
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class    a_ (			_a    ):
			# to overwrite at feature extractactor specific tests
			a	:			Dict					   =							None
			a	:			Optional[int]					   =							None






			@property
			def    UpperCamelCase_				(      self  ):
									return self.feat_extract_tester.prepare_feat_extract_dict()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""feature_size"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""sampling_rate"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""padding_value"""  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common()
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}  )

									self.assertTrue(all(len(__UpperCamelCase  ) == len(__UpperCamelCase  ) for x, y in zip(__UpperCamelCase				,			processed_features[input_name]  )  )  )

									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCamelCase  )
									_lowercase					       =					BatchFeature({input_name: speech_inputs}				,			tensor_type="""np"""  )

									_lowercase					       =					processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
															_lowercase					       =					batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.feature_size)  )






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCamelCase  )
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}				,			tensor_type="""pt"""  )

									_lowercase					       =					processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
															_lowercase					       =					batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.feature_size)  )






			@require_tf
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCamelCase  )
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}				,			tensor_type="""tf"""  )

									_lowercase					       =					processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
															_lowercase					       =					batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.feature_size)  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=False  ):
									def _inputs_have_equal_length(__UpperCamelCase  ):
															_lowercase					       =					len(input[0]  )
															for input_slice in input[1:]:
																					if len(__UpperCamelCase  ) != length:
																											return False
															return True

									def _inputs_are_equal(__UpperCamelCase				,			__UpperCamelCase  ):
															if len(__UpperCamelCase  ) != len(__UpperCamelCase  ):
																					return False

															for input_slice_a, input_slice_a in zip(__UpperCamelCase				,			__UpperCamelCase  ):
																					if not np.allclose(np.asarray(__UpperCamelCase  )				,			np.asarray(__UpperCamelCase  )				,			atol=1E-3  ):
																											return False
															return True

									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCamelCase  )
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}  )

									_lowercase					       =					self.feat_extract_tester.seq_length_diff
									_lowercase					       =					self.feat_extract_tester.max_seq_length + pad_diff
									_lowercase					       =					self.feat_extract_tester.min_seq_length
									_lowercase					       =					self.feat_extract_tester.batch_size
									_lowercase					       =					self.feat_extract_tester.feature_size

									# test padding for List[int] + numpy
									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding=__UpperCamelCase  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[-1]  )  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			return_tensors="""np"""  )
									_lowercase					       =					input_a[input_name]

									# max_length parameter has to be provided when setting `padding="max_length"`
									with self.assertRaises(__UpperCamelCase  ):
															feat_extract.pad(__UpperCamelCase				,			padding="""max_length"""  )[input_name]

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=__UpperCamelCase				,			return_tensors="""np"""  )
									_lowercase					       =					input_a[input_name]

									self.assertFalse(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertTrue(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertTrue(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertTrue(_inputs_are_equal(__UpperCamelCase				,			__UpperCamelCase  )  )
									self.assertTrue(len(input_a[0]  ) == pad_min_length  )
									self.assertTrue(len(input_a[1]  ) == pad_min_length + pad_diff  )
									self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]  ))  )
									self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)  )

									if feature_size > 1:
															self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size  )

									# test padding for `pad_to_multiple_of` for List[int] + numpy
									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			pad_to_multiple_of=10  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			pad_to_multiple_of=10  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			pad_to_multiple_of=10				,			max_length=__UpperCamelCase  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			pad_to_multiple_of=10				,			max_length=__UpperCamelCase				,			return_tensors="""np"""				,			)
									_lowercase					       =					input_a[input_name]

									self.assertTrue(all(len(__UpperCamelCase  ) % 10 == 0 for x in input_a  )  )
									self.assertTrue(_inputs_are_equal(__UpperCamelCase				,			__UpperCamelCase  )  )

									_lowercase					       =					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
									self.assertTrue(all(len(__UpperCamelCase  ) == expected_mult_pad_length for x in input_a  )  )
									self.assertEqual(input_a.shape[:2]				,			(batch_size, expected_mult_pad_length)  )

									if feature_size > 1:
															self.assertTrue(input_a.shape[2] == feature_size  )

									# Check padding value is correct
									_lowercase					       =					(np.ones(self.feat_extract_tester.feature_size  ) * feat_extract.padding_value).sum()
									self.assertTrue(
									    abs(np.asarray(input_a[0]  )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)  )
									    < 1E-3  )
									self.assertTrue(
									    abs(
									        np.asarray(input_a[1]  )[pad_min_length + pad_diff :].sum()
									        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)  )
									    < 1E-3  )
									self.assertTrue(
									    abs(
									        np.asarray(input_a[2]  )[pad_min_length + 2 * pad_diff :].sum()
									        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)  )
									    < 1E-3  )
									self.assertTrue(
									    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)  ) < 1E-3  )
									self.assertTrue(
									    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)  )
									    < 1E-3  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=False  ):
									def _inputs_have_equal_length(__UpperCamelCase  ):
															_lowercase					       =					len(input[0]  )
															for input_slice in input[1:]:
																					if len(__UpperCamelCase  ) != length:
																											return False
															return True

									def _inputs_are_equal(__UpperCamelCase				,			__UpperCamelCase  ):
															if len(__UpperCamelCase  ) != len(__UpperCamelCase  ):
																					return False

															for input_slice_a, input_slice_a in zip(__UpperCamelCase				,			__UpperCamelCase  ):
																					if not np.allclose(np.asarray(__UpperCamelCase  )				,			np.asarray(__UpperCamelCase  )				,			atol=1E-3  ):
																											return False
															return True

