




'''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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase      :   Optional[Any]      =       logging.get_logger(__name__)







class 					_a (UpperCamelCase_	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = 0.9							,__a = PILImageResampling.BICUBIC							,__a = True							,__a = None							,__a = 1 / 255							,__a = True							,__a = True							,__a = None							,__a = None							,**__a							,)      ->				List[str]:
          super().__init__(**a__							)
          snake_case			:      int        =			size if size is not None else {"shortest_edge": 224}
          snake_case			:      Union[str, Any]        =			get_size_dict(a__							,default_to_square=a__							)
          snake_case			:      Optional[int]        =			crop_size if crop_size is not None else {"height": 224, "width": 224}
          snake_case			:      int        =			get_size_dict(a__							,param_name="""crop_size"""							)

          snake_case			:      List[str]        =			do_resize
          snake_case			:      Tuple        =			size
          snake_case			:      Optional[int]        =			crop_pct
          snake_case			:      Tuple        =			resample
          snake_case			:      Optional[Any]        =			do_center_crop
          snake_case			:      str        =			crop_size
          snake_case			:      List[Any]        =			do_rescale
          snake_case			:      Any        =			rescale_factor
          snake_case			:      Tuple        =			do_normalize
          snake_case			:      Optional[int]        =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          snake_case			:      List[Any]        =			image_std if image_std is not None else IMAGENET_DEFAULT_STD







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,__a = PILImageResampling.BICUBIC							,__a = None							,**__a							,)      ->				Tuple:
          snake_case			:      int        =			get_size_dict(a__							,default_to_square=a__							)
          if "shortest_edge" not in size and ("height" not in size or "width" not in size):
               raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
          if crop_pct is not None:
               if "shortest_edge" in size:
                    snake_case			:      str        =			int(size["""shortest_edge"""] / crop_pct							)
               elif "height" in size and "width" in size:
                    if size["height"] == size["width"]:
                         snake_case			:      Union[str, Any]        =			int(size["""height"""] / crop_pct							)
                    else:
                         snake_case			:      Optional[int]        =			(int(size["""height"""] / crop_pct							), int(size["""width"""] / crop_pct							))
               else:
                    raise ValueError("""Invalid size for resize: {}""".format(a__							)							)

               snake_case			:      str        =			get_resize_output_image_size(a__							,size=a__							,default_to_square=a__							)
          else:
               if "shortest_edge" in size:
                    snake_case			:      Optional[Any]        =			get_resize_output_image_size(a__							,size=size["""shortest_edge"""]							,default_to_square=a__							)
               elif "height" in size and "width" in size:
                    snake_case			:      Dict        =			(size["height"], size["width"])
               else:
                    raise ValueError("""Invalid size for resize: {}""".format(a__							)							)

          return resize(a__							,size=a__							,resample=a__							,data_format=a__							,**a__							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				Optional[int]:
          snake_case			:      Optional[int]        =			get_size_dict(a__							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''							)
          return center_crop(a__							,size=(size["""height"""], size["""width"""])							,data_format=a__							,**a__							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				Tuple:
          return rescale(a__							,scale=a__							,data_format=a__							,**a__							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				Union[str, Any]:
          return normalize(a__							,mean=a__							,std=a__							,data_format=a__							,**a__							)







     def 			snake_case_      (				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							,)      ->				Dict:
          snake_case			:      Dict        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      Optional[Any]        =			crop_pct if crop_pct is not None else self.crop_pct
          snake_case			:      List[str]        =			resample if resample is not None else self.resample
          snake_case			:      Dict        =			do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case			:      Optional[Any]        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      str        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      str        =			do_normalize if do_normalize is not None else self.do_normalize
          snake_case			:      Optional[int]        =			image_mean if image_mean is not None else self.image_mean
          snake_case			:      str        =			image_std if image_std is not None else self.image_std

          snake_case			:      str        =			size if size is not None else self.size
          snake_case			:      List[str]        =			get_size_dict(a__							,default_to_square=a__							)
          snake_case			:      Optional[int]        =			crop_size if crop_size is not None else self.crop_size
          snake_case			:      Optional[Any]        =			get_size_dict(a__							,param_name="""crop_size"""							)

          snake_case			:      List[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 or resample is None:
               raise ValueError("""Size and resample must be specified if do_resize is True."""							)

          if do_center_crop and crop_pct is None:
               raise ValueError("""Crop_pct 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.
          snake_case			:      List[Any]        =			[to_numpy_array(a__							) for image in images]

          if do_resize:
               snake_case			:      Any        =			[self.resize(image=a__							,size=a__							,crop_pct=a__							,resample=a__							) for image in images]

          if do_center_crop:
               snake_case			:      Optional[int]        =			[self.center_crop(image=a__							,size=a__							) for image in images]

          if do_rescale:
               snake_case			:      Optional[Any]        =			[self.rescale(image=a__							,scale=a__							) for image in images]

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

          snake_case			:      List[str]        =			[to_channel_dimension_format(a__							,a__							) for image in images]

          snake_case			:      Tuple        =			{"pixel_values": images}
          return BatchFeature(data=a__							,tensor_type=a__							)





'''simple docstring'''

from ....configuration_utils import PretrainedConfig
from ....utils import logging


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Tuple	=			"""van"""


 def __init__(    self  : List[Any]			,							a__  : Dict=224			,							a__  : Dict=3			,							a__  : Union[str, Any]=[7, 3, 3, 3]			,							a__  : Optional[Any]=[4, 2, 2, 2]			,							a__  : Optional[Any]=[64, 128, 320, 512]			,							a__  : List[str]=[3, 3, 12, 3]			,							a__  : Any=[8, 8, 4, 4]			,							a__  : Optional[int]="gelu"			,							a__  : List[Any]=0.02			,							a__  : Tuple=1e-6			,							a__  : List[str]=1e-2			,							a__  : List[str]=0.0			,							a__  : List[Any]=0.0			,							**a__  : Tuple			,							):
  '''simple docstring'''

  super().__init__(**a__    )
  lowerCAmelCase__     :   Any     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[Any]     		=	patch_sizes
  lowerCAmelCase__     :   Dict     		=	strides
  lowerCAmelCase__     :   List[str]     		=	hidden_sizes
  lowerCAmelCase__     :   Union[str, Any]     		=	depths
  lowerCAmelCase__     :   Tuple     		=	mlp_ratios
  lowerCAmelCase__     :   Optional[Any]     		=	hidden_act
  lowerCAmelCase__     :   Dict     		=	initializer_range
  lowerCAmelCase__     :   int     		=	layer_norm_eps
  lowerCAmelCase__     :   Optional[Any]     		=	layer_scale_init_value
  lowerCAmelCase__     :   List[str]     		=	drop_path_rate
  lowerCAmelCase__     :   Any     		=	dropout_rate





