





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE_						(					SCREAMING_SNAKE_CASE__					):






						def __init__(      self      :      int		,   _A      :      List[Any]		,   _A      :      List[str]		,   _A      :      List[str]		,   _A      :      str = None		,   )							->    Dict:







							"""simple docstring"""


							super().__init__()
							self.register_modules(transformer=snake_case__		,   vae=snake_case__		,   scheduler=snake_case__	)

							# create a imagenet -> id dictionary for easier use
							snake_case_		:			Union[str, Any]											=	{}
							if idalabel is not None:
								for key, value in idalabel.items():
									for label in value.split(','	):
										snake_case_		:			Optional[int]											=	int(snake_case__	)
								snake_case_		:			Optional[Any]											=	dict(sorted(self.labels.items()	)	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


							if not isinstance(snake_case__		,   snake_case__	):
								snake_case_		:			int											=	list(snake_case__	)

							for l in label:
								if l not in self.labels:
									raise ValueError(
									    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""	)

							return [self.labels[l] for l in label]






						@torch.no_grad()
						def __call__(      self      :      List[Any]		,   _A      :      Optional[Any]		,   _A      :      Any = 4.0		,   _A      :      List[Any] = None		,   _A      :      List[str] = 50		,   _A      :      List[Any] = "pil"		,   _A      :      Tuple = True		,   )							->    int:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	len(snake_case__	)
							snake_case_		:			Any											=	self.transformer.config.sample_size
							snake_case_		:			List[Any]											=	self.transformer.config.in_channels

							snake_case_		:			List[Any]											=	randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)		,   generator=snake_case__		,   device=self.device		,   dtype=self.transformer.dtype		,   )
							snake_case_		:			str											=	torch.cat([latents] * 2	) if guidance_scale > 1 else latents

							snake_case_		:			List[str]											=	torch.tensor(snake_case__		,   device=self.device	).reshape(-1	)
							snake_case_		:			Optional[Any]											=	torch.tensor([1000] * batch_size		,   device=self.device	)
							snake_case_		:			int											=	torch.cat([class_labels, class_null]		,   0	) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(snake_case__	)

							for t in self.progress_bar(self.scheduler.timesteps	):
								if guidance_scale > 1:
									snake_case_		:			Optional[Any]											=	latent_model_input[: len(snake_case__	) // 2]
									snake_case_		:			Tuple											=	torch.cat([half, half]		,   dim=0	)
								snake_case_		:			Optional[int]											=	self.scheduler.scale_model_input(snake_case__		,   snake_case__	)

								snake_case_		:			Union[str, Any]											=	t
								if not torch.is_tensor(snake_case__	):
									# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
									# This would be a good case for the `match` statement (Python 3.10+)
									snake_case_		:			Any											=	latent_model_input.device.type == "mps"
									if isinstance(snake_case__		,   snake_case__	):
										snake_case_		:			Tuple											=	torch.floataa if is_mps else torch.floataa
									else:
										snake_case_		:			Tuple											=	torch.intaa if is_mps else torch.intaa
									snake_case_		:			List[Any]											=	torch.tensor([timesteps]		,   dtype=snake_case__		,   device=latent_model_input.device	)
								elif len(timesteps.shape	) == 0:
									snake_case_		:			Dict											=	timesteps[None].to(latent_model_input.device	)
								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								snake_case_		:			Optional[int]											=	timesteps.expand(latent_model_input.shape[0]	)
								# predict noise model_output
								snake_case_		:			Optional[Any]											=	self.transformer(
								    snake_case__		,   timestep=snake_case__		,   class_labels=snake_case__	).sample

								# perform guidance
								if guidance_scale > 1:
									snake_case_		:			Optional[int]											=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
									snake_case_		:			Dict											=	torch.split(snake_case__		,   len(snake_case__	) // 2		,   dim=0	)

									snake_case_		:			Optional[Any]											=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
									snake_case_		:			Tuple											=	torch.cat([half_eps, half_eps]		,   dim=0	)

									snake_case_		:			Optional[int]											=	torch.cat([eps, rest]		,   dim=1	)

								# learned sigma
								if self.transformer.config.out_channels // 2 == latent_channels:
									snake_case_		:			Dict											=	torch.split(snake_case__		,   snake_case__		,   dim=1	)
								else:
									snake_case_		:			str											=	noise_pred

								# compute previous image: x_t -> x_t-1
								snake_case_		:			Any											=	self.scheduler.step(snake_case__		,   snake_case__		,   snake_case__	).prev_sample

							if guidance_scale > 1:
								snake_case_		:			Any											=	latent_model_input.chunk(2		,   dim=0	)
							else:
								snake_case_		:			List[str]											=	latent_model_input

							snake_case_		:			Any											=	1 / self.vae.config.scaling_factor * latents
							snake_case_		:			Dict											=	self.vae.decode(snake_case__	).sample

							snake_case_		:			Dict											=	(samples / 2 + 0.5).clamp(0		,   1	)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							snake_case_		:			Tuple											=	samples.cpu().permute(0		,   2		,   3		,   1	).float().numpy()

							if output_type == "pil":
								snake_case_		:			Tuple											=	self.numpy_to_pil(snake_case__	)

							if not return_dict:
								return (samples,)

							return ImagePipelineOutput(images=snake_case__	)








from statistics import mean

import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	0
	# Number of processes finished
	snake_case_		:			List[str]											=	0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	snake_case_		:			str											=	[0] * no_of_process
	# List to include calculation results
	snake_case_		:			Optional[int]											=	[0] * no_of_process

	# Sort by arrival time.
	snake_case_		:			str											=	[burst_time[i] for i in np.argsort(__a   )]
	snake_case_		:			str											=	[process_name[i] for i in np.argsort(__a   )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		snake_case_		:			int											=	0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			snake_case_		:			str											=	arrival_time[i]

		snake_case_		:			Optional[Any]											=	0
		# Index showing the location of the process being performed
		snake_case_		:			Tuple											=	0
		# Saves the current response ratio.
		snake_case_		:			List[Any]											=	0
		for i in range(0   , __a   ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				snake_case_		:			Optional[Any]											=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				snake_case_		:			Optional[int]											=	temp
				snake_case_		:			Optional[Any]											=	i

        # Calculate the turn around time
		snake_case_		:			Any											=	current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		snake_case_		:			Optional[Any]											=	1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	[0] * no_of_process
	for i in range(0   , __a   ):
		snake_case_		:			Optional[int]											=	turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		5
						_SCREAMING_SNAKE_CASE      	=		["""A""", """B""", """C""", """D""", """E"""]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]

						_SCREAMING_SNAKE_CASE      	=		calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						_SCREAMING_SNAKE_CASE      	=		calculate_waiting_time(
						    process_name, turn_around_time, burst_time, no_of_process
						)

						print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
						for i in range(0, no_of_process):
												print(
												    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
												    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
												)

						print(F'''average waiting time : {mean(waiting_time):.5f}''')
						print(F'''average turn around time : {mean(turn_around_time):.5f}''')

