


"""simple docstring"""



import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				get_activation('''swish'''				)

              self.assertIsInstance(snake_case__  ,     nn.SiLU				)

              self.assertEqual(act(torch.tensor(-1_0_0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertNotEqual(act(torch.tensor(-1  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(2_0  ,     dtype=torch.floataa				)				).item()  ,     2_0				)






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				get_activation('''silu'''				)

              self.assertIsInstance(snake_case__  ,     nn.SiLU				)

              self.assertEqual(act(torch.tensor(-1_0_0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertNotEqual(act(torch.tensor(-1  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(2_0  ,     dtype=torch.floataa				)				).item()  ,     2_0				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				get_activation('''mish'''				)

              self.assertIsInstance(snake_case__  ,     nn.Mish				)

              self.assertEqual(act(torch.tensor(-2_0_0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertNotEqual(act(torch.tensor(-1  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(2_0  ,     dtype=torch.floataa				)				).item()  ,     2_0				)







       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:int       							=				get_activation('''gelu'''				)

              self.assertIsInstance(snake_case__  ,     nn.GELU				)

              self.assertEqual(act(torch.tensor(-1_0_0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertNotEqual(act(torch.tensor(-1  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(0  ,     dtype=torch.floataa				)				).item()  ,     0				)
              self.assertEqual(act(torch.tensor(2_0  ,     dtype=torch.floataa				)				).item()  ,     2_0				)





"""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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Any        =   1






       @register_to_config
       def __init__(   self  :    int  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    float = 0.15  ,     snake_case__  :    float = 0.01  ,     snake_case__  :    float = 13_48.0  ,     snake_case__  :    float = 1e-5  ,     snake_case__  :    int = 1  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				sigma_max

              # setable values
              lowercase		:List[str]       							=				None

              self.set_sigmas(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[int] = None				):

              '''simple docstring'''






              return sample






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    Union[str, torch.device] = None				):

              '''simple docstring'''






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

              lowercase		:Tuple       							=				torch.linspace(1  ,     snake_case__  ,     snake_case__  ,     device=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None				):

              '''simple docstring'''






              lowercase		:int       							=				sigma_min if sigma_min is not None else self.config.sigma_min
              lowercase		:Any       							=				sigma_max if sigma_max is not None else self.config.sigma_max
              lowercase		:Tuple       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
              if self.timesteps is None:
                     self.set_timesteps(snake_case__  ,     snake_case__				)

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






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    int				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''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		:Optional[int]       							=				(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		:Union[str, Any]       							=				timesteps.to(self.discrete_sigmas.device				)

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

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






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''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		:Tuple       							=				randn_tensor(sample.shape  ,     layout=sample.layout  ,     generator=snake_case__				).to(sample.device				)

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

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     ):

              '''simple docstring'''






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







       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return self.config.num_train_timesteps

