code: 
  
  """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, 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, is_vision_available, logging
  
  
  if is_vision_available():
      import PIL
  
  
  _A						=     logging.get_logger(__name__)
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     ['pixel_values']
  
  
  
    def __init__(self       ,					_lowerCamelCase = True       ,					_lowerCamelCase = None       ,					_lowerCamelCase = PIL.Image.NEAREST       ,					_lowerCamelCase = False       ,					_lowerCamelCase = None       ,					_lowerCamelCase = 1 / 255       ,					_lowerCamelCase = False       ,					_lowerCamelCase = True       ,					_lowerCamelCase = True       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = True       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          super().__init__(**_lowerCamelCase  )
          UpperCAmelCase__ :       int						=						size if size is not None else {"""height""": 346, """width""": 346}
          UpperCAmelCase__ :       Tuple						=						get_size_dict(_lowerCamelCase  )
          UpperCAmelCase__ :       str						=						crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
          UpperCAmelCase__ :       Tuple						=						get_size_dict(_lowerCamelCase       ,					param_name="""crop_size"""  )
  
          UpperCAmelCase__ :       Optional[int]						=						do_resize
          UpperCAmelCase__ :       str						=						size
          UpperCAmelCase__ :       Optional[int]						=						resample
          UpperCAmelCase__ :       Any						=						do_center_crop
          UpperCAmelCase__ :       Optional[int]						=						crop_size
          UpperCAmelCase__ :       List[str]						=						do_rescale
          UpperCAmelCase__ :       Tuple						=						rescale_factor
          UpperCAmelCase__ :       Dict						=						rescale_offset
          UpperCAmelCase__ :       List[str]						=						do_normalize
          UpperCAmelCase__ :       List[Any]						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase__ :       int						=						image_std if image_std is not None else IMAGENET_STANDARD_STD
          UpperCAmelCase__ :       Optional[Any]						=						include_top
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = PIL.Image.NEAREST       ,					_lowerCamelCase = None       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						get_size_dict(_lowerCamelCase  )
          if "height" not in size or "width" not in size:
                raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""  )
          return resize(
              _lowerCamelCase       ,					size=(size["""height"""], size["""width"""])       ,					resample=_lowerCamelCase       ,					data_format=_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						get_size_dict(_lowerCamelCase  )
          if "height" not in size or "width" not in size:
                raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""  )
          return center_crop(_lowerCamelCase       ,					size=(size["""height"""], size["""width"""])       ,					data_format=_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = True       ,					_lowerCamelCase = None       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          if offset:
                UpperCAmelCase__ :       Tuple						=						(image - 127.5) * scale
                if data_format is not None:
                      UpperCAmelCase__ :       int						=						to_channel_dimension_format(_lowerCamelCase       ,					_lowerCamelCase  )
                UpperCAmelCase__ :       Tuple						=						rescaled_image.astype(np.floataa  )
          else:
                UpperCAmelCase__ :       Dict						=						rescale(_lowerCamelCase       ,					scale=_lowerCamelCase       ,					data_format=_lowerCamelCase       ,					**_lowerCamelCase  )
          return rescaled_image
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          return normalize(_lowerCamelCase       ,					mean=_lowerCamelCase       ,					std=_lowerCamelCase       ,					data_format=_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase=None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = ChannelDimension.FIRST       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__ :       str						=						resample if resample is not None else self.resample
          UpperCAmelCase__ :       List[str]						=						do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__ :       int						=						do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__ :       List[str]						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__ :       List[Any]						=						rescale_offset if rescale_offset is not None else self.rescale_offset
          UpperCAmelCase__ :       str						=						do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__ :       List[str]						=						image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__ :       Union[str, Any]						=						image_std if image_std is not None else self.image_std
          UpperCAmelCase__ :       Dict						=						include_top if include_top is not None else self.include_top
  
          UpperCAmelCase__ :       Union[str, Any]						=						size if size is not None else self.size
          UpperCAmelCase__ :       Optional[int]						=						get_size_dict(_lowerCamelCase  )
          UpperCAmelCase__ :       str						=						crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__ :       Optional[Any]						=						get_size_dict(_lowerCamelCase       ,					param_name="""crop_size"""  )
  
          UpperCAmelCase__ :       List[str]						=						make_list_of_images(_lowerCamelCase  )
  
          if not valid_images(_lowerCamelCase  ):
                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 or resample is None:
                raise ValueError("""Size and resample 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.
          UpperCAmelCase__ :       Tuple						=						[to_numpy_array(_lowerCamelCase  ) for image in images]
  
          if do_resize:
                UpperCAmelCase__ :       Optional[Any]						=						[self.resize(image=_lowerCamelCase       ,					size=_lowerCamelCase       ,					resample=_lowerCamelCase  ) for image in images]
  
          if do_center_crop:
                UpperCAmelCase__ :       Optional[int]						=						[self.center_crop(image=_lowerCamelCase       ,					size=_lowerCamelCase  ) for image in images]
  
          if do_rescale:
                UpperCAmelCase__ :       str						=						[self.rescale(image=_lowerCamelCase       ,					scale=_lowerCamelCase       ,					offset=_lowerCamelCase  ) for image in images]
  
          if do_normalize:
                UpperCAmelCase__ :       List[Any]						=						[self.normalize(image=_lowerCamelCase       ,					mean=_lowerCamelCase       ,					std=_lowerCamelCase  ) for image in images]
  
          if include_top:
                UpperCAmelCase__ :       Optional[int]						=						[self.normalize(image=_lowerCamelCase       ,					mean=[0, 0, 0]       ,					std=_lowerCamelCase  ) for image in images]
  
          UpperCAmelCase__ :       int						=						[to_channel_dimension_format(_lowerCamelCase       ,					_lowerCamelCase  ) for image in images]
  
          UpperCAmelCase__ :       Dict						=						{"""pixel_values""": images}
          return BatchFeature(data=_lowerCamelCase       ,					tensor_type=_lowerCamelCase  )
  
  
style_context: 
  
  """simple docstring"""
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
    SCREAMING_SNAKE_CASE							=     ()
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						4
          UpperCAmelCase__ :       Tuple						=						3
          UpperCAmelCase__ :       Optional[Any]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )
  
          return sample
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						4
          UpperCAmelCase__ :       Optional[Any]						=						3
          UpperCAmelCase__ :       List[str]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
          UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						sample / num_elems
          UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )
  
          return sample
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
                return sample * t / (t + 1)
  
          return model
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						{
              """num_train_timesteps""": 2000,
              """snr""": 0.15,
              """sigma_min""": 0.01,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }
  
          config.update(**_lowerCamelCase  )
          return config
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       int						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
          kwargs.update(_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       List[str]						=						self.dummy_sample
                UpperCAmelCase__ :       List[str]						=						0.1 * sample
  
                UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
                UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
                self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
          UpperCAmelCase__ :       int						=						self.get_scheduler_config()
          UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						3
  
          UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
          UpperCAmelCase__ :       int						=						self.dummy_sample_deter
  
          scheduler.set_sigmas(_lowerCamelCase  )
          scheduler.set_timesteps(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )
  
          for i, t in enumerate(scheduler.timesteps  ):
                UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]
  
                for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                            UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
                UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean
  
          UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
          UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )
  
          assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
          assert np.isclose(result_mean.item()       ,					18_714_530.0  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
                UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      scheduler.set_timesteps(_lowerCamelCase  )
                elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      UpperCAmelCase__ :       Dict						=						num_inference_steps
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                self.assertEqual(output_a.shape       ,					sample.shape  )
                self.assertEqual(output_a.shape       ,					output_a.shape  )
  
  