



"""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


__snake_case							:     Tuple   =					8


def     _lowercase       (						__snake_case      ,__snake_case=BITS			)		->     Optional[int]:
			__lowerCAmelCase   :						List[Any]				= x.device

			__lowerCAmelCase   :						Optional[Any]				= (x * 255).int().clamp(0      ,255			)

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

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


def     _lowercase       (						__snake_case      ,__snake_case=BITS			)		->     Optional[Any]:
			__lowerCAmelCase   :						Union[str, Any]				= x.device

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

			__lowerCAmelCase   :						int				= rearrange(__snake_case      ,"d -> d 1 1"			)
			__lowerCAmelCase   :						Tuple				= rearrange(__snake_case      ,"b (c d) h w -> b c d h w"      ,d=8			)
			__lowerCAmelCase   :						List[Any]				= reduce(x * mask      ,"b c d h w -> b c h w"      ,"sum"			)
			return (dec / 255).clamp(0.0      ,1.0			)


def     _lowercase       (						self      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case = 0.0      ,__snake_case = True      ,__snake_case=None      ,__snake_case = True      ,)		->     Union[DDIMSchedulerOutput, Tuple]:
			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   :						List[Any]				= timestep - self.config.num_train_timesteps // self.num_inference_steps

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

			__lowerCAmelCase   :						Union[str, 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   :						Any				= self.bit_scale
			if self.config.clip_sample:
						__lowerCAmelCase   :						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)
			__lowerCAmelCase   :						str				= self._get_variance(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= 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   :						List[str]				= (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   :						Dict				= 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   :						int				= model_output.device if torch.is_tensor(__snake_case			) else 'cpu'
						__lowerCAmelCase   :						Any				= torch.randn(model_output.shape      ,dtype=model_output.dtype      ,generator=__snake_case			).to(__snake_case			)
						__lowerCAmelCase   :						Union[str, Any]				= self._get_variance(__snake_case      ,__snake_case			) ** 0.5 * eta * noise

						__lowerCAmelCase   :						List[str]				= prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=__snake_case      ,pred_original_sample=__snake_case			)


def     _lowercase       (						self      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case="epsilon"      ,__snake_case=None      ,__snake_case = True      ,)		->     Union[DDPMSchedulerOutput, Tuple]:
			__lowerCAmelCase   :						Tuple				= timestep

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

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

			# 3. Clip "predicted x_0"
			__lowerCAmelCase   :						Dict				= self.bit_scale
			if self.config.clip_sample:
						__lowerCAmelCase   :						Any				= 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
			__lowerCAmelCase   :						Dict				= (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			__lowerCAmelCase   :						List[Any]				= 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   :						str				= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

			__lowerCAmelCase   :						Union[str, 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     A__     (						UpperCAmelCase_							):




	'''simple docstring'''





	def __init__(  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] = 1.0 ,    )       -> Union[str, Any]:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase   :						Union[str, Any]				= bit_scale
				__lowerCAmelCase   :						Union[str, Any]				= (
				    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:	str ,    _SCREAMING_SNAKE_CASE:	Tuple = 256 ,    _SCREAMING_SNAKE_CASE:	int = 256 ,    _SCREAMING_SNAKE_CASE:	Optional[int] = 50 ,    _SCREAMING_SNAKE_CASE:	int = None ,    _SCREAMING_SNAKE_CASE:	str = 1 ,    _SCREAMING_SNAKE_CASE:	Dict = "pil" ,    _SCREAMING_SNAKE_CASE:	str = True ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> Union[Tuple, ImagePipelineOutput]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width) ,    generator=_lowercase ,    )
				__lowerCAmelCase   :						Union[str, Any]				= 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   :						Union[str, Any]				= self.unet(_lowercase ,    _lowercase).sample

							# compute the previous noisy sample x_t -> x_t-1
							__lowerCAmelCase   :						Tuple				= self.scheduler.step(_lowercase ,    _lowercase ,    _lowercase).prev_sample

				__lowerCAmelCase   :						List[str]				= bits_to_decimal(_lowercase)

				if output_type == "pil":
							__lowerCAmelCase   :						Optional[int]				= self.numpy_to_pil(_lowercase)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_lowercase)




"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	ArgumentParser)       -> str:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> str:

				"""simple docstring"""

				raise NotImplementedError()