






"""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,
    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


lowerCamelCase_					=      logging.get_logger(__name__)


if is_vision_available():
			import PIL







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				['''pixel_values''']





	def __init__(      self					: Union[str, Any]	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Dict[str, int] = None	,	lowerCAmelCase_					: PILImageResampling = PILImageResampling.BICUBIC	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Dict[str, int] = None	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Union[int, float] = 1 / 255	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	lowerCAmelCase_					: bool = True	,	**lowerCAmelCase_					: Tuple	,	)						->			None:
				super().__init__(**lowerCAmelCase_						)
				UpperCAmelCase_     : int			     =					size if size is not None else {"shortest_edge": 224}
				UpperCAmelCase_     : Dict			     =					get_size_dict(lowerCAmelCase_	,	default_to_square=lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					crop_size if crop_size is not None else {"height": 224, "width": 224}
				UpperCAmelCase_     : Any			     =					get_size_dict(lowerCAmelCase_	,	default_to_square=lowerCAmelCase_	,	param_name="crop_size"						)

				UpperCAmelCase_     : str			     =					do_resize
				UpperCAmelCase_     : Any			     =					size
				UpperCAmelCase_     : Optional[int]			     =					resample
				UpperCAmelCase_     : List[str]			     =					do_center_crop
				UpperCAmelCase_     : str			     =					crop_size
				UpperCAmelCase_     : List[Any]			     =					do_rescale
				UpperCAmelCase_     : Any			     =					rescale_factor
				UpperCAmelCase_     : List[Any]			     =					do_normalize
				UpperCAmelCase_     : int			     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				UpperCAmelCase_     : Tuple			     =					image_std if image_std is not None else OPENAI_CLIP_STD
				UpperCAmelCase_     : List[str]			     =					do_convert_rgb





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: np.ndarray	,	lowerCAmelCase_					: Dict[str, int]	,	lowerCAmelCase_					: PILImageResampling = PILImageResampling.BICUBIC	,	lowerCAmelCase_					: Optional[Union[str, ChannelDimension]] = None	,	**lowerCAmelCase_					: Tuple	,	)						->			np.ndarray:
				UpperCAmelCase_     : Dict			     =					get_size_dict(lowerCAmelCase_	,	default_to_square=lowerCAmelCase_						)
				if "shortest_edge" not in size:
							raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
				UpperCAmelCase_     : Dict			     =					get_resize_output_image_size(lowerCAmelCase_	,	size=size["shortest_edge"]	,	default_to_square=lowerCAmelCase_						)
				return resize(lowerCAmelCase_	,	size=lowerCAmelCase_	,	resample=lowerCAmelCase_	,	data_format=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: np.ndarray	,	lowerCAmelCase_					: Dict[str, int]	,	lowerCAmelCase_					: Optional[Union[str, ChannelDimension]] = None	,	**lowerCAmelCase_					: Optional[int]	,	)						->			np.ndarray:
				UpperCAmelCase_     : Optional[int]			     =					get_size_dict(lowerCAmelCase_						)
				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(lowerCAmelCase_	,	size=(size["height"], size["width"])	,	data_format=lowerCAmelCase_	,	**lowerCAmelCase_						)





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





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





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: ImageInput	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: Dict[str, int] = None	,	lowerCAmelCase_					: PILImageResampling = None	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: int = None	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: float = None	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: Optional[Union[str, TensorType]] = None	,	lowerCAmelCase_					: Optional[ChannelDimension] = ChannelDimension.FIRST	,	**lowerCAmelCase_					: List[str]	,	)						->			PIL.Image.Image:
				UpperCAmelCase_     : Any			     =					do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_     : str			     =					size if size is not None else self.size
				UpperCAmelCase_     : int			     =					get_size_dict(lowerCAmelCase_	,	param_name="size"	,	default_to_square=lowerCAmelCase_						)
				UpperCAmelCase_     : int			     =					resample if resample is not None else self.resample
				UpperCAmelCase_     : Any			     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCAmelCase_     : Any			     =					crop_size if crop_size is not None else self.crop_size
				UpperCAmelCase_     : Optional[Any]			     =					get_size_dict(lowerCAmelCase_	,	param_name="crop_size"	,	default_to_square=lowerCAmelCase_						)
				UpperCAmelCase_     : Any			     =					do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase_     : str			     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase_     : List[Any]			     =					do_normalize if do_normalize is not None else self.do_normalize
				UpperCAmelCase_     : Dict			     =					image_mean if image_mean is not None else self.image_mean
				UpperCAmelCase_     : int			     =					image_std if image_std is not None else self.image_std
				UpperCAmelCase_     : Optional[int]			     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				UpperCAmelCase_     : Any			     =					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:
							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:
							UpperCAmelCase_     : List[str]			     =					[convert_to_rgb(lowerCAmelCase_						) for image in images]

				# All transformations expect numpy arrays.
				UpperCAmelCase_     : str			     =					[to_numpy_array(lowerCAmelCase_						) for image in images]

				if do_resize:
							UpperCAmelCase_     : List[Any]			     =					[self.resize(image=lowerCAmelCase_	,	size=lowerCAmelCase_	,	resample=lowerCAmelCase_						) for image in images]

				if do_center_crop:
							UpperCAmelCase_     : Dict			     =					[self.center_crop(image=lowerCAmelCase_	,	size=lowerCAmelCase_						) for image in images]

				if do_rescale:
							UpperCAmelCase_     : Optional[Any]			     =					[self.rescale(image=lowerCAmelCase_	,	scale=lowerCAmelCase_						) for image in images]

				if do_normalize:
							UpperCAmelCase_     : Dict			     =					[self.normalize(image=lowerCAmelCase_	,	mean=lowerCAmelCase_	,	std=lowerCAmelCase_						) for image in images]

				UpperCAmelCase_     : Optional[int]			     =					[to_channel_dimension_format(lowerCAmelCase_	,	lowerCAmelCase_						) for image in images]

				UpperCAmelCase_     : str			     =					{"pixel_values": images}
				return BatchFeature(data=lowerCAmelCase_	,	tensor_type=lowerCAmelCase_						)











"""simple docstring"""
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)



