




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						List[str]		,							A_      :						TransformeraDModel		,							A_      :						AutoencoderKL		,							A_      :						KarrasDiffusionSchedulers		,							A_      :						Optional[Dict[int, str]] = None		,							)     ->			Tuple:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_		,							vae=A_		,							scheduler=A_						)

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_	  =					{}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','						):
                         lowerCamelCase_	  =					int(A_						)
             lowerCamelCase_	  =					dict(sorted(self.labels.items()						)						)

 def        a__				(       self      :						Dict		,							A_      :						Union[str, List[str]]						)     ->			List[int]:

       """simple docstring"""






       if not isinstance(A_		,							A_						):
             lowerCamelCase_	  =					list(A_						)

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""						)

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(       self      :						int		,							A_      :						List[int]		,							A_      :						float = 4.0		,							A_      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,							A_      :						int = 50		,							A_      :						Optional[str] = "pil"		,							A_      :						bool = True		,							)     ->			Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_	  =					len(A_						)
       lowerCamelCase_	  =					self.transformer.config.sample_size
       lowerCamelCase_	  =					self.transformer.config.in_channels

       lowerCamelCase_	  =					randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)		,							generator=A_		,							device=self.device		,							dtype=self.transformer.dtype		,							)
       lowerCamelCase_	  =					torch.cat([latents] * 2						) if guidance_scale > 1 else latents

       lowerCamelCase_	  =					torch.tensor(A_		,							device=self.device						).reshape(-1						)
       lowerCamelCase_	  =					torch.tensor([1000] * batch_size		,							device=self.device						)
       lowerCamelCase_	  =					torch.cat([class_labels, class_null]		,							0						) if guidance_scale > 1 else class_labels

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

       for t in self.progress_bar(self.scheduler.timesteps						):
             if guidance_scale > 1:
                   lowerCamelCase_	  =					latent_model_input[: len(A_						) // 2]
                   lowerCamelCase_	  =					torch.cat([half, half]		,							dim=0						)
             lowerCamelCase_	  =					self.scheduler.scale_model_input(A_		,							A_						)

             lowerCamelCase_	  =					t
             if not torch.is_tensor(A_						):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_	  =					latent_model_input.device.type == 'mps'
                   if isinstance(A_		,							A_						):
                         lowerCamelCase_	  =					torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_	  =					torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_	  =					torch.tensor([timesteps]		,							dtype=A_		,							device=latent_model_input.device						)
             elif len(timesteps.shape						) == 0:
                   lowerCamelCase_	  =					timesteps[None].to(latent_model_input.device						)
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_	  =					timesteps.expand(latent_model_input.shape[0]						)
             # predict noise model_output
             lowerCamelCase_	  =					self.transformer(
                 A_		,							timestep=A_		,							class_labels=A_						).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_  ,       lowerCamelCase_	  =					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_  ,       lowerCamelCase_	  =					torch.split(A_		,							len(A_						) // 2		,							dim=0						)

                   lowerCamelCase_	  =					uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_	  =					torch.cat([half_eps, half_eps]		,							dim=0						)

                   lowerCamelCase_	  =					torch.cat([eps, rest]		,							dim=1						)

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_  ,       lowerCamelCase_	  =					torch.split(A_		,							A_		,							dim=1						)
             else:
                   lowerCamelCase_	  =					noise_pred

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

       if guidance_scale > 1:
             lowerCamelCase_  ,       lowerCamelCase_	  =					latent_model_input.chunk(2		,							dim=0						)
       else:
             lowerCamelCase_	  =					latent_model_input

       lowerCamelCase_	  =					1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_	  =					self.vae.decode(A_						).sample

       lowerCamelCase_	  =					(samples / 2 + 0.5).clamp(0		,							1						)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_	  =					samples.cpu().permute(0		,							2		,							3		,							1						).float().numpy()

       if output_type == "pil":
             lowerCamelCase_	  =					self.numpy_to_pil(A_						)

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_						)









from manim import *







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					Rectangle(height=0.5		,							width=0.5						)
       lowerCamelCase_	  =					Rectangle(height=0.46		,							width=0.46						).set_stroke(width=0						)

       lowerCamelCase_	  =					Rectangle(height=0.25		,							width=0.25						)

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

       lowerCamelCase_	  =					[mem.copy() for i in range(4						)]
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					Text('GPU'		,							font_size=24						)
       lowerCamelCase_	  =					Group(A_		,							A_						).arrange(A_		,							buff=0.5		,							aligned_edge=A_						)
       gpu.move_to([-1, -1, 0]						)
       self.add(A_						)

