
'''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


__lowerCAmelCase												=	8






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=BITS				):
							_snake_case       =  x.device

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

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

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=BITS				):
							_snake_case       =  x.device

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

							_snake_case       =  rearrange(_SCREAMING_SNAKE_CASE							, """d -> d 1 1"""				)
							_snake_case       =  rearrange(_SCREAMING_SNAKE_CASE							, """b (c d) h w -> b c d h w"""							, d=8				)
							_snake_case       =  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							, _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							, ):
							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       =  timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

							# 4. Clip "predicted x_0"
							_snake_case       =  self.bit_scale
							if self.config.clip_sample:
														_snake_case       =  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)
							_snake_case       =  self._get_variance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
							_snake_case       =  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       =  (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       =  (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       =  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       =  model_output.device if torch.is_tensor(_SCREAMING_SNAKE_CASE				) else """cpu"""
														_snake_case       =  torch.randn(model_output.shape							, dtype=model_output.dtype							, generator=_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)
														_snake_case       =  self._get_variance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) ** 0.5 * eta * noise

														_snake_case       =  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							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE="epsilon"							, _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE = True							, ):
							_snake_case       =  timestep

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

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

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

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

							_snake_case       =  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  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 1.0  ,  )					->       Union[str, Any]:
														super().__init__()
														_snake_case       =  bit_scale
														_snake_case       =  (
														    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  ,  )					->       Union[Tuple, ImagePipelineOutput]:
														_snake_case       =  torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)  ,  generator=UpperCAmelCase  ,  )
														_snake_case       =  decimal_to_bits(UpperCAmelCase  ) * self.bit_scale
														_snake_case       =  latents.to(self.device  )

														self.scheduler.set_timesteps(UpperCAmelCase  )

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

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

														_snake_case       =  bits_to_decimal(UpperCAmelCase  )

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

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCAmelCase  )

'''simple docstring'''
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)