





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        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      torch.FloatTensor
							snake_case     :      torch.FloatTensor
							snake_case     :      Optional[torch.FloatTensor]						     =       None






class        __SCREAMING_SNAKE_CASE					(     _a   ,		_a       ):
							snake_case     :      str						     =       2






							@register_to_config
							def __init__(						self  , __lowerCAmelCase = 0.02  , __lowerCAmelCase = 100  , __lowerCAmelCase = 1.007  , __lowerCAmelCase = 80  , __lowerCAmelCase = 0.05  , __lowerCAmelCase = 50  , ):
														# standard deviation of the initial noise distribution
														UpperCamelCase__				     =			sigma_max

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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase = None  ):
														return sample






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase = None  ):
														UpperCamelCase__				     =			num_inference_steps
														UpperCamelCase__				     =			np.arange(0  , self.num_inference_steps  )[::-1].copy()
														UpperCamelCase__				     =			torch.from_numpy(__lowerCAmelCase  ).to(__lowerCAmelCase  )
														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(__lowerCAmelCase  , dtype=torch.floataa  , device=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  ):
														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=__lowerCAmelCase  ).to(sample.device  )
														UpperCamelCase__				     =			sigma + gamma * sigma
														UpperCamelCase__				     =			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

														return sample_hat, sigma_hat






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = True  , ):
														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=__lowerCAmelCase  , derivative=__lowerCAmelCase  , pred_original_sample=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = True  , ):
														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=__lowerCAmelCase  , derivative=__lowerCAmelCase  , pred_original_sample=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														raise NotImplementedError()









import inspect
import unittest

class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							def 				_lowerCamelCase		(						self  ):
														try:
																					import diffusers  # noqa: F401
														except ImportError:
																					assert False







							def 				_lowerCamelCase		(						self  ):
														import diffusers
														from diffusers.dependency_versions_table import deps

														UpperCamelCase__				     =			inspect.getmembers(__lowerCAmelCase  , inspect.isclass  )

														for cls_name, cls_module in all_classes:
																					if "dummy_" in cls_module.__module__:
																												for backend in cls_module._backends:
																																			if backend == "k_diffusion":
																																										UpperCamelCase__				     =			"""k-diffusion"""
																																			elif backend == "invisible_watermark":
																																										UpperCamelCase__				     =			"""invisible-watermark"""
																																			assert backend in deps, f"""{backend} is not in the deps table!"""




