





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						torch.Tensor:
       # The first operation in a checkpoint can't be in-place, but it's
       # nice to have in-place addition during inference. Thus...
       if not inplace:
              _snake_case      :							Any     				=						ma + ma
       else:
              ma += ma

       return ma




def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
       _snake_case      :							str     				=						-1 * len(lowercase_  )
       _snake_case      :							Any     				=						list(range(len(tensor.shape[:zero_index]  )  )  )
       return tensor.permute(first_inds + [zero_index + i for i in inds]  )




def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
       return t.reshape(t.shape[:-no_dims] + (-1,)  )




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_ = 1E-4  )			->						torch.Tensor:
       _snake_case      :							Any     				=						mask.expand(*value.shape  )
       return torch.sum(mask * value      ,		dim=lowercase_  ) / (eps + torch.sum(lowercase_      ,		dim=lowercase_  ))




def 						A_			(  lowercase_      ,		lowercase_ = 2.31_25      ,		lowercase_ = 21.68_75      ,		lowercase_ = 64  )			->						torch.Tensor:
       _snake_case      :							int     				=						torch.linspace(lowercase_      ,		lowercase_      ,		no_bins - 1      ,		device=pts.device  )
       _snake_case      :							Optional[Any]     				=						torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2      ,		dim=-1  )  )
       return torch.bucketize(lowercase_      ,		lowercase_  )




def 						A_			(  lowercase_      ,		lowercase_  )			->						dict:
       _snake_case      :							Dict     				=						dicts[0]
       _snake_case      :							str     				=						{}
       for k, v in first.items():
              _snake_case      :							List[Any]     				=						[d[k] for d in dicts]
              if isinstance(lowercase_      ,		lowercase_  ):
                     _snake_case      :							Any     				=						dict_multimap(lowercase_      ,		lowercase_  )
              else:
                     _snake_case      :							Dict     				=						fn(lowercase_  )

       return new_dict




def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
       _snake_case      :							str     				=						v_bins.view(((1,) * len(x.shape  )) + (len(lowercase_  ),)  )
       _snake_case      :							Union[str, Any]     				=						x[..., None] - reshaped_bins
       _snake_case      :							Optional[Any]     				=						torch.argmin(torch.abs(lowercase_  )      ,		dim=-1  )
       return nn.functional.one_hot(lowercase_      ,		num_classes=len(lowercase_  )  ).float()




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_ = 0      ,		lowercase_ = 0  )			->						torch.Tensor:
       _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[]
       for i, s in enumerate(data.shape[:no_batch_dims]  ):
              _snake_case      :							Optional[Any]     				=						torch.arange(lowercase_  )
              _snake_case      :							Union[str, Any]     				=						r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
              ranges.append(lowercase_  )

       _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[slice(lowercase_  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
       _snake_case      :							Union[str, Any]     				=						inds
       ranges.extend(lowercase_  )
       # Matt note: Editing this to get around the behaviour of using a list as an array index changing
       # in recent Numpy versions
       return data[tuple(lowercase_  )]


lowerCAmelCase_   =   TypeVar("T")




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict[Any, Union[dict, list, tuple, Any]]:
       _snake_case      :							Dict[Any, Union[dict, list, tuple, Any]]     				=						{}
       for k, v in dic.items():
              if isinstance(lowercase_      ,		lowercase_  ):
                     _snake_case      :							Dict     				=						dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
              else:
                     _snake_case      :							List[Any]     				=						tree_map(lowercase_      ,		lowercase_      ,		lowercase_  )

       return new_dict




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
       ...




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						dict:
       ...




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						list:
       ...




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						tuple:
       ...




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
       if isinstance(lowercase_      ,		lowercase_  ):
              return dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
       elif isinstance(lowercase_      ,		lowercase_  ):
              return [tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree]
       elif isinstance(lowercase_      ,		lowercase_  ):
              return tuple(tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree  )
       elif isinstance(lowercase_      ,		lowercase_  ):
              return fn(lowercase_  )
       else:
              print(type(lowercase_  )  )
              raise ValueError('''Not supported'''  )
lowerCAmelCase_   =   partial(tree_map, leaf_type=torch.Tensor)













import cva
import numpy as np




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:

       try:
              return int(image[x_coordinate][y_coordinate] >= center  )
       except (IndexError, TypeError):
              return 0




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
       _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
       _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]

       # skip get_neighbors_pixel if center is null
       if center is None:
              return 0

       # Starting from the top right, assigning value to pixels clockwise
       _snake_case      :							Any     				=						[
           get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
       ]

       # Converting the binary value to decimal.
       return sum(
           binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )


if __name__ == "__main__":
   # Reading the image and converting it to grayscale.
   lowerCAmelCase_   =   cva.imread(
       "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
   )

   # Create a numpy array as the same height and width of read image
   lowerCAmelCase_   =   np.zeros((image.shape[0], image.shape[1]))

   # Iterating through the image and calculating the
   # local binary pattern value for each pixel.
   for i in range(0, image.shape[0]):
      for j in range(0, image.shape[1]):
         lowerCAmelCase_   =   local_binary_value(image, i, j)

   cva.imshow("local binary pattern", lbp_image)
   cva.waitKey(0)
   cva.destroyAllWindows()






