"""simple docstring"""





from manim import *


class 					UpperCAmelCase_     (   _a):


      def 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__      :		Optional[int]          =     Rectangle(height=0.5		,      width=0.5							)
             lowercase__      :		Tuple          =     Rectangle(height=0.46		,      width=0.46							).set_stroke(width=0							)
             lowercase__      :		int          =     Rectangle(height=0.25		,      width=0.25							)

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

             lowercase__      :		List[str]          =     [mem.copy() for i in range(4							)]
             lowercase__      :		Optional[Any]          =     VGroup(*a							).arrange(a		,      buff=0							)
             lowercase__      :		int          =     Text('GPU'		,      font_size=2_4							)
             lowercase__      :		Any          =     Group(a		,      a							).arrange(a		,      buff=0.5		,      aligned_edge=a							)
             gpu.move_to([-1, -1, 0]							)
             self.add(a							)

             lowercase__      :		List[Any]          =     [mem.copy() for i in range(6							)]
             lowercase__      :		Any          =     VGroup(*a							).arrange(a		,      buff=0							)

             lowercase__      :		str          =     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__      :		str          =     []
             lowercase__      :		Dict          =     []

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

                    lowercase__      :		Union[str, Any]          =     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							)

             lowercase__      :		Union[str, Any]          =     [meta_mem.copy() for i in range(6							)]
             lowercase__      :		Tuple          =     [meta_mem.copy() for i in range(6							)]
             lowercase__      :		str          =     VGroup(*a							).arrange(a		,      buff=0							)
             lowercase__      :		Tuple          =     VGroup(*a							).arrange(a		,      buff=0							)
             lowercase__      :		int          =     VGroup(a		,      a							).arrange(a		,      buff=0							)
             lowercase__      :		Optional[Any]          =     Text('Disk'		,      font_size=2_4							)
             lowercase__      :		Dict          =     Group(a		,      a							).arrange(a		,      buff=0.5		,      aligned_edge=a							)
             disk.move_to([-4.0, -1.25, 0]							)
             self.add(a		,      a							)

             lowercase__      :		Dict          =     []

             for i in range(6							):
                    lowercase__      :		List[Any]          =     fill.copy().set_fill(a		,      opacity=0.8							)
                    target.move_to(disk_left_col_base[i]							).scale(0.5							)
                    cpu_disk_arr.append(a							)

             self.add(*a							)

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

             lowercase__      :		Union[str, Any]          =     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__      :		int          =     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[str]          =     MarkupText(f"""The offloaded weights are all sent to the CPU."""		,      font_size=2_4							)
             step_a.move_to([2, 2, 0]							)

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

             for i in range(6							):
                    lowercase__      :		List[str]          =     cpu_disk_arr[i]
                    lowercase__      :		Any          =     rect.copy().set_fill(a		,      opacity=0.8							).scale(2.0							)
                    cpa.generate_target()
                    cpa.target.move_to(model_base[i]							)

                    if i == 0:
                           rect.set_fill(a		,      opacity=0.8							)
                           rect.generate_target()
                           rect.target.move_to(cpu_left_col_base[0]							).scale(2.0							)

                           self.remove(
                               *a		,      *a		,      )

                    else:
                           rect.generate_target()
                           rect.target.move_to(cpu_left_col_base[i]							).scale(2.0							)
                    self.play(MoveToTarget(a							)		,      MoveToTarget(a							)		,      model_base[i].animate.set_stroke(a							)							)
             self.play(FadeOut(a							)							)

             lowercase__      :		str          =     MarkupText(
                 f"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."""		,      font_size=2_4		,      )
             step_a.move_to([2, 2, 0]							)

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

             lowercase__      :		List[str]          =     []
             lowercase__      :		Dict          =     []
             for i in range(6							):
                    lowercase__      :		int          =     Arrow(start=a		,      end=a		,      color=a		,      buff=0.5							)
                    a.next_to(model_base[i].get_left()		,      a		,      buff=0.2							)
                    arrows.append(a							)
                    animations.append(Write(a							)							)
             self.play(*a							)
             self.wait()


"""simple docstring"""





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

             super().__init__()
             self.register_modules(
                 vae=a		,      text_encoder=a		,      tokenizer=a		,      unet=a		,      scheduler=a		,      )

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    lowercase__      :		Tuple          =     self.noise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

                    # j - model_input index, k - base index
                    for j in range(2							):
                           lowercase__      :		Any          =     j ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 0:
                                         lowercase__   ,				lowercase__      :		Tuple          =     j, k

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		Any          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

             self.scheduler.set_timesteps(a		,      self.device							)

             lowercase__      :		Dict          =     num_inference_steps - int(num_inference_steps * strength							)
             lowercase__      :		str          =     self.scheduler.timesteps[t_limit:]
             lowercase__      :		int          =     fwd_timesteps.flip(0							)

             lowercase__      :		int          =     self.prepare_latents(a		,      a		,      a		,      a		,      a							)

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    # j - model_input index, k - base index
                    for k in range(2							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 1:
                                         lowercase__   ,				lowercase__      :		int          =     j, k

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		int          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        f"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'		,      '1.0.0'		,      a		,      standard_warn=a							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

