




'''simple docstring'''






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


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

lowercase	:      Optional[int]				=       {
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     Optional[int]			   =      "vit-hybrid"



 def __init__(  self   ,		A=None   ,		A=768   ,		A=12   ,		A=12   ,		A=3072   ,		A="gelu"   ,		A=0.0   ,		A=0.0   ,		A=0.0_2   ,		A=1E-12   ,		A=224   ,		A=1   ,		A=3   ,		A=[1, 1024, 24, 24]   ,		A=True   ,		**A   ,		):






        '''simple docstring'''




        super().__init__(**A )

        if backbone_config is None:
               logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.''' )
               lowercase  :		List[str]						=      {
                   '''global_padding''': '''same''',
                   '''layer_type''': '''bottleneck''',
                   '''depths''': [3, 4, 9],
                   '''out_features''': ['''stage3'''],
                   '''embedding_dynamic_padding''': True,
               }

        if isinstance(A   ,		A ):
               if "model_type" in backbone_config:
                      lowercase  :		str						=      CONFIG_MAPPING[backbone_config['''model_type''']]
               else:
                      logger.info(
                          '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.''' )
                      lowercase  :		Union[str, Any]						=      BitConfig
               lowercase  :		Dict						=      backbone_config_class(**A )

        lowercase  :		Dict						=      backbone_featmap_shape
        lowercase  :		int						=      backbone_config
        lowercase  :		Union[str, Any]						=      hidden_size
        lowercase  :		Optional[int]						=      num_hidden_layers
        lowercase  :		Optional[Any]						=      num_attention_heads
        lowercase  :		List[str]						=      intermediate_size
        lowercase  :		Optional[int]						=      hidden_act
        lowercase  :		Tuple						=      hidden_dropout_prob
        lowercase  :		List[Any]						=      attention_probs_dropout_prob
        lowercase  :		str						=      initializer_range
        lowercase  :		str						=      layer_norm_eps
        lowercase  :		List[Any]						=      image_size
        lowercase  :		Tuple						=      patch_size
        lowercase  :		Tuple						=      num_channels
        lowercase  :		Optional[int]						=      qkv_bias





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      copy.deepcopy(self.__dict__ )
        lowercase  :		Any						=      self.backbone_config.to_dict()
        lowercase  :		Any						=      self.__class__.model_type
        return output










'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




