






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)


if is_vision_available():
    import PIL






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[int]					=['''pixel_values''']





       def __init__(				self	:Tuple,      snake_case	:bool = True,      snake_case	:Dict[str, int] = None,      snake_case	:PILImageResampling = PILImageResampling.BICUBIC,      snake_case	:bool = True,      snake_case	:Dict[str, int] = None,      snake_case	:bool = True,      snake_case	:Union[int, float] = 1 / 255,      snake_case	:bool = True,      snake_case	:Optional[Union[float, List[float]]] = None,      snake_case	:Optional[Union[float, List[float]]] = None,      snake_case	:bool = True,      **snake_case	:Optional[Any],      ):



          """simple docstring"""

          super().__init__(**snake_case)
          _lowercase              =size if size is not None else {'shortest_edge': 224}
          _lowercase              =get_size_dict(snake_case,      default_to_square=snake_case)
          _lowercase              =crop_size if crop_size is not None else {'height': 224, 'width': 224}
          _lowercase              =get_size_dict(snake_case,      default_to_square=snake_case,      param_name='crop_size')

          _lowercase              =do_resize
          _lowercase              =size
          _lowercase              =resample
          _lowercase              =do_center_crop
          _lowercase              =crop_size
          _lowercase              =do_rescale
          _lowercase              =rescale_factor
          _lowercase              =do_normalize
          _lowercase              =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          _lowercase              =image_std if image_std is not None else OPENAI_CLIP_STD
          _lowercase              =do_convert_rgb





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:np.ndarray,      snake_case	:Dict[str, int],      snake_case	:PILImageResampling = PILImageResampling.BICUBIC,      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:List[Any],      ):



          """simple docstring"""

          _lowercase              =get_size_dict(snake_case,      default_to_square=snake_case)
          if "shortest_edge" not in size:
             raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
          _lowercase              =get_resize_output_image_size(snake_case,      size=size['shortest_edge'],      default_to_square=snake_case)
          return resize(snake_case,      size=snake_case,      resample=snake_case,      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:np.ndarray,      snake_case	:Dict[str, int],      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =get_size_dict(snake_case)
          if "height" not in size or "width" not in size:
             raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
          return center_crop(snake_case,      size=(size['height'], size['width']),      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:np.ndarray,      snake_case	:Union[int, float],      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:List[str],      ):



          """simple docstring"""

          return rescale(snake_case,      scale=snake_case,      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:np.ndarray,      snake_case	:Union[float, List[float]],      snake_case	:Union[float, List[float]],      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:Optional[int],      ):



          """simple docstring"""

          return normalize(snake_case,      mean=snake_case,      std=snake_case,      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Optional[int],      snake_case	:ImageInput,      snake_case	:bool = None,      snake_case	:Dict[str, int] = None,      snake_case	:PILImageResampling = None,      snake_case	:bool = None,      snake_case	:int = None,      snake_case	:bool = None,      snake_case	:float = None,      snake_case	:bool = None,      snake_case	:Optional[Union[float, List[float]]] = None,      snake_case	:Optional[Union[float, List[float]]] = None,      snake_case	:bool = None,      snake_case	:Optional[Union[str, TensorType]] = None,      snake_case	:Optional[ChannelDimension] = ChannelDimension.FIRST,      **snake_case	:str,      ):



          """simple docstring"""

          _lowercase              =do_resize if do_resize is not None else self.do_resize
          _lowercase              =size if size is not None else self.size
          _lowercase              =get_size_dict(snake_case,      param_name='size',      default_to_square=snake_case)
          _lowercase              =resample if resample is not None else self.resample
          _lowercase              =do_center_crop if do_center_crop is not None else self.do_center_crop
          _lowercase              =crop_size if crop_size is not None else self.crop_size
          _lowercase              =get_size_dict(snake_case,      param_name='crop_size',      default_to_square=snake_case)
          _lowercase              =do_rescale if do_rescale is not None else self.do_rescale
          _lowercase              =rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowercase              =do_normalize if do_normalize is not None else self.do_normalize
          _lowercase              =image_mean if image_mean is not None else self.image_mean
          _lowercase              =image_std if image_std is not None else self.image_std
          _lowercase              =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          _lowercase              =make_list_of_images(snake_case)

          if not valid_images(snake_case):
             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.')

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
             _lowercase              =[convert_to_rgb(snake_case) for image in images]

          # All transformations expect numpy arrays.
          _lowercase              =[to_numpy_array(snake_case) for image in images]

          if do_resize:
             _lowercase              =[self.resize(image=snake_case,      size=snake_case,      resample=snake_case) for image in images]

          if do_center_crop:
             _lowercase              =[self.center_crop(image=snake_case,      size=snake_case) for image in images]

          if do_rescale:
             _lowercase              =[self.rescale(image=snake_case,      scale=snake_case) for image in images]

          if do_normalize:
             _lowercase              =[self.normalize(image=snake_case,      mean=snake_case,      std=snake_case) for image in images]

          _lowercase              =[to_channel_dimension_format(snake_case,      snake_case) for image in images]

          _lowercase              ={'pixel_values': images}
          return BatchFeature(data=snake_case,      tensor_type=snake_case)










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =UNetaDModel(
              block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=3,      out_channels=3,      down_block_types=('DownBlock2D', 'AttnDownBlock2D'),      up_block_types=('AttnUpBlock2D', 'UpBlock2D'),      )
          return model





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              0
          ]

          _lowercase              =image[0, -3:, -3:, -1]
          _lowercase              =image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _lowercase              =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2






@slow
@require_torch
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

          _lowercase              =image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          _lowercase              =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


