


'''simple docstring'''


from manim import *





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      Rectangle(height=0.5    ,					width=0.5  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      Rectangle(height=0.25    ,					width=0.25  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      Rectangle(height=0.46    ,					width=0.46  ).set_stroke(width=0  )

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

											_SCREAMING_SNAKE_CASE :       int     		=      [mem.copy() for i in range(4  )]
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      VGroup(*__snake_case  ).arrange(__snake_case    ,					buff=0  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      Text("""GPU"""    ,					font_size=24  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      Group(__snake_case    ,					__snake_case  ).arrange(__snake_case    ,					buff=0.5    ,					aligned_edge=__snake_case  )
											gpu.move_to([-1, -1, 0]  )
											self.add(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      [mem.copy() for i in range(6  )]
											_SCREAMING_SNAKE_CASE :       List[str]     		=      VGroup(*__snake_case  ).arrange(__snake_case    ,					buff=0  )

											_SCREAMING_SNAKE_CASE :       str     		=      Text("""Model"""    ,					font_size=24  )
											_SCREAMING_SNAKE_CASE :       Any     		=      Group(__snake_case    ,					__snake_case  ).arrange(__snake_case    ,					buff=0.5    ,					aligned_edge=__snake_case  )
											model.move_to([3, -1.0, 0]  )
											self.add(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      []
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      []
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      []

											for i, rect in enumerate(__snake_case  ):
															rect.set_stroke(__snake_case  )

															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      Rectangle(height=0.46 / 4    ,					width=0.46 / 3  ).set_stroke(width=0.0  ).set_fill(__snake_case    ,					opacity=0.7  )

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

											self.add(*__snake_case    ,					*__snake_case    ,					*__snake_case  )

											_SCREAMING_SNAKE_CASE :       Any     		=      [mem.copy() for i in range(6  )]
											_SCREAMING_SNAKE_CASE :       Dict     		=      VGroup(*__snake_case  ).arrange(__snake_case    ,					buff=0  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      Text("""Loaded Checkpoint"""    ,					font_size=24  )
											_SCREAMING_SNAKE_CASE :       Any     		=      Group(__snake_case    ,					__snake_case  ).arrange(__snake_case    ,					buff=0.5    ,					aligned_edge=__snake_case  )
											checkpoint.move_to([3, 0.5, 0]  )

											self.add(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Any     		=      []
											_SCREAMING_SNAKE_CASE :       int     		=      []

											for i, rect in enumerate(__snake_case  ):
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      fill.copy().set_fill(__snake_case    ,					opacity=0.7  )
															target.move_to(__snake_case  )
															ckpt_arr.append(__snake_case  )

															_SCREAMING_SNAKE_CASE :       List[str]     		=      target.copy()
															if i < 5:
																			cpu_target.move_to(cpu_left_col_base[i + 1]  )
															else:
																			cpu_target.move_to(cpu_right_col_base[i - 5]  )
															ckpt_cpu_arr.append(__snake_case  )
											self.add(*__snake_case    ,					*__snake_case  )

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

											_SCREAMING_SNAKE_CASE :       List[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]  )

											self.add(__snake_case    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       List[str]     		=      MarkupText(
											    f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""    ,					font_size=18    ,					)

											blue_text.next_to(__snake_case    ,					DOWN * 2.4    ,					aligned_edge=key_text.get_left()  )
											self.add(__snake_case  )

											_SCREAMING_SNAKE_CASE :       int     		=      MarkupText(
											    f"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device."""    ,					font_size=24    ,					)
											step_a.move_to([2, 2, 0]  )

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      [meta_mem.copy() for i in range(6  )]
											_SCREAMING_SNAKE_CASE :       Any     		=      [meta_mem.copy() for i in range(6  )]
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      VGroup(*__snake_case  ).arrange(__snake_case    ,					buff=0  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      VGroup(*__snake_case  ).arrange(__snake_case    ,					buff=0  )
											_SCREAMING_SNAKE_CASE :       str     		=      VGroup(__snake_case    ,					__snake_case  ).arrange(__snake_case    ,					buff=0  )
											_SCREAMING_SNAKE_CASE :       int     		=      Text("""Disk"""    ,					font_size=24  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      Group(__snake_case    ,					__snake_case  ).arrange(__snake_case    ,					buff=0.5    ,					aligned_edge=__snake_case  )
											disk.move_to([-4.0, -1.25, 0]  )
											self.play(Write(__snake_case    ,					run_time=3  )    ,					Write(__snake_case    ,					run_time=1  )    ,					Create(__snake_case    ,					run_time=1  )  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      []
											for i, rect in enumerate(__snake_case  ):
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      rect.copy()
															target.generate_target()
															target.target.move_to(disk_left_col_base[i]  ).scale(0.5  )
															animations.append(MoveToTarget(__snake_case    ,					run_time=1.5  )  )
											self.play(*__snake_case  )

											self.play(FadeOut(__snake_case  )  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      MarkupText(f"""Then, the checkpoint is removed from memory\nthrough garbage collection."""    ,					font_size=24  )
											step_a.move_to([2, 2, 0]  )

											self.play(Write(__snake_case    ,					run_time=3  )  )

											self.play(
											    FadeOut(__snake_case    ,					__snake_case    ,					*__snake_case    ,					*__snake_case  )    ,					)

											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     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      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(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




