




'''simple docstring'''





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


UpperCamelCase_						:      Optional[Any]       =						8





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   List[str]=BITS   )						->			List[Any]:

     """simple docstring"""




     _snake_case							=				x.device

     _snake_case							=				(x * 255).int().clamp(0 ,						255   )

     _snake_case							=				2 ** torch.arange(bits - 1 ,						-1 ,						-1 ,						device=_UpperCamelCase   )
     _snake_case							=				rearrange(_UpperCamelCase ,						"d -> d 1 1"   )
     _snake_case							=				rearrange(_UpperCamelCase ,						"b c h w -> b c 1 h w"   )

     _snake_case							=				((x & mask) != 0).float()
     _snake_case							=				rearrange(_UpperCamelCase ,						"b c d h w -> b (c d) h w"   )
     _snake_case							=				bits * 2 - 1
     return bits





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Dict=BITS   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				x.device

     _snake_case							=				(x > 0).int()
     _snake_case							=				2 ** torch.arange(bits - 1 ,						-1 ,						-1 ,						device=_UpperCamelCase ,						dtype=torch.intaa   )

     _snake_case							=				rearrange(_UpperCamelCase ,						"d -> d 1 1"   )
     _snake_case							=				rearrange(_UpperCamelCase ,						"b (c d) h w -> b c d h w" ,						d=8   )
     _snake_case							=				reduce(x * mask ,						"b c d h w -> b c h w" ,						"sum"   )
     return (dec / 255).clamp(0.0 ,						1.0   )





def      __a     (       self:   Any ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   int ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   float = 0.0 ,						_UpperCamelCase:   bool = True ,						_UpperCamelCase:   Any=None ,						_UpperCamelCase:   bool = True ,						)						->			Union[DDIMSchedulerOutput, Tuple]:

     """simple docstring"""




     if self.num_inference_steps is None:
          raise ValueError(
              "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"   )

     # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
     # Ideally, read DDIM paper in-detail understanding

     # Notation (<variable name> -> <name in paper>
     # - pred_noise_t -> e_theta(x_t, t)
     # - pred_original_sample -> f_theta(x_t, t) or x_0
     # - std_dev_t -> sigma_t
     # - eta -> η
     # - pred_sample_direction -> "direction pointing to x_t"
     # - pred_prev_sample -> "x_t-1"

     # 1. get previous step value (=t-1)
     _snake_case							=				timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     _snake_case							=				self.alphas_cumprod[timestep]
     _snake_case							=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     _snake_case							=				1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     _snake_case							=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     _snake_case							=				self.bit_scale
     if self.config.clip_sample:
          _snake_case							=				torch.clamp(_UpperCamelCase ,						-scale ,						_UpperCamelCase   )

     # 5. compute variance: "sigma_t(η)" -> see formula (16)
     # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
     _snake_case							=				self._get_variance(_UpperCamelCase ,						_UpperCamelCase   )
     _snake_case							=				eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          _snake_case							=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

     # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     _snake_case							=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     _snake_case							=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if eta > 0:
          # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
          _snake_case							=				model_output.device if torch.is_tensor(_UpperCamelCase   ) else "cpu"
          _snake_case							=				torch.randn(model_output.shape ,						dtype=model_output.dtype ,						generator=_UpperCamelCase   ).to(_UpperCamelCase   )
          _snake_case							=				self._get_variance(_UpperCamelCase ,						_UpperCamelCase   ) ** 0.5 * eta * noise

          _snake_case							=				prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=_UpperCamelCase ,						pred_original_sample=_UpperCamelCase   )





def      __a     (       self:   Optional[Any] ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   int ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   Dict="epsilon" ,						_UpperCamelCase:   Dict=None ,						_UpperCamelCase:   bool = True ,						)						->			Union[DDPMSchedulerOutput, Tuple]:

     """simple docstring"""




     _snake_case							=				timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          _snake_case							, _snake_case							=				torch.split(_UpperCamelCase ,						sample.shape[1] ,						dim=1   )
     else:
          _snake_case							=				None

     # 1. compute alphas, betas
     _snake_case							=				self.alphas_cumprod[t]
     _snake_case							=				self.alphas_cumprod[t - 1] if t > 0 else self.one
     _snake_case							=				1 - alpha_prod_t
     _snake_case							=				1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if prediction_type == "epsilon":
          _snake_case							=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          _snake_case							=				model_output
     else:
          raise ValueError(F"""Unsupported prediction_type {prediction_type}."""   )

