


'''simple docstring'''




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	torch.FloatTensor
     lowerCamelCase				:	Optional[torch.FloatTensor]		=     None





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     List[Any]=0.999	, lowerCamelCase	:     Union[str, Any]="cosine"	, ):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowerCamelCase	:     Optional[int]				):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(lowerCamelCase	:     Tuple				):
         return math.exp(t * -12.0				)

   else:
      raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

   lowerCAmelCase     =			[]
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			i / num_diffusion_timesteps
      lowerCAmelCase     =			(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(lowerCamelCase				) / alpha_bar_fn(lowerCamelCase				)	, lowerCamelCase				)				)
   return torch.tensor(lowerCamelCase	, dtype=torch.floataa				)




class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase		):
     lowerCamelCase				:	Union[str, Any]		=     1





     @register_to_config
     def __init__( self       :      List[Any]    ,      UpperCAmelCase__       :      int = 1_0_0_0    ,      UpperCAmelCase__       :      float = 0.0_001    ,      UpperCAmelCase__       :      float = 0.02    ,      UpperCAmelCase__       :      str = "linear"    ,      UpperCAmelCase__       :      Optional[Union[np.ndarray, List[float]]] = None    ,      UpperCAmelCase__       :      bool = True    ,      UpperCAmelCase__       :      bool = True    ,      UpperCAmelCase__       :      int = 0    ,      UpperCAmelCase__       :      str = "epsilon"    ,      UpperCAmelCase__       :      float = 1.0    ,      **UpperCAmelCase__       :      str    ,      )				->  Any:
        if kwargs.get('set_alpha_to_one'    ,      UpperCAmelCase__    ) is not None:
           lowerCAmelCase     =			(
               'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
           )
           deprecate('set_alpha_to_one'    ,      '1.0.0'    ,      UpperCAmelCase__    ,      standard_warn=UpperCAmelCase__    )
           lowerCAmelCase     =			kwargs['set_alpha_to_one']
        if trained_betas is not None:
           lowerCAmelCase     =			torch.tensor(UpperCAmelCase__    ,      dtype=torch.floataa    )
        elif beta_schedule == "linear":
           lowerCAmelCase     =			torch.linspace(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      dtype=torch.floataa    )
        elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           lowerCAmelCase     =			(
               torch.linspace(beta_start**0.5    ,      beta_end**0.5    ,      UpperCAmelCase__    ,      dtype=torch.floataa    ) ** 2
           )
        elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           lowerCAmelCase     =			betas_for_alpha_bar(UpperCAmelCase__    )
        else:
           raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

        lowerCAmelCase     =			1.0 - self.betas
        lowerCAmelCase     =			torch.cumprod(self.alphas    ,      dim=0    )

        # At every step in inverted ddim, we are looking into the next alphas_cumprod
        # For the final step, there is no next alphas_cumprod, and the index is out of bounds
        # `set_alpha_to_zero` decides whether we set this parameter simply to zero
        # in this case, self.step() just output the predicted noise
        # or whether we use the final alpha of the "non-previous" one.
        lowerCAmelCase     =			torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        lowerCAmelCase     =			1.0

        # setable values
        lowerCAmelCase     =			None
        lowerCAmelCase     =			torch.from_numpy(np.arange(0    ,      UpperCAmelCase__    ).copy().astype(np.intaa    )    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      torch.FloatTensor    ,      UpperCAmelCase__       :      Optional[int] = None    )				->  torch.FloatTensor:
        return sample





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Union[str, torch.device] = None    )				->  Dict:

        if num_inference_steps > self.config.num_train_timesteps:
           raise ValueError(
               F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
               F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
               F''' maximal {self.config.num_train_timesteps} timesteps.'''    )

        lowerCAmelCase     =			num_inference_steps
        lowerCAmelCase     =			self.config.num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        lowerCAmelCase     =			(np.arange(0    ,      UpperCAmelCase__    ) * step_ratio).round().copy().astype(np.intaa    )
        lowerCAmelCase     =			torch.from_numpy(UpperCAmelCase__    ).to(UpperCAmelCase__    )
        self.timesteps += self.config.steps_offset





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      torch.FloatTensor    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      torch.FloatTensor    ,      UpperCAmelCase__       :      float = 0.0    ,      UpperCAmelCase__       :      bool = False    ,      UpperCAmelCase__       :      Optional[torch.FloatTensor] = None    ,      UpperCAmelCase__       :      bool = True    ,      )				->  Union[DDIMSchedulerOutput, Tuple]:
        # 1. get previous step value (=t+1)
        lowerCAmelCase     =			timestep + self.config.num_train_timesteps // self.num_inference_steps

        # 2. compute alphas, betas
        # change original implementation to exactly match noise levels for analogous forward process
        lowerCAmelCase     =			self.alphas_cumprod[timestep]
        lowerCAmelCase     =			(
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

        lowerCAmelCase     =			1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
           lowerCAmelCase     =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           lowerCAmelCase     =			model_output
        elif self.config.prediction_type == "sample":
           lowerCAmelCase     =			model_output
           lowerCAmelCase     =			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
           lowerCAmelCase     =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           lowerCAmelCase     =			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
           raise ValueError(
               F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
               ' `v_prediction`'    )

        # 4. Clip or threshold "predicted x_0"
        if self.config.clip_sample:
           lowerCAmelCase     =			pred_original_sample.clamp(
               -self.config.clip_sample_range    ,      self.config.clip_sample_range    )

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        lowerCAmelCase     =			(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        lowerCAmelCase     =			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
           return (prev_sample, pred_original_sample)
        return DDIMSchedulerOutput(prev_sample=UpperCAmelCase__    ,      pred_original_sample=UpperCAmelCase__    )





     def __len__( self       :      Any    )				->  Optional[Any]:
        return self.config.num_train_timesteps









'''simple docstring'''




from __future__ import annotations

from statistics import mean





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     list[int]	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			[0] * no_of_processes
   lowerCAmelCase     =			[0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i]
   lowerCAmelCase     =			[]

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

   return waiting_time





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     int	, lowerCamelCase	:     list[int]				):
   lowerCAmelCase     =			[0] * no_of_processes
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i] + waiting_time[i]
   return turn_around_time


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

       __snake_case    						=4
       __snake_case    						=[2, 5, 3, 7]
       __snake_case    						=[0, 0, 0, 0]
       __snake_case    						=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
       __snake_case    						=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}''')