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

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

       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					[]

       for i, rect in enumerate(A_						):
             lowerCamelCase_	  =					fill.copy().set_fill(A_		,							opacity=0.8						)
             target.move_to(A_						)
             model_arr.append(A_						)

             lowerCamelCase_	  =					Rectangle(height=0.46		,							width=0.46						).set_stroke(width=0.0						).set_fill(A_		,							opacity=0.8						)
             cpu_target.move_to(cpu_left_col_base[i]						)
             model_cpu_arr.append(A_						)

       self.add(*A_		,							*A_						)

       lowerCamelCase_	  =					[meta_mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					[meta_mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(A_		,							A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					Text('Disk'		,							font_size=24						)
       lowerCamelCase_	  =					Group(A_		,							A_						).arrange(A_		,							buff=0.5		,							aligned_edge=A_						)
       disk.move_to([-4, -1.25, 0]						)
       self.add(A_		,							A_						)

       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]						)

       self.add(A_		,							A_						)

       lowerCamelCase_	  =					MarkupText(
           f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""		,							font_size=18		,							)

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

       lowerCamelCase_	  =					MarkupText(
           f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""		,							font_size=24		,							)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_						)						)

       lowerCamelCase_	  =					Square(0.3						)
       input.set_fill(A_		,							opacity=1.0						)
       input.set_stroke(width=0.0						)
       input.next_to(model_base[0]		,							A_		,							buff=0.5						)

       self.play(Write(A_						)						)

       input.generate_target()
       input.target.next_to(model_arr[0]		,							direction=A_		,							buff=0.02						)
       self.play(MoveToTarget(A_						)						)

       self.play(FadeOut(A_						)						)

       lowerCamelCase_	  =					Arrow(start=A_		,							end=A_		,							color=A_		,							buff=0.5						)
       a.next_to(model_arr[0].get_left()		,							A_		,							buff=0.2						)

       model_cpu_arr[0].generate_target()
       model_cpu_arr[0].target.move_to(gpu_rect[0]						)

       lowerCamelCase_	  =					MarkupText(
           f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""		,							font_size=24		,							)
       step_a.move_to([2, 2, 0]						)

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

       lowerCamelCase_	  =					{'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

       self.play(
           Write(A_						)		,							Circumscribe(model_arr[0]		,							color=A_		,							**A_						)		,							Circumscribe(model_cpu_arr[0]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							)
       self.play(MoveToTarget(model_cpu_arr[0]						)						)

       lowerCamelCase_	  =					a.copy()
       for i in range(6						):
             a_c.next_to(model_arr[i].get_right() + 0.02		,							A_		,							buff=0.2						)

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02						)

             lowerCamelCase_	  =					AnimationGroup(
                 FadeOut(A_		,							run_time=0.5						)		,							MoveToTarget(A_		,							run_time=0.5						)		,							FadeIn(A_		,							run_time=0.5						)		,							lag_ratio=0.2						)

             self.play(A_						)

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]						)

             if i < 5:
                   model_cpu_arr[i + 1].generate_target()
                   model_cpu_arr[i + 1].target.move_to(gpu_rect[0]						)
                   if i >= 1:
                         lowerCamelCase_	  =					0.7

                   self.play(
                       Circumscribe(model_arr[i]		,							**A_						)		,							Circumscribe(cpu_left_col_base[i]		,							**A_						)		,							Circumscribe(cpu_left_col_base[i + 1]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							Circumscribe(model_arr[i + 1]		,							color=A_		,							**A_						)		,							)
                   if i < 1:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]						)		,							MoveToTarget(model_cpu_arr[i + 1]						)		,							)
                   else:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]		,							run_time=0.7						)		,							MoveToTarget(model_cpu_arr[i + 1]		,							run_time=0.7						)		,							)
             else:
                   model_cpu_arr[i].generate_target()
                   model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]						)
                   input.generate_target()
                   input.target.next_to(model_arr[-1].get_right()		,							RIGHT + 0.02		,							buff=0.2						)

                   self.play(
                       Circumscribe(model_arr[-1]		,							color=A_		,							**A_						)		,							Circumscribe(cpu_left_col_base[-1]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							)

                   self.play(MoveToTarget(model_cpu_arr[i]						)						)

             lowerCamelCase_	  =					a_c
             lowerCamelCase_	  =					a_c.copy()

       input.generate_target()
       input.target.next_to(model_base[-1]		,							RIGHT + 0.02		,							buff=0.5						)
       self.play(
           FadeOut(A_						)		,							FadeOut(A_		,							run_time=0.5						)		,							)

       lowerCamelCase_	  =					MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""		,							font_size=24						)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_		,							run_time=3						)		,							MoveToTarget(A_						)						)

       self.wait()



