






'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


UpperCAmelCase__						:Any          =			{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase__						:Union[str, Any]          =			{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase__						:Optional[int]          =			{
    """google/electra-small-generator""": 512,
    """google/electra-base-generator""": 512,
    """google/electra-large-generator""": 512,
    """google/electra-small-discriminator""": 512,
    """google/electra-base-discriminator""": 512,
    """google/electra-large-discriminator""": 512,
}

UpperCAmelCase__						:str          =			{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     int   		=			VOCAB_FILES_NAMES
       snake_case__    :     Optional[Any]   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     int   		=			PRETRAINED_INIT_CONFIGURATION
       snake_case__    :     Tuple   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     Optional[int]   		=			ElectraTokenizer




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


            """simple docstring"""







            super().__init__(
                A__	,    tokenizer_file=A__	,    do_lower_case=A__	,    unk_token=A__	,    sep_token=A__	,    pad_token=A__	,    cls_token=A__	,    mask_token=A__	,    tokenize_chinese_chars=A__	,    strip_accents=A__	,    **A__	,    )

            __lowerCamelCase						:       Union[str, Any]				   =				json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
            if (
                normalizer_state.get("""lowercase"""	,    A__					) != do_lower_case
                or normalizer_state.get("""strip_accents"""	,    A__					) != strip_accents
                or normalizer_state.get("""handle_chinese_chars"""	,    A__					) != tokenize_chinese_chars
            ):
                 __lowerCamelCase						:       Union[str, Any]				   =				getattr(A__	,    normalizer_state.pop("""type"""					)					)
                 __lowerCamelCase						:       Optional[int]				   =				do_lower_case
                 __lowerCamelCase						:       Optional[int]				   =				strip_accents
                 __lowerCamelCase						:       Union[str, Any]				   =				tokenize_chinese_chars
                 __lowerCamelCase						:       Union[str, Any]				   =				normalizer_class(**A__					)

            __lowerCamelCase						:       Dict				   =				do_lower_case




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


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

            if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

            return output




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


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				[self.sep_token_id]
            __lowerCamelCase						:       Any				   =				[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 					a_					(					self      : Tuple	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self._tokenizer.model.save(A__	,    name=A__					)
            return tuple(A__					)











'''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}''')



