'''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


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






class 						A   (   UpperCAmelCase   ):
							a_        =      ['''pixel_values''']





							def __init__(      self				:							int     ,		__a				:							bool = True     ,		__a				:							Dict[str, int] = None     ,		__a				:							PILImageResampling = PIL.Image.BICUBIC     ,		__a				:							bool = True     ,		__a				:							Dict[str, int] = None     ,		__a				:							Union[int, float] = 1 / 2_5_5     ,		__a				:							bool = True     ,		__a				:							bool = True     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		**__a				:							Optional[Any]     ,		)							->	None:
								super().__init__(**__a	)
								__UpperCAmelCase							=			size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
								__UpperCAmelCase							=			get_size_dict(__a	)
								__UpperCAmelCase							=			crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								__UpperCAmelCase							=			get_size_dict(__a     ,		param_name='''crop_size'''	)

								__UpperCAmelCase							=			do_resize
								__UpperCAmelCase							=			size
								__UpperCAmelCase							=			resample
								__UpperCAmelCase							=			do_center_crop
								__UpperCAmelCase							=			crop_size
								__UpperCAmelCase							=			do_rescale
								__UpperCAmelCase							=			rescale_factor
								__UpperCAmelCase							=			do_normalize
								__UpperCAmelCase							=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__UpperCAmelCase							=			image_std if image_std is not None else IMAGENET_STANDARD_STD





							def       snake_case__						(      self				:							Any     ,		__a				:							np.ndarray     ,		__a				:							Dict[str, int]     ,		__a				:							PILImageResampling = PIL.Image.BICUBIC     ,		__a				:							Optional[Union[str, ChannelDimension]] = None     ,		**__a				:							Any     ,		)							->	np.ndarray:
								__UpperCAmelCase							=			get_size_dict(__a	)
								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(
								    __a     ,		size=(size['''height'''], size['''width'''])     ,		resample=__a     ,		data_format=__a     ,		**__a	)





							def       snake_case__						(      self				:							str     ,		__a				:							np.ndarray     ,		__a				:							Dict[str, int]     ,		__a				:							Optional[Union[str, ChannelDimension]] = None     ,		**__a				:							List[str]     ,		)							->	np.ndarray:
								__UpperCAmelCase							=			get_size_dict(__a	)
								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(__a     ,		size=(size['''height'''], size['''width'''])     ,		data_format=__a     ,		**__a	)





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





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






							def       snake_case__						(      self				:							Tuple     ,		__a				:							ImageInput     ,		__a				:							bool = None     ,		__a				:							Dict[str, int] = None     ,		__a				:							Optional[int]=None     ,		__a				:							bool = None     ,		__a				:							Dict[str, int] = None     ,		__a				:							bool = None     ,		__a				:							float = None     ,		__a				:							bool = None     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		__a				:							Optional[Union[str, TensorType]] = None     ,		__a				:							ChannelDimension = ChannelDimension.FIRST     ,		**__a				:							List[str]     ,		)							->	PIL.Image.Image:
								__UpperCAmelCase							=			do_resize if do_resize is not None else self.do_resize
								__UpperCAmelCase							=			resample if resample is not None else self.resample
								__UpperCAmelCase							=			do_center_crop if do_center_crop is not None else self.do_center_crop
								__UpperCAmelCase							=			do_rescale if do_rescale is not None else self.do_rescale
								__UpperCAmelCase							=			rescale_factor if rescale_factor is not None else self.rescale_factor
								__UpperCAmelCase							=			do_normalize if do_normalize is not None else self.do_normalize
								__UpperCAmelCase							=			image_mean if image_mean is not None else self.image_mean
								__UpperCAmelCase							=			image_std if image_std is not None else self.image_std

								__UpperCAmelCase							=			size if size is not None else self.size
								__UpperCAmelCase							=			get_size_dict(__a	)
								__UpperCAmelCase							=			crop_size if crop_size is not None else self.crop_size
								__UpperCAmelCase							=			get_size_dict(__a     ,		param_name='''crop_size'''	)

								__UpperCAmelCase							=			make_list_of_images(__a	)

								if not valid_images(__a	):
									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.
								__UpperCAmelCase							=			[to_numpy_array(__a	) for image in images]

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

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

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

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

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

								__UpperCAmelCase							=			{'''pixel_values''': images}
								return BatchFeature(data=__a     ,		tensor_type=__a	)

'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)
