
from collections import deque



class    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					process_name  # process name
									_lowercase					       =					arrival_time  # arrival time of the process
									# completion time of finished process or last interrupted time
									_lowercase					       =					arrival_time
									_lowercase					       =					burst_time  # remaining burst time
									_lowercase					       =					0  # total time of the process wait in ready queue
									_lowercase					       =					0  # time from arrival time to completion time







class    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			):
									# total number of mlfq's queues
									_lowercase					       =					number_of_queues
									# time slice of queues that round robin algorithm applied
									_lowercase					       =					time_slices
									# unfinished process is in this ready_queue
									_lowercase					       =					queue
									# current time
									_lowercase					       =					current_time
									# finished process is in this sequence queue
									_lowercase					       =					deque()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					[]
									for i in range(len(self.finish_queue  )  ):
															sequence.append(self.finish_queue[i].process_name  )
									return sequence






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					[]
									for i in range(len(__UpperCamelCase  )  ):
															waiting_times.append(queue[i].waiting_time  )
									return waiting_times






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					[]
									for i in range(len(__UpperCamelCase  )  ):
															turnaround_times.append(queue[i].turnaround_time  )
									return turnaround_times






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					[]
									for i in range(len(__UpperCamelCase  )  ):
															completion_times.append(queue[i].stop_time  )
									return completion_times






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return [q.burst_time for q in queue]






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									process.waiting_time += self.current_time - process.stop_time
									return process.waiting_time






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					deque()  # sequence deque of finished process
									while len(__UpperCamelCase  ) != 0:
															_lowercase					       =					ready_queue.popleft()  # current process

															# if process's arrival time is later than current time, update current time
															if self.current_time < cp.arrival_time:
																					self.current_time += cp.arrival_time

															# update waiting time of current process
															self.update_waiting_time(__UpperCamelCase  )
															# update current time
															self.current_time += cp.burst_time
															# finish the process and set the process's burst-time 0
															_lowercase					       =					0
															# set the process's turnaround time because it is finished
															_lowercase					       =					self.current_time - cp.arrival_time
															# set the completion time
															_lowercase					       =					self.current_time
															# add the process to queue that has finished queue
															finished.append(__UpperCamelCase  )

									self.finish_queue.extend(__UpperCamelCase  )  # add finished process to finish queue
									# FCFS will finish all remaining processes
									return finished






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					deque()  # sequence deque of terminated process
									# just for 1 cycle and unfinished processes will go back to queue
									for _ in range(len(__UpperCamelCase  )  ):
															_lowercase					       =					ready_queue.popleft()  # current process

															# if process's arrival time is later than current time, update current time
															if self.current_time < cp.arrival_time:
																					self.current_time += cp.arrival_time

															# update waiting time of unfinished processes
															self.update_waiting_time(__UpperCamelCase  )
															# if the burst time of process is bigger than time-slice
															if cp.burst_time > time_slice:
																					# use CPU for only time-slice
																					self.current_time += time_slice
																					# update remaining burst time
																					cp.burst_time -= time_slice
																					# update end point time
																					_lowercase					       =					self.current_time
																					# locate the process behind the queue because it is not finished
																					ready_queue.append(__UpperCamelCase  )
															else:
																					# use CPU for remaining burst time
																					self.current_time += cp.burst_time
																					# set burst time 0 because the process is finished
																					_lowercase					       =					0
																					# set the finish time
																					_lowercase					       =					self.current_time
																					# update the process' turnaround time because it is finished
																					_lowercase					       =					self.current_time - cp.arrival_time
																					# add the process to queue that has finished queue
																					finished.append(__UpperCamelCase  )

									self.finish_queue.extend(__UpperCamelCase  )  # add finished process to finish queue
									# return finished processes queue and remaining processes queue
									return finished, ready_queue






			def    UpperCamelCase_				(      self  ):

									#  all queues except last one have round_robin algorithm
									for i in range(self.number_of_queues - 1  ):
															_lowercase       ,						_lowercase					       =					self.round_robin(
															    self.ready_queue				,			self.time_slices[i]  )
									#  the last queue has first_come_first_served algorithm
									self.first_come_first_served(self.ready_queue  )

									return self.finish_queue


if __name__ == "__main__":
		import doctest

		A		: Tuple								=     Process('''P1''', 0, 53)
		A		: Dict								=     Process('''P2''', 0, 17)
		A		: Dict								=     Process('''P3''', 0, 68)
		A		: Optional[int]								=     Process('''P4''', 0, 24)
		A		: str								=     3
		A		: Union[str, Any]								=     [17, 25]
		A		: int								=     deque([Pa, Pa, Pa, Pa])

		if len(time_slices) != number_of_queues - 1:
				raise SystemExit(0)

		doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

		A		: List[Any]								=     Process('''P1''', 0, 53)
		A		: List[Any]								=     Process('''P2''', 0, 17)
		A		: int								=     Process('''P3''', 0, 68)
		A		: Union[str, Any]								=     Process('''P4''', 0, 24)
		A		: List[Any]								=     3
		A		: Union[str, Any]								=     [17, 25]
		A		: Optional[Any]								=     deque([Pa, Pa, Pa, Pa])
		A		: Optional[Any]								=     MLFQ(number_of_queues, time_slices, queue, 0)
		A		: int								=     mlfq.multi_level_feedback_queue()

		# print total waiting times of processes(P1, P2, P3, P4)
		print(
		    F'waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'
		)
		# print completion times of processes(P1, P2, P3, P4)
		print(
		    F'completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'
		)
		# print total turnaround times of processes(P1, P2, P3, P4)
		print(
		    F'turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'
		)
		# print sequence of finished processes
		print(
		    F'sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'
		)

