


'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Any:
														super().__init__()
														self.register_modules(unet=lowerCamelCase_      ,					scheduler=lowerCamelCase_     )







							@torch.no_grad()
							def __call__(	self      ,					lowerCamelCase_ = 1      ,					lowerCamelCase_ = 1_00      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = True      ,					) ->							Union[AudioPipelineOutput, Tuple]:

														if audio_length_in_s is None:
																					lowerCAmelCase__      =			self.unet.config.sample_size / self.unet.config.sample_rate

														lowerCAmelCase__      =			audio_length_in_s * self.unet.config.sample_rate

														lowerCAmelCase__      =			2 ** len(self.unet.up_blocks     )
														if sample_size < 3 * down_scale_factor:
																					raise ValueError(
																					    F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
																					    F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""     )

														lowerCAmelCase__      =			int(lowerCamelCase_     )
														if sample_size % down_scale_factor != 0:
																					lowerCAmelCase__      =			(
																					    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
																					) * down_scale_factor
																					logger.info(
																					    F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
																					    F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
																					    ''' process.'''     )
														lowerCAmelCase__      =			int(lowerCamelCase_     )

														lowerCAmelCase__      =			next(iter(self.unet.parameters()     )     ).dtype
														lowerCAmelCase__      =			(batch_size, self.unet.config.in_channels, sample_size)
														if isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and len(lowerCamelCase_     ) != batch_size:
																					raise ValueError(
																					    F"""You have passed a list of generators of length {len(lowerCamelCase_     )}, but requested an effective batch"""
																					    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

														lowerCAmelCase__      =			randn_tensor(lowerCamelCase_      ,					generator=lowerCamelCase_      ,					device=self.device      ,					dtype=lowerCamelCase_     )

														# set step values
														self.scheduler.set_timesteps(lowerCamelCase_      ,					device=audio.device     )
														lowerCAmelCase__      =			self.scheduler.timesteps.to(lowerCamelCase_     )

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

																					# 2. compute previous image: x_t -> t_t-1
																					lowerCAmelCase__      =			self.scheduler.step(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ).prev_sample

														lowerCAmelCase__      =			audio.clamp(-1      ,					1     ).float().cpu().numpy()

														lowerCAmelCase__      =			audio[:, :, :original_sample_size]

														if not return_dict:
																					return (audio,)

														return AudioPipelineOutput(audios=lowerCamelCase_     )



'''simple docstring'''






from manim import *




class 	a__       (							a__						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			Rectangle(height=0.5      ,					width=0.5     )
														lowerCAmelCase__      =			Rectangle(height=0.46      ,					width=0.46     ).set_stroke(width=0     )

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

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

														self.add(lowerCamelCase_     )

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

														lowerCAmelCase__      =			Text('''Model'''      ,					font_size=24     )
														lowerCAmelCase__      =			Group(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0.5      ,					aligned_edge=lowerCamelCase_     )
														model.move_to([3, -1.0, 0]     )

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

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

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

														lowerCAmelCase__      =			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(lowerCamelCase_      ,					run_time=2.5     )      ,					Write(lowerCamelCase_     )      ,					Write(lowerCamelCase_     )     )

														self.add(lowerCamelCase_     )

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

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

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

														self.play(*lowerCamelCase_     )
														self.play(*lowerCamelCase_     )

														self.wait()