





'''simple docstring'''






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class  _A (					__lowercase		):
		lowercase__:   torch.FloatTensor
		lowercase__:   torch.FloatTensor





class  _A (					__lowercase						,					__lowercase		):
		lowercase__:   List[Any]				     =   1

		@register_to_config
		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   int = 20_00  ,      __magic_name__					:   float = 0.15  ,      __magic_name__					:   float = 0.01  ,      __magic_name__					:   float = 1348.0  ,      __magic_name__					:   float = 1E-5  ,      __magic_name__					:   int = 1  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      Dict							      =     sigma_max

					# setable values
					__snake_case					:      Optional[Any]							      =     None

					self.set_sigmas(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      __magic_name__			)

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   Optional[int] = None			)					->    torch.FloatTensor:


					"""simple docstring"""

					return sample

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   float = None  ,      __magic_name__					:   Union[str, torch.device] = None			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

					__snake_case					:      List[str]							      =     torch.linspace(1  ,      __magic_name__  ,      __magic_name__  ,      device=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   float = None  ,      __magic_name__					:   float = None  ,      __magic_name__					:   float = None			)					->    int:


					"""simple docstring"""

					__snake_case					:      Any							      =     sigma_min if sigma_min is not None else self.config.sigma_min
					__snake_case					:      Optional[Any]							      =     sigma_max if sigma_max is not None else self.config.sigma_max
					__snake_case					:      int							      =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
					if self.timesteps is None:
								self.set_timesteps(__magic_name__  ,      __magic_name__			)

					__snake_case					:      Union[str, Any]							      =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
					__snake_case					:      Any							      =     torch.exp(torch.linspace(math.log(__magic_name__			)  ,      math.log(__magic_name__			)  ,      __magic_name__			)			)
					__snake_case					:      List[Any]							      =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					return torch.where(
					    timesteps == 0  ,      torch.zeros_like(t.to(timesteps.device			)			)  ,      self.discrete_sigmas[timesteps - 1].to(timesteps.device			)  ,      )

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   int  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   Optional[torch.Generator] = None  ,      __magic_name__					:   bool = True  ,      )					->    Union[SdeVeOutput, Tuple]:


					"""simple docstring"""

					if self.timesteps is None:
								raise ValueError(
								    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

					__snake_case					:      List[str]							      =     timestep * torch.ones(
					    sample.shape[0]  ,      device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
					__snake_case					:      Dict							      =     (timestep * (len(self.timesteps			) - 1)).long()

					# mps requires indices to be in the same device, so we use cpu as is the default with cuda
					__snake_case					:      int							      =     timesteps.to(self.discrete_sigmas.device			)

					__snake_case					:      Dict							      =     self.discrete_sigmas[timesteps].to(sample.device			)
					__snake_case					:      Union[str, Any]							      =     self.get_adjacent_sigma(__magic_name__  ,      __magic_name__			).to(sample.device			)
					__snake_case					:      List[str]							      =     torch.zeros_like(__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     (sigma**2 - adjacent_sigma**2) ** 0.5

					# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
					# also equation 47 shows the analog from SDE models to ancestral sampling methods
					__snake_case					:      int							      =     diffusion.flatten()
					while len(diffusion.shape			) < len(sample.shape			):
								__snake_case					:      List[str]							      =     diffusion.unsqueeze(-1			)
					__snake_case					:      Optional[int]							      =     drift - diffusion**2 * model_output

					#  equation 6: sample noise for the diffusion term of
					__snake_case					:      Any							      =     randn_tensor(
					    sample.shape  ,      layout=sample.layout  ,      generator=__magic_name__  ,      device=sample.device  ,      dtype=sample.dtype			)
					__snake_case					:      Union[str, Any]							      =     sample - drift  # subtract because `dt` is a small negative timestep
					# TODO is the variable diffusion the correct scaling term for the noise?
					__snake_case					:      Tuple							      =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

					if not return_dict:
								return (prev_sample, prev_sample_mean)

					return SdeVeOutput(prev_sample=__magic_name__  ,      prev_sample_mean=__magic_name__			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   Optional[torch.Generator] = None  ,      __magic_name__					:   bool = True  ,      )					->    Union[SchedulerOutput, Tuple]:


					"""simple docstring"""

					if self.timesteps is None:
								raise ValueError(
								    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

					# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
					# sample noise for correction
					__snake_case					:      str							      =     randn_tensor(sample.shape  ,      layout=sample.layout  ,      generator=__magic_name__			).to(sample.device			)

					# compute step size from the model_output, the noise, and the snr
					__snake_case					:      List[str]							      =     torch.norm(model_output.reshape(model_output.shape[0]  ,      -1			)  ,      dim=-1			).mean()
					__snake_case					:      Optional[Any]							      =     torch.norm(noise.reshape(noise.shape[0]  ,      -1			)  ,      dim=-1			).mean()
					__snake_case					:      Optional[Any]							      =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
					__snake_case					:      Union[str, Any]							      =     step_size * torch.ones(sample.shape[0]			).to(sample.device			)
					# self.repeat_scalar(step_size, sample.shape[0])

					# compute corrected sample: model_output term and noise term
					__snake_case					:      int							      =     step_size.flatten()
					while len(step_size.shape			) < len(sample.shape			):
								__snake_case					:      int							      =     step_size.unsqueeze(-1			)
					__snake_case					:      Any							      =     sample + step_size * model_output
					__snake_case					:      List[Any]							      =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

					if not return_dict:
								return (prev_sample,)

					return SchedulerOutput(prev_sample=__magic_name__			)

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   torch.FloatTensor  ,      )					->    torch.FloatTensor:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     timesteps.to(original_samples.device			)
					__snake_case					:      Union[str, Any]							      =     self.discrete_sigmas.to(original_samples.device			)[timesteps]
					__snake_case					:      Union[str, Any]							      =     (
					    noise * sigmas[:, None, None, None]
					    if noise is not None
					    else torch.randn_like(__magic_name__			) * sigmas[:, None, None, None]
					)
					__snake_case					:      Union[str, Any]							      =     noise + original_samples
					return noisy_samples

		def __len__(   self					:   Optional[int]			)					->    List[str]:


					"""simple docstring"""

					return self.config.num_train_timesteps









'''simple docstring'''






def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			if not isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
						raise TypeError("""only integers accepted as input""" )
			else:
						__snake_case					:      List[Any]							      =     str(abs(_lowerCamelCase ) )
						__snake_case					:      Union[str, Any]							      =     [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
						for index in range(len(_lowerCamelCase ) ):
									num_transpositions[index].pop(_lowerCamelCase )
						return max(
						    int("""""".join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
	__import__("doctest").testmod()


