
"""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




"""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}""")


