




import math

import tensorflow as tf
from packaging import version







def 		_A(       UpperCamelCase__							:			List[Any]				)    -> List[Any]:
							'''simple docstring'''


							__lowercase      =    tf.convert_to_tensor(UpperCamelCase__				)
							__lowercase      =    0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0				)				,       x.dtype				)				))

							return x * cdf







def 		_A(       UpperCamelCase__							:			Tuple				)    -> int:
							'''simple docstring'''


							__lowercase      =    tf.convert_to_tensor(UpperCamelCase__				)
							__lowercase      =    tf.cast(math.pi				,       x.dtype				)
							__lowercase      =    tf.cast(0.04_4715				,       x.dtype				)
							__lowercase      =    0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi				) * (x + coeff * tf.pow(UpperCamelCase__				,       3				))				))

							return x * cdf







def 		_A(       UpperCamelCase__							:			List[str]				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    tf.convert_to_tensor(UpperCamelCase__				)

							return x * tf.tanh(tf.math.softplus(UpperCamelCase__				)				)







def 		_A(       UpperCamelCase__							:			Optional[int]				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    tf.convert_to_tensor(UpperCamelCase__				)
							__lowercase      =    tf.cast(0.04_4715				,       x.dtype				)
							__lowercase      =    tf.cast(0.79_7884_5608				,       x.dtype				)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)				))







def 		_A(       UpperCamelCase__							:			List[Any]				)    -> Any:
							'''simple docstring'''


							__lowercase      =    tf.convert_to_tensor(UpperCamelCase__				)
							__lowercase      =    tf.cast(1.702				,       x.dtype				)
							return x * tf.math.sigmoid(coeff * x				)







def 		_A(       UpperCamelCase__							:			Optional[Any]				)    -> Any:
							'''simple docstring'''


							return tf.clip_by_value(_gelu(UpperCamelCase__				)				,       -10				,       10				)







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			List[str]=-1				)    -> Tuple:
							'''simple docstring'''


							__lowercase     ,     __lowercase      =    tf.split(UpperCamelCase__				,       2				,       axis=UpperCamelCase__				)
							return a * tf.math.sigmoid(UpperCamelCase__				)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):







			def 		_A(       UpperCamelCase__							:			Optional[Any]				)    -> List[Any]:
										'''simple docstring'''


										return tf.keras.activations.gelu(UpperCamelCase__				,       approximate=UpperCamelCase__				)

			UpperCAmelCase__				     = tf.keras.activations.gelu



			UpperCAmelCase__				     = approximate_gelu_wrap
else:
			UpperCAmelCase__				     = _gelu
			UpperCAmelCase__				     = _gelu_new


UpperCAmelCase__				     = {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}







def 		_A(       UpperCamelCase__							:			Optional[int]				)    -> str:
							'''simple docstring'''


							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()				)}'				)







import cva
import numpy as np







class a :
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     float				,							lowerCamelCase__ :     int				)    ->						Dict:
													"""simple docstring"""


													if k in (0.0_4, 0.0_6):
																				__lowercase      =    k
																				__lowercase      =    window_size
													else:
																				raise ValueError('''invalid k value'''				)






						def __str__( self :     int				)    ->						str:
													"""simple docstring"""


													return str(self.k				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     str				)    ->						tuple[cva.Mat, list[list[int]]]:
													"""simple docstring"""


													__lowercase      =    cva.imread(lowerCamelCase__				,							0				)
													__lowercase     ,     __lowercase      =    img.shape
													__lowercase      =    []
													__lowercase      =    img.copy()
													__lowercase      =    cva.cvtColor(lowerCamelCase__				,							cva.COLOR_GRAY2RGB				)
													__lowercase     ,     __lowercase      =    np.gradient(lowerCamelCase__				)
													__lowercase      =    dx**2
													__lowercase      =    dy**2
													__lowercase      =    dx * dy
													__lowercase      =    0.0_4
													__lowercase      =    self.window_size // 2
													for y in range(lowerCamelCase__				,							h - offset				):
																				for x in range(lowerCamelCase__				,							w - offset				):
																											__lowercase      =    ixx[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    iyy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    ixy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()

																											__lowercase      =    (wxx * wyy) - (wxy**2)
																											__lowercase      =    wxx + wyy
																											__lowercase      =    det - k * (trace**2)
																											# Can change the value
																											if r > 0.5:
																																		corner_list.append([x, y, r]				)
																																		color_img.itemset((y, x, 0)				,							0				)
																																		color_img.itemset((y, x, 1)				,							0				)
																																		color_img.itemset((y, x, 2)				,							255				)
													return color_img, corner_list


if __name__ == "__main__":
			UpperCAmelCase__				     = HarrisCorner(0.04, 3)
			UpperCAmelCase__     ,							UpperCAmelCase__				     = edge_detect.detect("path_to_image")
			cva.imwrite("detect.png", color_img)






