






'''simple docstring'''







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_xlm_roberta import XLMRobertaTokenizer
else:
       __A   :							Union[str, Any]     	=						None


__A   :							Tuple     	=						logging.get_logger(__name__)

__A   :							Any     	=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A   :							Dict     	=						{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

__A   :							List[Any]     	=						{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	VOCAB_FILES_NAMES
     _snake_case							   =	PRETRAINED_VOCAB_FILES_MAP
     _snake_case							   =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case							   =	['input_ids', 'attention_mask']
     _snake_case							   =	XLMRobertaTokenizer






     def __init__(self     :						Optional[int]					,					lowercase__     :						Tuple=None					,					lowercase__     :						List[str]=None					,					lowercase__     :						Optional[Any]="<s>"					,					lowercase__     :						str="</s>"					,					lowercase__     :						Union[str, Any]="</s>"					,					lowercase__     :						Optional[Any]="<s>"					,					lowercase__     :						Union[str, Any]="<unk>"					,					lowercase__     :						List[Any]="<pad>"					,					lowercase__     :						Tuple="<mask>"					,					**lowercase__     :						Optional[Any]					,					)	->							Any:
           # Mask token behave like a normal word, i.e. include the space before it
           __A		 =   AddedToken(lowercase__					,					lstrip=lowercase__					,					rstrip=lowercase__       ) if isinstance(lowercase__					,					lowercase__       ) else mask_token

           super().__init__(
               lowercase__					,					tokenizer_file=lowercase__					,					bos_token=lowercase__					,					eos_token=lowercase__					,					sep_token=lowercase__					,					cls_token=lowercase__					,					unk_token=lowercase__					,					pad_token=lowercase__					,					mask_token=lowercase__					,					**lowercase__					,					)

           __A		 =   vocab_file
           __A		 =   False if not self.vocab_file else True






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

           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           __A		 =   [self.cls_token_id]
           __A		 =   [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep






     def      _UpperCamelCase (self     :						Optional[int]					,					lowercase__     :						List[int]					,					lowercase__     :						Optional[List[int]] = None       )	->							List[int]:
           __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 + sep + token_ids_a + sep       ) * [0]






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						str					,					lowercase__     :						Optional[str] = None       )	->							Tuple[str]:
           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                     "tokenizer."       )

           if not os.path.isdir(lowercase__       ):
                 logger.error(f"Vocabulary path ({save_directory}) should be a directory."       )
                 return
           __A		 =   os.path.join(
               lowercase__					,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

           return (out_vocab_file,)










'''simple docstring'''







from __future__ import annotations


def     _UpperCamelCase				(  snake_case__				):
      __A		 =   [0] * len(snake_case__				)
      for i in range(1				,      len(snake_case__				)				):
            __A		 =   duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(snake_case__				)
      ]


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


if __name__ == "__main__":
       # process id's
       __A   :							List[str]     	=						[1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print('Zero amount of processes')
              raise SystemExit(0)

       # duration time of all processes
       __A   :							List[Any]     	=						[1_9, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print('Unable to match all id\'s with their duration time')
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       __A   :							Dict     	=						calculate_waiting_times(duration_times)
       __A   :							Optional[int]     	=						calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       __A   :							Dict     	=						calculate_average_waiting_time(waiting_times)
       __A   :							Tuple     	=						calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
       for i, process in enumerate(processes):
              print(
                  F"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
                  F"""{turnaround_times[i]}"""
              )
       print(F"""Average waiting time = {average_waiting_time}""")
       print(F"""Average turn around time = {average_turnaround_time}""")


