



'''simple docstring'''

from manim import *


class 	a__     (				_lowercase	):


	def 				lowercase__  (self : Optional[Any]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= Rectangle(height=0.5,					width=0.5      )
					SCREAMING_SNAKE_CASE  :						str      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= VGroup(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= Text('''CPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					cpu.move_to([-2.5, -0.5, 0]      )
					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= [mem.copy() for i in range(1      )]
					SCREAMING_SNAKE_CASE  :						Dict      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= Text('''GPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					gpu.align_to(__UpperCAmelCase,					__UpperCAmelCase      )
					gpu.set_x(gpu.get_x() - 1      )

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Any      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Text('''Model''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					model.move_to([3, -1.0, 0]      )

					self.play(
					    Create(__UpperCAmelCase,					run_time=1      ),					Create(__UpperCAmelCase,					run_time=1      ),					Create(__UpperCAmelCase,					run_time=1      ),					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= MarkupText(
					    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',					font_size=24,					)

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= Square(side_length=2.2      )
					key.move_to([-5, 2, 0]      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',					font_size=18,					)

					key_text.move_to([-5, 2.4, 0]      )

					step_a.move_to([2, 2, 0]      )
					self.play(Write(__UpperCAmelCase,					run_time=2.5      ),					Write(__UpperCAmelCase      ),					Write(__UpperCAmelCase      )      )

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						Dict      					= []
					for i, rect in enumerate(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0.0      ).set_fill(__UpperCAmelCase,					opacity=0.7      )
									cpu_target.move_to(__UpperCAmelCase      )
									cpu_target.generate_target()
									SCREAMING_SNAKE_CASE  :						List[str]      					= 0.46 / 4
									SCREAMING_SNAKE_CASE  :						Any      					= 0.46 / 3

									if i == 0:
													cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      ),					buff=0.02,					direction=__UpperCAmelCase      )
													cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
									elif i == 3:
													cpu_target.target.next_to(cpu_targs[0].target,					direction=__UpperCAmelCase,					buff=0.0      )
									else:
													cpu_target.target.next_to(cpu_targs[i - 1].target,					direction=__UpperCAmelCase,					buff=0.0      )
									cpu_targs.append(__UpperCAmelCase      )

									first_animations.append(rect.animate(run_time=0.5      ).set_stroke(__UpperCAmelCase      )      )
									second_animations.append(MoveToTarget(__UpperCAmelCase,					run_time=1.5      )      )

					self.play(*__UpperCAmelCase      )
					self.play(*__UpperCAmelCase      )

					self.wait()









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




