






"""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


_UpperCamelCase		:  Optional[Any]						=		logging.get_logger(__name__)

class        a     (   a_	):
    UpperCAmelCase_ :     int	     =["pixel_values"]







    def __init__(			self							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = PIL.Image.BICUBIC							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = 1 / 2_5_5							,		_lowerCamelCase = True							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      super().__init__(**_lowerCamelCase       )
      lowercase							    =       size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
      lowercase							    =       get_size_dict(_lowerCamelCase       )
      lowercase							    =       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
      lowercase							    =       get_size_dict(_lowerCamelCase							,		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 IMAGENET_STANDARD_MEAN
      lowercase							    =       image_std if image_std is not None else IMAGENET_STANDARD_STD







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = PIL.Image.BICUBIC							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      return rescale(_lowerCamelCase							,		scale=_lowerCamelCase							,		data_format=_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      return normalize(_lowerCamelCase							,		mean=_lowerCamelCase							,		std=_lowerCamelCase							,		data_format=_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			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 = ChannelDimension.FIRST							,		**_lowerCamelCase							,		):
      lowercase							    =       do_resize if do_resize is not None else self.do_resize
      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							    =       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							    =       size if size is not None else self.size
      lowercase							    =       get_size_dict(_lowerCamelCase       )
      lowercase							    =       crop_size if crop_size is not None else self.crop_size
      lowercase							    =       get_size_dict(_lowerCamelCase							,		param_name='crop_size'       )

      lowercase							    =       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.
      lowercase							    =       [to_numpy_array(_lowerCamelCase       ) for image in images]

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

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

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

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

      lowercase							    =       [to_channel_dimension_format(_lowerCamelCase							,		_lowerCamelCase       ) for image in images]

      lowercase							    =       {'pixel_values': images}
      return BatchFeature(data=_lowerCamelCase							,		tensor_type=_lowerCamelCase       )










"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


