
"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a														=		logging.get_logger(__name__)






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				['pixel_values']



		def __init__( self    ,		__a = True    ,		__a = None    ,		__a = PILImageResampling.BICUBIC    ,		__a = True    ,		__a = True    ,		__a = 1 / 2_55    ,		__a = None    ,		__a = True    ,		__a = None    ,		__a = None    ,		**__a    ,		)	->	None:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase										=  size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase										=  get_size_dict(__a)
							_UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase										=  get_size_dict(__a    ,		default_to_square=__a    ,		param_name='''crop_size''')

							_UpperCamelCase										=  do_resize
							_UpperCamelCase										=  do_rescale
							_UpperCamelCase										=  do_normalize
							_UpperCamelCase										=  do_center_crop
							_UpperCamelCase										=  crop_size
							_UpperCamelCase										=  size
							_UpperCamelCase										=  resample
							_UpperCamelCase										=  rescale_factor
							_UpperCamelCase										=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase										=  image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = PILImageResampling.BILINEAR    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  get_size_dict(__a)

							if "shortest_edge" in size:
												_UpperCamelCase										=  get_resize_output_image_size(__a    ,		size=size['''shortest_edge''']    ,		default_to_square=__a)
												# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
												_UpperCamelCase										=  (size['''height'''], size['''width'''])
							else:
												raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
							return resize(__a    ,		size=__a    ,		resample=__a    ,		data_format=__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = None    ,		**__a)	->	np.ndarray:






							'''simple docstring'''
							return rescale(__a    ,		scale=__a    ,		data_format=__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






							'''simple docstring'''
							return normalize(__a    ,		mean=__a    ,		std=__a    ,		data_format=__a    ,		**__a)




		def 						UpperCAmelCase							( 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 = ChannelDimension.FIRST    ,		**__a    ,		)	->	BatchFeature:






							'''simple docstring'''
							_UpperCamelCase										=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase										=  do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase										=  do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase										=  do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase										=  crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase										=  get_size_dict(__a    ,		param_name='''crop_size'''    ,		default_to_square=__a)
							_UpperCamelCase										=  resample if resample is not None else self.resample
							_UpperCamelCase										=  rescale_factor if rescale_factor is not None else self.rescale_factor
							_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)

							if not is_batched(__a):
												_UpperCamelCase										=  [images]

							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.''')

							# 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"""





from ...utils import is_torch_available, is_transformers_available


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






