




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

import math
from typing import Union

import torch

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


class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   1


    @register_to_config
    def __init__(    self   :			Any      ,				UpperCAmelCase__   :			Tuple=2_000      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			List[str]=20      ,				UpperCAmelCase__   :			Any=1e-3)		->Dict:





           '''simple docstring'''

           A__   				=					None
           A__   				=					None
           A__   				=					None


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Union[str, torch.device] = None)		->int:





           '''simple docstring'''

           A__   				=					torch.linspace(1      ,				self.config.sampling_eps      ,				UpperCAmelCase__      ,				device=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			str=None)		->List[Any]:





           '''simple docstring'''

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

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           A__   				=					(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           A__   				=					torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
           A__   				=					std.flatten()
           while len(std.shape) < len(score.shape):
                  A__   				=					std.unsqueeze(-1)
           A__   				=					-score / std

           # compute
           A__   				=					-1.0 / len(self.timesteps)

           A__   				=					self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           A__   				=					beta_t.flatten()
           while len(beta_t.shape) < len(x.shape):
                  A__   				=					beta_t.unsqueeze(-1)
           A__   				=					-0.5 * beta_t * x

           A__   				=					torch.sqrt(UpperCAmelCase__)
           A__   				=					drift - diffusion**2 * score
           A__   				=					x + drift * dt

           # add noise
           A__   				=					randn_tensor(x.shape      ,				layout=x.layout      ,				generator=UpperCAmelCase__      ,				device=x.device      ,				dtype=x.dtype)
           A__   				=					x_mean + diffusion * math.sqrt(-dt) * noise

           return x, x_mean





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





           '''simple docstring'''

           return self.config.num_train_timesteps








from __future__ import annotations

import numpy as np







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

       return np.maximum(0				,       lowercase_		)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


