

'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:			Union[str, Any]       =	{
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase					:			List[Any]       =	[
      'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TrajectoryTransformerModel',
      'TrajectoryTransformerPreTrainedModel',
      'load_tf_weights_in_trajectory_transformer',
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase					:			Optional[Any]       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''



import math
import sys

import cva
import numpy as np







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = math.sqrt(a__		)
     __SCREAMING_SNAKE_CASE      = 1 / (sigma * math.sqrt(2 * math.pi		))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros((kernel_size, kernel_size)		)
     for i in range(0 ,       a__		):
          for j in range(0 ,       a__		):
               __SCREAMING_SNAKE_CASE      = math.sqrt(
                   abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
     return vec_gaussian(a__ ,       a__		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros(img.shape		)
     __SCREAMING_SNAKE_CASE      = get_gauss_kernel(a__ ,       a__		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = img.shape
     for i in range(kernel_size // 2 ,       size_x - kernel_size // 2		):
          for j in range(kernel_size // 2 ,       size_y - kernel_size // 2		):
               __SCREAMING_SNAKE_CASE      = get_slice(a__ ,       a__ ,       a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = img_s - img_s[kernel_size // 2, kernel_size // 2]
               __SCREAMING_SNAKE_CASE      = vec_gaussian(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.sum(a__		) / np.sum(a__		)
               __SCREAMING_SNAKE_CASE      = val
     return imga







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = args[1] if args[1:] else """../image_data/lena.jpg"""
     __SCREAMING_SNAKE_CASE      = float(args[2]		) if args[2:] else 1.0
     __SCREAMING_SNAKE_CASE      = float(args[3]		) if args[3:] else 1.0
     if args[4:]:
          __SCREAMING_SNAKE_CASE      = int(args[4]		)
          __SCREAMING_SNAKE_CASE      = kernel_size + abs(kernel_size % 2 - 1		)
     else:
          __SCREAMING_SNAKE_CASE      = 5
     return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase					:			Any       =	parse_args(sys.argv)
  UpperCAmelCase					:			Union[str, Any]       =	cva.imread(filename, 0)
  cva.imshow('input image', img)

  UpperCAmelCase					:			Optional[int]       =	img / 2_5_5
  UpperCAmelCase					:			Optional[Any]       =	out.astype('float32')
  UpperCAmelCase					:			Tuple       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  UpperCAmelCase					:			List[Any]       =	out * 2_5_5
  UpperCAmelCase					:			Any       =	np.uinta(out)
  cva.imshow('output image', out)
  cva.waitKey(0)
  cva.destroyAllWindows()






