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

from digital_image_processing.filters.convolve import img_convolve


def  A			(   lowercase					)  ->    List[str]:
		'''simple docstring'''

		UpperCamelCase            =	np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
		UpperCamelCase            =	np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

		UpperCamelCase            =	np.abs(img_convolve(lowercase       ,			lowercase					)					)
		UpperCamelCase            =	np.abs(img_convolve(lowercase       ,			lowercase					)					)
		# modify the pix within [0, 255]
		UpperCamelCase            =	dst_x * 255 / np.max(lowercase					)
		UpperCamelCase            =	dst_y * 255 / np.max(lowercase					)

		UpperCamelCase            =	np.sqrt((np.square(lowercase					)) + (np.square(lowercase					))					)
		UpperCamelCase            =	dst_xy * 255 / np.max(lowercase					)
		UpperCamelCase            =	dst_xy.astype(np.uinta					)

		UpperCamelCase            =	np.arctana(lowercase       ,			lowercase					)
		return dst, theta


if __name__ == "__main__":
					# read original image
					_UpperCAmelCase   :					List[str]    =			imread("../image_data/lena.jpg")
					# turn image in gray scale value
					_UpperCAmelCase   :					Optional[Any]    =			cvtColor(img, COLOR_BGR2GRAY)

					_UpperCAmelCase   :					Optional[int]    =			sobel_filter(gray)

					# show result images
					imshow("sobel filter", sobel_grad)
					imshow("sobel theta", sobel_theta)
					waitKey(0)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__						:	Any	     = {
    'configuration_decision_transformer': [
        'DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DecisionTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:	Optional[Any]	     = [
       'DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DecisionTransformerGPT2Model',
       'DecisionTransformerGPT2PreTrainedModel',
       'DecisionTransformerModel',
       'DecisionTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

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



