'''simple docstring'''

import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class 							lowercase_    (     enum.Enum						):
    a_             =   """timm"""
    a_             =   """transformers"""



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ):
 if stage_names is None:
  raise ValueError("Stage_names must be set for transformers backbones"       )

 if out_features is not None:
  if not isinstance(A_       ,			(list,)       ):
   raise ValueError(F"""out_features must be a list {type(A_       )}"""       )
  if any(feat not in stage_names for feat in out_features       ):
   raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""       )

 if out_indices is not None:
  if not isinstance(A_       ,			(list, tuple)       ):
   raise ValueError(F"""out_indices must be a list or tuple, got {type(A_       )}"""       )
  if any(idx >= len(A_       ) for idx in out_indices       ):
   raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"       )

 if out_features is not None and out_indices is not None:
  if len(A_       ) != len(A_       ):
   raise ValueError("out_features and out_indices should have the same length if both are set"       )
  if out_features != [stage_names[idx] for idx in out_indices]:
   raise ValueError("out_features and out_indices should correspond to the same stages if both are set"       )



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ,			):
 if out_indices is None and out_features is None:
  UpperCAmelCase_				       =					[len(A_       ) - 1]
  UpperCAmelCase_				       =					[stage_names[-1]]
 elif out_indices is None and out_features is not None:
  UpperCAmelCase_				       =					[stage_names.index(A_       ) for layer in out_features]
 elif out_features is None and out_indices is not None:
  UpperCAmelCase_				       =					[stage_names[idx] for idx in out_indices]
 return out_features, out_indices



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ,			):
 verify_out_features_out_indices(out_features=A_       ,			out_indices=A_       ,			stage_names=A_       )
 UpperCAmelCase_       ,       UpperCAmelCase_				       =					_align_output_features_output_indices(
     out_features=A_       ,			out_indices=A_       ,			stage_names=A_       )
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=A_       ,			out_indices=A_       ,			stage_names=A_       )
 return output_features, output_indices
class 							lowercase_    :
    a_             =   None







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       None:





     """simple docstring"""

     if getattr(self       ,     "_backbone"       ,     UpperCamelCase__				) is None:
      raise ValueError("self._backbone must be set before calling _init_timm_backbone"				)

     # These will diagree with the defaults for the transformers models e.g. for resnet50
     # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
     # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
     UpperCAmelCase_				       =					[stage["module"] for stage in self._backbone.feature_info.info]
     UpperCAmelCase_				       =					[stage["num_chs"] for stage in self._backbone.feature_info.info]
     UpperCAmelCase_				       =					self._backbone.feature_info.out_indices
     UpperCAmelCase_				       =					self._backbone.feature_info.module_name()

     # We verify the out indices and out features are valid
     verify_out_features_out_indices(
         out_features=UpperCamelCase__       ,     out_indices=UpperCamelCase__       ,     stage_names=self.stage_names				)
     UpperCAmelCase_       ,       UpperCAmelCase_				       =					out_features, out_indices







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       None:





     """simple docstring"""

     UpperCAmelCase_				       =					getattr(UpperCamelCase__       ,     "stage_names"				)
     UpperCAmelCase_				       =					getattr(UpperCamelCase__       ,     "out_features"       ,     UpperCamelCase__				)
     UpperCAmelCase_				       =					getattr(UpperCamelCase__       ,     "out_indices"       ,     UpperCamelCase__				)

     UpperCAmelCase_				       =					stage_names
     UpperCAmelCase_       ,       UpperCAmelCase_				       =					get_aligned_output_features_output_indices(
         out_features=UpperCamelCase__       ,     out_indices=UpperCamelCase__       ,     stage_names=UpperCamelCase__				)
     # Number of channels for each stage. This is set in the transformer backbone model init
     UpperCAmelCase_				       =					None







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       None:





     """simple docstring"""

