



'''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							:Optional[Any]       = 8



def       a      (			lowerCamelCase__						,				lowerCamelCase__=BITS   ):
			'''simple docstring'''





			A_     :						List[Any]     =					x.device

			A_     :						Any     =					(x * 2_55).int().clamp(0						,				2_55   )

			A_     :						List[str]     =					2 ** torch.arange(bits - 1						,				-1						,				-1						,				device=_a   )
			A_     :						Optional[Any]     =					rearrange(_a						,				"""d -> d 1 1"""   )
			A_     :						Tuple     =					rearrange(_a						,				"""b c h w -> b c 1 h w"""   )

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



def       a      (			lowerCamelCase__						,				lowerCamelCase__=BITS   ):
			'''simple docstring'''





			A_     :						List[str]     =					x.device

			A_     :						int     =					(x > 0).int()
			A_     :						List[str]     =					2 ** torch.arange(bits - 1						,				-1						,				-1						,				device=_a						,				dtype=torch.intaa   )

			A_     :						Optional[int]     =					rearrange(_a						,				"""d -> d 1 1"""   )
			A_     :						Union[str, Any]     =					rearrange(_a						,				"""b (c d) h w -> b c d h w"""						,				d=8   )
			A_     :						str     =					reduce(x * mask						,				"""b c d h w -> b c h w"""						,				"""sum"""   )
			return (dec / 2_55).clamp(0.0						,				1.0   )



def       a      (			self						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = 0.0						,				lowerCamelCase__ = True						,				lowerCamelCase__=None						,				lowerCamelCase__ = True						,				):
			'''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_     :						Optional[Any]     =					timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

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

						A_     :						List[str]     =					prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=_a						,				pred_original_sample=_a   )



def       a      (			self						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__="epsilon"						,				lowerCamelCase__=None						,				lowerCamelCase__ = True						,				):
			'''simple docstring'''





			A_     :						Optional[int]     =					timestep

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

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

			# 3. Clip "predicted x_0"
			A_     :						str     =					self.bit_scale
			if self.config.clip_sample:
						A_     :						Optional[Any]     =					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
			A_     :						Optional[Any]     =					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			A_     :						Dict     =					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_     :						Optional[int]     =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

			A_     :						Optional[int]     =					pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=_a						,				pred_original_sample=_a   )







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase	,				lowercase = 1.0	,				):
				super().__init__()
				A_     :						Tuple     =					bit_scale
				A_     :						Optional[int]     =					(
				    ddim_bit_scheduler_step if isinstance(lowerCamelCase_	,				lowerCamelCase_							) else ddpm_bit_scheduler_step
				)

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


	@torch.no_grad()
	def __call__(self	,				lowercase = 256	,				lowercase = 256	,				lowercase = 50	,				lowercase = None	,				lowercase = 1	,				lowercase = "pil"	,				lowercase = True	,				**lowercase	,				):
				A_     :						Any     =					torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width)	,				generator=lowerCamelCase_	,				)
				A_     :						Tuple     =					decimal_to_bits(lowerCamelCase_							) * self.bit_scale
				A_     :						Tuple     =					latents.to(self.device							)

				self.scheduler.set_timesteps(lowerCamelCase_							)

				for t in self.progress_bar(self.scheduler.timesteps							):
							# predict the noise residual
							A_     :						Union[str, Any]     =					self.unet(lowerCamelCase_	,				lowerCamelCase_							).sample

							# compute the previous noisy sample x_t -> x_t-1
							A_     :						Any     =					self.scheduler.step(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_							).prev_sample

				A_     :						List[str]     =					bits_to_decimal(lowerCamelCase_							)

				if output_type == "pil":
							A_     :						str     =					self.numpy_to_pil(lowerCamelCase_							)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=lowerCamelCase_							)




'''simple docstring'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()