






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  _lowercase   (   _lowerCamelCase						):

      '''simple docstring'''





      _a							:						Union[str, Any]										=							42
      _a							:						Tuple										=							42
      def __init__(     self				,			a				,			a   ):
       super().__init__()
       self.register_modules(unet=A__				,			scheduler=A__   )



      @torch.no_grad()
      def __call__(     self				,			a = 1				,			a = 2_0_0_0				,			a = None				,			a = "pil"				,			a = True				,			**a				,			):
       snake_case__  :    Dict							       =self.unet.config.sample_size
       snake_case__  :    Optional[int]							       =(batch_size, 3, img_size, img_size)

       snake_case__  :    Tuple							       =self.unet

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

       self.scheduler.set_timesteps(A__   )
       self.scheduler.set_sigmas(A__   )

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

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

        # prediction step
        snake_case__  :    str							       =model(A__				,			A__   ).sample
        snake_case__  :    List[str]							       =self.scheduler.step_pred(A__				,			A__				,			A__				,			generator=A__   )

        snake_case__       ,   snake_case__  :    Any							       =output.prev_sample, output.prev_sample_mean

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

       if not return_dict:
        return (sample,)

       return ImagePipelineOutput(images=A__   )













from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
        raise ValueError("""size_divisor is required for resizing"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

       # All transformations expect numpy arrays.
       snake_case__  :    str							       =[to_numpy_array(a   ) for img in images]

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

       if do_rescale:
        snake_case__  :    Union[str, Any]							       =[self.rescale(a				,			scale=1 / 2_5_5   ) for image in images]

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )





