


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

import numpy as np
import torch

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







@dataclass
class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					torch.FloatTensor
			_UpperCamelCase		:					torch.FloatTensor
			_UpperCamelCase		:					Optional[torch.FloatTensor]    =		None







class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		2




			@register_to_config
			def __init__(							self      ,						snake_case = 0.02      ,						snake_case = 100      ,						snake_case = 1.007      ,						snake_case = 80      ,						snake_case = 0.05      ,						snake_case = 50      ,						):

							'''simple docstring'''


							UpperCamelCase__					=       sigma_max

							# setable values
							UpperCamelCase__					=       None
							UpperCamelCase__					=       None
							UpperCamelCase__					=       None  # sigma(t_i)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

							'''simple docstring'''


							return sample




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

							'''simple docstring'''


							UpperCamelCase__					=       num_inference_steps
							UpperCamelCase__					=       np.arange(0      ,						self.num_inference_steps     )[::-1].copy()
							UpperCamelCase__					=       torch.from_numpy(snake_case     ).to(snake_case     )
							UpperCamelCase__					=       [
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							UpperCamelCase__					=       torch.tensor(snake_case      ,						dtype=torch.floataa      ,						device=snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None     ):

							'''simple docstring'''


							if self.config.s_min <= sigma <= self.config.s_max:
											UpperCamelCase__					=       min(self.config.s_churn / self.num_inference_steps      ,						2**0.5 - 1     )
							else:
											UpperCamelCase__					=       0

							# sample eps ~ N(0, S_noise^2 * I)
							UpperCamelCase__					=       self.config.s_noise * randn_tensor(sample.shape      ,						generator=snake_case     ).to(sample.device     )
							UpperCamelCase__					=       sigma + gamma * sigma
							UpperCamelCase__					=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = True      ,						):

							'''simple docstring'''


							UpperCamelCase__					=       sample_hat + sigma_hat * model_output
							UpperCamelCase__					=       (sample_hat - pred_original_sample) / sigma_hat
							UpperCamelCase__					=       sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
											return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=snake_case      ,						derivative=snake_case      ,						pred_original_sample=snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = True      ,						):

							'''simple docstring'''


							UpperCamelCase__					=       sample_prev + sigma_prev * model_output
							UpperCamelCase__					=       (sample_prev - pred_original_sample) / sigma_prev
							UpperCamelCase__					=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
											return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=snake_case      ,						derivative=snake_case      ,						pred_original_sample=snake_case     )







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

							'''simple docstring'''


							raise NotImplementedError()






from __future__ import annotations

import numpy as np
def        UpperCamelCase_(				_A   :list[float]  )->		Union[str, Any]:
			return np.maximum(0				,     _A  )


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