									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCamelCase  )
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}  )

									# truncate to smallest
									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]  )				,			truncation=__UpperCamelCase  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]  )  )
									_lowercase					       =					input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertFalse(_inputs_have_equal_length(__UpperCamelCase  )  )

									# truncate to smallest with np
									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]  )				,			return_tensors="""np"""				,			truncation=__UpperCamelCase				,			)
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]  )				,			return_tensors="""np"""  )
									_lowercase					       =					input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertTrue(input_a.shape[1] == len(speech_inputs[0]  )  )

									# since truncation forces padding to be smaller than longest input
									# function can't return `np.ndarray`, but has to return list
									self.assertFalse(_inputs_have_equal_length(__UpperCamelCase  )  )

									# truncate to middle
									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[1]  )				,			truncation=__UpperCamelCase				,			return_tensors="""np"""				,			)
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[1]  )				,			truncation=__UpperCamelCase  )
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[1]  )				,			return_tensors="""np"""  )
									_lowercase					       =					input_a[input_name]

									self.assertTrue(input_a.shape[1] == len(speech_inputs[1]  )  )
									self.assertTrue(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertTrue(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertTrue(_inputs_are_equal(__UpperCamelCase				,			__UpperCamelCase  )  )

									# since truncation forces padding to be smaller than longest input
									# function can't return `np.ndarray`, but has to return list
									self.assertFalse(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertTrue(len(input_a[-1]  ) == len(speech_inputs[-1]  )  )

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(__UpperCamelCase  ):
															feat_extract.pad(__UpperCamelCase				,			truncation=__UpperCamelCase  )[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(__UpperCamelCase  ):
															feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			truncation=__UpperCamelCase  )[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(__UpperCamelCase  ):
															feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			truncation=__UpperCamelCase  )[input_name]

									# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
									with self.assertRaises(__UpperCamelCase  ):
															feat_extract.pad(__UpperCamelCase				,			padding="""max_length"""				,			truncation=__UpperCamelCase  )[input_name]

									# test truncation for `pad_to_multiple_of` for List[int] + numpy
									_lowercase					       =					12
									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]  )				,			pad_to_multiple_of=__UpperCamelCase				,			truncation=__UpperCamelCase				,			)
									_lowercase					       =					input_a[input_name]

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]  )				,			pad_to_multiple_of=__UpperCamelCase				,			)
									_lowercase					       =					input_a[input_name]

									# retrieve expected_length as multiple of pad_to_multiple_of
									_lowercase					       =					len(speech_inputs[0]  )
									if expected_length % pad_to_multiple_of != 0:
															_lowercase					       =					((len(speech_inputs[0]  ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

									self.assertTrue(len(input_a[0]  ) == expected_length  )
									self.assertTrue(_inputs_have_equal_length(__UpperCamelCase  )  )
									self.assertFalse(_inputs_have_equal_length(__UpperCamelCase  )  )






			def    UpperCamelCase_				(      self  ):
									self._check_padding(numpify=__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									self._check_padding(numpify=__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									self._check_truncation(numpify=__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									self._check_truncation(numpify=__UpperCamelCase  )






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common()
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}  )

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			return_tensors="""np"""  )[input_name]
									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			return_tensors="""pt"""  )[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa  ).sum() - input_pt.numpy().astype(np.floataa  ).sum()  ) < 1E-2  )






			@require_tf
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common()
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}  )

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			return_tensors="""np"""  )[input_name]
									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			return_tensors="""tf"""  )[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa  ).sum() - input_tf.numpy().astype(np.floataa  ).sum()  ) < 1E-2  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feat_extract_dict
									_lowercase					       =					True
									_lowercase					       =					self.feature_extraction_class(**__UpperCamelCase  )
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common()
									_lowercase					       =					[len(__UpperCamelCase  ) for x in speech_inputs]
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}  )

									_lowercase					       =					feat_extract.pad(__UpperCamelCase				,			padding="""longest"""				,			return_tensors="""np"""  )
									self.assertIn("""attention_mask"""				,			__UpperCamelCase  )
									self.assertListEqual(list(processed.attention_mask.shape  )				,			list(processed[input_name].shape[:2]  )  )
									self.assertListEqual(processed.attention_mask.sum(-1  ).tolist()				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feat_extract_dict
									_lowercase					       =					True
									_lowercase					       =					self.feature_extraction_class(**__UpperCamelCase  )
									_lowercase					       =					self.feat_extract_tester.prepare_inputs_for_common()
									_lowercase					       =					[len(__UpperCamelCase  ) for x in speech_inputs]
									_lowercase					       =					feat_extract.model_input_names[0]

									_lowercase					       =					BatchFeature({input_name: speech_inputs}  )
									_lowercase					       =					min(__UpperCamelCase  )

									_lowercase					       =					feat_extract.pad(
									    __UpperCamelCase				,			padding="""max_length"""				,			max_length=__UpperCamelCase				,			truncation=__UpperCamelCase				,			return_tensors="""np"""  )
									self.assertIn("""attention_mask"""				,			__UpperCamelCase  )
									self.assertListEqual(
									    list(processed_pad.attention_mask.shape  )				,			[processed_pad[input_name].shape[0], max_length]  )
									self.assertListEqual(
									    processed_pad.attention_mask[:, :max_length].sum(-1  ).tolist()				,			[max_length for x in speech_inputs]  )

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)