





'''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_     =	8
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : Tuple=BITS		)		->  Union[str, Any]:
    A       :       str							=						x.device

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

    A       :       List[Any]							=						2 ** torch.arange(bits - 1    ,       -1    ,       -1    ,       device=UpperCamelCase__		)
    A       :       Any							=						rearrange(UpperCamelCase__    ,       '''d -> d 1 1'''		)
    A       :       Any							=						rearrange(UpperCamelCase__    ,       '''b c h w -> b c 1 h w'''		)

    A       :       Optional[int]							=						((x & mask) != 0).float()
    A       :       int							=						rearrange(UpperCamelCase__    ,       '''b c d h w -> b (c d) h w'''		)
    A       :       List[str]							=						bits * 2 - 1
    return bits
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : int=BITS		)		->  Dict:
    A       :       Dict							=						x.device

    A       :       Union[str, Any]							=						(x > 0).int()
    A       :       Dict							=						2 ** torch.arange(bits - 1    ,       -1    ,       -1    ,       device=UpperCamelCase__    ,       dtype=torch.intaa		)

    A       :       str							=						rearrange(UpperCamelCase__    ,       '''d -> d 1 1'''		)
    A       :       Dict							=						rearrange(UpperCamelCase__    ,       '''b (c d) h w -> b c d h w'''    ,       d=8		)
    A       :       Tuple							=						reduce(x * mask    ,       '''b c d h w -> b c h w'''    ,       '''sum'''		)
    return (dec / 255).clamp(0.0    ,       1.0		)
def 							_lowerCamelCase(  self    : int    ,       UpperCamelCase__    : torch.FloatTensor    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : torch.FloatTensor    ,       UpperCamelCase__    : float = 0.0    ,       UpperCamelCase__    : bool = True    ,       UpperCamelCase__    : Optional[Any]=None    ,       UpperCamelCase__    : 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)
    A       :       Any							=						timestep - self.config.num_train_timesteps // self.num_inference_steps

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

    A       :       Tuple							=						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
    A       :       List[Any]							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    A       :       Dict							=						self.bit_scale
    if self.config.clip_sample:
        A       :       List[Any]							=						torch.clamp(UpperCamelCase__    ,       -scale    ,       UpperCamelCase__		)

    # 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)
    A       :       Optional[Any]							=						self._get_variance(UpperCamelCase__    ,       UpperCamelCase__		)
    A       :       Optional[Any]							=						eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        A       :       str							=						(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
    A       :       Optional[Any]							=						(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
    A       :       List[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
        A       :       Optional[int]							=						model_output.device if torch.is_tensor(UpperCamelCase__		) else '''cpu'''
        A       :       str							=						torch.randn(model_output.shape    ,       dtype=model_output.dtype    ,       generator=UpperCamelCase__		).to(UpperCamelCase__		)
        A       :       Tuple							=						self._get_variance(UpperCamelCase__    ,       UpperCamelCase__		) ** 0.5 * eta * noise

        A       :       Any							=						prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=UpperCamelCase__    ,       pred_original_sample=UpperCamelCase__		)
def 							_lowerCamelCase(  self    : Optional[Any]    ,       UpperCamelCase__    : torch.FloatTensor    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : torch.FloatTensor    ,       UpperCamelCase__    : List[str]="epsilon"    ,       UpperCamelCase__    : Tuple=None    ,       UpperCamelCase__    : bool = True    ,       )		->  Union[DDPMSchedulerOutput, Tuple]:
    A       :       List[str]							=						timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        A,	A       :       List[str]							=						torch.split(UpperCamelCase__    ,       sample.shape[1]    ,       dim=1		)
    else:
        A       :       Dict							=						None

    # 1. compute alphas, betas
    A       :       List[str]							=						self.alphas_cumprod[t]
    A       :       List[str]							=						self.alphas_cumprod[t - 1] if t > 0 else self.one
    A       :       Dict							=						1 - alpha_prod_t
    A       :       List[Any]							=						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":
        A       :       str							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        A       :       Tuple							=						model_output
    else:
        raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''		)

    # 3. Clip "predicted x_0"
    A       :       int							=						self.bit_scale
    if self.config.clip_sample:
        A       :       Optional[Any]							=						torch.clamp(UpperCamelCase__    ,       -scale    ,       UpperCamelCase__		)

    # 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
    A       :       Dict							=						(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    A       :       Optional[int]							=						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
    A       :       List[Any]							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

    A       :       Tuple							=						pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=UpperCamelCase__    ,       pred_original_sample=UpperCamelCase__		)





class   _lowercase						(							a				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 1.0    ,   ):
       super().__init__()
       A       :       Optional[Any]							=						bit_scale
       A       :       List[str]							=						(
           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    ,   _UpperCAmelCase = 256    ,   _UpperCAmelCase = 256    ,   _UpperCAmelCase = 50    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = 1    ,   _UpperCAmelCase = "pil"    ,   _UpperCAmelCase = True    ,   **_UpperCAmelCase    ,   ):
       A       :       Union[str, Any]							=						torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)    ,   generator=_UpperCAmelCase    ,   )
       A       :       Any							=						decimal_to_bits(_UpperCAmelCase					) * self.bit_scale
       A       :       Any							=						latents.to(self.device					)

       self.scheduler.set_timesteps(_UpperCAmelCase					)

       for t in self.progress_bar(self.scheduler.timesteps					):
           # predict the noise residual
           A       :       List[str]							=						self.unet(_UpperCAmelCase    ,   _UpperCAmelCase					).sample

           # compute the previous noisy sample x_t -> x_t-1
           A       :       List[str]							=						self.scheduler.step(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					).prev_sample

       A       :       Optional[int]							=						bits_to_decimal(_UpperCAmelCase					)

       if output_type == "pil":
           A       :       int							=						self.numpy_to_pil(_UpperCAmelCase					)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase					)







'''simple docstring'''






from ..utils import DummyObject, requires_backends


class   _lowercase						(							metaclass=a				):
   _UpperCamelCase       		=   ["""onnx"""]


   def __init__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(self    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)
