





'''simple docstring'''







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

from digital_image_processing.filters.convolve import img_convolve



def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			Any					   = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]							)
     snake_case :			Optional[Any]					   = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]							)

     snake_case :			str					   = np.abs(img_convolve(__snake_case    ,      __snake_case							)							)
     snake_case :			str					   = np.abs(img_convolve(__snake_case    ,      __snake_case							)							)
     # modify the pix within [0, 255]
     snake_case :			Tuple					   = dst_x * 255 / np.max(__snake_case							)
     snake_case :			List[str]					   = dst_y * 255 / np.max(__snake_case							)

     snake_case :			str					   = np.sqrt((np.square(__snake_case							)) + (np.square(__snake_case							))							)
     snake_case :			Union[str, Any]					   = dst_xy * 255 / np.max(__snake_case							)
     snake_case :			Dict					   = dst_xy.astype(np.uinta							)

     snake_case :			Union[str, Any]					   = np.arctana(__snake_case    ,      __snake_case							)
     return dst, theta


if __name__ == "__main__":
      # read original image
      lowercase			:      Tuple			=					imread("""../image_data/lena.jpg""")
      # turn image in gray scale value
      lowercase			:      str			=					cvtColor(img, COLOR_BGR2GRAY)

      lowercase,  lowercase			:      str			=					sobel_filter(gray)

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









'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase			:      Tuple			=					{"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      Tuple			=					["""BertGenerationTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      List[Any]			=					[
          """BertGenerationDecoder""",
          """BertGenerationEncoder""",
          """BertGenerationPreTrainedModel""",
          """load_tf_weights_in_bert_generation""",
      ]


if TYPE_CHECKING:
      from .configuration_bert_generation import BertGenerationConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_generation import BertGenerationTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert_generation import (
                BertGenerationDecoder,
                BertGenerationEncoder,
                BertGenerationPreTrainedModel,
                load_tf_weights_in_bert_generation,
            )

else:
      import sys

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



