


"""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


a_       							=    8






def      a__ (				__lowercase     ,			__lowercase=BITS   )	->	Optional[int]:
				_A					  =			x.device

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

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

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






def      a__ (				__lowercase     ,			__lowercase=BITS   )	->	str:
				_A					  =			x.device

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

				_A					  =			rearrange(snake_case__     ,			"d -> d 1 1"   )
				_A					  =			rearrange(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      a__ (				self     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = 0.0     ,			__lowercase = True     ,			__lowercase=None     ,			__lowercase = True     ,			)	->	List[str]:
				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(snake_case__     ,			-scale     ,			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(snake_case__     ,			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(snake_case__   ) else "cpu"
								_A					  =			torch.randn(model_output.shape     ,			dtype=model_output.dtype     ,			generator=snake_case__   ).to(snake_case__   )
								_A					  =			self._get_variance(snake_case__     ,			snake_case__   ) ** 0.5 * eta * noise

								_A					  =			prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=snake_case__     ,			pred_original_sample=snake_case__   )






def      a__ (				self     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase="epsilon"     ,			__lowercase=None     ,			__lowercase = True     ,			)	->	List[str]:
				_A					  =			timestep

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

				_A					  =			pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=snake_case__     ,			pred_original_sample=snake_case__   )




class snake_case      (  _lowercase):







		def __init__(					self      :       Union[str, Any]    ,   a__      :       UNetaDConditionModel    ,   a__      :       Union[DDIMScheduler, DDPMScheduler]    ,   a__      :       Optional[float] = 1.0    ,   )       ->						Optional[int]:





						'''simple docstring'''







						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      :       Tuple    ,   a__      :       Optional[int] = 2_56    ,   a__      :       Optional[int] = 2_56    ,   a__      :       Optional[int] = 50    ,   a__      :       Optional[torch.Generator] = None    ,   a__      :       Optional[int] = 1    ,   a__      :       Optional[str] = "pil"    ,   a__      :       bool = True    ,   **a__      :       Optional[Any]    ,   )       ->						Union[Tuple, ImagePipelineOutput]:





						'''simple docstring'''







						_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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)