
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[int]	       =							8



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   int=BITS  )   ->			int:






						'''simple docstring'''

						_lowercase       :	Tuple           =   x.device

						_lowercase       :	Tuple           =   (x * 255).int().clamp(0 ,			255  )

						_lowercase       :	Optional[int]           =   2 ** torch.arange(bits - 1 ,			-1 ,			-1 ,			device=_snake_case  )
						_lowercase       :	Tuple           =   rearrange(_snake_case ,			"d -> d 1 1"  )
						_lowercase       :	Optional[Any]           =   rearrange(_snake_case ,			"b c h w -> b c 1 h w"  )

						_lowercase       :	Any           =   ((x & mask) != 0).float()
						_lowercase       :	Optional[int]           =   rearrange(_snake_case ,			"b c d h w -> b (c d) h w"  )
						_lowercase       :	Tuple           =   bits * 2 - 1
						return bits



def 		UpperCamelCase     (       _UpperCAmelCase  :   Tuple ,			_UpperCAmelCase  :   int=BITS  )   ->			List[Any]:






						'''simple docstring'''

						_lowercase       :	List[str]           =   x.device

						_lowercase       :	Optional[Any]           =   (x > 0).int()
						_lowercase       :	str           =   2 ** torch.arange(bits - 1 ,			-1 ,			-1 ,			device=_snake_case ,			dtype=torch.intaa  )

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



def 		UpperCamelCase     (       self  :   Dict ,			_UpperCAmelCase  :   torch.FloatTensor ,			_UpperCAmelCase  :   int ,			_UpperCAmelCase  :   torch.FloatTensor ,			_UpperCAmelCase  :   float = 0.0 ,			_UpperCAmelCase  :   bool = True ,			_UpperCAmelCase  :   str=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)
						_lowercase       :	List[Any]           =   timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						_lowercase       :	List[Any]           =   self.alphas_cumprod[timestep]
						_lowercase       :	List[str]           =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						_lowercase       :	int           =   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
						_lowercase       :	List[str]           =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

						# 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)
						_lowercase       :	Tuple           =   self._get_variance(_snake_case ,			_snake_case  )
						_lowercase       :	Dict           =   eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												_lowercase       :	List[Any]           =   (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
						_lowercase       :	Any           =   (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
						_lowercase       :	Union[str, Any]           =   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
												_lowercase       :	Union[str, Any]           =   model_output.device if torch.is_tensor(_snake_case  ) else "cpu"
												_lowercase       :	Optional[int]           =   torch.randn(model_output.shape ,			dtype=model_output.dtype ,			generator=_snake_case  ).to(_snake_case  )
												_lowercase       :	Any           =   self._get_variance(_snake_case ,			_snake_case  ) ** 0.5 * eta * noise

												_lowercase       :	str           =   prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=_snake_case ,			pred_original_sample=_snake_case  )



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






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   timestep

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

						# 1. compute alphas, betas
						_lowercase       :	Dict           =   self.alphas_cumprod[t]
						_lowercase       :	Any           =   self.alphas_cumprod[t - 1] if t > 0 else self.one
						_lowercase       :	Dict           =   1 - alpha_prod_t
						_lowercase       :	int           =   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":
												_lowercase       :	Any           =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												_lowercase       :	Union[str, Any]           =   model_output
						else:
												raise ValueError(f"""Unsupported prediction_type {prediction_type}."""  )

						# 3. Clip "predicted x_0"
						_lowercase       :	Optional[Any]           =   self.bit_scale
						if self.config.clip_sample:
												_lowercase       :	str           =   torch.clamp(_snake_case ,			-scale ,			_snake_case  )

						# 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
						_lowercase       :	Tuple           =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						_lowercase       :	int           =   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
						_lowercase       :	int           =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

						_lowercase       :	List[Any]           =   pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=_snake_case ,			pred_original_sample=_snake_case  )




class   __lowercase						(						__lowerCAmelCase       ):






	def __init__(self				: Optional[int]      ,	snake_case				: UNetaDConditionModel      ,	snake_case				: Union[DDIMScheduler, DDPMScheduler]      ,	snake_case				: Optional[float] = 1.0      ,	) ->			List[Any]:
							super().__init__()
							_lowercase       :	Union[str, Any]           =   bit_scale
							_lowercase       :	int           =   (
							    ddim_bit_scheduler_step if isinstance(_UpperCAmelCase      ,	_UpperCAmelCase    ) else ddpm_bit_scheduler_step
							)

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






	@torch.no_grad()
	def __call__(self				: List[str]      ,	snake_case				: Optional[int] = 256      ,	snake_case				: Optional[int] = 256      ,	snake_case				: Optional[int] = 50      ,	snake_case				: Optional[torch.Generator] = None      ,	snake_case				: Optional[int] = 1      ,	snake_case				: Optional[str] = "pil"      ,	snake_case				: bool = True      ,	**snake_case				: Union[str, Any]      ,	) ->			Dict:
							_lowercase       :	str           =   torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)      ,	generator=_UpperCAmelCase      ,	)
							_lowercase       :	Union[str, Any]           =   decimal_to_bits(_UpperCAmelCase    ) * self.bit_scale
							_lowercase       :	Dict           =   latents.to(self.device    )

							self.scheduler.set_timesteps(_UpperCAmelCase    )

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

													# compute the previous noisy sample x_t -> x_t-1
													_lowercase       :	Union[str, Any]           =   self.scheduler.step(_UpperCAmelCase      ,	_UpperCAmelCase      ,	_UpperCAmelCase    ).prev_sample

							_lowercase       :	str           =   bits_to_decimal(_UpperCAmelCase    )

							if output_type == "pil":
													_lowercase       :	Dict           =   self.numpy_to_pil(_UpperCAmelCase    )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=_UpperCAmelCase    )










"""simple docstring"""

from manim import *





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  Rectangle(height=0.5       ,				width=0.5						)
       _A     =  Rectangle(height=0.46       ,				width=0.46						).set_stroke(width=0						)

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

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

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

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

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

       _A     =  []
       _A     =  []

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

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

       self.add(*_UpperCAmelCase       ,				*_UpperCAmelCase						)

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

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

       _A     =  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(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  MarkupText(
           F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''       ,				font_size=18       ,				)

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

       _A     =  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(_UpperCAmelCase						)						)

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

       self.play(Write(_UpperCAmelCase						)						)

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

       self.play(FadeOut(_UpperCAmelCase						)						)

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

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

       _A     =  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(_UpperCAmelCase       ,				run_time=3						)						)

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

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

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

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

             _A     =  AnimationGroup(
                 FadeOut(_UpperCAmelCase       ,				run_time=0.5						)       ,				MoveToTarget(_UpperCAmelCase       ,				run_time=0.5						)       ,				FadeIn(_UpperCAmelCase       ,				run_time=0.5						)       ,				lag_ratio=0.2						)

             self.play(_UpperCAmelCase						)

             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:
                         _A     =  0.7

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

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

             _A     =  a_c
             _A     =  a_c.copy()

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

       _A     =  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(_UpperCAmelCase       ,				run_time=3						)       ,				MoveToTarget(_UpperCAmelCase						)						)

       self.wait()

