




def    A						(__A   :    int       )   ->      int:







     """simple docstring"""


     if a < 0:
          raise ValueError('''Input value must be a positive integer'''       )
     elif isinstance(__A   ,				__A       ):
          raise TypeError('''Input value must be a \'int\' type'''       )
     return bin(__A       ).count('''1'''       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()












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_ : List[str]    		=       8


def    A						(__A   :    Union[str, Any]   ,				__A   :    List[Any]=BITS       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

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

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

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


def    A						(__A   :    Dict   ,				__A   :    Tuple=BITS       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

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

     UpperCAmelCase_          =							rearrange(__A   ,				'''d -> d 1 1'''       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''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 / 255).clamp(0.0   ,				1.0       )


def    A						(self   :    List[Any]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    float = 0.0   ,				__A   :    bool = True   ,				__A   :    Tuple=None   ,				__A   :    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(__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)
     UpperCAmelCase_          =							self._get_variance(__A   ,				__A       )
     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(__A       ) else '''cpu'''
          UpperCAmelCase_          =							torch.randn(model_output.shape   ,				dtype=model_output.dtype   ,				generator=__A       ).to(__A       )
          UpperCAmelCase_          =							self._get_variance(__A   ,				__A       ) ** 0.5 * eta * noise

          UpperCAmelCase_          =							prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )


def    A						(self   :    Optional[int]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    int="epsilon"   ,				__A   :    Optional[Any]=None   ,				__A   :    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(__A   ,				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(__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
     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=__A       ).to(model_output.device       )
          UpperCAmelCase_          =							(self._get_variance(__A   ,				predicted_variance=__A       ) ** 0.5) * noise

     UpperCAmelCase_          =							pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )







class __snake_case			(			a		):







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						UNetaDConditionModel					,						_snake_case		:						Union[DDIMScheduler, DDPMScheduler]					,						_snake_case		:						Optional[float] = 1.0					,						):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							bit_scale
        UpperCAmelCase_          =							(
            ddim_bit_scheduler_step if isinstance(_snake_case					,						_snake_case) else ddpm_bit_scheduler_step
        )

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







   @torch.no_grad()
   def __call__(      self		:						Union[str, Any]					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 50					,						_snake_case		:						Optional[torch.Generator] = None					,						_snake_case		:						Optional[int] = 1					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						**_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)					,						generator=_snake_case					,						)
        UpperCAmelCase_          =							decimal_to_bits(_snake_case) * self.bit_scale
        UpperCAmelCase_          =							latents.to(self.device)

        self.scheduler.set_timesteps(_snake_case)

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

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

        UpperCAmelCase_          =							bits_to_decimal(_snake_case)

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

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case)






