



"""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,
    get_resize_output_image_size,
    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, logging


UpperCamelCase__						=			logging.get_logger(__name__)




class   a__			(       UpperCamelCase_  ):
						snake_case__             = ['''pixel_values''']





						def __init__(							self      :	List[str]     ,a__      :	bool = True     ,a__      :	Optional[Dict[str, int]] = None     ,a__      :	PILImageResampling = PILImageResampling.BILINEAR     ,a__      :	bool = True     ,a__      :	Dict[str, int] = None     ,a__      :	bool = True     ,a__      :	Union[int, float] = 1 / 255     ,a__      :	bool = True     ,a__      :	Optional[Union[float, List[float]]] = None     ,a__      :	Optional[Union[float, List[float]]] = None     ,**a__      :	Dict     ,)						->       None:




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:int									=  size if size is not None else {'''shortest_edge''': 256}
											_lowerCAmelCase:Dict									=  get_size_dict(a__     ,default_to_square=a__)
											_lowerCAmelCase:List[str]									=  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											_lowerCAmelCase:List[str]									=  get_size_dict(a__)
											_lowerCAmelCase:int									=  do_resize
											_lowerCAmelCase:List[Any]									=  size
											_lowerCAmelCase:Dict									=  resample
											_lowerCAmelCase:List[str]									=  do_center_crop
											_lowerCAmelCase:Any									=  crop_size
											_lowerCAmelCase:List[Any]									=  do_rescale
											_lowerCAmelCase:Tuple									=  rescale_factor
											_lowerCAmelCase:Dict									=  do_normalize
											_lowerCAmelCase:Optional[Any]									=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											_lowerCAmelCase:List[Any]									=  image_std if image_std is not None else IMAGENET_STANDARD_STD





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	np.ndarray     ,a__      :	Dict[str, int]     ,a__      :	PILImageResampling = PILImageResampling.BICUBIC     ,a__      :	Optional[Union[str, ChannelDimension]] = None     ,**a__      :	List[Any]     ,)						->       np.ndarray:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  get_size_dict(a__     ,default_to_square=a__)
											if "shortest_edge" not in size:
																raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
											_lowerCAmelCase:List[str]									=  get_resize_output_image_size(a__     ,size=size['''shortest_edge''']     ,default_to_square=a__)
											return resize(a__     ,size=a__     ,resample=a__     ,data_format=a__     ,**a__)





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	np.ndarray     ,a__      :	Dict[str, int]     ,a__      :	Optional[Union[str, ChannelDimension]] = None     ,**a__      :	int     ,)						->       np.ndarray:




											"""simple docstring"""







											_lowerCAmelCase:str									=  get_size_dict(a__)
											return center_crop(a__     ,size=(size['''height'''], size['''width'''])     ,data_format=a__     ,**a__)





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	np.ndarray     ,a__      :	float     ,a__      :	Optional[Union[str, ChannelDimension]] = None     ,**a__      :	str)						->       np.ndarray:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	np.ndarray     ,a__      :	Union[float, List[float]]     ,a__      :	Union[float, List[float]]     ,a__      :	Optional[Union[str, ChannelDimension]] = None     ,**a__      :	Tuple     ,)						->       np.ndarray:




											"""simple docstring"""







											return normalize(a__     ,mean=a__     ,std=a__     ,data_format=a__     ,**a__)







						def       __UpperCamelCase     (							self      :	int     ,a__      :	ImageInput     ,a__      :	Optional[bool] = None     ,a__      :	Dict[str, int] = None     ,a__      :	PILImageResampling = None     ,a__      :	bool = None     ,a__      :	Dict[str, int] = None     ,a__      :	Optional[bool] = None     ,a__      :	Optional[float] = None     ,a__      :	Optional[bool] = None     ,a__      :	Optional[Union[float, List[float]]] = None     ,a__      :	Optional[Union[float, List[float]]] = None     ,a__      :	Optional[Union[str, TensorType]] = None     ,a__      :	Union[str, ChannelDimension] = ChannelDimension.FIRST     ,**a__      :	int     ,)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  do_resize if do_resize is not None else self.do_resize
											_lowerCAmelCase:Tuple									=  size if size is not None else self.size
											_lowerCAmelCase:str									=  get_size_dict(a__     ,default_to_square=a__)
											_lowerCAmelCase:Dict									=  resample if resample is not None else self.resample
											_lowerCAmelCase:Optional[int]									=  do_center_crop if do_center_crop is not None else self.do_center_crop
											_lowerCAmelCase:List[str]									=  crop_size if crop_size is not None else self.crop_size
											_lowerCAmelCase:Union[str, Any]									=  get_size_dict(a__)
											_lowerCAmelCase:Any									=  do_rescale if do_rescale is not None else self.do_rescale
											_lowerCAmelCase:Dict									=  rescale_factor if rescale_factor is not None else self.rescale_factor
											_lowerCAmelCase:Optional[Any]									=  do_normalize if do_normalize is not None else self.do_normalize
											_lowerCAmelCase:str									=  image_mean if image_mean is not None else self.image_mean
											_lowerCAmelCase:Dict									=  image_std if image_std is not None else self.image_std

											_lowerCAmelCase:Union[str, Any]									=  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:
																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.''')

											# All transformations expect numpy arrays.
											_lowerCAmelCase:List[Any]									=  [to_numpy_array(a__) for image in images]

											if do_resize:
																_lowerCAmelCase:Optional[Any]									=  [self.resize(image=a__     ,size=a__     ,resample=a__) for image in images]

											if do_center_crop:
																_lowerCAmelCase:Union[str, Any]									=  [self.center_crop(image=a__     ,size=a__) for image in images]

											if do_rescale:
																_lowerCAmelCase:int									=  [self.rescale(image=a__     ,scale=a__) for image in images]

											if do_normalize:
																_lowerCAmelCase:List[str]									=  [self.normalize(image=a__     ,mean=a__     ,std=a__) for image in images]

											_lowerCAmelCase:Dict									=  [to_channel_dimension_format(a__     ,a__) for image in images]

											_lowerCAmelCase:List[str]									=  {'''pixel_values''': images}
											return BatchFeature(data=a__     ,tensor_type=a__)












"""simple docstring"""






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)






