"""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


_a      :		Dict           =   8





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Tuple=BITS	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       Union[str, Any]  			=     x.device

  _lowerCAmelCase						:       Any  			=     (x * 255).int().clamp(0			,255	)

  _lowerCAmelCase						:       Union[str, Any]  			=     2 ** torch.arange(bits - 1			,-1			,-1			,device=_lowerCamelCase	)
  _lowerCAmelCase						:       Any  			=     rearrange(_lowerCamelCase			,"""d -> d 1 1"""	)
  _lowerCAmelCase						:       str  			=     rearrange(_lowerCamelCase			,"""b c h w -> b c 1 h w"""	)

  _lowerCAmelCase						:       Dict  			=     ((x & mask) != 0).float()
  _lowerCAmelCase						:       Any  			=     rearrange(_lowerCamelCase			,"""b c d h w -> b (c d) h w"""	)
  _lowerCAmelCase						:       str  			=     bits * 2 - 1
  return bits





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Optional[Any]=BITS	)       ->      List[Any]:
  _lowerCAmelCase						:       List[str]  			=     x.device

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

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





def 							SCREAMING_SNAKE_CASE     (			self						:       List[Any]			,_lowerCamelCase						:       torch.FloatTensor			,_lowerCamelCase						:       int			,_lowerCamelCase						:       torch.FloatTensor			,_lowerCamelCase						:       float = 0.0			,_lowerCamelCase						:       bool = True			,_lowerCamelCase						:       Union[str, Any]=None			,_lowerCamelCase						:       bool = 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						:       Tuple  			=     timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  _lowerCAmelCase						:       List[Any]  			=     self.alphas_cumprod[timestep]
  _lowerCAmelCase						:       Optional[int]  			=     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						:       List[str]  			=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  _lowerCAmelCase						:       List[Any]  			=     self.bit_scale
  if self.config.clip_sample:
    _lowerCAmelCase						:       Union[str, Any]  			=     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)
  _lowerCAmelCase						:       Any  			=     self._get_variance(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       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						:       Dict  			=     (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						:       Dict  			=     (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[Any]  			=     model_output.device if torch.is_tensor(_lowerCamelCase	) else """cpu"""
    _lowerCAmelCase						:       Optional[Any]  			=     torch.randn(model_output.shape			,dtype=model_output.dtype			,generator=_lowerCamelCase	).to(_lowerCamelCase	)
    _lowerCAmelCase						:       Tuple  			=     self._get_variance(_lowerCamelCase			,_lowerCamelCase	) ** 0.5 * eta * noise

    _lowerCAmelCase						:       int  			=     prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCamelCase			,pred_original_sample=_lowerCamelCase	)





def 							SCREAMING_SNAKE_CASE     (			self						:       Optional[Any]			,_lowerCamelCase						:       torch.FloatTensor			,_lowerCamelCase						:       int			,_lowerCamelCase						:       torch.FloatTensor			,_lowerCamelCase						:       Any="epsilon"			,_lowerCamelCase						:       str=None			,_lowerCamelCase						:       bool = True			,)       ->      Union[DDPMSchedulerOutput, Tuple]:
  _lowerCAmelCase						:       str  			=     timestep

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

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

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

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

  _lowerCAmelCase						:       Optional[Any]  			=     pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCamelCase			,pred_original_sample=_lowerCamelCase	)






class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__				,     a__ = 1.0				,     ):
       super().__init__()
       _lowerCAmelCase						:       Optional[int]  			=     bit_scale
       _lowerCAmelCase						:       int  			=     (
           ddim_bit_scheduler_step if isinstance(a__				,     a__  ) else ddpm_bit_scheduler_step
       )

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






     @torch.no_grad()
     def __call__(		self				,     a__ = 256				,     a__ = 256				,     a__ = 50				,     a__ = None				,     a__ = 1				,     a__ = "pil"				,     a__ = True				,     **a__				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)				,     generator=a__				,     )
       _lowerCAmelCase						:       Dict  			=     decimal_to_bits(a__  ) * self.bit_scale
       _lowerCAmelCase						:       Dict  			=     latents.to(self.device  )

       self.scheduler.set_timesteps(a__  )

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

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

       _lowerCAmelCase						:       Dict  			=     bits_to_decimal(a__  )

       if output_type == "pil":
         _lowerCAmelCase						:       Optional[Any]  			=     self.numpy_to_pil(a__  )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__  )





"""simple docstring"""




# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)




