



import numpy as np
from PIL import Image





def 	__snake_case      (     _UpperCAmelCase							):
    __a			,  __a			,  __a					=							rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def 	__snake_case      (     _UpperCAmelCase							):
    return (gray > 127) & (gray <= 255)





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							np.zeros_like(_A							)
    __a					=							np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    __a					=							image

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


# kernel to be applied
__snake_case		:Tuple 						=	np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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








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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






     super().__init__(pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       **lowercase_			)

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
