




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





def 				A__ (snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int				)     ->						np.ndarray:

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

					# each value
					for y in range(snake_case				):
										for x in range(snake_case				):
															# distance from center
															__UpperCamelCase					:	Dict             =      x - ksize // 2
															__UpperCamelCase					:	Optional[int]             =      y - ksize // 2

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

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

															# get kernel y
															__UpperCamelCase					:	Union[str, Any]             =      -sin_theta * px + cos_theta * py

															# fill kernel
															__UpperCamelCase					:	List[str]             =      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
	a__		     =     imread('''../image_data/lena.jpg''')
	# turn image in gray scale value
	a__		     =     cvtColor(img, COLOR_BGR2GRAY)

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

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

	waitKey(0)












from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      SMALL_MODEL_IDENTIFIER
						__UpperCamelCase					:	List[str]             =      """pt"""
						__UpperCamelCase					:	List[Any]             =      """tf"""






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						Any	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      TFAutoModel.from_pretrained(self.test_model					,     from_pt=lowerCAmelCase	)
						model_tf.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      """mock_framework"""

						# Framework provided - return whatever the user provides
						__UpperCamelCase					:	List[Any]             =      FeaturesManager.determine_framework(self.test_model					,     lowerCAmelCase	)
						self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Optional[Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						__UpperCamelCase					:	Optional[int]             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Both in environment -> use PyTorch
						__UpperCamelCase					:	Dict             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# Both not in environment -> raise error
						__UpperCamelCase					:	Union[str, Any]             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	str             =      FeaturesManager.determine_framework(self.test_model	)






