






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

def   lowerCAmelCase__						(     _a							:      int	,   _a							:      int	,   _a							:      int	,   _a							:      int	,   _a							:      int	,   _a							:      int     ):





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

		# each value
		for y in range(_a     ):
				for x in range(_a     ):
						# distance from center
						snake_case_					:				str    			=							x - ksize // 2
						snake_case_					:				List[str]    			=							y - ksize // 2

						# degree to radiant
						snake_case_					:				Tuple    			=							theta / 1_80 * np.pi
						snake_case_					:				List[str]    			=							np.cos(_theta     )
						snake_case_					:				List[str]    			=							np.sin(_theta     )

						# get kernel x
						snake_case_					:				List[str]    			=							cos_theta * px + sin_theta * py

						# get kernel y
						snake_case_					:				Optional[Any]    			=							-sin_theta * px + cos_theta * py

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

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

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

							waitKey(0)




"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





