times[i] > quantum:
                    t += quantum
                    rem_burst_times[i] -= quantum
                else:
                    t += rem_burst_times[i]
                    snake_case:						Optional[Any]         =   t - burst_time
                    snake_case:						Tuple         =   0
        if done is True:
            return waiting_times

def   a_					(_lowerCAmelCase  :   list[int]			,	_lowerCAmelCase  :   list[int]       )->				list[int]:
    return [burst + waiting for burst, waiting in zip(_lowerCAmelCase			,	_lowerCAmelCase       )]


if __name__ == "__main__":
    __lowerCAmelCase    :  List[Any]										= [3, 5, 7]
    __lowerCAmelCase    :  List[Any]										= calculate_waiting_times(burst_times)
    __lowerCAmelCase    :  str										= calculate_turn_around_times(burst_times, waiting_times)
    print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
    for i, burst_time in enumerate(burst_times):
        print(
            F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
            F"""{turn_around_times[i]}"""
        )
    print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
    print(F"""Average turn around time = {mean(turn_around_times):.5f}""")


