

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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      int        =  "graphormer"
   lowerCamelCase__					:      Tuple        =  ["past_key_values"]



   def __init__(							self,       lowercase_ = 1,       lowercase_ = 512 * 9,       lowercase_ = 512 * 3,       lowercase_ = 512,       lowercase_ = 512,       lowercase_ = 512,       lowercase_ = 128,       lowercase_ = 5,       lowercase_ = 1024,       lowercase_ = "multi_hop",       lowercase_ = 512,       lowercase_ = False,       lowercase_ = 12,       lowercase_ = 768,       lowercase_ = 768,       lowercase_ = 32,       lowercase_ = 0.1,       lowercase_ = 0.1,       lowercase_ = 0.0,       lowercase_ = False,       lowercase_ = False,       lowercase_ = False,       lowercase_ = "gelu",       lowercase_ = None,       lowercase_ = False,       lowercase_ = 0,       lowercase_ = False,       lowercase_ = 0.0,       lowercase_ = 8,       lowercase_ = None,       lowercase_ = None,       lowercase_ = True,       lowercase_ = True,       lowercase_=0,       lowercase_=1,       lowercase_=2,       **lowercase_,       )				->       Optional[int]:






     """simple docstring"""






     a__    =num_classes
     a__    =num_atoms
     a__    =num_in_degree
     a__    =num_out_degree
     a__    =num_edges
     a__    =num_spatial
     a__    =num_edge_dis
     a__    =edge_type
     a__    =multi_hop_max_dist
     a__    =spatial_pos_max
     a__    =max_nodes
     a__    =num_hidden_layers
     a__    =embedding_dim
     a__    =embedding_dim
     a__    =ffn_embedding_dim
     a__    =num_attention_heads
     a__    =dropout
     a__    =attention_dropout
     a__    =layerdrop
     a__    =encoder_normalize_before
     a__    =pre_layernorm
     a__    =apply_graphormer_init
     a__    =activation_fn
     a__    =embed_scale
     a__    =freeze_embeddings
     a__    =num_trans_layers_to_freeze
     a__    =share_input_output_embed
     a__    =traceable
     a__    =q_noise
     a__    =qn_block_size

     # These parameters are here for future extensions
     # atm, the model only supports self attention
     a__    =kdim
     a__    =vdim
     a__    =self_attention
     a__    =bias

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

'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

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


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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