     # 3. Clip "predicted x_0"
     _snake_case							=				self.bit_scale
     if self.config.clip_sample:
          _snake_case							=				torch.clamp(_UpperCamelCase ,						-scale ,						_UpperCamelCase   )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     _snake_case							=				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     _snake_case							=				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     _snake_case							=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     _snake_case							=				0
     if t > 0:
          _snake_case							=				torch.randn(
              model_output.size() ,						dtype=model_output.dtype ,						layout=model_output.layout ,						generator=_UpperCamelCase   ).to(model_output.device   )
          _snake_case							=				(self._get_variance(_UpperCamelCase ,						predicted_variance=_UpperCamelCase   ) ** 0.5) * noise

     _snake_case							=				pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=_UpperCamelCase ,						pred_original_sample=_UpperCamelCase   )





class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = 1.0 ,)							->      Tuple:
          super().__init__()
          _snake_case							=				bit_scale
          _snake_case							=				(
              ddim_bit_scheduler_step if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else ddpm_bit_scheduler_step
          )

          self.register_modules(unet=_SCREAMING_SNAKE_CASE ,scheduler=_SCREAMING_SNAKE_CASE   )






     @torch.no_grad()
     def __call__(			self ,_SCREAMING_SNAKE_CASE = 256 ,_SCREAMING_SNAKE_CASE = 256 ,_SCREAMING_SNAKE_CASE = 50 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 1 ,_SCREAMING_SNAKE_CASE = "pil" ,_SCREAMING_SNAKE_CASE = True ,**_SCREAMING_SNAKE_CASE ,)							->      Union[Tuple, ImagePipelineOutput]:
          _snake_case							=				torch.randn(
              (batch_size, self.unet.config.in_channels, height, width) ,generator=_SCREAMING_SNAKE_CASE ,)
          _snake_case							=				decimal_to_bits(_SCREAMING_SNAKE_CASE   ) * self.bit_scale
          _snake_case							=				latents.to(self.device   )

          self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

          for t in self.progress_bar(self.scheduler.timesteps   ):
               # predict the noise residual
               _snake_case							=				self.unet(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).sample

               # compute the previous noisy sample x_t -> x_t-1
               _snake_case							=				self.scheduler.step(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).prev_sample

          _snake_case							=				bits_to_decimal(_SCREAMING_SNAKE_CASE   )

          if output_type == "pil":
               _snake_case							=				self.numpy_to_pil(_SCREAMING_SNAKE_CASE   )

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''





from manim import *

class        _a    (						__lowerCAmelCase							):






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				Rectangle(height=0.5 ,width=0.5   )
          _snake_case							=				Rectangle(height=0.4_6 ,width=0.4_6   ).set_stroke(width=0   )

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

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

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

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

          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               rect.set_stroke(_SCREAMING_SNAKE_CASE   )
               # target = fill.copy().set_fill(YELLOW, opacity=0.7)
               # target.move_to(rect)
               # self.add(target)

               _snake_case							=				Rectangle(height=0.4_6 / 4 ,width=0.4_6 / 3   ).set_stroke(width=0.0   ).set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )

               if i == 0:
                    cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   ) ,buff=0.0_2 ,direction=_SCREAMING_SNAKE_CASE   )
                    cpu_target.set_x(cpu_target.get_x() + 0.1   )
               elif i == 3:
                    cpu_target.next_to(cpu_targs[0] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               else:
                    cpu_target.next_to(cpu_targs[i - 1] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               self.add(_SCREAMING_SNAKE_CASE   )
               cpu_targs.append(_SCREAMING_SNAKE_CASE   )

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

          _snake_case							=				Text("Loaded Checkpoint" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,aligned_edge=_SCREAMING_SNAKE_CASE ,buff=0.4   )
          checkpoint.move_to([3, 0.5, 0]   )

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

          _snake_case							=				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(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

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

          blue_text.next_to(_SCREAMING_SNAKE_CASE ,DOWN * 2.4 ,aligned_edge=key_text.get_left()   )

          _snake_case							=				MarkupText(
              f"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" ,font_size=24 ,)
          step_a.move_to([2, 2, 0]   )
          self.play(Write(_SCREAMING_SNAKE_CASE   ) ,Write(_SCREAMING_SNAKE_CASE   )   )

          self.play(Write(_SCREAMING_SNAKE_CASE ,run_time=1   ) ,Create(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

          _snake_case							=				[]
          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				fill.copy().set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )
               target.move_to(_SCREAMING_SNAKE_CASE   )
               first_animations.append(GrowFromCenter(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

               _snake_case							=				target.copy()
               cpu_target.generate_target()
               if i < 5:
                    cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
               else:
                    cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
               second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE ,run_time=1.5   )   )

          self.play(*_SCREAMING_SNAKE_CASE   )
          self.play(*_SCREAMING_SNAKE_CASE   )
          self.wait()


