



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						  =		8



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       Optional[Any]=BITS	)->  Dict:
				A__		 =					x.device

				A__		 =					(x * 2_5_5).int().clamp(0 ,       2_5_5	)

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

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Union[str, Any] ,       UpperCamelCase_				:       Tuple=BITS	)->  List[str]:
				A__		 =					x.device

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

				A__		 =					rearrange(UpperCamelCase_ ,       '''d -> d 1 1'''	)
				A__		 =					rearrange(UpperCamelCase_ ,       '''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 / 2_5_5).clamp(0.0 ,       1.0	)



def 		lowerCAmelCase__    (					self				:       Any ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       float = 0.0 ,       UpperCamelCase_				:       bool = True ,       UpperCamelCase_				:       Optional[int]=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__		 =					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(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__		 =					self._get_variance(UpperCamelCase_ ,       UpperCamelCase_	)
				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(UpperCamelCase_	) else '''cpu'''
								A__		 =					torch.randn(model_output.shape ,       dtype=model_output.dtype ,       generator=UpperCamelCase_	).to(UpperCamelCase_	)
								A__		 =					self._get_variance(UpperCamelCase_ ,       UpperCamelCase_	) ** 0.5 * eta * noise

								A__		 =					prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=UpperCamelCase_ ,       pred_original_sample=UpperCamelCase_	)



def 		lowerCAmelCase__    (					self				:       List[Any] ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       Optional[Any]="epsilon" ,       UpperCamelCase_				:       Optional[int]=None ,       UpperCamelCase_				:       bool = True ,       )->  Union[DDPMSchedulerOutput, Tuple]:
				A__		 =					timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								A__ ,	A__		 =					torch.split(UpperCamelCase_ ,       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(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__		 =					(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=UpperCamelCase_	).to(model_output.device	)
								A__		 =					(self._get_variance(UpperCamelCase_ ,       predicted_variance=UpperCamelCase_	) ** 0.5) * noise

				A__		 =					pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=UpperCamelCase_ ,       pred_original_sample=UpperCamelCase_	)



class     _UpperCAmelCase			(       A__     ):




					def __init__(				self ,						a__ ,						a__ ,						a__ = 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 ,						a__ = 2_5_6 ,						a__ = 2_5_6 ,						a__ = 5_0 ,						a__ = None ,						a__ = 1 ,						a__ = "pil" ,						a__ = True ,						**a__ ,						):
									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__)





import numpy as np



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return 1 / (1 + np.exp(-vector	))



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return vector * sigmoid(1.702 * vector	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
