






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__       			=   logging.get_logger(__name__)

lowerCAmelCase__       			=   {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''mobilenet_v1'''





	def __init__(   self      :				int			,			lowerCAmelCase_      :				Optional[Any]=3			,			lowerCAmelCase_      :				Optional[int]=224			,			lowerCAmelCase_      :				List[Any]=1.0			,			lowerCAmelCase_      :				List[str]=8			,			lowerCAmelCase_      :				Optional[Any]="relu6"			,			lowerCAmelCase_      :				Union[str, Any]=True			,			lowerCAmelCase_      :				int=0.9_9_9			,			lowerCAmelCase_      :				List[str]=0.0_2			,			lowerCAmelCase_      :				Tuple=0.0_0_1			,			**lowerCAmelCase_      :				Optional[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)

				if depth_multiplier <= 0:
							raise ValueError("depth_multiplier must be greater than zero."		)

				UpperCAmelCase_  :				Optional[int]      =      num_channels
				UpperCAmelCase_  :				Any      =      image_size
				UpperCAmelCase_  :				Union[str, Any]      =      depth_multiplier
				UpperCAmelCase_  :				List[str]      =      min_depth
				UpperCAmelCase_  :				Optional[int]      =      hidden_act
				UpperCAmelCase_  :				List[str]      =      tf_padding
				UpperCAmelCase_  :				List[Any]      =      classifier_dropout_prob
				UpperCAmelCase_  :				Dict      =      initializer_range
				UpperCAmelCase_  :				Tuple      =      layer_norm_eps







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict([("pixel_values", {0: "batch"})]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Mapping[str, Mapping[int, str]]:
				if self.task == "image-classification":
							return OrderedDict([("logits", {0: "batch"})]		)
				else:
							return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    float:
				return 1e-4











"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)



