code: 
  '''simple docstring'''
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  _UpperCAmelCase   :      Union[str, Any] 						=  180
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      Union[str, Any]:
  		_UpperCamelCase       : str    =      k_size // 2
  		_UpperCamelCase       : str    =      np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  		_UpperCamelCase       : Any    =      (
  		    1
  		    / (2 * np.pi * sigma)
  		    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
  		)
  		return g
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      str:
  		_UpperCamelCase       : List[Any]    =      np.zeros(_SCREAMING_SNAKE_CASE	)
  
  		for row in range(1			,image_shape[0] - 1	):
  				for col in range(1			,image_shape[1] - 1	):
  						_UpperCamelCase       : int    =      gradient_direction[row, col]
  
  						if (
  						    0 <= direction < PI / 8
  						    or 15 * PI / 8 <= direction <= 2 * PI
  						    or 7 * PI / 8 <= direction <= 9 * PI / 8
  						):
  								_UpperCamelCase       : int    =      sobel_grad[row, col - 1]
  								_UpperCamelCase       : int    =      sobel_grad[row, col + 1]
  								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
  										_UpperCamelCase       : List[str]    =      sobel_grad[row, col]
  
  						elif (
  						    PI / 8 <= direction < 3 * PI / 8
  						    or 9 * PI / 8 <= direction < 11 * PI / 8
  						):
  								_UpperCamelCase       : Optional[int]    =      sobel_grad[row + 1, col - 1]
  								_UpperCamelCase       : Optional[int]    =      sobel_grad[row - 1, col + 1]
  								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
  										_UpperCamelCase       : int    =      sobel_grad[row, col]
  
  						elif (
  						    3 * PI / 8 <= direction < 5 * PI / 8
  						    or 11 * PI / 8 <= direction < 13 * PI / 8
  						):
  								_UpperCamelCase       : List[str]    =      sobel_grad[row - 1, col]
  								_UpperCamelCase       : str    =      sobel_grad[row + 1, col]
  								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
  										_UpperCamelCase       : Optional[Any]    =      sobel_grad[row, col]
  
  						elif (
  						    5 * PI / 8 <= direction < 7 * PI / 8
  						    or 13 * PI / 8 <= direction < 15 * PI / 8
  						):
  								_UpperCamelCase       : Dict    =      sobel_grad[row - 1, col - 1]
  								_UpperCamelCase       : str    =      sobel_grad[row + 1, col + 1]
  								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
  										_UpperCamelCase       : List[str]    =      sobel_grad[row, col]
  
  		return destination
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      Optional[Any]:
  		for row in range(1			,image_shape[0] - 1	):
  				for col in range(1			,image_shape[1] - 1	):
  						if destination[row, col] >= threshold_high:
  								_UpperCamelCase       : Tuple    =      strong
  						elif destination[row, col] <= threshold_low:
  								_UpperCamelCase       : Optional[Any]    =      0
  						else:
  								_UpperCamelCase       : int    =      weak
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      Tuple:
  		for row in range(1			,image_shape[0]	):
  				for col in range(1			,image_shape[1]	):
  						if destination[row, col] == weak:
  								if 2_55 in (
  								    destination[row, col + 1],
  								    destination[row, col - 1],
  								    destination[row - 1, col],
  								    destination[row + 1, col],
  								    destination[row - 1, col - 1],
  								    destination[row + 1, col - 1],
  								    destination[row - 1, col + 1],
  								    destination[row + 1, col + 1],
  								):
  										_UpperCamelCase       : List[Any]    =      strong
  								else:
  										_UpperCamelCase       : Optional[Any]    =      0
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=15			,UpperCamelCase=30			,UpperCamelCase=1_28			,UpperCamelCase=2_55	) ->      Tuple:
  		# gaussian_filter
  		_UpperCamelCase       : List[str]    =      img_convolve(_SCREAMING_SNAKE_CASE			,gen_gaussian_kernel(9			,sigma=1.4	)	)
  		# get the gradient and degree by sobel_filter
  		_UpperCamelCase       : Union[str, Any]    =      sobel_filter(_SCREAMING_SNAKE_CASE	)
  		_UpperCamelCase       : Tuple    =      PI + np.radadeg(_SCREAMING_SNAKE_CASE	)
  
  		_UpperCamelCase       : Tuple    =      suppress_non_maximum(image.shape			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE	)
  
  		detect_high_low_threshold(
  		    image.shape			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE	)
  
  		track_edge(image.shape			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE	)
  
  		return destination
  
  
  if __name__ == "__main__":
  	# read original image in gray mode
  	_UpperCAmelCase   :      List[str] 						=  cva.imread(R"""../image_data/lena.jpg""", 0)
  	# canny edge detection
  	_UpperCAmelCase   :      int 						=  canny(lena)
  	cva.imshow("""canny""", canny_destination)
  	cva.waitKey(0)
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _UpperCAmelCase   :      int 						=  {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	_UpperCAmelCase   :      int 						=  [
  	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """GPTSanJapaneseForConditionalGeneration""",
  	    """GPTSanJapaneseModel""",
  	    """GPTSanJapanesePreTrainedModel""",
  	]
  	_UpperCAmelCase   :      str 						=  [
  	    """GPTSanJapaneseTokenizer""",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_gptsan_japanese import (
  		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    GPTSanJapaneseForConditionalGeneration,
  		    GPTSanJapaneseModel,
  		    GPTSanJapanesePreTrainedModel,
  		)
  		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
  	import sys
  
  	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  