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



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''

					# prepare kernel
					# the kernel size have to be odd
					if (ksize % 2) == 0:
										lowercase						     =						ksize + 1
					lowercase						     =						np.zeros((ksize, ksize)			,      dtype=np.floataa		)

					# each value
					for y in range(lowerCAmelCase__		):
										for x in range(lowerCAmelCase__		):
															# distance from center
															lowercase						     =						x - ksize // 2
															lowercase						     =						y - ksize // 2

															# degree to radiant
															lowercase						     =						theta / 180 * np.pi
															lowercase						     =						np.cos(_theta		)
															lowercase						     =						np.sin(_theta		)

															# get kernel x
															lowercase						     =						cos_theta * px + sin_theta * py

															# get kernel y
															lowercase						     =						-sin_theta * px + cos_theta * py

															# fill kernel
															lowercase						     =						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
							lowercase__		:Union[str, Any]			  =   imread("../image_data/lena.jpg")
							# turn image in gray scale value
							lowercase__		:int			  =   cvtColor(img, COLOR_BGR2GRAY)

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

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

							waitKey(0)






import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	int	 =ComputeEnvironment.AMAZON_SAGEMAKER
							lowercase_	:	Optional[int]	 =True
							lowercase_	:	Any	 ='''ml.p3.2xlarge'''
							lowercase_	:	Any	 ='''accelerate_sagemaker_execution_role'''
							lowercase_	:	Union[str, Any]	 ='''hf-sm'''
							lowercase_	:	Any	 ='''us-east-1'''
							lowercase_	:	List[str]	 =1
							lowercase_	:	Any	 ='''accelerate-sagemaker-1'''
							lowercase_	:	Union[str, Any]	 ='''1.6'''
							lowercase_	:	Any	 ='''4.4'''
							lowercase_	:	Any	 ='''train.py'''
							lowercase_	:	int	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''False''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]
							lowercase_	:	List[Any]	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''--do_test''',
							    '''False''',
							    '''--do_predict''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]







class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												# If no defaults are changed, `to_kwargs` returns an empty dict.
												lowercase						     =						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
												assert isinstance(converted_args['''model_name_or_path''']       ,A__)
												assert isinstance(converted_args['''do_train''']       ,A__)
												assert isinstance(converted_args['''epochs''']       ,A__)
												assert isinstance(converted_args['''learning_rate''']       ,A__)
												assert isinstance(converted_args['''max_steps''']       ,A__)

												with pytest.raises(A__):
																	_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args)




