"""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 convert_to_rgb, 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


if is_vision_available():
       import PIL


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





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      ['pixel_values']

      def __init__(		self						,			__lowerCAmelCase = True						,			__lowerCAmelCase = None						,			__lowerCAmelCase = PILImageResampling.BICUBIC						,			__lowerCAmelCase = True						,			__lowerCAmelCase = 1 / 255						,			__lowerCAmelCase = True						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)
            lowercase								=      size if size is not None else {"""height""": 384, """width""": 384}
            lowercase								=      get_size_dict(__lowerCAmelCase						,			default_to_square=__lowerCAmelCase		)

            lowercase								=      do_resize
            lowercase								=      size
            lowercase								=      resample
            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 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = PILImageResampling.BICUBIC						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      get_size_dict(__lowerCAmelCase						,			default_to_square=__lowerCAmelCase		)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'		)
            lowercase								=      (size["""height"""], size["""width"""])
            return resize(__lowerCAmelCase						,			size=__lowerCAmelCase						,			resample=__lowerCAmelCase						,			data_format=__lowerCAmelCase						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



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

      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 = ChannelDimension.FIRST						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            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_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								=      size if size is not None else self.size
            lowercase								=      get_size_dict(__lowerCAmelCase						,			default_to_square=__lowerCAmelCase		)

            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_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(__lowerCAmelCase		) for image in images]

            # 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_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								=      BatchFeature(data={"""pixel_values""": images}						,			tensor_type=__lowerCAmelCase		)

            return encoded_outputs




"""simple docstring"""






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase       :			Optional[Any]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Optional[Any]:
      '''simple docstring'''


      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""   ,						lowerCAmelCase__   ,						)
      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return image
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [image]

      if isinstance(image[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      image[0].size
            lowercase				,				lowercase								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            lowercase								=      [np.array(i.resize((w, h)   ,						resample=PIL_INTERPOLATION["""lanczos"""]						)						)[None, :] for i in image]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      np.array(lowerCAmelCase__						).astype(np.floataa						) / 255.0
            lowercase								=      image.transpose(0   ,						3   ,						1   ,						2						)
            lowercase								=      2.0 * image - 1.0
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(image[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return image


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Dict:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return mask
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [mask]

      if isinstance(mask[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      mask[0].size
            lowercase				,				lowercase								=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            lowercase								=      [np.array(m.convert("""L"""						).resize((w, h)   ,						resample=PIL_INTERPOLATION["""nearest"""]						)						)[None, :] for m in mask]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      mask.astype(np.floataa						) / 255.0
            lowercase								=      0
            lowercase								=      1
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(mask[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return mask






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  UNetaDModel
      snake_case__     :  RePaintScheduler

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 250						,			__lowerCAmelCase = 0.0						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			):







            """simple docstring"""



            lowercase								=      image

            lowercase								=      _preprocess_image(__lowerCAmelCase		)
            lowercase								=      original_image.to(device=self.device						,			dtype=self.unet.dtype		)
            lowercase								=      _preprocess_mask(__lowerCAmelCase		)
            lowercase								=      mask_image.to(device=self.device						,			dtype=self.unet.dtype		)

            lowercase								=      original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) and len(__lowerCAmelCase		) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(__lowerCAmelCase		)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

            lowercase								=      original_image.shape
            lowercase								=      randn_tensor(__lowerCAmelCase						,			generator=__lowerCAmelCase						,			device=self.device						,			dtype=self.unet.dtype		)

            # set step values
            self.scheduler.set_timesteps(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			self.device		)
            lowercase								=      eta

            lowercase								=      self.scheduler.timesteps[0] + 1
            lowercase								=      generator[0] if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
                  if t < t_last:
                        # predict the noise residual
                        lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample
                        # compute previous image: x_t -> x_t-1
                        lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

                  else:
                        # compute the reverse: x_t-1 -> x_t
                        lowercase								=      self.scheduler.undo_step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      t

            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,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



