



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


a     :    Tuple      						=					logging.get_logger(__name__)


if is_vision_available():
							import PIL

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





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

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





	def 				_lowercase(						self	,	A	,	A	,	A = PILImageResampling.BICUBIC	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	Dict							  =	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()}'''					)
								UpperCAmelCase  :	Tuple							  =	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 				_lowercase(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	Union[str, Any]							  =	get_size_dict(A					)
								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(A	,	size=(size["""height"""], size["""width"""])	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    Optional[int]:
								return rescale(A	,	scale=A	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A = None	,	**A	,	)    ->    np.ndarray:
								return normalize(A	,	mean=A	,	std=A	,	data_format=A	,	**A					)






	def 				_lowercase(						self	,	A	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = ChannelDimension.FIRST	,	**A	,	)    ->    PIL.Image.Image:
								UpperCAmelCase  :	str							  =	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase  :	Tuple							  =	size if size is not None else self.size
								UpperCAmelCase  :	List[str]							  =	get_size_dict(A	,	param_name="""size"""	,	default_to_square=A					)
								UpperCAmelCase  :	int							  =	resample if resample is not None else self.resample
								UpperCAmelCase  :	Optional[int]							  =	do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase  :	Dict							  =	crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase  :	Optional[int]							  =	get_size_dict(A	,	param_name="""crop_size"""	,	default_to_square=A					)
								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  :	int							  =	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase  :	int							  =	image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase  :	List[str]							  =	image_std if image_std is not None else self.image_std
								UpperCAmelCase  :	Any							  =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase  :	Optional[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."""					)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
															UpperCAmelCase  :	Union[str, Any]							  =	[convert_to_rgb(A					) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase  :	Any							  =	[to_numpy_array(A					) for image in images]

								if do_resize:
															UpperCAmelCase  :	Any							  =	[self.resize(image=A	,	size=A	,	resample=A					) for image in images]

								if do_center_crop:
															UpperCAmelCase  :	Tuple							  =	[self.center_crop(image=A	,	size=A					) for image in images]

								if do_rescale:
															UpperCAmelCase  :	List[str]							  =	[self.rescale(image=A	,	scale=A					) for image in images]

								if do_normalize:
															UpperCAmelCase  :	int							  =	[self.normalize(image=A	,	mean=A	,	std=A					) for image in images]

								UpperCAmelCase  :	int							  =	[to_channel_dimension_format(A	,	A					) for image in images]

								UpperCAmelCase  :	Optional[Any]							  =	{"""pixel_values""": images}
								return BatchFeature(data=A	,	tensor_type=A					)






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

