


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


UpperCAmelCase   :    int     =		logging.get_logger(__name__)







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[Any]			       =		["pixel_values"]







		def __init__( self		,      A = True		,      A = 32		,      A=PILImageResampling.BILINEAR		,      A = True		,      **A		,      )  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  do_resize
					lowerCamelCase    		=  do_rescale
					lowerCamelCase    		=  size_divisor
					lowerCamelCase    		=  resample
					super().__init__(**A	)







		def 					__A   ( self		,      A		,      A		,      A		,      A = None		,      **A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase    		=  get_image_size(A	)
					# Rounds the height and width down to the closest multiple of size_divisor
					lowerCamelCase    		=  height // size_divisor * size_divisor
					lowerCamelCase    		=  width // size_divisor * size_divisor
					lowerCamelCase    		=  resize(A		,      (new_h, new_w)		,      resample=A		,      data_format=A		,      **A	)
					return image







		def 					__A   ( self		,      A		,      A		,      A = None		,      **A	)  -> np.ndarray:







					'''simple docstring'''







					return rescale(image=A		,      scale=A		,      data_format=A		,      **A	)







		def 					__A   ( self		,      A		,      A = None		,      A = None		,      A=None		,      A = None		,      A = None		,      A = ChannelDimension.FIRST		,      **A		,      )  -> BatchFeature:







					'''simple docstring'''







					lowerCamelCase    		=  do_resize if do_resize is not None else self.do_resize
					lowerCamelCase    		=  do_rescale if do_rescale is not None else self.do_rescale
					lowerCamelCase    		=  size_divisor if size_divisor is not None else self.size_divisor
					lowerCamelCase    		=  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"""	)

					lowerCamelCase    		=  make_list_of_images(A	)

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

					# All transformations expect numpy arrays.
					lowerCamelCase    		=  [to_numpy_array(A	) for img in images]

					if do_resize:
								lowerCamelCase    		=  [self.resize(A		,      size_divisor=A		,      resample=A	) for image in images]

					if do_rescale:
								lowerCamelCase    		=  [self.rescale(A		,      scale=1 / 2_55	) for image in images]

					lowerCamelCase    		=  [to_channel_dimension_format(A		,      A	) for image in images]

					lowerCamelCase    		=  {"""pixel_values""": images}
					return BatchFeature(data=A		,      tensor_type=A	)





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

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

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).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
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

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

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

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

