


'''simple docstring'''





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 						a__					(					_a   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Any	)      ->		Optional[int]:
						__A=	SMALL_MODEL_IDENTIFIER
						__A=	'pt'
						__A=	'tf'
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[Any]	)      ->		Tuple:
						__A=	AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(__UpperCamelCase	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			int	)      ->		Optional[int]:
						__A=	TFAutoModel.from_pretrained(self.test_model			,    from_pt=__UpperCamelCase	)
						model_tf.save_pretrained(__UpperCamelCase	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Optional[int]:
						__A=	'mock_framework'

						# Framework provided - return whatever the user provides
						__A=	FeaturesManager.determine_framework(self.test_model			,    __UpperCamelCase	)
						self.assertEqual(__UpperCamelCase			,    __UpperCamelCase	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__UpperCamelCase	)
									__A=	FeaturesManager.determine_framework(__UpperCamelCase			,    __UpperCamelCase	)
									self.assertEqual(__UpperCamelCase			,    __UpperCamelCase	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
									self._setup_tf_ckpt(__UpperCamelCase	)
									__A=	FeaturesManager.determine_framework(__UpperCamelCase			,    __UpperCamelCase	)
									self.assertEqual(__UpperCamelCase			,    __UpperCamelCase	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__UpperCamelCase	)
									__A=	FeaturesManager.determine_framework(__UpperCamelCase	)
									self.assertEqual(__UpperCamelCase			,    self.framework_pt	)

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

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

			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Optional[int]:
						__A=	MagicMock(return_value=__UpperCamelCase	)
						with patch('transformers.onnx.features.is_tf_available'			,    __UpperCamelCase	):
									__A=	FeaturesManager.determine_framework(self.test_model	)
									self.assertEqual(__UpperCamelCase			,    self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						__A=	MagicMock(return_value=__UpperCamelCase	)
						with patch('transformers.onnx.features.is_torch_available'			,    __UpperCamelCase	):
									__A=	FeaturesManager.determine_framework(self.test_model	)
									self.assertEqual(__UpperCamelCase			,    self.framework_tf	)

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

						# Both not in environment -> raise error
						__A=	MagicMock(return_value=__UpperCamelCase	)
						__A=	MagicMock(return_value=__UpperCamelCase	)
						with patch('transformers.onnx.features.is_tf_available'			,    __UpperCamelCase	), patch(
						    'transformers.onnx.features.is_torch_available'			,    __UpperCamelCase	):
									with self.assertRaises(__UpperCamelCase	):
												__A=	FeaturesManager.determine_framework(self.test_model	)





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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# 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(SCREAMING_SNAKE_CASE_       ):
												for x in range(SCREAMING_SNAKE_CASE_       ):
																		# distance from center
																		_lowercase					       =					x - ksize // 2
																		_lowercase					       =					y - ksize // 2

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * 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
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

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

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

		waitKey(0)