




'''simple docstring'''
from __future__ import annotations

from statistics import mean
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    __lowercase     							=		2
    __lowercase     							=		list(lowerCamelCase_     )
    __lowercase     							=		[0] * len(lowerCamelCase_     )
    __lowercase     							=		0
    while True:
        __lowercase     							=		True
        for i, burst_time in enumerate(lowerCamelCase_     ):
            if rem_burst_times[i] > 0:
                __lowercase     							=		False
                if rem_burst_times[i] > quantum:
                    t += quantum
                    rem_burst_times[i] -= quantum
                else:
                    t += rem_burst_times[i]
                    __lowercase     							=		t - burst_time
                    __lowercase     							=		0
        if done is True:
            return waiting_times
def 							snake_case_    (  a__						:      Optional[int]       ,a__						:      Optional[int]     ):



    """simple docstring"""




    return [burst + waiting for burst, waiting in zip(lowerCamelCase_       ,lowerCamelCase_     )]


if __name__ == "__main__":
  A				: List[Any]					      =			[3, 5, 7]
  A				: Optional[int]					      =			calculate_waiting_times(burst_times)
  A				: int					      =			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}""")












"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a							:						Union[str, Any]   							=			logging.get_logger(__name__)

__a							:						Optional[Any]   							=			{
    'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/config.json',
    'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json',
    'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/config.json',
    'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     ='splinter'




							def __init__( self:  Dict					,		__A:  List[Any]=30522					,		__A:  str=768					,		__A:  int=12					,		__A:  str=12					,		__A:  Tuple=3072					,		__A:  Dict="gelu"					,		__A:  Optional[int]=0.1					,		__A:  List[str]=0.1					,		__A:  str=512					,		__A:  List[Any]=2					,		__A:  Any=0.0_2					,		__A:  Any=1e-12					,		__A:  Tuple=True					,		__A:  List[Any]=0					,		__A:  List[str]=104					,		**__A:  Union[str, Any]					,		):

												'''simple docstring'''

												super().__init__(pad_token_id=__A					,		**__A     )

												a__ 							=	vocab_size
												a__ 							=	max_position_embeddings
												a__ 							=	hidden_size
												a__ 							=	num_hidden_layers
												a__ 							=	num_attention_heads
												a__ 							=	intermediate_size
												a__ 							=	hidden_act
												a__ 							=	hidden_dropout_prob
												a__ 							=	attention_probs_dropout_prob
												a__ 							=	initializer_range
												a__ 							=	type_vocab_size
												a__ 							=	layer_norm_eps
												a__ 							=	use_cache
												a__ 							=	question_token_id


