
'''simple docstring'''

import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

__UpperCAmelCase												=   180



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[Any]   ,     snake_case_				:	Union[str, Any]							)       ->	Any:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		k_size // 2
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 SCREAMING_SNAKE_CASE  :  Dict								=		(
     1
     / (2 * np.pi * sigma)
     * np.exp(-(np.square(snake_case_							) + np.square(snake_case_							)) / (2 * np.square(snake_case_							))							)
 )
 return g



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Dict   ,     snake_case_				:	str   ,     snake_case_				:	Any							)       ->	int:
 SCREAMING_SNAKE_CASE  :  int								=		np.zeros(snake_case_							)

 for row in range(1   ,     image_shape[0] - 1							):
  for col in range(1   ,     image_shape[1] - 1							):
   SCREAMING_SNAKE_CASE  :  str								=		gradient_direction[row, col]

   if (
       0 <= direction < PI / 8
       or 15 * PI / 8 <= direction <= 2 * PI
       or 7 * PI / 8 <= direction <= 9 * PI / 8
   ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		sobel_grad[row, col - 1]
    SCREAMING_SNAKE_CASE  :  List[str]								=		sobel_grad[row, col + 1]
    if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
     SCREAMING_SNAKE_CASE  :  str								=		sobel_grad[row, col]

   elif (
       PI / 8 <= direction < 3 * PI / 8
       or 9 * PI / 8 <= direction < 11 * PI / 8
   ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		sobel_grad[row + 1, col - 1]
    SCREAMING_SNAKE_CASE  :  Tuple								=		sobel_grad[row - 1, col + 1]
    if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		sobel_grad[row, col]

   elif (
       3 * PI / 8 <= direction < 5 * PI / 8
       or 11 * PI / 8 <= direction < 13 * PI / 8
   ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		sobel_grad[row - 1, col]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		sobel_grad[row + 1, col]
    if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		sobel_grad[row, col]

   elif (
       5 * PI / 8 <= direction < 7 * PI / 8
       or 13 * PI / 8 <= direction < 15 * PI / 8
   ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		sobel_grad[row - 1, col - 1]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		sobel_grad[row + 1, col + 1]
    if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		sobel_grad[row, col]

 return destination



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]   ,     snake_case_				:	Any   ,     snake_case_				:	int   ,     snake_case_				:	str   ,     snake_case_				:	Tuple   ,     snake_case_				:	int							)       ->	int:
 for row in range(1   ,     image_shape[0] - 1							):
  for col in range(1   ,     image_shape[1] - 1							):
   if destination[row, col] >= threshold_high:
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		strong
   elif destination[row, col] <= threshold_low:
    SCREAMING_SNAKE_CASE  :  Dict								=		0
   else:
    SCREAMING_SNAKE_CASE  :  Any								=		weak



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]   ,     snake_case_				:	Dict   ,     snake_case_				:	int   ,     snake_case_				:	Tuple							)       ->	Optional[Any]:
 for row in range(1   ,     image_shape[0]							):
  for col in range(1   ,     image_shape[1]							):
   if destination[row, col] == weak:
    if 255 in (
        destination[row, col + 1],
        destination[row, col - 1],
        destination[row - 1, col],
        destination[row + 1, col],
        destination[row - 1, col - 1],
        destination[row + 1, col - 1],
        destination[row - 1, col + 1],
        destination[row + 1, col + 1],
    ):
     SCREAMING_SNAKE_CASE  :  List[str]								=		strong
    else:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		0



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]   ,     snake_case_				:	Union[str, Any]=15   ,     snake_case_				:	Optional[int]=30   ,     snake_case_				:	Optional[Any]=128   ,     snake_case_				:	List[Any]=255							)       ->	List[Any]:
 # gaussian_filter
 SCREAMING_SNAKE_CASE  :  List[Any]								=		img_convolve(snake_case_   ,     gen_gaussian_kernel(9   ,     sigma=1.4							)							)
 # get the gradient and degree by sobel_filter
 SCREAMING_SNAKE_CASE  :  List[str]								=		sobel_filter(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Dict								=		PI + np.radadeg(snake_case_							)

