



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


__A       =						8



def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Any=BITS    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       x.device

						UpperCAmelCase_=       (x * 2_55).int().clamp(0      ,2_55    )

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

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



def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : List[Any]=BITS    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       x.device

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

						UpperCAmelCase_=       rearrange(lowerCAmelCase_      ,"""d -> d 1 1"""    )
						UpperCAmelCase_=       rearrange(lowerCAmelCase_      ,"""b (c d) h w -> b c d h w"""      ,d=8    )
						UpperCAmelCase_=       reduce(x * mask      ,"""b c d h w -> b c h w"""      ,"""sum"""    )
						return (dec / 2_55).clamp(0.0      ,1.0    )



def 	__a					(   self       : Dict      ,lowerCAmelCase_       : torch.FloatTensor      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : torch.FloatTensor      ,lowerCAmelCase_       : float = 0.0      ,lowerCAmelCase_       : bool = True      ,lowerCAmelCase_       : Optional[int]=None      ,lowerCAmelCase_       : 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)
						UpperCAmelCase_=       timestep - self.config.num_train_timesteps // self.num_inference_steps

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

						UpperCAmelCase_=       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
						UpperCAmelCase_=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

						# 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)
						UpperCAmelCase_=       self._get_variance(lowerCAmelCase_      ,lowerCAmelCase_    )
						UpperCAmelCase_=       eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												UpperCAmelCase_=       (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
						UpperCAmelCase_=       (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
						UpperCAmelCase_=       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
												UpperCAmelCase_=       model_output.device if torch.is_tensor(lowerCAmelCase_    ) else """cpu"""
												UpperCAmelCase_=       torch.randn(model_output.shape      ,dtype=model_output.dtype      ,generator=lowerCAmelCase_    ).to(lowerCAmelCase_    )
												UpperCAmelCase_=       self._get_variance(lowerCAmelCase_      ,lowerCAmelCase_    ) ** 0.5 * eta * noise

												UpperCAmelCase_=       prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_      ,pred_original_sample=lowerCAmelCase_    )



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






						'''simple docstring'''



						UpperCAmelCase_=       timestep

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

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

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

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

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

						UpperCAmelCase_=       pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_      ,pred_original_sample=lowerCAmelCase_    )






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						str						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  __UpperCAmelCase     :						Optional[float] = 1.0						,  )      ->			int:
							super().__init__()
							UpperCAmelCase_=       bit_scale
							UpperCAmelCase_=       (
							    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     :						Any						,  __UpperCAmelCase     :						Optional[int] = 256						,  __UpperCAmelCase     :						Optional[int] = 256						,  __UpperCAmelCase     :						Optional[int] = 50						,  __UpperCAmelCase     :						Optional[torch.Generator] = None						,  __UpperCAmelCase     :						Optional[int] = 1						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  **__UpperCAmelCase     :						List[Any]						,  )      ->			Union[Tuple, ImagePipelineOutput]:
							UpperCAmelCase_=       torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)						,  generator=__UpperCAmelCase						,  )
							UpperCAmelCase_=       decimal_to_bits(__UpperCAmelCase					) * self.bit_scale
							UpperCAmelCase_=       latents.to(self.device					)

							self.scheduler.set_timesteps(__UpperCAmelCase					)

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

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

							UpperCAmelCase_=       bits_to_decimal(__UpperCAmelCase					)

							if output_type == "pil":
													UpperCAmelCase_=       self.numpy_to_pil(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)









from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCAmelCase_=       DDIMScheduler.from_config(scheduler.config					)

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




	@torch.no_grad()
	def __call__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  __UpperCAmelCase     :						float = 0.0						,  __UpperCAmelCase     :						int = 50						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[ImagePipelineOutput, Tuple]:

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													UpperCAmelCase_=       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
							else:
													UpperCAmelCase_=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) and len(__UpperCAmelCase					) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(__UpperCAmelCase					)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase					)

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase_=       self.scheduler.step(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).prev_sample

							UpperCAmelCase_=       (image / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					).numpy()
							if output_type == "pil":
													UpperCAmelCase_=       self.numpy_to_pil(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