     UpperCAmelCase_				       =					config

     UpperCAmelCase_				       =					getattr(UpperCamelCase__       ,     "use_timm_backbone"       ,     UpperCamelCase__				)
     UpperCAmelCase_				       =					BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

     if self.backbone_type == BackboneType.TIMM:
      self._init_timm_backbone(UpperCamelCase__				)
     elif self.backbone_type == BackboneType.TRANSFORMERS:
      self._init_transformers_backbone(UpperCamelCase__				)
     else:
      raise ValueError(F"""backbone_type {self.backbone_type} not supported."""				)







    @property
    def   lowerCamelCase_    (	self				)				->       str:





     """simple docstring"""

     return self._out_features







    @out_features.setter
    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Dict:





     """simple docstring"""

     UpperCAmelCase_       ,       UpperCAmelCase_				       =					get_aligned_output_features_output_indices(
         out_features=UpperCamelCase__       ,     out_indices=UpperCamelCase__       ,     stage_names=self.stage_names				)







    @property
    def   lowerCamelCase_    (	self				)				->       Optional[Any]:





     """simple docstring"""

     return self._out_indices







    @out_indices.setter
    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Optional[int]:





     """simple docstring"""

     UpperCAmelCase_       ,       UpperCAmelCase_				       =					get_aligned_output_features_output_indices(
         out_features=UpperCamelCase__       ,     out_indices=UpperCamelCase__       ,     stage_names=self.stage_names				)







    @property
    def   lowerCamelCase_    (	self				)				->       Union[str, Any]:





     """simple docstring"""

     return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names				)}







    @property
    def   lowerCamelCase_    (	self				)				->       Optional[Any]:





     """simple docstring"""

     return [self.out_feature_channels[name] for name in self.out_features]







    def   lowerCamelCase_    (	self       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       List[Any]:





     """simple docstring"""

     UpperCAmelCase_				       =					dict(inspect.signature(self.forward				).parameters				)
     UpperCAmelCase_				       =					{k: v for k, v in kwargs.items() if k in signature}
     return self(*UpperCamelCase__       ,     **UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     )				->       Optional[Any]:





     """simple docstring"""

     raise NotImplementedError("This method should be implemented by the derived class."				)







    def   lowerCamelCase_    (	self				)				->       Dict:





     """simple docstring"""

     UpperCAmelCase_				       =					super().to_dict()
     UpperCAmelCase_				       =					output.pop("_out_features"				)
     UpperCAmelCase_				       =					output.pop("_out_indices"				)
     return output


class 							lowercase_    :







    @property
    def   lowerCamelCase_    (	self				)				->       int:





     """simple docstring"""

     return self._out_features







    @out_features.setter
    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Optional[Any]:





     """simple docstring"""

     UpperCAmelCase_       ,       UpperCAmelCase_				       =					get_aligned_output_features_output_indices(
         out_features=UpperCamelCase__       ,     out_indices=UpperCamelCase__       ,     stage_names=self.stage_names				)







    @property
    def   lowerCamelCase_    (	self				)				->       str:





     """simple docstring"""

     return self._out_indices







    @out_indices.setter
    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       List[Any]:





     """simple docstring"""

     UpperCAmelCase_       ,       UpperCAmelCase_				       =					get_aligned_output_features_output_indices(
         out_features=UpperCamelCase__       ,     out_indices=UpperCamelCase__       ,     stage_names=self.stage_names				)







    def   lowerCamelCase_    (	self				)				->       int:





     """simple docstring"""

     UpperCAmelCase_				       =					super().to_dict()
     UpperCAmelCase_				       =					output.pop("_out_features"				)
     UpperCAmelCase_				       =					output.pop("_out_indices"				)
     return output

'''simple docstring'''

import numpy as np
from PIL import Image



def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_       ,       UpperCAmelCase_       ,       UpperCAmelCase_				       =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2989 * r + 0.5870 * g + 0.1140 * b



def lowerCamelCase__						(       A_       ):
 return (gray > 127) & (gray <= 255)



def lowerCamelCase__						(       A_       ,			A_       ):
 UpperCAmelCase_				       =					np.zeros_like(A_       )
 UpperCAmelCase_				       =					np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

 # Copy image to padded image
 UpperCAmelCase_				       =					image

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


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

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