





'''simple docstring'''






from pathlib import Path

import numpy as np
from PIL import Image
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase	,				_lowerCAmelCase					=				rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
      return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      return (gray > 1_27) & (gray <= 2_55)
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				np.zeros_like(_A    )
      _lowerCAmelCase					=				np.zeros(
          (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

      # Copy image to padded image
      _lowerCAmelCase					=				image

      # Iterate over image & apply kernel
      for x in range(image.shape[1]    ):
            for y in range(image.shape[0]    ):
                  _lowerCAmelCase					=				(
                      kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                  ).sum()
                  _lowerCAmelCase					=				int(summation > 0    )
      return output


if __name__ == "__main__":
      # read original image
      A__							:  Optional[int]    =Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
      A__							:  Dict    =np.array(Image.open(lena_path))
      # kernel to be applied
      A__							:  int    =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
      A__							:  Tuple    =dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
      # Save the output image
      A__							:  Dict    =Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_dilation.png''')







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


