

"""simple docstring"""




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


A_							:			int											= logging.get_logger(__name__)

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			def constraint_to_multiple_of(snake_case__		,				snake_case__		,				snake_case__=0		,				snake_case__=None    ):
						SCREAMING_SNAKE_CASE__								=      round(val / multiple    ) * multiple

						if max_val is not None and x > max_val:
									SCREAMING_SNAKE_CASE__								=      math.floor(val / multiple    ) * multiple

						if x < min_val:
									SCREAMING_SNAKE_CASE__								=      math.ceil(val / multiple    ) * multiple

						return x

			SCREAMING_SNAKE_CASE__								=      (output_size, output_size) if isinstance(snake_case__		,				snake_case__    ) else output_size

			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      get_image_size(snake_case__    )
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      output_size

			# determine new height and width
			SCREAMING_SNAKE_CASE__								=      output_height / input_height
			SCREAMING_SNAKE_CASE__								=      output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width    ) < abs(1 - scale_height    ):
									# fit width
									SCREAMING_SNAKE_CASE__								=      scale_width
						else:
									# fit height
									SCREAMING_SNAKE_CASE__								=      scale_height

			SCREAMING_SNAKE_CASE__								=      constraint_to_multiple_of(scale_height * input_height		,				multiple=snake_case__    )
			SCREAMING_SNAKE_CASE__								=      constraint_to_multiple_of(scale_width * input_width		,				multiple=snake_case__    )

			return (new_height, new_width)





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[Any]							=							['pixel_values']

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Dict[str, int] = None    ,				__UpperCAmelCase				:						PILImageResampling = PILImageResampling.BILINEAR    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						int = 1    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Union[int, float] = 1 / 2_5_5    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				**__UpperCAmelCase				:						Dict    ,				)   ->    None:
						super().__init__(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      do_resize
						SCREAMING_SNAKE_CASE__								=      size
						SCREAMING_SNAKE_CASE__								=      keep_aspect_ratio
						SCREAMING_SNAKE_CASE__								=      ensure_multiple_of
						SCREAMING_SNAKE_CASE__								=      resample
						SCREAMING_SNAKE_CASE__								=      do_rescale
						SCREAMING_SNAKE_CASE__								=      rescale_factor
						SCREAMING_SNAKE_CASE__								=      do_normalize
						SCREAMING_SNAKE_CASE__								=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						SCREAMING_SNAKE_CASE__								=      image_std if image_std is not None else IMAGENET_STANDARD_STD

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						np.ndarray    ,				__UpperCAmelCase				:						Dict[str, int]    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						int = 1    ,				__UpperCAmelCase				:						PILImageResampling = PILImageResampling.BICUBIC    ,				__UpperCAmelCase				:						Optional[Union[str, ChannelDimension]] = None    ,				**__UpperCAmelCase				:						Tuple    ,				)   ->    np.ndarray:
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )
						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()}"""       )
						SCREAMING_SNAKE_CASE__								=      get_resize_output_image_size(
						    __UpperCAmelCase    ,				output_size=(size["""height"""], size["""width"""])    ,				keep_aspect_ratio=__UpperCAmelCase    ,				multiple=__UpperCAmelCase    ,				)
						return resize(__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						np.ndarray    ,				__UpperCAmelCase				:						Union[int, float]    ,				__UpperCAmelCase				:						Optional[Union[str, ChannelDimension]] = None    ,				**__UpperCAmelCase				:						str    ,				)   ->    Optional[Any]:
						return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						np.ndarray    ,				__UpperCAmelCase				:						Union[float, List[float]]    ,				__UpperCAmelCase				:						Union[float, List[float]]    ,				__UpperCAmelCase				:						Optional[Union[str, ChannelDimension]] = None    ,				**__UpperCAmelCase				:						Optional[int]    ,				)   ->    np.ndarray:
						return normalize(__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						ImageInput    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						int = None    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						int = None    ,				__UpperCAmelCase				:						PILImageResampling = None    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						float = None    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[str, TensorType]] = None    ,				__UpperCAmelCase				:						ChannelDimension = ChannelDimension.FIRST    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    PIL.Image.Image:
						SCREAMING_SNAKE_CASE__								=      do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__								=      size if size is not None else self.size
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						SCREAMING_SNAKE_CASE__								=      ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						SCREAMING_SNAKE_CASE__								=      resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE__								=      do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__								=      rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE__								=      do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE__								=      image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE__								=      image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE__								=      make_list_of_images(__UpperCAmelCase       )

						if not valid_images(__UpperCAmelCase       ):
									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."""       )

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

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

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

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

						SCREAMING_SNAKE_CASE__								=      [to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase       ) for image in images]

						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": images}
						return BatchFeature(data=__UpperCAmelCase    ,				tensor_type=__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Tuple] = None       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(__UpperCAmelCase       ) != len(__UpperCAmelCase       ):
												raise ValueError(
												    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""       )

									if is_torch_tensor(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__								=      target_sizes.numpy()

									SCREAMING_SNAKE_CASE__								=      []

									for idx in range(len(__UpperCAmelCase       )       ):
												SCREAMING_SNAKE_CASE__								=      torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0       )    ,				size=target_sizes[idx]    ,				mode="""bilinear"""    ,				align_corners=__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      resized_logits[0].argmax(dim=0       )
												semantic_segmentation.append(__UpperCAmelCase       )
						else:
									SCREAMING_SNAKE_CASE__								=      logits.argmax(dim=1       )
									SCREAMING_SNAKE_CASE__								=      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

						return semantic_segmentation





"""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


A_							:			Tuple											= logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

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

						SCREAMING_SNAKE_CASE__								=      [np.array(i.resize((w, h)		,				resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      np.array(snake_case__    ).astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      image.transpose(0		,				3		,				1		,				2    )
						SCREAMING_SNAKE_CASE__								=      2.0 * image - 1.0
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(image[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return image

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

			if isinstance(mask[0]		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      mask[0].size
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						SCREAMING_SNAKE_CASE__								=      [np.array(m.convert("""L"""    ).resize((w, h)		,				resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      mask.astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      0
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(mask[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return mask





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		UNetaDModel
			lowerCamelCase__   :		RePaintScheduler

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Dict       )   ->    Tuple:
						super().__init__()
						self.register_modules(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )





			@torch.no_grad()
			def __call__(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						int = 2_5_0    ,				__UpperCAmelCase				:						float = 0.0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				__UpperCAmelCase				:						Optional[str] = "pil"    ,				__UpperCAmelCase				:						bool = True    ,				)   ->    Union[ImagePipelineOutput, Tuple]:
						SCREAMING_SNAKE_CASE__								=      image

						SCREAMING_SNAKE_CASE__								=      _preprocess_image(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      original_image.to(device=self.device    ,				dtype=self.unet.dtype       )
						SCREAMING_SNAKE_CASE__								=      _preprocess_mask(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      mask_image.to(device=self.device    ,				dtype=self.unet.dtype       )

						SCREAMING_SNAKE_CASE__								=      original_image.shape[0]

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

						SCREAMING_SNAKE_CASE__								=      original_image.shape
						SCREAMING_SNAKE_CASE__								=      randn_tensor(__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				device=self.device    ,				dtype=self.unet.dtype       )

						# set step values
						self.scheduler.set_timesteps(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				self.device       )
						SCREAMING_SNAKE_CASE__								=      eta

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

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

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__UpperCAmelCase       )


