



'''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


snake_case_					:      Dict    			=    logging.get_logger(__name__)






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





	def __init__(					self   :							Optional[Any]					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = PIL.Image.BICUBIC					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							Union[int, float] = 1 / 255					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Optional[Union[float, List[float]]] = None					,lowerCamelCase__   :							Optional[Union[float, List[float]]] = None					,**lowerCamelCase__   :							Optional[Any]					,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						int												=  size if size is not None else {'height': 256, 'width': 256}
							_UpperCamelCase   :						Union[str, Any]												=  get_size_dict(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						List[Any]												=  get_size_dict(lowerCamelCase__					,param_name='crop_size'					)

							_UpperCamelCase   :						List[str]												=  do_resize
							_UpperCamelCase   :						Optional[int]												=  size
							_UpperCamelCase   :						Any												=  resample
							_UpperCamelCase   :						str												=  do_center_crop
							_UpperCamelCase   :						List[Any]												=  crop_size
							_UpperCamelCase   :						List[Any]												=  do_rescale
							_UpperCamelCase   :						Optional[Any]												=  rescale_factor
							_UpperCamelCase   :						Optional[Any]												=  do_normalize
							_UpperCamelCase   :						Optional[Any]												=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_UpperCamelCase   :						Dict												=  image_std if image_std is not None else IMAGENET_STANDARD_STD





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							PILImageResampling = PIL.Image.BICUBIC					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Dict					,):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  get_size_dict(lowerCamelCase__					)
							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(
							    lowerCamelCase__					,size=(size['height'], size['width'])					,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__   :							Any					,):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  get_size_dict(lowerCamelCase__					)
							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(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__   :							Optional[int]					,):







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





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							List[Any]					,):







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





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							Union[str, Any]=None					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Dict[str, 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__   :							Optional[Union[str, TensorType]] = None					,lowerCamelCase__   :							ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__   :							int					,):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						Optional[int]												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						Union[str, Any]												=  do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase   :						Any												=  do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase   :						int												=  rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase   :						str												=  do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase   :						Dict												=  image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase   :						Any												=  image_std if image_std is not None else self.image_std

							_UpperCamelCase   :						Tuple												=  size if size is not None else self.size
							_UpperCamelCase   :						List[str]												=  get_size_dict(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase   :						List[Any]												=  get_size_dict(lowerCamelCase__					,param_name='crop_size'					)

							_UpperCamelCase   :						Tuple												=  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 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   :						List[Any]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if do_resize:
													_UpperCamelCase   :						List[Any]												=  [self.resize(image=lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					) for image in images]

							if do_center_crop:
													_UpperCamelCase   :						Dict												=  [self.center_crop(image=lowerCamelCase__					,size=lowerCamelCase__					) for image in images]

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

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

							_UpperCamelCase   :						List[Any]												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						str												=  {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__					,tensor_type=lowerCamelCase__					)







'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_					:      List[Any]    			=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'VanForImageClassification',
					    'VanModel',
					    'VanPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      List[Any]    			=    _LazyModule(__name__, globals()['__file__'], _import_structure)


