





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							:			List[Any]         =			8





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=BITS				)							-> Tuple:




    """simple docstring"""




    _A     =							x.device

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

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

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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=BITS				)							-> Tuple:




    """simple docstring"""




    _A     =							x.device

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

    _A     =							rearrange(_SCREAMING_SNAKE_CASE							,     'd -> d 1 1'				)
    _A     =							rearrange(_SCREAMING_SNAKE_CASE							,     'b (c d) h w -> b c d h w'							,     d=8				)
    _A     =							reduce(x * mask							,     'b c d h w -> b c h w'							,     'sum'				)
    return (dec / 255).clamp(0.0							,     1.0				)





def 				__lowerCAmelCase(       self							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE = 0.0							,     _SCREAMING_SNAKE_CASE = True							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE = 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)
    _A     =							timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

    # 4. Clip "predicted x_0"
    _A     =							self.bit_scale
    if self.config.clip_sample:
        _A     =							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)
    _A     =							self._get_variance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    _A     =							eta * variance ** 0.5

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

        _A     =							prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE							,     pred_original_sample=_SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       self							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE="epsilon"							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE = True							,     )							-> Union[DDPMSchedulerOutput, Tuple]:




    """simple docstring"""




    _A     =							timestep

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

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

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

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

    _A     =							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      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_				,     snake_case_ = 1.0				,     ):
         super().__init__()
         _A     =							bit_scale
         _A     =							(
             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				,     snake_case_ = 256				,     snake_case_ = 256				,     snake_case_ = 50				,     snake_case_ = None				,     snake_case_ = 1				,     snake_case_ = "pil"				,     snake_case_ = True				,     **snake_case_				,     ):
         _A     =							torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)				,     generator=a_				,     )
         _A     =							decimal_to_bits(a_							) * self.bit_scale
         _A     =							latents.to(self.device							)

         self.scheduler.set_timesteps(a_							)

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

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

         _A     =							bits_to_decimal(a_							)

         if output_type == "pil":
             _A     =							self.numpy_to_pil(a_							)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=a_							)









'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