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A		: Tuple								=     logging.get_logger(__name__)  # pylint: disable=invalid-name

A		: Optional[Any]								=     '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	int=8       )  ->     Optional[int]:
						_lowercase					       =					height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_lowercase					       =					width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



class    a_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			):
									super().__init__()

									self.register_modules(
									    unet=__UpperCamelCase				,			scheduler=__UpperCamelCase				,			movq=__UpperCamelCase				,			)
									_lowercase					       =					2 ** (len(self.movq.config.block_out_channels  ) - 1)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									if latents is None:
															_lowercase					       =					randn_tensor(__UpperCamelCase				,			generator=__UpperCamelCase				,			device=__UpperCamelCase				,			dtype=__UpperCamelCase  )
									else:
															if latents.shape != shape:
																					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
															_lowercase					       =					latents.to(__UpperCamelCase  )

									_lowercase					       =					latents * scheduler.init_noise_sigma
									return latents






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=0  ):
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

									_lowercase					       =					torch.device(f"""cuda:{gpu_id}"""  )

									_lowercase					       =					[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=0  ):
									if is_accelerate_available() and is_accelerate_version(""">="""				,			"""0.17.0.dev0"""  ):
															from accelerate import cpu_offload_with_hook
									else:
															raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""  )

									_lowercase					       =					torch.device(f"""cuda:{gpu_id}"""  )

									if self.device.type != "cpu":
															self.to("""cpu"""				,			silence_dtype_warnings=__UpperCamelCase  )
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									_lowercase					       =					None
									for cpu_offloaded_model in [self.unet, self.movq]:
															_lowercase       ,						_lowercase					       =					cpu_offload_with_hook(__UpperCamelCase				,			__UpperCamelCase				,			prev_module_hook=__UpperCamelCase  )

									# We'll offload the last model manually.
									_lowercase					       =					hook






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def    UpperCamelCase_				(      self  ):
									if not hasattr(self.unet				,			"""_hf_hook"""  ):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(__UpperCamelCase				,			"""_hf_hook"""  )
															    and hasattr(module._hf_hook				,			"""execution_device"""  )
															    and module._hf_hook.execution_device is not None
															):
																					return torch.device(module._hf_hook.execution_device  )
									return self.device






			@torch.no_grad()
			@replace_example_docstring(__UpperCamelCase  )
			def __call__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = 512				,			__UpperCamelCase = 512				,			__UpperCamelCase = 100				,			__UpperCamelCase = 4.0				,			__UpperCamelCase = 1				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = "pil"				,			__UpperCamelCase = True				,			):
									_lowercase					       =					self._execution_device

									_lowercase					       =					guidance_scale > 1.0

									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )

									_lowercase					       =					image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
															_lowercase					       =					image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					negative_image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					hint.repeat_interleave(__UpperCamelCase				,			dim=0  )

									_lowercase					       =					torch.cat([negative_image_embeds, image_embeds]				,			dim=0  ).to(dtype=self.unet.dtype				,			device=__UpperCamelCase  )
									_lowercase					       =					torch.cat([hint, hint]				,			dim=0  ).to(dtype=self.unet.dtype				,			device=__UpperCamelCase  )
									self.scheduler.set_timesteps(__UpperCamelCase				,			device=__UpperCamelCase  )
									_lowercase					       =					self.scheduler.timesteps

									_lowercase					       =					self.movq.config.latent_channels

									_lowercase       ,						_lowercase					       =					downscale_height_and_width(__UpperCamelCase				,			__UpperCamelCase				,			self.movq_scale_factor  )

									# create initial latent
									_lowercase					       =					self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)				,			image_embeds.dtype				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			self.scheduler				,			)

									for i, t in enumerate(self.progress_bar(__UpperCamelCase  )  ):
															# expand the latents if we are doing classifier free guidance
															_lowercase					       =					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

															_lowercase					       =					{"""image_embeds""": image_embeds, """hint""": hint}
															_lowercase					       =					self.unet(
															    sample=__UpperCamelCase				,			timestep=__UpperCamelCase				,			encoder_hidden_states=__UpperCamelCase				,			added_cond_kwargs=__UpperCamelCase				,			return_dict=__UpperCamelCase				,			)[0]

															if do_classifier_free_guidance:
																					_lowercase       ,						_lowercase					       =					noise_pred.split(latents.shape[1]				,			dim=1  )
																					_lowercase       ,						_lowercase					       =					noise_pred.chunk(2  )
																					_lowercase       ,						_lowercase					       =					variance_pred.chunk(2  )
																					_lowercase					       =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					_lowercase					       =					torch.cat([noise_pred, variance_pred_text]				,			dim=1  )

															if not (
															    hasattr(self.scheduler.config				,			"""variance_type"""  )
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																					_lowercase       ,						_lowercase					       =					noise_pred.split(latents.shape[1]				,			dim=1  )

															# compute the previous noisy sample x_t -> x_t-1
															_lowercase					       =					self.scheduler.step(
															    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			generator=__UpperCamelCase				,			)[0]
									# post-processing
									_lowercase					       =					self.movq.decode(__UpperCamelCase				,			force_not_quantize=__UpperCamelCase  )["""sample"""]

									if output_type not in ["pt", "np", "pil"]:
															raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""  )

									if output_type in ["np", "pil"]:
															_lowercase					       =					image * 0.5 + 0.5
															_lowercase					       =					image.clamp(0				,			1  )
															_lowercase					       =					image.cpu().permute(0				,			2				,			3				,			1  ).float().numpy()

									if output_type == "pil":
															_lowercase					       =					self.numpy_to_pil(__UpperCamelCase  )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=__UpperCamelCase  )