

'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__   		=      {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__   		=      [
     '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MraForMaskedLM''',
     '''MraForMultipleChoice''',
     '''MraForQuestionAnswering''',
     '''MraForSequenceClassification''',
     '''MraForTokenClassification''',
     '''MraLayer''',
     '''MraModel''',
     '''MraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 lowerCAmelCase__   		=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)


"""simple docstring"""




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"""




		if (ksize % 2) == 0:
				lowerCAmelCase__   = ksize + 1
		lowerCAmelCase__   = np.zeros((ksize, ksize)      ,	dtype=np.floataa  )

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

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

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

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

						# fill kernel
						lowerCAmelCase__   = 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     :				Tuple    				=    imread("../image_data/lena.jpg")
			# turn image in gray scale value
			__lowerCAmelCase     :				List[str]    				=    cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			__lowerCAmelCase     :				Union[str, Any]    				=    np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 1_20, 1_50]:
						__lowerCAmelCase     :				Union[str, Any]    				=    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			__lowerCAmelCase     :				Optional[int]    				=    out / out.max() * 2_55
			__lowerCAmelCase     :				Tuple    				=    out.astype(np.uinta)

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

			waitKey(0)




