
from manim import *





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					Rectangle(height=0.5				,width=0.5					)
					__A       =					Rectangle(height=0.46				,width=0.46					).set_stroke(width=0					)

					__A       =					[mem.copy() for i in range(6					)]
					__A       =					[mem.copy() for i in range(6					)]
					__A       =					VGroup(*A					).arrange(A				,buff=0					)
					__A       =					VGroup(*A					).arrange(A				,buff=0					)
					__A       =					VGroup(A				,A					).arrange(A				,buff=0					)
					__A       =					Text("CPU"				,font_size=24					)
					__A       =					Group(A				,A					).arrange(A				,buff=0.5				,aligned_edge=A					)
					cpu.move_to([-2.5, -0.5, 0]					)
					self.add(A					)

					__A       =					[mem.copy() for i in range(1					)]
					__A       =					VGroup(*A					).arrange(A				,buff=0					)
					__A       =					Text("GPU"				,font_size=24					)
					__A       =					Group(A				,A					).arrange(A				,buff=0.5				,aligned_edge=A					)
					gpu.align_to(A				,A					)
					gpu.set_x(gpu.get_x() - 1					)

					self.add(A					)

					__A       =					[mem.copy() for i in range(6					)]
					__A       =					VGroup(*A					).arrange(A				,buff=0					)

					__A       =					Text("Model"				,font_size=24					)
					__A       =					Group(A				,A					).arrange(A				,buff=0.5				,aligned_edge=A					)
					model.move_to([3, -1.0, 0]					)

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

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

					__A       =					Square(side_length=2.2					)
					key.move_to([-5, 2, 0]					)

					__A       =					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(A				,run_time=2.5					)				,Write(A					)				,Write(A					)					)

					self.add(A					)

					__A       =					[]
					__A       =					[]
					__A       =					[]
					for i, rect in enumerate(A					):
									__A       =					Rectangle(height=0.46				,width=0.46					).set_stroke(width=0.0					).set_fill(A				,opacity=0.7					)
									cpu_target.move_to(A					)
									cpu_target.generate_target()
									__A       =					0.46 / 4
									__A       =					0.46 / 3

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

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

					self.play(*A					)
					self.play(*A					)

					self.wait()








from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

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






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

								snake_case_           =				(image / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
								if output_type == "pil":
															snake_case_           =				self.numpy_to_pil(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





