



"""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


lowercase__    :  List[str]							    =	8




def __lowercase			(							_a		,			_a=BITS    ):
    snake_case_ :     Any				=	x.device

    snake_case_ :     str				=	(x * 255).int().clamp(0		,			255    )

    snake_case_ :     Tuple				=	2 ** torch.arange(bits - 1		,			-1		,			-1		,			device=_a    )
    snake_case_ :     Tuple				=	rearrange(_a		,			'''d -> d 1 1'''    )
    snake_case_ :     Any				=	rearrange(_a		,			'''b c h w -> b c 1 h w'''    )

    snake_case_ :     List[str]				=	((x & mask) != 0).float()
    snake_case_ :     Optional[int]				=	rearrange(_a		,			'''b c d h w -> b (c d) h w'''    )
    snake_case_ :     Union[str, Any]				=	bits * 2 - 1
    return bits




def __lowercase			(							_a		,			_a=BITS    ):
    snake_case_ :     str				=	x.device

    snake_case_ :     Any				=	(x > 0).int()
    snake_case_ :     Any				=	2 ** torch.arange(bits - 1		,			-1		,			-1		,			device=_a		,			dtype=torch.intaa    )

    snake_case_ :     List[str]				=	rearrange(_a		,			'''d -> d 1 1'''    )
    snake_case_ :     Dict				=	rearrange(_a		,			'''b (c d) h w -> b c d h w'''		,			d=8    )
    snake_case_ :     Optional[Any]				=	reduce(x * mask		,			'''b c d h w -> b c h w'''		,			'''sum'''    )
    return (dec / 255).clamp(0.0		,			1.0    )




def __lowercase			(							self		,			_a		,			_a		,			_a		,			_a = 0.0		,			_a = True		,			_a=None		,			_a = True		,			):
    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)
    snake_case_ :     List[Any]				=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

    snake_case_ :     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
    snake_case_ :     Optional[int]				=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    snake_case_ :     Tuple				=	self.bit_scale
    if self.config.clip_sample:
        snake_case_ :     Dict				=	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)
    snake_case_ :     Optional[int]				=	self._get_variance(_a		,			_a    )
    snake_case_ :     Union[str, Any]				=	eta * variance ** 0.5

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

        snake_case_ :     Optional[int]				=	prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=_a		,			pred_original_sample=_a    )




def __lowercase			(							self		,			_a		,			_a		,			_a		,			_a="epsilon"		,			_a=None		,			_a = True		,			):
    snake_case_ :     Optional[Any]				=	timestep

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

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

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

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

    snake_case_ :     List[str]				=	pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=_a		,			pred_original_sample=_a    )






class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Union[str, Any]  ,     lowercase_		:       UNetaDConditionModel  ,     lowercase_		:       Union[DDIMScheduler, DDPMScheduler]  ,     lowercase_		:       Optional[float] = 1.0  ,     ):
     super().__init__()
     snake_case_ :     Union[str, Any]				=	bit_scale
     snake_case_ :     Any				=	(
         ddim_bit_scheduler_step if isinstance(lowercase_  ,     lowercase_				) else ddpm_bit_scheduler_step
     )

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






 @torch.no_grad()
 def __call__(     self		:       Tuple  ,     lowercase_		:       Optional[int] = 256  ,     lowercase_		:       Optional[int] = 256  ,     lowercase_		:       Optional[int] = 50  ,     lowercase_		:       Optional[torch.Generator] = None  ,     lowercase_		:       Optional[int] = 1  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     **lowercase_		:       List[Any]  ,     ):
     snake_case_ :     int				=	torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)  ,     generator=lowercase_  ,     )
     snake_case_ :     str				=	decimal_to_bits(lowercase_				) * self.bit_scale
     snake_case_ :     Dict				=	latents.to(self.device				)

     self.scheduler.set_timesteps(lowercase_				)

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

         # compute the previous noisy sample x_t -> x_t-1
         snake_case_ :     Dict				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

     snake_case_ :     Tuple				=	bits_to_decimal(lowercase_				)

     if output_type == "pil":
         snake_case_ :     List[Any]				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowercase_				)










"""simple docstring"""

from functools import lru_cache




@lru_cache
def __lowercase			(							_a    ):
    if num < 0:
        raise ValueError('''Number should not be negative.'''    )

    return 1 if num in (0, 1) else num * factorial(num - 1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





