





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __magic_name__	(				A_				):





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  42



     def __init__(				self							:List[Any]      ,   snake_case							:UNetaDModel      ,   snake_case							:ScoreSdeVeScheduler	):





        '''simple docstring'''




        super().__init__()
        self.register_modules(unet=snake_case__      ,   scheduler=snake_case__	)





     @torch.no_grad()
     def __call__(				self							:Optional[int]      ,   snake_case							:int = 1      ,   snake_case							:int = 2_000      ,   snake_case							:Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,   snake_case							:Optional[str] = "pil"      ,   snake_case							:bool = True      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        A_       :					Dict        =      self.unet.config.sample_size
        A_       :					List[str]        =      (batch_size, 3, img_size, img_size)

        A_       :					Any        =      self.unet

        A_       :					List[str]        =      randn_tensor(snake_case__      ,   generator=snake_case__	) * self.scheduler.init_noise_sigma
        A_       :					List[str]        =      sample.to(self.device	)

        self.scheduler.set_timesteps(snake_case__	)
        self.scheduler.set_sigmas(snake_case__	)

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
           A_       :					int        =      self.scheduler.sigmas[i] * torch.ones(shape[0]      ,   device=self.device	)

           # correction step
           for _ in range(self.scheduler.config.correct_steps	):
              A_       :					List[str]        =      self.unet(snake_case__      ,   snake_case__	).sample
              A_       :					Tuple        =      self.scheduler.step_correct(snake_case__      ,   snake_case__      ,   generator=snake_case__	).prev_sample

           # prediction step
           A_       :					Union[str, Any]        =      model(snake_case__      ,   snake_case__	).sample
           A_       :					Tuple        =      self.scheduler.step_pred(snake_case__      ,   snake_case__      ,   snake_case__      ,   generator=snake_case__	)

           A_       :					List[Any]        =      output.prev_sample, output.prev_sample_mean

        A_       :					str        =      sample_mean.clamp(0      ,   1	)
        A_       :					Dict        =      sample.cpu().permute(0      ,   2      ,   3      ,   1	).numpy()
        if output_type == "pil":
           A_       :					Optional[Any]        =      self.numpy_to_pil(snake_case__	)

        if not return_dict:
           return (sample,)

        return ImagePipelineOutput(images=snake_case__	)


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





