code: 
  """simple docstring"""
  
  
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  _a	 =							logging.get_logger(__name__)
  
  _a	 =							{
      'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
      'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
      'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
      'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
  }
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Optional[Any]						  =	"""longt5"""
    SCREAMING_SNAKE_CASE__    :		str						  =	["""past_key_values"""]
    SCREAMING_SNAKE_CASE__    :		str						  =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_=3_2128       ,     lowercase_=512       ,     lowercase_=64       ,     lowercase_=2048       ,     lowercase_=6       ,     lowercase_=None       ,     lowercase_=8       ,     lowercase_=127       ,     lowercase_=16       ,     lowercase_=32       ,     lowercase_=128       ,     lowercase_=0.1       ,     lowercase_=1E-6       ,     lowercase_=1.0       ,     lowercase_="relu"       ,     lowercase_=True       ,     lowercase_="local"       ,     lowercase_=True       ,     lowercase_=0       ,     lowercase_=1       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					vocab_size
       UpperCAmelCase_      :      Optional[Any]      =					d_model
       UpperCAmelCase_      :      int      =					d_kv
       UpperCAmelCase_      :      int      =					d_ff
       UpperCAmelCase_      :      List[Any]      =					num_layers
       # default = symmetry
       UpperCAmelCase_      :      Any      =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
       UpperCAmelCase_      :      Optional[Any]      =					num_heads
       UpperCAmelCase_      :      Optional[int]      =					local_radius
       UpperCAmelCase_      :      Optional[int]      =					global_block_size
       UpperCAmelCase_      :      Any      =					relative_attention_num_buckets
       UpperCAmelCase_      :      Tuple      =					relative_attention_max_distance
       UpperCAmelCase_      :      Union[str, Any]      =					dropout_rate
       UpperCAmelCase_      :      List[str]      =					layer_norm_epsilon
       UpperCAmelCase_      :      Any      =					initializer_factor
       UpperCAmelCase_      :      Dict      =					feed_forward_proj
       UpperCAmelCase_      :      Dict      =					encoder_attention_type
       UpperCAmelCase_      :      List[str]      =					use_cache
  
       UpperCAmelCase_      :      Union[str, Any]      =					self.feed_forward_proj.split("-"						)
       UpperCAmelCase_      :      Dict      =					act_info[-1]
       UpperCAmelCase_      :      Optional[Any]      =					act_info[0] == "gated"
  
       if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
          raise ValueError(
              F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
              "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
              "'gated-gelu' or 'relu'"						)
  
       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
          UpperCAmelCase_      :      List[str]      =					"gelu_new"
  
       super().__init__(
           pad_token_id=lowercase_       ,     eos_token_id=lowercase_       ,     is_encoder_decoder=lowercase_       ,     **lowercase_       ,     )
  
  
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					{
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
          UpperCAmelCase_      :      Tuple      =					"past_encoder_sequence + sequence"
          UpperCAmelCase_      :      Union[str, Any]      =					{0: "batch"}
          UpperCAmelCase_      :      Tuple      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          UpperCAmelCase_      :      int      =					{0: "batch", 1: "decoder_sequence"}
          UpperCAmelCase_      :      Optional[Any]      =					{0: "batch", 1: "decoder_sequence"}
  
       if self.use_past:
          self.fill_with_past_key_values_(lowercase_       ,     direction="inputs"						)
  
       return common_inputs
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return 13
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from __future__ import annotations
  
  from statistics import mean
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     UpperCAmelCase_      :      Dict      =					2
     UpperCAmelCase_      :      Any      =					list(__lowerCamelCase						)
     UpperCAmelCase_      :      List[Any]      =					[0] * len(__lowerCamelCase						)
     UpperCAmelCase_      :      str      =					0
     while True:
        UpperCAmelCase_      :      int      =					True
        for i, burst_time in enumerate(__lowerCamelCase						):
           if rem_burst_times[i] > 0:
              UpperCAmelCase_      :      List[Any]      =					False
              if rem_burst_times[i] > quantum:
                 t += quantum
                 rem_burst_times[i] -= quantum
              else:
                 t += rem_burst_times[i]
                 UpperCAmelCase_      :      Optional[int]      =					t - burst_time
                 UpperCAmelCase_      :      Union[str, Any]      =					0
        if done is True:
           return waiting_times
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
     return [burst + waiting for burst, waiting in zip(__lowerCamelCase,	__lowerCamelCase						)]
  
  
  if __name__ == "__main__":
      _a	 =							[3, 5, 7]
      _a	 =							calculate_waiting_times(burst_times)
      _a	 =							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}""")
  
  
  