
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 		A						(  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__):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:List[Any] 		=    fields(self	)

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

									UpperCAmelCase__		:Dict 		=    getattr(self  , class_fields[0].name	)
									UpperCAmelCase__		: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():
															UpperCAmelCase__		:Optional[Any] 		=    value
									else:
												for field in class_fields:
															UpperCAmelCase__		:int 		=    getattr(self  , field.name	)
															if v is not None:
																		UpperCAmelCase__		:Union[str, Any] 		=    v


						def __delitem__(  self  , *A  , **A	)  ->List[Any]:
									raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""	)


						def    A__       (  self  , *A  , **A	)  ->List[Any]:
									raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""	)


						def    A__       (  self  , *A  , **A	)  ->Union[str, Any]:
									raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""	)


						def    A__       (  self  , *A  , **A	)  ->List[Any]:
									raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance."""	)


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


						def __setattr__(  self  , A  , A	)  ->Tuple:
									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  , A  , A	)  ->Optional[int]:
									# Will raise a KeyException if needed
									super().__setitem__(lowercase_  , lowercase_	)
									# Don't call self.__setattr__ to avoid recursion errors
									super().__setattr__(lowercase_  , lowercase_	)


						def    A__       (  self	)  ->str:
									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")




