
'''simple docstring'''
from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if is_torch_available():
        import torch

        if isinstance(__SCREAMING_SNAKE_CASE,		torch.Tensor							):
            return True

    return isinstance(__SCREAMING_SNAKE_CASE,		np.ndarray							)






class  UpperCAmelCase_					(						snake_case__       ):





 def 							snake_case__		(     self):
     snake_case_			:     List[Any]        =    fields(self)

     # Safety and consistency checks
     if not len(lowercase_):
         raise ValueError(F'{self.__class__.__name__} has no fields.')

     snake_case_			:     Dict        =    getattr(self			,							class_fields[0].name)
     snake_case_			:     List[Any]        =    all(getattr(self			,							field.name) is None for field in class_fields[1:])

     if other_fields_are_none and isinstance(lowercase_			,							lowercase_):
         for key, value in first_field.items():
             snake_case_			:     Optional[Any]        =    value
     else:
         for field in class_fields:
             snake_case_			:     int        =    getattr(self			,							field.name)
             if v is not None:
                 snake_case_			:     Union[str, Any]        =    v





 def __delitem__(     self			,							*lowercase_			,							**lowercase_):
     raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.')





 def 							snake_case__		(     self			,							*lowercase_			,							**lowercase_):
     raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.')





 def 							snake_case__		(     self			,							*lowercase_			,							**lowercase_):
     raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.')





 def 							snake_case__		(     self			,							*lowercase_			,							**lowercase_):
     raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.')





 def __getitem__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         snake_case_			:     List[str]        =    dict(self.items())
         return inner_dict[k]
     else:
         return self.to_tuple()[k]





 def __setattr__(     self			,							lowercase_			,							lowercase_):
     if name in self.keys() and value is not None:
         # Don't call self.__setitem__ to avoid recursion errors
         super().__setitem__(lowercase_			,							lowercase_)
     super().__setattr__(lowercase_			,							lowercase_)





 def __setitem__(     self			,							lowercase_			,							lowercase_):
     # Will raise a KeyException if needed
     super().__setitem__(lowercase_			,							lowercase_)
     # Don't call self.__setattr__ to avoid recursion errors
     super().__setattr__(lowercase_			,							lowercase_)





 def 							snake_case__		(     self):
     return tuple(self[k] for k in self.keys())






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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_      ,			snake_case_      ,			snake_case_			:     List[str]        =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    return (gray > 1_2_7) & (gray <= 2_5_5)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Any        =    np.zeros_like(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Tuple        =    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    snake_case_			:     Union[str, Any]        =    image

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


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

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




