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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class 		_lowerCamelCase		(   SCREAMING_SNAKE_CASE__    ):
    __a  =      42
    __a  =      42
    __a  =      None






class 		_lowerCamelCase		(   SCREAMING_SNAKE_CASE__    ,     SCREAMING_SNAKE_CASE__    ):
    __a  =      2
    @register_to_config
    def __init__(  self , lowerCAmelCase = 0.02 , lowerCAmelCase = 100 , lowerCAmelCase = 1.007 , lowerCAmelCase = 80 , lowerCAmelCase = 0.05 , lowerCAmelCase = 50 , )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   int=      sigma_max

     # setable values
     SCREAMING_SNAKE_CASE__:   int=      None
     SCREAMING_SNAKE_CASE__:   np.IntTensor=      None
     SCREAMING_SNAKE_CASE__:   torch.FloatTensor=      None  # sigma(t_i)
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					int:
     return sample
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      num_inference_steps
     SCREAMING_SNAKE_CASE__:   Any=      np.arange(0 , self.num_inference_steps   )[::-1].copy()
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.from_numpy(snake_case__   ).to(snake_case__   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [
         (
             self.config.sigma_max**2
             * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
         )
         for i in self.timesteps
     ]
     SCREAMING_SNAKE_CASE__:   int=      torch.tensor(snake_case__ , dtype=torch.floataa , device=snake_case__   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None   )       ->					str:
     if self.config.s_min <= sigma <= self.config.s_max:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1   )
     else:
      SCREAMING_SNAKE_CASE__:   str=      0

     # sample eps ~ N(0, S_noise^2 * I)
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.config.s_noise * randn_tensor(sample.shape , generator=snake_case__   ).to(sample.device   )
     SCREAMING_SNAKE_CASE__:   str=      sigma + gamma * sigma
     SCREAMING_SNAKE_CASE__:   Any=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

     return sample_hat, sigma_hat
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = True , )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      sample_hat + sigma_hat * model_output
     SCREAMING_SNAKE_CASE__:   Optional[int]=      (sample_hat - pred_original_sample) / sigma_hat
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      sample_hat + (sigma_prev - sigma_hat) * derivative

     if not return_dict:
      return (sample_prev, derivative)

     return KarrasVeOutput(
         prev_sample=snake_case__ , derivative=snake_case__ , pred_original_sample=snake_case__   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = True , )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   int=      sample_prev + sigma_prev * model_output
     SCREAMING_SNAKE_CASE__:   Optional[int]=      (sample_prev - pred_original_sample) / sigma_prev
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

     if not return_dict:
      return (sample_prev, derivative)

     return KarrasVeOutput(
         prev_sample=snake_case__ , derivative=snake_case__ , pred_original_sample=snake_case__   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[Any]:
     raise NotImplementedError()


from __future__ import annotations

from statistics import mean







def 			A__      (	snake_case_ :		list[int]       ,				snake_case_ :		list[int]       ,				snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Dict=      [0] * no_of_processes
 SCREAMING_SNAKE_CASE__:   Dict=      [0] * no_of_processes

 # Initialize remaining_time to waiting_time.

 for i in range(snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[str]=      burst_time[i]
 SCREAMING_SNAKE_CASE__:   list[int]=      []

 SCREAMING_SNAKE_CASE__:   List[Any]=      0
 SCREAMING_SNAKE_CASE__:   List[Any]=      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:
  SCREAMING_SNAKE_CASE__:   Optional[int]=      []
  SCREAMING_SNAKE_CASE__:   List[str]=      -1

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

  if len(snake_case_      ) > 0:
   SCREAMING_SNAKE_CASE__:   Optional[int]=      ready_process[0]
   for i in ready_process:
    if remaining_time[i] < remaining_time[target_process]:
     SCREAMING_SNAKE_CASE__:   int=      i
   total_time += burst_time[target_process]
   completed += 1
   SCREAMING_SNAKE_CASE__:   str=      0
   SCREAMING_SNAKE_CASE__:   Tuple=      (
       total_time - arrival_time[target_process] - burst_time[target_process]
   )
  else:
   total_time += 1

 return waiting_time







def 			A__      (	snake_case_ :		list[int]       ,				snake_case_ :		int       ,				snake_case_ :		list[int]      ):
 SCREAMING_SNAKE_CASE__:   List[str]=      [0] * no_of_processes
 for i in range(snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Dict=      burst_time[i] + waiting_time[i]
 return turn_around_time


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

      lowercase_     :   List[Any] 				=				4
      lowercase_     :   str 				=				[2, 5, 3, 7]
      lowercase_     :   Union[str, Any] 				=				[0, 0, 0, 0]
      lowercase_     :   Any 				=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)
      lowercase_     :   Optional[Any] 				=				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}''')

