

"""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  :      List[str]							      =		8

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=BITS ):
	lowerCAmelCase__	:						Any  				=	x.device

	lowerCAmelCase__	:						Optional[int]  				=	(x * 2_55).int().clamp(0						,     2_55 )

	lowerCAmelCase__	:						str  				=	2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=A_ )
	lowerCAmelCase__	:						List[Any]  				=	rearrange(A_						,     '''d -> d 1 1''' )
	lowerCAmelCase__	:						Any  				=	rearrange(A_						,     '''b c h w -> b c 1 h w''' )

	lowerCAmelCase__	:						Dict  				=	((x & mask) != 0).float()
	lowerCAmelCase__	:						Any  				=	rearrange(A_						,     '''b c d h w -> b (c d) h w''' )
	lowerCAmelCase__	:						Union[str, Any]  				=	bits * 2 - 1
	return bits

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=BITS ):
	lowerCAmelCase__	:						str  				=	x.device

	lowerCAmelCase__	:						Dict  				=	(x > 0).int()
	lowerCAmelCase__	:						Tuple  				=	2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=A_						,     dtype=torch.intaa )

	lowerCAmelCase__	:						Dict  				=	rearrange(A_						,     '''d -> d 1 1''' )
	lowerCAmelCase__	:						List[str]  				=	rearrange(A_						,     '''b (c d) h w -> b c d h w'''						,     d=8 )
	lowerCAmelCase__	:						Union[str, Any]  				=	reduce(x * mask						,     '''b c d h w -> b c h w'''						,     '''sum''' )
	return (dec / 2_55).clamp(0.0						,     1.0 )

def   __SCREAMING_SNAKE_CASE  (      self						,     A_						,     A_						,     A_						,     A_ = 0.0						,     A_ = True						,     A_=None						,     A_ = True						,     ):
	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)
	lowerCAmelCase__	:						int  				=	timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	lowerCAmelCase__	:						Tuple  				=	self.alphas_cumprod[timestep]
	lowerCAmelCase__	:						Dict  				=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	lowerCAmelCase__	:						Any  				=	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
	lowerCAmelCase__	:						Union[str, Any]  				=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	lowerCAmelCase__	:						str  				=	self.bit_scale
	if self.config.clip_sample:
		lowerCAmelCase__	:						Dict  				=	torch.clamp(A_						,     -scale						,     A_ )

	# 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)
	lowerCAmelCase__	:						str  				=	self._get_variance(A_						,     A_ )
	lowerCAmelCase__	:						List[Any]  				=	eta * variance ** 0.5

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

		lowerCAmelCase__	:						Optional[int]  				=	prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=A_						,     pred_original_sample=A_ )

def   __SCREAMING_SNAKE_CASE  (      self						,     A_						,     A_						,     A_						,     A_="epsilon"						,     A_=None						,     A_ = True						,     ):
	lowerCAmelCase__	:						Union[str, Any]  				=	timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	torch.split(A_						,     sample.shape[1]						,     dim=1 )
	else:
		lowerCAmelCase__	:						List[Any]  				=	None

	# 1. compute alphas, betas
	lowerCAmelCase__	:						List[str]  				=	self.alphas_cumprod[t]
	lowerCAmelCase__	:						Tuple  				=	self.alphas_cumprod[t - 1] if t > 0 else self.one
	lowerCAmelCase__	:						List[str]  				=	1 - alpha_prod_t
	lowerCAmelCase__	:						List[Any]  				=	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":
		lowerCAmelCase__	:						Optional[int]  				=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		lowerCAmelCase__	:						Any  				=	model_output
	else:
		raise ValueError(f'Unsupported prediction_type {prediction_type}.' )

	# 3. Clip "predicted x_0"
	lowerCAmelCase__	:						List[Any]  				=	self.bit_scale
	if self.config.clip_sample:
		lowerCAmelCase__	:						Dict  				=	torch.clamp(A_						,     -scale						,     A_ )

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

	# 6. Add noise
	lowerCAmelCase__	:						int  				=	0
	if t > 0:
		lowerCAmelCase__	:						Optional[Any]  				=	torch.randn(
		    model_output.size()						,     dtype=model_output.dtype						,     layout=model_output.layout						,     generator=A_ ).to(model_output.device )
		lowerCAmelCase__	:						Union[str, Any]  				=	(self._get_variance(A_						,     predicted_variance=A_ ) ** 0.5) * noise

	lowerCAmelCase__	:						Any  				=	pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=A_						,     pred_original_sample=A_ )







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				UNetaDConditionModel   ,lowercase_			:				Union[DDIMScheduler, DDPMScheduler]   ,lowercase_			:				Optional[float] = 1.0   ,):
						super().__init__()
						lowerCAmelCase__	:						Any  				=	bit_scale
						lowerCAmelCase__	:						Optional[int]  				=	(
						    ddim_bit_scheduler_step if isinstance(lowercase_   ,lowercase_		) else ddpm_bit_scheduler_step
						)

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







					@torch.no_grad()
					def __call__(       self			:				List[str]   ,lowercase_			:				Optional[int] = 2_5_6   ,lowercase_			:				Optional[int] = 2_5_6   ,lowercase_			:				Optional[int] = 5_0   ,lowercase_			:				Optional[torch.Generator] = None   ,lowercase_			:				Optional[int] = 1   ,lowercase_			:				Optional[str] = "pil"   ,lowercase_			:				bool = True   ,**lowercase_			:				Tuple   ,):
						lowerCAmelCase__	:						List[Any]  				=	torch.randn(
						    (batch_size, self.unet.config.in_channels, height, width)   ,generator=lowercase_   ,)
						lowerCAmelCase__	:						int  				=	decimal_to_bits(lowercase_		) * self.bit_scale
						lowerCAmelCase__	:						Optional[int]  				=	latents.to(self.device		)

						self.scheduler.set_timesteps(lowercase_		)

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

							# compute the previous noisy sample x_t -> x_t-1
							lowerCAmelCase__	:						int  				=	self.scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						lowerCAmelCase__	:						Optional[Any]  				=	bits_to_decimal(lowercase_		)

						if output_type == "pil":
							lowerCAmelCase__	:						Union[str, Any]  				=	self.numpy_to_pil(lowercase_		)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowercase_		)






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)