


"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase    					=      logging.get_logger(__name__)






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Union[str, Any]	=       ["""pixel_values"""]






    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int = True				, __UpperCAmelCase				:    int = None				, __UpperCAmelCase				:    Any = PILImageResampling.BILINEAR				, __UpperCAmelCase				:    Optional[Any] = True				, __UpperCAmelCase				:    Union[str, Any] = None				, __UpperCAmelCase				:    Optional[Any] = True				, __UpperCAmelCase				:    Optional[int] = 1 / 255				, __UpperCAmelCase				:    Union[str, Any] = True				, __UpperCAmelCase				:    Dict = None				, __UpperCAmelCase				:    Optional[Any] = None				, **__UpperCAmelCase				:    int				, ):
      super().__init__(**__UpperCAmelCase)
      a						:		Union[str, Any]      							=			size if size is not None else {"shortest_edge": 256}
      a						:		Union[str, Any]      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      a						:		Any      							=			crop_size if crop_size is not None else {"height": 224, "width": 224}
      a						:		Optional[int]      							=			get_size_dict(__UpperCAmelCase)
      a						:		Optional[int]      							=			do_resize
      a						:		Dict      							=			size
      a						:		str      							=			resample
      a						:		Union[str, Any]      							=			do_center_crop
      a						:		Any      							=			crop_size
      a						:		int      							=			do_rescale
      a						:		Any      							=			rescale_factor
      a						:		Union[str, Any]      							=			do_normalize
      a						:		str      							=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a						:		Optional[int]      							=			image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    Tuple = PILImageResampling.BICUBIC				, __UpperCAmelCase				:    List[Any] = None				, **__UpperCAmelCase				:    List[str]				, ):
      a						:		Tuple      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
      a						:		str      							=			get_resize_output_image_size(__UpperCAmelCase				, size=size["shortest_edge"]				, default_to_square=__UpperCAmelCase)
      return resize(__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    List[str] = None				, **__UpperCAmelCase				:    int				, ):
      a						:		Tuple      							=			get_size_dict(__UpperCAmelCase)
      return center_crop(__UpperCAmelCase				, size=(size["height"], size["width"])				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Union[str, Any] = None				, **__UpperCAmelCase				:    Union[str, Any]):
      return rescale(__UpperCAmelCase				, scale=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = None				, **__UpperCAmelCase				:    Union[str, Any]				, ):
      return normalize(__UpperCAmelCase				, mean=__UpperCAmelCase				, std=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Union[str, Any] = None				, __UpperCAmelCase				:    Any = None				, __UpperCAmelCase				:    Optional[Any] = None				, __UpperCAmelCase				:    int = None				, __UpperCAmelCase				:    Optional[int] = None				, __UpperCAmelCase				:    Tuple = None				, __UpperCAmelCase				:    Dict = None				, __UpperCAmelCase				:    str = None				, __UpperCAmelCase				:    Tuple = None				, __UpperCAmelCase				:    Optional[Any] = None				, __UpperCAmelCase				:    Optional[int] = None				, __UpperCAmelCase				:    List[Any] = ChannelDimension.FIRST				, **__UpperCAmelCase				:    Tuple				, ):
      a						:		Dict      							=			do_resize if do_resize is not None else self.do_resize
      a						:		int      							=			size if size is not None else self.size
      a						:		List[str]      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      a						:		Dict      							=			resample if resample is not None else self.resample
      a						:		Optional[int]      							=			do_center_crop if do_center_crop is not None else self.do_center_crop
      a						:		Optional[Any]      							=			crop_size if crop_size is not None else self.crop_size
      a						:		Optional[Any]      							=			get_size_dict(__UpperCAmelCase)
      a						:		Tuple      							=			do_rescale if do_rescale is not None else self.do_rescale
      a						:		List[str]      							=			rescale_factor if rescale_factor is not None else self.rescale_factor
      a						:		List[Any]      							=			do_normalize if do_normalize is not None else self.do_normalize
      a						:		Optional[int]      							=			image_mean if image_mean is not None else self.image_mean
      a						:		Optional[int]      							=			image_std if image_std is not None else self.image_std

      a						:		Optional[int]      							=			make_list_of_images(__UpperCAmelCase)

      if not valid_images(__UpperCAmelCase):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "torch.Tensor, tf.Tensor or jax.ndarray.")

      if do_resize and size is None:
        raise ValueError("Size must be specified if do_resize is True.")

      if do_center_crop and crop_size is None:
        raise ValueError("Crop size must be specified if do_center_crop is True.")

      if do_rescale and rescale_factor is None:
        raise ValueError("Rescale factor must be specified if do_rescale is True.")

      if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True.")

      # All transformations expect numpy arrays.
      a						:		Optional[int]      							=			[to_numpy_array(__UpperCAmelCase) for image in images]

      if do_resize:
        a						:		Dict      							=			[self.resize(image=__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase) for image in images]

      if do_center_crop:
        a						:		List[str]      							=			[self.center_crop(image=__UpperCAmelCase				, size=__UpperCAmelCase) for image in images]

      if do_rescale:
        a						:		Union[str, Any]      							=			[self.rescale(image=__UpperCAmelCase				, scale=__UpperCAmelCase) for image in images]

      if do_normalize:
        a						:		Union[str, Any]      							=			[self.normalize(image=__UpperCAmelCase				, mean=__UpperCAmelCase				, std=__UpperCAmelCase) for image in images]

      a						:		List[str]      							=			[to_channel_dimension_format(__UpperCAmelCase				, __UpperCAmelCase) for image in images]

      a						:		int      							=			{"pixel_values": images}
      return BatchFeature(data=__UpperCAmelCase				, tensor_type=__UpperCAmelCase)










'''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      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

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

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



