


"""simple docstring"""



from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Optional[int]        =   "van"






       def __init__(   self  :    Union[str, Any]  ,     snake_case__  :    List[str]=2_2_4  ,     snake_case__  :    Tuple=3  ,     snake_case__  :    Dict=[7, 3, 3, 3]  ,     snake_case__  :    str=[4, 2, 2, 2]  ,     snake_case__  :    int=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,     snake_case__  :    Any=[3, 3, 1_2, 3]  ,     snake_case__  :    List[str]=[8, 8, 4, 4]  ,     snake_case__  :    List[str]="gelu"  ,     snake_case__  :    Tuple=0.02  ,     snake_case__  :    int=1e-6  ,     snake_case__  :    Union[str, Any]=1e-2  ,     snake_case__  :    str=0.0  ,     snake_case__  :    List[Any]=0.0  ,     **snake_case__  :    List[Any]  ,     ):

              '''simple docstring'''






              super().__init__(**snake_case__				)
              lowercase		:Optional[int]       							=				image_size
              lowercase		:str       							=				num_channels
              lowercase		:Optional[int]       							=				patch_sizes
              lowercase		:List[str]       							=				strides
              lowercase		:Any       							=				hidden_sizes
              lowercase		:Tuple       							=				depths
              lowercase		:Union[str, Any]       							=				mlp_ratios
              lowercase		:Optional[Any]       							=				hidden_act
              lowercase		:Optional[int]       							=				initializer_range
              lowercase		:Any       							=				layer_norm_eps
              lowercase		:Dict       							=				layer_scale_init_value
              lowercase		:List[Any]       							=				drop_path_rate
              lowercase		:str       							=				dropout_rate





"""simple docstring"""



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__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[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  :    Tuple  ,     snake_case__  :    int = 1  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				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				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

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

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

