


'''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        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				42
				_snake_case						       =				42

				def __init__(     self							,      a_							,      a_							)    ->  Optional[int]:
					super().__init__()
					self.register_modules(unet=a_							,      scheduler=a_							)


				@torch.no_grad()
				def __call__(     self							,      a_ = 1							,      a_ = 2_0_0_0							,      a_ = None							,      a_ = "pil"							,      a_ = True							,      **a_							,      )    ->  Union[ImagePipelineOutput, Tuple]:
					lowercase						:		Dict   					=		self.unet.config.sample_size
					lowercase						:		int   					=		(batch_size, 3, img_size, img_size)

					lowercase						:		List[Any]   					=		self.unet

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

					self.scheduler.set_timesteps(a_							)
					self.scheduler.set_sigmas(a_							)

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

						# correction step
						for _ in range(self.scheduler.config.correct_steps							):
							lowercase						:		str   					=		self.unet(a_							,      a_							).sample
							lowercase						:		List[str]   					=		self.scheduler.step_correct(a_							,      a_							,      generator=a_							).prev_sample

						# prediction step
						lowercase						:		Dict   					=		model(a_							,      a_							).sample
						lowercase						:		Optional[Any]   					=		self.scheduler.step_pred(a_							,      a_							,      a_							,      generator=a_							)

						lowercase					,		lowercase						:		Tuple   					=		output.prev_sample, output.prev_sample_mean

					lowercase						:		Dict   					=		sample_mean.clamp(0							,      1							)
					lowercase						:		List[Any]   					=		sample.cpu().permute(0							,      2							,      3							,      1							).numpy()
					if output_type == "pil":
						lowercase						:		int   					=		self.numpy_to_pil(a_							)

					if not return_dict:
						return (sample,)

					return ImagePipelineOutput(images=a_							)






'''simple docstring'''
from manim import *




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		List[Any]   					=		Rectangle(height=0.5							,      width=0.5							)
					lowercase						:		str   					=		Rectangle(height=0.25							,      width=0.25							)
					lowercase						:		Union[str, Any]   					=		Rectangle(height=0.46							,      width=0.46							).set_stroke(width=0							)

					lowercase						:		List[str]   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Any   					=		[mem.copy() for i in range(6							)]
					lowercase						:		List[str]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Union[str, Any]   					=		Text("CPU"							,      font_size=2_4							)
					lowercase						:		List[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					cpu.move_to([-2.5, -0.5, 0]							)
					self.add(a_							)

					lowercase						:		List[Any]   					=		[mem.copy() for i in range(4							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		Text("GPU"							,      font_size=2_4							)
					lowercase						:		Tuple   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					gpu.move_to([-1, -1, 0]							)
					self.add(a_							)

					lowercase						:		Tuple   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Optional[int]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)

					lowercase						:		Any   					=		Text("Model"							,      font_size=2_4							)
					lowercase						:		str   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					model.move_to([3, -1.0, 0]							)
					self.add(a_							)

					lowercase						:		Dict   					=		[]
					lowercase						:		Tuple   					=		[]
					lowercase						:		List[Any]   					=		[]

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

						lowercase						:		Tuple   					=		Rectangle(height=0.46 / 4							,      width=0.46 / 3							).set_stroke(width=0.0							).set_fill(a_							,      opacity=0.7							)

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

					self.add(*a_							,      *a_							,      *a_							)

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

					lowercase						:		List[str]   					=		Text("Loaded Checkpoint"							,      font_size=2_4							)
					lowercase						:		Optional[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					checkpoint.move_to([3, 0.5, 0]							)

					self.add(a_							)

					lowercase						:		Any   					=		[]
					lowercase						:		int   					=		[]

					for i, rect in enumerate(a_							):
						lowercase						:		str   					=		fill.copy().set_fill(a_							,      opacity=0.7							)
						target.move_to(a_							)
						ckpt_arr.append(a_							)

						lowercase						:		Optional[Any]   					=		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(a_							)
					self.add(*a_							,      *a_							)

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

					lowercase						:		str   					=		MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''							,      font_size=1_8							,      )

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

					self.add(a_							,      a_							)

					lowercase						:		Any   					=		MarkupText(
					    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''							,      font_size=1_8							,      )

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

					lowercase						:		List[Any]   					=		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=2_4							,      )
					step_a.move_to([2, 2, 0]							)

					lowercase						:		Any   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Dict   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Any   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Optional[Any]   					=		Text("Disk"							,      font_size=2_4							)
					lowercase						:		List[str]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					disk.move_to([-4.0, -1.25, 0]							)
					self.play(Write(a_							,      run_time=3							)							,      Write(a_							,      run_time=1							)							,      Create(a_							,      run_time=1							)							)

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

					self.play(FadeOut(a_							)							)

					lowercase						:		List[Any]   					=		MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''							,      font_size=2_4							)
					step_a.move_to([2, 2, 0]							)

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

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

					self.wait()


