

'''simple docstring'''







import math

import tensorflow as tf
from packaging import version
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
							A_						=      tf.convert_to_tensor(UpperCAmelCase__				)
							A_						=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0				),       x.dtype				)				))

							return x * cdf
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Tuple:
							A_						=      tf.convert_to_tensor(UpperCAmelCase__				)
							A_						=      tf.cast(math.pi,       x.dtype				)
							A_						=      tf.cast(0.044_715,       x.dtype				)
							A_						=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi				) * (x + coeff * tf.pow(UpperCAmelCase__,       3				))				))

							return x * cdf
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
							A_						=      tf.convert_to_tensor(UpperCAmelCase__				)

							return x * tf.tanh(tf.math.softplus(UpperCAmelCase__				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
							A_						=      tf.convert_to_tensor(UpperCAmelCase__				)
							A_						=      tf.cast(0.044_715,       x.dtype				)
							A_						=      tf.cast(0.7_978_845_608,       x.dtype				)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)				))
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
							A_						=      tf.convert_to_tensor(UpperCAmelCase__				)
							A_						=      tf.cast(1.702,       x.dtype				)
							return x * tf.math.sigmoid(coeff * x				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
							return tf.clip_by_value(_gelu(UpperCAmelCase__				),       -10,       10				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__=-1				)			->			Optional[int]:
							A_					,			A_						=      tf.split(UpperCAmelCase__,       2,       axis=UpperCAmelCase__				)
							return a * tf.math.sigmoid(UpperCAmelCase__				)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
						def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Tuple:
													return tf.keras.activations.gelu(UpperCAmelCase__,       approximate=UpperCAmelCase__				)

						__lowerCamelCase		       =    tf.keras.activations.gelu







						__lowerCamelCase		       =    approximate_gelu_wrap
else:
						__lowerCamelCase		       =    _gelu
						__lowerCamelCase		       =    _gelu_new


__lowerCamelCase		       =    {
    '''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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()				)}'''				)



'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

       # Reading the image and converting it to grayscale.
       A_						=      imread(UpperCAmelCase__,       0				)

       # Test for get_neighbors_pixel function() return not None
       A_						=      0
       A_						=      0
       A_						=      image[x_coordinate][y_coordinate]

       A_						=      lbp.get_neighbors_pixel(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert neighbors_pixels is not None

       # Test for local_binary_pattern function()
       # Create a numpy array as the same height and width of read image
       A_						=      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]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
