
"""simple docstring"""




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 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   None







class 			snake_case  (				__lowercase							,      __lowercase ):
							UpperCAmelCase__      =   2

							@register_to_config
							def __init__(self							,							SCREAMING_SNAKE_CASE_ = 0.02							,							SCREAMING_SNAKE_CASE_ = 1_00							,							SCREAMING_SNAKE_CASE_ = 1.0_07							,							SCREAMING_SNAKE_CASE_ = 80							,							SCREAMING_SNAKE_CASE_ = 0.05							,							SCREAMING_SNAKE_CASE_ = 50							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			sigma_max

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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									return sample

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			num_inference_steps
									SCREAMING_SNAKE_CASE_    			=			np.arange(0							,							self.num_inference_steps     )[::-1].copy()
									SCREAMING_SNAKE_CASE_    			=			torch.from_numpy(SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			[
									    (
									        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
									]
									SCREAMING_SNAKE_CASE_    			=			torch.tensor(SCREAMING_SNAKE_CASE_							,							dtype=torch.floataa							,							device=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







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

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

									return sample_hat, sigma_hat

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = True							,							):

									"""simple docstring"""







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

									if not return_dict:
											return (sample_prev, derivative)

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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = True							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			sample_prev + sigma_prev * model_output
									SCREAMING_SNAKE_CASE_    			=			(sample_prev - pred_original_sample) / sigma_prev
									SCREAMING_SNAKE_CASE_    			=			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=SCREAMING_SNAKE_CASE_							,							derivative=SCREAMING_SNAKE_CASE_							,							pred_original_sample=SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									raise NotImplementedError()

"""simple docstring"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_pt_objects import *  # noqa F403
else:
						from .scheduling_consistency_models import CMStochasticIterativeScheduler
						from .scheduling_ddim import DDIMScheduler
						from .scheduling_ddim_inverse import DDIMInverseScheduler
						from .scheduling_ddim_parallel import DDIMParallelScheduler
						from .scheduling_ddpm import DDPMScheduler
						from .scheduling_ddpm_parallel import DDPMParallelScheduler
						from .scheduling_deis_multistep import DEISMultistepScheduler
						from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
						from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
						from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
						from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
						from .scheduling_euler_discrete import EulerDiscreteScheduler
						from .scheduling_heun_discrete import HeunDiscreteScheduler
						from .scheduling_ipndm import IPNDMScheduler
						from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
						from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
						from .scheduling_karras_ve import KarrasVeScheduler
						from .scheduling_pndm import PNDMScheduler
						from .scheduling_repaint import RePaintScheduler
						from .scheduling_sde_ve import ScoreSdeVeScheduler
						from .scheduling_sde_vp import ScoreSdeVpScheduler
						from .scheduling_unclip import UnCLIPScheduler
						from .scheduling_unipc_multistep import UniPCMultistepScheduler
						from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
						from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_flax_objects import *  # noqa F403
else:
						from .scheduling_ddim_flax import FlaxDDIMScheduler
						from .scheduling_ddpm_flax import FlaxDDPMScheduler
						from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
						from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
						from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
						from .scheduling_pndm_flax import FlaxPNDMScheduler
						from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
						from .scheduling_utils_flax import (
						    FlaxKarrasDiffusionSchedulers,
						    FlaxSchedulerMixin,
						    FlaxSchedulerOutput,
						    broadcast_to_shape_from_left,
						)


try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler