




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


lowerCAmelCase_						 =					logging.get_logger(__name__)
class 		_A   (    __A ):
  _UpperCamelCase  :     Optional[Any]					=     ['''pixel_values''']






  def __init__(    self						:      Tuple ,		_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						:      List[str] ,		)						->       None:






       """simple docstring"""







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






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






       """simple docstring"""







       lowercase   :  Optional[int]        =					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()}""" )
       lowercase   :  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    __a     (    self						:      Tuple ,		_A						:      np.ndarray ,		_A						:      Dict[str, int] ,		_A						:      Optional[Union[str, ChannelDimension]] = None ,		**_A						:      str ,		)						->       np.ndarray:






       """simple docstring"""







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






  def    __a     (    self						:      Optional[int] ,		_A						:      np.ndarray ,		_A						:      float ,		_A						:      Optional[Union[str, ChannelDimension]] = None ,		**_A						:      List[str] )						->       np.ndarray:






       """simple docstring"""







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






  def    __a     (    self						:      Optional[int] ,		_A						:      np.ndarray ,		_A						:      Union[float, List[float]] ,		_A						:      Union[float, List[float]] ,		_A						:      Optional[Union[str, ChannelDimension]] = None ,		**_A						:      int ,		)						->       np.ndarray:






       """simple docstring"""







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






  def    __a     (    self						:      Any ,		_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						:      Any ,		)						->       Union[str, Any]:






       """simple docstring"""







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

       lowercase   :  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.
       lowercase   :  Dict        =					[to_numpy_array(_A ) for image in images]

       if do_resize:
            lowercase   :  Dict        =					[self.resize(image=_A ,		size=_A ,		resample=_A ) for image in images]

       if do_center_crop:
            lowercase   :  List[Any]        =					[self.center_crop(image=_A ,		size=_A ) for image in images]

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

       if do_normalize:
            lowercase   :  str        =					[self.normalize(image=_A ,		mean=_A ,		std=_A ) for image in images]

       lowercase   :  Union[str, Any]        =					[to_channel_dimension_format(_A ,		_A ) for image in images]

       lowercase   :  Tuple        =					{"pixel_values": images}
       return BatchFeature(data=_A ,		tensor_type=_A )


from typing import TYPE_CHECKING

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


a_      =     {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    '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

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)