




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   :						List[Any]				=			logging.get_logger(__name__)


if is_vision_available():
					import PIL


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=['''pixel_values''']

			def __init__( self			:   List[str]       ,						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_			:   Union[str, Any]       ,						)						->   None:
								super().__init__(**lowerCamelCase_  )
								__magic_name__							:	int    							=							size if size is not None else {'''shortest_edge''': 224}
								__magic_name__							:	Optional[Any]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								__magic_name__							:	int    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_       ,						param_name='''crop_size'''  )

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

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   List[Any]       ,						)						->   np.ndarray:
								__magic_name__							:	Union[str, Any]    							=							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()}'''  )
								__magic_name__							:	List[Any]    							=							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 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   List[Any]       ,						)						->   np.ndarray:
								__magic_name__							:	List[str]    							=							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 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Union[int, float]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Dict       ,						)						->   Dict:
								return rescale(lowerCamelCase_       ,						scale=lowerCamelCase_       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

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


			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						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_			:   Dict       ,						)						->   PIL.Image.Image:
								__magic_name__							:	Optional[Any]    							=							do_resize if do_resize is not None else self.do_resize
								__magic_name__							:	Tuple    							=							size if size is not None else self.size
								__magic_name__							:	Union[str, Any]    							=							get_size_dict(lowerCamelCase_       ,						param_name='''size'''       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	int    							=							resample if resample is not None else self.resample
								__magic_name__							:	int    							=							do_center_crop if do_center_crop is not None else self.do_center_crop
								__magic_name__							:	int    							=							crop_size if crop_size is not None else self.crop_size
								__magic_name__							:	List[Any]    							=							get_size_dict(lowerCamelCase_       ,						param_name='''crop_size'''       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	Dict    							=							do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__							:	Tuple    							=							rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__							:	Optional[Any]    							=							do_normalize if do_normalize is not None else self.do_normalize
								__magic_name__							:	Optional[Any]    							=							image_mean if image_mean is not None else self.image_mean
								__magic_name__							:	Union[str, Any]    							=							image_std if image_std is not None else self.image_std
								__magic_name__							:	int    							=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__magic_name__							:	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:
													__magic_name__							:	List[str]    							=							[convert_to_rgb(lowerCamelCase_  ) for image in images]

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

								if do_resize:
													__magic_name__							:	List[str]    							=							[self.resize(image=lowerCamelCase_       ,						size=lowerCamelCase_       ,						resample=lowerCamelCase_  ) for image in images]

								if do_center_crop:
													__magic_name__							:	str    							=							[self.center_crop(image=lowerCamelCase_       ,						size=lowerCamelCase_  ) for image in images]

								if do_rescale:
													__magic_name__							:	Union[str, Any]    							=							[self.rescale(image=lowerCamelCase_       ,						scale=lowerCamelCase_  ) for image in images]

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

								__magic_name__							:	str    							=							[to_channel_dimension_format(lowerCamelCase_       ,						lowerCamelCase_  ) for image in images]

								__magic_name__							:	str    							=							{'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase_       ,						tensor_type=lowerCamelCase_  )









# 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,
)



