


"""simple docstring"""


from collections import deque
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			process_name  # process name
										__lowerCAmelCase				    =			arrival_time  # arrival time of the process
										# completion time of finished process or last interrupted time
										__lowerCAmelCase				    =			arrival_time
										__lowerCAmelCase				    =			burst_time  # remaining burst time
										__lowerCAmelCase				    =			0  # total time of the process wait in ready queue
										__lowerCAmelCase				    =			0  # time from arrival time to completion time
class   _UpperCamelCase  :
							'''simple docstring'''







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

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

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[]
										for i in range(len(__a	)	):
													waiting_times.append(queue[i].waiting_time	)
										return waiting_times

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[]
										for i in range(len(__a	)	):
													turnaround_times.append(queue[i].turnaround_time	)
										return turnaround_times

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[]
										for i in range(len(__a	)	):
													completion_times.append(queue[i].stop_time	)
										return completion_times

							def 			snake_case				(     self    ,   __a	):
										return [q.burst_time for q in queue]

							def 			snake_case				(     self    ,   __a	):
										process.waiting_time += self.current_time - process.stop_time
										return process.waiting_time

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			deque()  # sequence deque of finished process
										while len(__a	) != 0:
													__lowerCAmelCase				    =			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(__a	)
													# update current time
													self.current_time += cp.burst_time
													# finish the process and set the process's burst-time 0
													__lowerCAmelCase				    =			0
													# set the process's turnaround time because it is finished
													__lowerCAmelCase				    =			self.current_time - cp.arrival_time
													# set the completion time
													__lowerCAmelCase				    =			self.current_time
													# add the process to queue that has finished queue
													finished.append(__a	)

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

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			deque()  # sequence deque of terminated process
										# just for 1 cycle and unfinished processes will go back to queue
										for _ in range(len(__a	)	):
													__lowerCAmelCase				    =			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(__a	)
													# 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
																__lowerCAmelCase				    =			self.current_time
																# locate the process behind the queue because it is not finished
																ready_queue.append(__a	)
													else:
																# use CPU for remaining burst time
																self.current_time += cp.burst_time
																# set burst time 0 because the process is finished
																__lowerCAmelCase				    =			0
																# set the finish time
																__lowerCAmelCase				    =			self.current_time
																# update the process' turnaround time because it is finished
																__lowerCAmelCase				    =			self.current_time - cp.arrival_time
																# add the process to queue that has finished queue
																finished.append(__a	)

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



							def 			snake_case				(     self	):

										#  all queues except last one have round_robin algorithm
										for i in range(self.number_of_queues - 1	):
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			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, 5_3)
	A     :   Optional[int]			    =			Process("P2", 0, 1_7)
	A     :   Union[str, Any]			    =			Process("P3", 0, 6_8)
	A     :   Dict			    =			Process("P4", 0, 2_4)
	A     :   Any			    =			3
	A     :   Optional[Any]			    =			[1_7, 2_5]
	A     :   Any			    =			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[str]			    =			Process("P1", 0, 5_3)
	A     :   int			    =			Process("P2", 0, 1_7)
	A     :   Tuple			    =			Process("P3", 0, 6_8)
	A     :   List[Any]			    =			Process("P4", 0, 2_4)
	A     :   int			    =			3
	A     :   str			    =			[1_7, 2_5]
	A     :   str			    =			deque([Pa, Pa, Pa, Pa])
	A     :   List[Any]			    =			MLFQ(number_of_queues, time_slices, queue, 0)
	A     :   Optional[Any]			    =			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 dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

__A				='''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''




@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 42




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					super().__init__()

					self.register_modules(
					    prior=lowercase						,  image_encoder=lowercase						,  image_processor=lowercase						,  scheduler=lowercase						,  renderer=lowercase						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					if latents is None:
									lowerCamelCase_						=			randn_tensor(lowercase						,  generator=lowercase						,  device=lowercase						,  dtype=lowercase )
					else:
									if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
									lowerCamelCase_						=			latents.to(lowercase )

					lowerCamelCase_						=			latents * scheduler.init_noise_sigma
					return latents



