





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 				=       8

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :int=BITS     )      ->						Dict:
  _lowercase							= x.device

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

  _lowercase							= 2 ** torch.arange(bits - 1			,							-1			,							-1			,							device=SCREAMING_SNAKE_CASE_     )
  _lowercase							= rearrange(SCREAMING_SNAKE_CASE_			,							'd -> d 1 1'     )
  _lowercase							= rearrange(SCREAMING_SNAKE_CASE_			,							'b c h w -> b c 1 h w'     )

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :Optional[Any]=BITS     )      ->						Union[str, Any]:
  _lowercase							= x.device

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

  _lowercase							= rearrange(SCREAMING_SNAKE_CASE_			,							'd -> d 1 1'     )
  _lowercase							= rearrange(SCREAMING_SNAKE_CASE_			,							'b (c d) h w -> b c d h w'			,							d=8     )
  _lowercase							= 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[str]			,							snake_case__  :Optional[int]			,							snake_case__  :Dict			,							snake_case__  :List[Any]			,							snake_case__  :Optional[Any] = 0.0			,							snake_case__  :Tuple = True			,							snake_case__  :Tuple=None			,							snake_case__  :List[str] = 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)
  _lowercase							= timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

  # 4. Clip "predicted x_0"
  _lowercase							= self.bit_scale
  if self.config.clip_sample:
    _lowercase							= torch.clamp(SCREAMING_SNAKE_CASE_			,							-scale			,							SCREAMING_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							= self._get_variance(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_     )
  _lowercase							= eta * variance ** 0.5

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

    _lowercase							= prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_			,							pred_original_sample=SCREAMING_SNAKE_CASE_     )

def 							SCREAMING_SNAKE_CASE__       (  self  :Tuple			,							snake_case__  :Tuple			,							snake_case__  :int			,							snake_case__  :str			,							snake_case__  :str="epsilon"			,							snake_case__  :Union[str, Any]=None			,							snake_case__  :Optional[int] = True			,							)      ->						Union[DDPMSchedulerOutput, Tuple]:
  _lowercase							= timestep

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

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

  # 3. Clip "predicted x_0"
  _lowercase							= self.bit_scale
  if self.config.clip_sample:
    _lowercase							= torch.clamp(SCREAMING_SNAKE_CASE_			,							-scale			,							SCREAMING_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							= (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  _lowercase							= 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							= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

  _lowercase							= pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_			,							pred_original_sample=SCREAMING_SNAKE_CASE_     )







class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





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

         self.scheduler.set_timesteps(__A  )

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

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

         _lowercase							= bits_to_decimal(__A  )

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

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=__A  )










# 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.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)