




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					(  a__      ):
  _UpperCamelCase   :			Tuple =			4_2
  _UpperCamelCase   :			str =			4_2


class 						_A					(  a__			,   a__      ):
  _UpperCamelCase   :			Tuple =			1






  @register_to_config
  def __init__(  self		:	str      ,						_A		:	int = 2_000      ,						_A		:	float = 0.15      ,						_A		:	float = 0.01      ,						_A		:	float = 1_348.0      ,						_A		:	float = 1E-5      ,						_A		:	int = 1      ,						)		->    int:






       """simple docstring"""







       lowercase					:   Union[str, Any]								= sigma_max

       # setable values
       lowercase					:   Dict								= None

       self.set_sigmas(UpperCamelCase_      ,						UpperCamelCase_      ,						UpperCamelCase_      ,						UpperCamelCase_      )






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






       """simple docstring"""







       return sample






  def 					__a			(  self		:	Optional[int]      ,						_A		:	int      ,						_A		:	float = None      ,						_A		:	Union[str, torch.device] = None      )		->    Tuple:






       """simple docstring"""







       lowercase					:   Optional[Any]								= sampling_eps if sampling_eps is not None else self.config.sampling_eps

       lowercase					:   Dict								= torch.linspace(1      ,						UpperCamelCase_      ,						UpperCamelCase_      ,						device=UpperCamelCase_      )






  def 					__a			(  self		:	str      ,						_A		:	int      ,						_A		:	float = None      ,						_A		:	float = None      ,						_A		:	float = None      )		->    str:






       """simple docstring"""







       lowercase					:   Optional[Any]								= sigma_min if sigma_min is not None else self.config.sigma_min
       lowercase					:   Union[str, Any]								= sigma_max if sigma_max is not None else self.config.sigma_max
       lowercase					:   int								= sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
            self.set_timesteps(UpperCamelCase_      ,						UpperCamelCase_      )

       lowercase					:   Optional[int]								= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       lowercase					:   Any								= torch.exp(torch.linspace(math.log(UpperCamelCase_      )      ,						math.log(UpperCamelCase_      )      ,						UpperCamelCase_      )      )
       lowercase					:   Dict								= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )






  def 					__a			(  self		:	Union[str, Any]      ,						_A		:	Union[str, Any]      ,						_A		:	List[Any]      )		->    Any:






       """simple docstring"""







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






  def 					__a			(  self		:	List[Any]      ,						_A		:	torch.FloatTensor      ,						_A		:	int      ,						_A		:	torch.FloatTensor      ,						_A		:	Optional[torch.Generator] = None      ,						_A		:	bool = True      ,						)		->    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'''      )

       lowercase					:   Optional[int]								= timestep * torch.ones(
           sample.shape[0]      ,						device=sample.device      )  # torch.repeat_interleave(timestep, sample.shape[0])
       lowercase					:   Any								= (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
       lowercase					:   int								= timesteps.to(self.discrete_sigmas.device      )

       lowercase					:   Union[str, Any]								= self.discrete_sigmas[timesteps].to(sample.device      )
       lowercase					:   Any								= self.get_adjacent_sigma(UpperCamelCase_      ,						UpperCamelCase_      ).to(sample.device      )
       lowercase					:   Union[str, Any]								= torch.zeros_like(UpperCamelCase_      )
       lowercase					:   str								= (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
       lowercase					:   List[str]								= diffusion.flatten()
       while len(diffusion.shape      ) < len(sample.shape      ):
            lowercase					:   List[Any]								= diffusion.unsqueeze(-1      )
       lowercase					:   int								= drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       lowercase					:   str								= randn_tensor(
           sample.shape      ,						layout=sample.layout      ,						generator=UpperCamelCase_      ,						device=sample.device      ,						dtype=sample.dtype      )
       lowercase					:   Optional[Any]								= sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       lowercase					:   Union[str, Any]								= 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=UpperCamelCase_      ,						prev_sample_mean=UpperCamelCase_      )






  def 					__a			(  self		:	Any      ,						_A		:	torch.FloatTensor      ,						_A		:	torch.FloatTensor      ,						_A		:	Optional[torch.Generator] = None      ,						_A		:	bool = True      ,						)		->    List[str]:






       """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
       lowercase					:   Union[str, Any]								= randn_tensor(sample.shape      ,						layout=sample.layout      ,						generator=UpperCamelCase_      ).to(sample.device      )

       # compute step size from the model_output, the noise, and the snr
       lowercase					:   Tuple								= torch.norm(model_output.reshape(model_output.shape[0]      ,						-1      )      ,						dim=-1      ).mean()
       lowercase					:   str								= torch.norm(noise.reshape(noise.shape[0]      ,						-1      )      ,						dim=-1      ).mean()
       lowercase					:   Optional[int]								= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       lowercase					:   Tuple								= 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
       lowercase					:   Any								= step_size.flatten()
       while len(step_size.shape      ) < len(sample.shape      ):
            lowercase					:   str								= step_size.unsqueeze(-1      )
       lowercase					:   Optional[Any]								= sample + step_size * model_output
       lowercase					:   List[str]								= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=UpperCamelCase_      )






  def 					__a			(  self		:	str      ,						_A		:	torch.FloatTensor      ,						_A		:	torch.FloatTensor      ,						_A		:	torch.FloatTensor      ,						)		->    List[Any]:






       """simple docstring"""







       lowercase					:   List[Any]								= timesteps.to(original_samples.device      )
       lowercase					:   Optional[int]								= self.discrete_sigmas.to(original_samples.device      )[timesteps]
       lowercase					:   str								= (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(UpperCamelCase_      ) * sigmas[:, None, None, None]
       )
       lowercase					:   int								= noise + original_samples
       return noisy_samples






  def __len__(  self		:	Tuple      )		->    Union[str, Any]:






       """simple docstring"""







       return self.config.num_train_timesteps



"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def    _snake_case  (  _snake_case :	int	):
      for param in module.parameters():
            lowerCAmelCase    :		Optional[int]				=      False


def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            lowerCAmelCase    :		Any				=      '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''	)
      return device


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      plt.imshow(_snake_case	)
      fig.axes.get_xaxis().set_visible(_snake_case	)
      fig.axes.get_yaxis().set_visible(_snake_case	)
      plt.show()






def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datetime.now()
      lowerCAmelCase    :		Union[str, Any]				=      current_time.strftime('''%H:%M:%S'''	)
      return timestamp


