
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a   :      Optional[Any]       =				logging.get_logger(__name__)

_a   :      Dict       =				{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Tuple	=       """electra"""
    def __init__(     self    ,     snake_case_=3_05_22    ,     snake_case_=1_28    ,     snake_case_=2_56    ,     snake_case_=12    ,     snake_case_=4    ,     snake_case_=10_24    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=2    ,     snake_case_=0.02    ,     snake_case_=1E-12    ,     snake_case_="first"    ,     snake_case_=True    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0    ,     snake_case_="absolute"    ,     snake_case_=True    ,     snake_case_=None    ,     **snake_case_    ,     ):
      super().__init__(pad_token_id=snake_case_    ,     **snake_case_  )

      _snake_case  :    List[Any]            = vocab_size
      _snake_case  :    int            = embedding_size
      _snake_case  :    Optional[int]            = hidden_size
      _snake_case  :    int            = num_hidden_layers
      _snake_case  :    int            = num_attention_heads
      _snake_case  :    Union[str, Any]            = intermediate_size
      _snake_case  :    Tuple            = hidden_act
      _snake_case  :    Tuple            = hidden_dropout_prob
      _snake_case  :    Dict            = attention_probs_dropout_prob
      _snake_case  :    int            = max_position_embeddings
      _snake_case  :    str            = type_vocab_size
      _snake_case  :    Union[str, Any]            = initializer_range
      _snake_case  :    str            = layer_norm_eps

      _snake_case  :    Any            = summary_type
      _snake_case  :    Optional[Any]            = summary_use_proj
      _snake_case  :    List[Any]            = summary_activation
      _snake_case  :    List[str]            = summary_last_dropout
      _snake_case  :    Any            = position_embedding_type
      _snake_case  :    str            = use_cache
      _snake_case  :    Any            = classifier_dropout






class   _UpperCAmelCase					(       _snake_case):
    @property
    def        lowerCamelCase__						(     self  ):
      if self.task == "multiple-choice":
        _snake_case  :    Tuple            = {0: "batch", 1: "choice", 2: "sequence"}
      else:
        _snake_case  :    Tuple            = {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ]  )



"""simple docstring"""
from __future__ import annotations

from statistics import mean
def 					a__ ( a :					list[int]		):

  """simple docstring"""







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

  """simple docstring"""







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


if __name__ == "__main__":
    _a   :      Dict       =				[3, 5, 7]
    _a   :      Dict       =				calculate_waiting_times(burst_times)
    _a   :      Tuple       =				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}')

