






'''simple docstring'''

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

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


UpperCAmelCase__						:Optional[int]          =			logging.get_logger(__name__)

UpperCAmelCase__						:Tuple          =			"""▁"""

UpperCAmelCase__						:Any          =			{"""vocab_file""": """prophetnet.tokenizer"""}

UpperCAmelCase__						:Tuple          =			{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

UpperCAmelCase__						:List[Any]          =			{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

UpperCAmelCase__						:List[str]          =			{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}
def     __lowercase  (_lowercase     )   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				collections.OrderedDict()
     with open(__A,						"""r""",						encoding="""utf-8"""     ) as reader:
          __lowerCamelCase						:       List[Any]				   =				reader.readlines()
     for index, token in enumerate(__A     ):
          __lowerCamelCase						:       Optional[int]				   =				token.rstrip("""\n"""     )
          __lowerCamelCase						:       str				   =				index
     return vocab







class 	SCREAMING_SNAKE_CASE	(      _snake_case  ):
       snake_case__    :     Any   		=			VOCAB_FILES_NAMES
       snake_case__    :     Union[str, Any]   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     str   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     Union[str, Any]   		=			['input_ids', 'attention_mask']




       def __init__(					self      : List[str]	,    A__      : Any	,    A__      : Optional[Any]="[SEP]"	,    A__      : Any="[SEP]"	,    A__      : Tuple="[SEP]"	,    A__      : List[Any]="[UNK]"	,    A__      : Dict="[PAD]"	,    A__      : Tuple="[CLS]"	,    A__      : str="[MASK]"	,    A__      : Optional[Dict[str, Any]] = None	,    **A__      : int	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=lowerCAmelCase__	,    eos_token=lowerCAmelCase__	,    sep_token=lowerCAmelCase__	,    unk_token=lowerCAmelCase__	,    pad_token=lowerCAmelCase__	,    cls_token=lowerCAmelCase__	,    mask_token=lowerCAmelCase__	,    sp_model_kwargs=self.sp_model_kwargs	,    **lowerCAmelCase__	,    )

            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
                     """ pip install sentencepiece"""					)
                 raise

            __lowerCamelCase						:       int				   =				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(str(lowerCAmelCase__					)					)
            __lowerCamelCase						:       List[Any]				   =				vocab_file

            # Original fairseq vocab and spm vocab must be "aligned":
            # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
            # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
            # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
            # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

            # put special tokens and [unused] tokens into the vocab
            __lowerCamelCase						:       List[Any]				   =				{"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

            for i in range(10					):
                 __lowerCamelCase						:       Tuple				   =				f"[unused{i}]"
                 __lowerCamelCase						:       Optional[Any]				   =				5 + i

            # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
            __lowerCamelCase						:       List[str]				   =				12
            __lowerCamelCase						:       Union[str, Any]				   =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
            for k in self.fairseq_tokens_to_ids.keys():
                 self.unique_no_split_tokens.append(lowerCAmelCase__					)




       def __getstate__(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.__dict__.copy()
            __lowerCamelCase						:       str				   =				None
            return state




       def __setstate__(					self      : List[Any]	,    A__      : str					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				d
            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
                     """ pip install sentencepiece"""					)
                 raise

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

            __lowerCamelCase						:       List[str]				   =				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)




       def 					a_					(					self      : Optional[Any]	,    A__      : List[int]	,    A__      : Optional[List[int]] = None	,    A__      : 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 None:
                 return ([0] * len(lowerCAmelCase__					)) + [1]
            return ([0] * len(lowerCAmelCase__					)) + [1] + ([0] * len(lowerCAmelCase__					)) + [1]




       def 					a_					(					self      : int	,    A__      : List[int]	,    A__      : Optional[List[int]] = None					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				[self.sep_token_id]

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




       @property
       def 					a_					(					self      : int					):


            """simple docstring"""







            return len(self.sp_model					) + self.fairseq_offset




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				{self.convert_ids_to_tokens(lowerCAmelCase__					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab




       def 					a_					(					self      : Optional[Any]	,    A__      : str					):


            """simple docstring"""







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




       def 					a_					(					self      : Any	,    A__      : int					):


            """simple docstring"""







            if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
            __lowerCamelCase						:       Tuple				   =				self.sp_model.PieceToId(lowerCAmelCase__					)

            # Need to return unknown token if the SP model returned 0
            return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




       def 					a_					(					self      : str	,    A__      : int					):


            """simple docstring"""







            if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(index - self.fairseq_offset					)




       def 					a_					(					self      : Union[str, Any]	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				"""""".join(lowerCAmelCase__					).replace(lowerCAmelCase__	,    """ """					).strip()
            return out_string




       def 					a_					(					self      : Any	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







            if not os.path.isdir(lowerCAmelCase__					):
                 logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
                 return
            __lowerCamelCase						:       List[Any]				   =				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						:       Optional[Any]				   =				self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__					)

            return (out_vocab_file,)




       def 					a_					(					self      : Dict	,    A__      : List[int]	,    A__      : Optional[List[int]] = None					):


            """simple docstring"""







            if token_ids_a is None:
                 return token_ids_a + [self.sep_token_id]
            __lowerCamelCase						:       Any				   =				[self.sep_token_id]
            return token_ids_a + sep + token_ids_a + sep











'''simple docstring'''

from statistics import mean

import numpy as np
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				0
     # Number of processes finished
     __lowerCamelCase						:       List[Any]				   =				0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     __lowerCamelCase						:       int				   =				[0] * no_of_process
     # List to include calculation results
     __lowerCamelCase						:       Optional[int]				   =				[0] * no_of_process

     # Sort by arrival time.
     __lowerCamelCase						:       int				   =				[burst_time[i] for i in np.argsort(_lowercase     )]
     __lowerCamelCase						:       Union[str, Any]				   =				[process_name[i] for i in np.argsort(_lowercase     )]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          __lowerCamelCase						:       Tuple				   =				0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               __lowerCamelCase						:       List[str]				   =				arrival_time[i]

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

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

     return turn_around_time
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				[0] * no_of_process
     for i in range(0,						_lowercase     ):
          __lowerCamelCase						:       Tuple				   =				turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
   UpperCAmelCase__						:List[Any]          =			5
   UpperCAmelCase__						:Tuple          =			["""A""", """B""", """C""", """D""", """E"""]
   UpperCAmelCase__						:Tuple          =			[1, 2, 3, 4, 5]
   UpperCAmelCase__						:List[Any]          =			[1, 2, 3, 4, 5]

   UpperCAmelCase__						:Union[str, Any]          =			calculate_turn_around_time(
       process_name, arrival_time, burst_time, no_of_process
   )
   UpperCAmelCase__						:Optional[int]          =			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}''')



