





'''simple docstring'''

from __future__ import annotations

from statistics import mean





def 		UpperCamelCase							(				_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   int			):
    A__							=     [0] * no_of_processes
    A__							=     [0] * no_of_processes

    # Initialize remaining_time to waiting_time.

    for i in range(_lowerCamelCase			):
        A__							=     burst_time[i]
    A__							=     []

    A__							=     0
    A__							=     0

    # When processes are not completed,
    # A process whose arrival time has passed \
    # and has remaining execution time is put into the ready_process.
    # The shortest process in the ready_process, target_process is executed.

    while completed != no_of_processes:
        A__							=     []
        A__							=     -1

        for i in range(_lowerCamelCase			):
            if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                ready_process.append(_lowerCamelCase			)

        if len(_lowerCamelCase			) > 0:
            A__							=     ready_process[0]
            for i in ready_process:
                if remaining_time[i] < remaining_time[target_process]:
                    A__							=     i
            total_time += burst_time[target_process]
            completed += 1
            A__							=     0
            A__							=     (
                total_time - arrival_time[target_process] - burst_time[target_process]
            )
        else:
            total_time += 1

    return waiting_time





def 		UpperCamelCase							(				_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   list[int]			):
    A__							=     [0] * no_of_processes
    for i in range(_lowerCamelCase			):
        A__							=     burst_time[i] + waiting_time[i]
    return turn_around_time


if __name__ == "__main__":
  print("[TEST CASE 01]")

  __lowerCAmelCase				:      Optional[int]						      =4
  __lowerCAmelCase				:      Union[str, Any]						      =[2, 5, 3, 7]
  __lowerCAmelCase				:      Optional[Any]						      =[0, 0, 0, 0]
  __lowerCAmelCase				:      str						      =calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  __lowerCAmelCase				:      Optional[int]						      =calculate_turnaroundtime(
      burst_time, no_of_processes, waiting_time
  )

  # Printing the Result
  print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
  for i, process_id in enumerate(list(range(1, 5))):
    print(
        f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
        f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
    )
  print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
  print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")










'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							42






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :Tuple		,			lowercase_ :int = 3		,			lowercase_ :int = 3		,			lowercase_ :Tuple[str] = ("DownEncoderBlock2D",)		,			lowercase_ :Tuple[str] = ("UpDecoderBlock2D",)		,			lowercase_ :Tuple[int] = (64,)		,			lowercase_ :int = 1		,			lowercase_ :str = "silu"		,			lowercase_ :int = 3		,			lowercase_ :int = 32		,			lowercase_ :int = 2_56		,			lowercase_ :int = 32		,			lowercase_ :Optional[int] = None		,			lowercase_ :float = 0.1_8_2_1_5		,			lowercase_ :str = "group"		,			)->				Optional[int]:
     super().__init__()

     # pass init params to Encoder
     A__							=     Encoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			down_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			double_z=lowercase_		,			)

     A__							=     vq_embed_dim if vq_embed_dim is not None else latent_channels

     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)
     A__							=     VectorQuantizer(lowercase_		,			lowercase_		,			beta=0.2_5		,			remap=lowercase_		,			sane_index_shape=lowercase_				)
     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)

     # pass init params to Decoder
     A__							=     Decoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			up_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			norm_type=lowercase_		,			)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				VQEncoderOutput:
     A__							=     self.encoder(lowercase_				)
     A__							=     self.quant_conv(lowercase_				)

     if not return_dict:
         return (h,)

     return VQEncoderOutput(latents=lowercase_				)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = False		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     # also go through quantization layer
     if not force_not_quantize:
         A__,      A__,      A__							=     self.quantize(lowercase_				)
     else:
         A__							=     h
     A__							=     self.post_quant_conv(lowercase_				)
     A__							=     self.decoder(lowercase_		,			quant if self.config.norm_type == "spatial" else None				)

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     A__							=     sample
     A__							=     self.encode(lowercase_				).latents
     A__							=     self.decode(lowercase_				).sample

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



