
from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


SCREAMING_SNAKE_CASE__   :			int												=    8




def  __magic_name__ (   __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						str=BITS							)			->   Optional[int]:
				__lowerCamelCase    					=	x.device

				__lowerCamelCase    					=	(x * 255).int().clamp(0		,  255							)

				__lowerCamelCase    					=	2 ** torch.arange(bits - 1		,  -1		,  -1		,  device=__lowerCAmelCase							)
				__lowerCamelCase    					=	rearrange(__lowerCAmelCase		,  '''d -> d 1 1'''							)
				__lowerCamelCase    					=	rearrange(__lowerCAmelCase		,  '''b c h w -> b c 1 h w'''							)

				__lowerCamelCase    					=	((x & mask) != 0).float()
				__lowerCamelCase    					=	rearrange(__lowerCAmelCase		,  '''b c d h w -> b (c d) h w'''							)
				__lowerCamelCase    					=	bits * 2 - 1
				return bits




def  __magic_name__ (   __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Any=BITS							)			->   List[Any]:
				__lowerCamelCase    					=	x.device

				__lowerCamelCase    					=	(x > 0).int()
				__lowerCamelCase    					=	2 ** torch.arange(bits - 1		,  -1		,  -1		,  device=__lowerCAmelCase		,  dtype=torch.intaa							)

				__lowerCamelCase    					=	rearrange(__lowerCAmelCase		,  '''d -> d 1 1'''							)
				__lowerCamelCase    					=	rearrange(__lowerCAmelCase		,  '''b (c d) h w -> b c d h w'''		,  d=8							)
				__lowerCamelCase    					=	reduce(x * mask		,  '''b c d h w -> b c h w'''		,  '''sum'''							)
				return (dec / 255).clamp(0.0		,  1.0							)




def  __magic_name__ (   self	:						Union[str, Any]		,  __lowerCAmelCase	:						torch.FloatTensor		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						torch.FloatTensor		,  __lowerCAmelCase	:						float = 0.0		,  __lowerCAmelCase	:						bool = True		,  __lowerCAmelCase	:						Optional[int]=None		,  __lowerCAmelCase	:						bool = True		,  )			->   Union[DDIMSchedulerOutput, Tuple]:
				if self.num_inference_steps is None:
								raise ValueError(
								    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''							)

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				__lowerCamelCase    					=	timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				__lowerCamelCase    					=	self.alphas_cumprod[timestep]
				__lowerCamelCase    					=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 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
				__lowerCamelCase    					=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				__lowerCamelCase    					=	self.bit_scale
				if self.config.clip_sample:
								__lowerCamelCase    					=	torch.clamp(__lowerCAmelCase		,  -scale		,  __lowerCAmelCase							)

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				__lowerCamelCase    					=	self._get_variance(__lowerCAmelCase		,  __lowerCAmelCase							)
				__lowerCamelCase    					=	eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								__lowerCamelCase    					=	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__lowerCamelCase    					=	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. 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 eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								__lowerCamelCase    					=	model_output.device if torch.is_tensor(__lowerCAmelCase							) else '''cpu'''
								__lowerCamelCase    					=	torch.randn(model_output.shape		,  dtype=model_output.dtype		,  generator=__lowerCAmelCase							).to(__lowerCAmelCase							)
								__lowerCamelCase    					=	self._get_variance(__lowerCAmelCase		,  __lowerCAmelCase							) ** 0.5 * eta * noise

								__lowerCamelCase    					=	prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase		,  pred_original_sample=__lowerCAmelCase							)




def  __magic_name__ (   self	:						List[str]		,  __lowerCAmelCase	:						torch.FloatTensor		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						torch.FloatTensor		,  __lowerCAmelCase	:						Union[str, Any]="epsilon"		,  __lowerCAmelCase	:						Optional[int]=None		,  __lowerCAmelCase	:						bool = True		,  )			->   Union[DDPMSchedulerOutput, Tuple]:
				__lowerCamelCase    					=	timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								__lowerCamelCase					,    __lowerCamelCase    					=	torch.split(__lowerCAmelCase		,  sample.shape[1]		,  dim=1							)
				else:
								__lowerCamelCase    					=	None

				# 1. compute alphas, betas
				__lowerCamelCase    					=	self.alphas_cumprod[t]
				__lowerCamelCase    					=	self.alphas_cumprod[t - 1] if t > 0 else self.one
				__lowerCamelCase    					=	1 - alpha_prod_t
				__lowerCamelCase    					=	1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								__lowerCamelCase    					=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								__lowerCamelCase    					=	model_output
				else:
								raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''							)

				# 3. Clip "predicted x_0"
				__lowerCamelCase    					=	self.bit_scale
				if self.config.clip_sample:
								__lowerCamelCase    					=	torch.clamp(__lowerCAmelCase		,  -scale		,  __lowerCAmelCase							)

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__lowerCamelCase    					=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				__lowerCamelCase    					=	self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__lowerCamelCase    					=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				__lowerCamelCase    					=	0
				if t > 0:
								__lowerCamelCase    					=	torch.randn(
								    model_output.size()		,  dtype=model_output.dtype		,  layout=model_output.layout		,  generator=__lowerCAmelCase							).to(model_output.device							)
								__lowerCamelCase    					=	(self._get_variance(__lowerCAmelCase		,  predicted_variance=__lowerCAmelCase							) ** 0.5) * noise

				__lowerCamelCase    					=	pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase		,  pred_original_sample=__lowerCAmelCase							)







class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							UNetaDConditionModel							,			SCREAMING_SNAKE_CASE__		:							Union[DDIMScheduler, DDPMScheduler]							,			SCREAMING_SNAKE_CASE__		:							Optional[float] = 1.0							,			)     ->							str:
						super().__init__()
						__lowerCamelCase    					=	bit_scale
						__lowerCamelCase    					=	(
						    ddim_bit_scheduler_step if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else ddpm_bit_scheduler_step
						)

						self.register_modules(unet=SCREAMING_SNAKE_CASE__							,			scheduler=SCREAMING_SNAKE_CASE__  )







		@torch.no_grad()
		def __call__(		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = 2_56							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = 2_56							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = 50							,			SCREAMING_SNAKE_CASE__		:							Optional[torch.Generator] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = 1							,			SCREAMING_SNAKE_CASE__		:							Optional[str] = "pil"							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			**SCREAMING_SNAKE_CASE__		:							List[Any]							,			)     ->							Union[Tuple, ImagePipelineOutput]:
						__lowerCamelCase    					=	torch.randn(
						    (batch_size, self.unet.config.in_channels, height, width)							,			generator=SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	decimal_to_bits(SCREAMING_SNAKE_CASE__  ) * self.bit_scale
						__lowerCamelCase    					=	latents.to(self.device  )

						self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
										# predict the noise residual
										__lowerCamelCase    					=	self.unet(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ).sample

										# compute the previous noisy sample x_t -> x_t-1
										__lowerCamelCase    					=	self.scheduler.step(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ).prev_sample

						__lowerCamelCase    					=	bits_to_decimal(SCREAMING_SNAKE_CASE__  )

						if output_type == "pil":
										__lowerCamelCase    					=	self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )





from __future__ import annotations

from statistics import mean




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int							)			->   list[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  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						list[int]							)			->   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]")

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