 SCREAMING_SNAKE_CASE  :  str								=		suppress_non_maximum(image.shape   ,     snake_case_   ,     snake_case_							)

 detect_high_low_threshold(
     image.shape   ,     snake_case_   ,     snake_case_   ,     snake_case_   ,     snake_case_   ,     snake_case_							)

 track_edge(image.shape   ,     snake_case_   ,     snake_case_   ,     snake_case_							)

 return destination


if __name__ == "__main__":
   # read original image in gray mode
   __UpperCAmelCase												=   cva.imread(r'../image_data/lena.jpg', 0)
   # canny edge detection
   __UpperCAmelCase												=   canny(lena)
   cva.imshow('canny', canny_destination)
   cva.waitKey(0)




import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


UpperCAmelCase__						:	Any	     = logging.get_logger(__name__)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				List[str]	):

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__	) < version.parse('''1.12.0'''	):
         raise ImportError(
             F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
             '''PytorchGELUTanh. Please upgrade torch.'''	)


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return nn.functional.gelu(lowerCAmelCase_     ,     approximate='''tanh'''	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.044715 * torch.pow(lowerCAmelCase_     ,     3.0	))	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Tuple     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      super().__init__()
      if use_gelu_python:
         _A:    Union[str, Any]						     =			self._gelu_python
      else:
         _A:    Union[str, Any]						     =			nn.functional.gelu


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0	)	))


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return self.act(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * torch.sigmoid(1.702 * input	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				str     ,     lowerCAmelCase_	:				float     ,     lowerCAmelCase_	:				float	):

      """simple docstring"""
      if min > max:
         raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""	)

      super().__init__()
      _A:    Tuple						     =			min
      _A:    str						     =			max


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return torch.clip(gelu(lowerCAmelCase_	)     ,     self.min     ,     self.max	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Optional[Any]	):

      """simple docstring"""
      super().__init__()
      _A:    Any						     =			math.sqrt(2 / math.pi	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(lowerCAmelCase_     ,     3	))	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return nn.functional.silu(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]	):

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__	) < version.parse('''1.9.0'''	):
         _A:    Optional[Any]						     =			self._mish_python
      else:
         _A:    int						     =			nn.functional.mish


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * torch.tanh(nn.functional.softplus(lowerCAmelCase_	)	)


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return self.act(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				List[str]     ,     lowerCAmelCase_	:				Tuple=0.707107     ,     lowerCAmelCase_	:				Union[str, Any]=0.282095	):

      """simple docstring"""
      _A:    Any						     =			(input - mu).div(sigma * math.sqrt(2.0	)	)
      return 0.5 * (1.0 + torch.erf(lowerCAmelCase_	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				List[Any]	):

      """simple docstring"""
      _A:    Optional[int]						     =			nn.functional.relu(lowerCAmelCase_	)
      _A:    Any						     =			torch.square(lowerCAmelCase_	)
      return squared






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   def __getitem__(   self	:				List[Any]     ,     lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			super().__getitem__(lowerCAmelCase_	)
      _A     ,    _A:    List[str]						     =			content if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else (content, {})
      return cls(**lowerCAmelCase_	)

UpperCAmelCase__						:	List[str]	     = {
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -10, 'max': 10}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
UpperCAmelCase__						:	List[str]	     = ClassInstantier(ACTaCLS)






def     lowerCamelCase__				(   a ) ->  List[str]:
   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )


# For backwards compatibility with: from activations import gelu_python
UpperCAmelCase__						:	Any	     = get_activation('gelu_python')
UpperCAmelCase__						:	Dict	     = get_activation('gelu_new')
UpperCAmelCase__						:	List[Any]	     = get_activation('gelu')
UpperCAmelCase__						:	Dict	     = get_activation('gelu_fast')
UpperCAmelCase__						:	str	     = get_activation('quick_gelu')
UpperCAmelCase__						:	Union[str, Any]	     = get_activation('silu')
UpperCAmelCase__						:	List[Any]	     = get_activation('mish')





UpperCAmelCase__						:	int	     = get_activation('linear')