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase=0 )				->       int:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

					lowerCamelCase_						=			torch.device(f'cuda:{gpu_id}' )

					lowerCamelCase_						=			[self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(lowercase						,  lowercase )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					if self.device != torch.device("meta" ) or not hasattr(self.image_encoder						,  "_hf_hook" ):
									return self.device
					for module in self.image_encoder.modules():
									if (
									    hasattr(lowercase						,  "_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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					if isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  torch.Tensor ):
									lowerCamelCase_						=			torch.cat(lowercase						,  axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase						,  axis=0 )

					if not isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			self.image_processor(lowercase						,  return_tensors="pt" ).pixel_values[0].unsqueeze(0 )

					lowerCamelCase_						=			image.to(dtype=self.image_encoder.dtype						,  device=lowercase )

					lowerCamelCase_						=			self.image_encoder(lowercase )["last_hidden_state"]
					lowerCamelCase_						=			image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					lowerCamelCase_						=			image_embeds.repeat_interleave(lowercase						,  dim=0 )

					if do_classifier_free_guidance:
									lowerCamelCase_						=			torch.zeros_like(lowercase )

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowerCamelCase_						=			torch.cat([negative_image_embeds, image_embeds] )

					return image_embeds



	@torch.no_grad()
	@replace_example_docstring(lowercase )
	def __call__(    self						,  lowercase						,  lowercase = 1						,  lowercase = 25						,  lowercase = None						,  lowercase = None						,  lowercase = 4.0						,  lowercase = 64						,  lowercase = "pil"						,  lowercase = True						,  )				->       Union[str, Any]:

					if isinstance(lowercase						,  PIL.Image.Image ):
									lowerCamelCase_						=			1
					elif isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			image.shape[0]
					elif isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  (torch.Tensor, PIL.Image.Image) ):
									lowerCamelCase_						=			len(lowercase )
					else:
									raise ValueError(
									    f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase )}' )

					lowerCamelCase_						=			self._execution_device

					lowerCamelCase_						=			batch_size * num_images_per_prompt

					lowerCamelCase_						=			guidance_scale > 1.0
					lowerCamelCase_						=			self._encode_image(lowercase						,  lowercase						,  lowercase						,  lowercase )

					# prior

					self.scheduler.set_timesteps(lowercase						,  device=lowercase )
					lowerCamelCase_						=			self.scheduler.timesteps

					lowerCamelCase_						=			self.prior.config.num_embeddings
					lowerCamelCase_						=			self.prior.config.embedding_dim

					lowerCamelCase_						=			self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)						,  image_embeds.dtype						,  lowercase						,  lowercase						,  lowercase						,  self.scheduler						,  )

					# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
					lowerCamelCase_						=			latents.reshape(latents.shape[0]						,  lowercase						,  lowercase )

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

									lowerCamelCase_						=			self.prior(
									    lowercase						,  timestep=lowercase						,  proj_embedding=lowercase						,  ).predicted_image_embedding

									# remove the variance
									lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.split(
									    scaled_model_input.shape[2]						,  dim=2 )  # batch_size, num_embeddings, embedding_dim

									if do_classifier_free_guidance is not None:
													lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.chunk(2 )
													lowerCamelCase_						=			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

									lowerCamelCase_						=			self.scheduler.step(
									    lowercase						,  timestep=lowercase						,  sample=lowercase						,  ).prev_sample

					if output_type == "latent":
									return ShapEPipelineOutput(images=lowercase )

					lowerCamelCase_						=			[]
					for i, latent in enumerate(lowercase ):
									print()
									lowerCamelCase_						=			self.renderer.decode(
									    latent[None, :]						,  lowercase						,  size=lowercase						,  ray_batch_size=4096						,  n_coarse_samples=64						,  n_fine_samples=128						,  )

									images.append(lowercase )

					lowerCamelCase_						=			torch.stack(lowercase )

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

					lowerCamelCase_						=			images.cpu().numpy()

					if output_type == "pil":
									lowerCamelCase_						=			[self.numpy_to_pil(lowercase ) for image in images]

					# Offload last model to CPU
					if hasattr(self						,  "final_offload_hook" ) and self.final_offload_hook is not None:
									self.final_offload_hook.offload()

					if not return_dict:
									return (images,)

					return ShapEPipelineOutput(images=lowercase )


