
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :      str       =				logging.get_logger(__name__)

_a   :      Union[str, Any]       =				{
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       """mpnet"""
    def __init__(     self    ,     snake_case_=3_05_27    ,     snake_case_=7_68    ,     snake_case_=12    ,     snake_case_=12    ,     snake_case_=30_72    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=0.02    ,     snake_case_=1E-12    ,     snake_case_=32    ,     snake_case_=1    ,     snake_case_=0    ,     snake_case_=2    ,     **snake_case_    ,     ):
      super().__init__(pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     **snake_case_  )

      _snake_case  :    Tuple            = vocab_size
      _snake_case  :    int            = hidden_size
      _snake_case  :    str            = num_hidden_layers
      _snake_case  :    Any            = num_attention_heads
      _snake_case  :    Optional[Any]            = hidden_act
      _snake_case  :    Union[str, Any]            = intermediate_size
      _snake_case  :    Any            = hidden_dropout_prob
      _snake_case  :    Any            = attention_probs_dropout_prob
      _snake_case  :    Any            = max_position_embeddings
      _snake_case  :    int            = initializer_range
      _snake_case  :    Any            = layer_norm_eps
      _snake_case  :    Optional[int]            = relative_attention_num_buckets


'''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''')