# 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     _A   (    __UpperCamelCase							):
		SCREAMING_SNAKE_CASE_	:	torch.FloatTensor
		SCREAMING_SNAKE_CASE_	:	Optional[torch.FloatTensor]						=None







def  __UpperCamelCase    (						A ,							A=0.9_99 ,							A="cosine" ,							):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A							):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

					elif alpha_transform_type == "exp":

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

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

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



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):
		SCREAMING_SNAKE_CASE_	:	int						=1






		@register_to_config
		def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 1000   ,				SCREAMING_SNAKE_CASE_ = 0.0001   ,				SCREAMING_SNAKE_CASE_ = 0.02   ,				SCREAMING_SNAKE_CASE_ = "linear"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = 0   ,				SCREAMING_SNAKE_CASE_ = "epsilon"   ,				SCREAMING_SNAKE_CASE_ = 1.0   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Tuple:





							'''simple docstring'''





							if kwargs.get('''set_alpha_to_one'''   ,				SCREAMING_SNAKE_CASE_	) is not None:
												UpperCamelCase__										=      (
												    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
												)
												deprecate('''set_alpha_to_one'''   ,				'''1.0.0'''   ,				SCREAMING_SNAKE_CASE_   ,				standard_warn=SCREAMING_SNAKE_CASE_	)
												UpperCamelCase__										=      kwargs['''set_alpha_to_one''']
							if trained_betas is not None:
												UpperCamelCase__										=      torch.tensor(SCREAMING_SNAKE_CASE_   ,				dtype=torch.floataa	)
							elif beta_schedule == "linear":
												UpperCamelCase__										=      torch.linspace(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				dtype=torch.floataa	)
							elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												UpperCamelCase__										=      (
												    torch.linspace(beta_start**0.5   ,				beta_end**0.5   ,				SCREAMING_SNAKE_CASE_   ,				dtype=torch.floataa	) ** 2
												)
							elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												UpperCamelCase__										=      betas_for_alpha_bar(SCREAMING_SNAKE_CASE_	)
							else:
												raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

							UpperCamelCase__										=      1.0 - self.betas
							UpperCamelCase__										=      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.
							UpperCamelCase__										=      torch.tensor(0.0	) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							UpperCamelCase__										=      1.0

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






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      torch.FloatTensor:





							'''simple docstring'''





							return sample






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Any:





							'''simple docstring'''





							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."	)

							UpperCamelCase__										=      num_inference_steps
							UpperCamelCase__										=      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
							UpperCamelCase__										=      (np.arange(0   ,				SCREAMING_SNAKE_CASE_	) * step_ratio).round().copy().astype(np.intaa	)
							UpperCamelCase__										=      torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
							self.timesteps += self.config.steps_offset






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = 0.0   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = True   ,				)		->      Union[DDIMSchedulerOutput, Tuple]:





							'''simple docstring'''





							UpperCamelCase__										=      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
							UpperCamelCase__										=      self.alphas_cumprod[timestep]
							UpperCamelCase__										=      (
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							UpperCamelCase__										=      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":
												UpperCamelCase__										=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												UpperCamelCase__										=      model_output
							elif self.config.prediction_type == "sample":
												UpperCamelCase__										=      model_output
												UpperCamelCase__										=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
												UpperCamelCase__										=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
												UpperCamelCase__										=      (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:
												UpperCamelCase__										=      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
							UpperCamelCase__										=      (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
							UpperCamelCase__										=      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=SCREAMING_SNAKE_CASE_   ,				pred_original_sample=SCREAMING_SNAKE_CASE_	)






		def __len__(self	)		->      Union[str, Any]:





							'''simple docstring'''





							return self.config.num_train_timesteps




from __future__ import annotations

import pandas as pd







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     UpperCamelCase__										=      [0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i]

     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      999999999
     UpperCamelCase__										=      0
     UpperCamelCase__										=      False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(A							):
               if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                    if remaining_time[j] < minm:
                         UpperCamelCase__										=      remaining_time[j]
                         UpperCamelCase__										=      j
                         UpperCamelCase__										=      True

          if not check:
               increment_time += 1
               continue
          remaining_time[short] -= 1

          UpperCamelCase__										=      remaining_time[short]
          if minm == 0:
               UpperCamelCase__										=      999999999

          if remaining_time[short] == 0:
               complete += 1
               UpperCamelCase__										=      False

               # Find finish time of current process
               UpperCamelCase__										=      increment_time + 1

               # Calculate waiting time
               UpperCamelCase__										=      finish_time - arrival_time[short]
               UpperCamelCase__										=      finar - burst_time[short]

               if waiting_time[short] < 0:
                    UpperCamelCase__										=      0

        # Increment time
          increment_time += 1
     return waiting_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i] + waiting_time[i]
     return turn_around_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     for i in range(A							):
          UpperCamelCase__										=      total_waiting_time + waiting_time[i]
          UpperCamelCase__										=      total_turn_around_time + turn_around_time[i]
     print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}"							)
     print('''Average turn around time =''' ,							total_turn_around_time / no_of_processes							)


if __name__ == "__main__":
     print('''Enter how many process you want to analyze''')
     __magic_name__								=int(input())
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=list(range(1, no_of_processes + 1))

     for i in range(no_of_processes):
          print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
          __magic_name__		,  __magic_name__								=map(int, input().split())

     __magic_name__								=calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     __magic_name__								=burst_time
     __magic_name__								=no_of_processes
     __magic_name__								=waiting_time
     __magic_name__								=calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     __magic_name__								=pd.DataFrame(
         list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
         columns=[
             '''Process''',
             '''BurstTime''',
             '''ArrivalTime''',
             '''WaitingTime''',
             '''TurnAroundTime''',
         ],
     )

     # Printing the dataFrame
     pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
     print(fcfs)



