code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  lowerCAmelCase__   	=				{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
      if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				["""XLMProphetNetTokenizer"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """XLMProphetNetDecoder""",
          """XLMProphetNetEncoder""",
          """XLMProphetNetForCausalLM""",
          """XLMProphetNetForConditionalGeneration""",
          """XLMProphetNetModel""",
          """XLMProphetNetPreTrainedModel""",
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
      try:
          if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_xlm_prophetnet import (
              XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMProphetNetDecoder,
              XLMProphetNetEncoder,
              XLMProphetNetForCausalLM,
              XLMProphetNetForConditionalGeneration,
              XLMProphetNetModel,
              XLMProphetNetPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
style_context: 
  
  
  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
  
  lowerCAmelCase__   	=				1_80
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
         _A   : int       					=				k_size // 2
         _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
         _A   : Union[str, Any]       					=				(
             1
             / (2 * np.pi * sigma)
             * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
         )
         return g
  def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
         _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)
  
         for row in range(1       ,							image_shape[0] - 1		):
                for col in range(1       ,							image_shape[1] - 1		):
                       _A   : Dict       					=				gradient_direction[row, col]
  
                       if (
                           0 <= direction < PI / 8
                           or 15 * PI / 8 <= direction <= 2 * PI
                           or 7 * PI / 8 <= direction <= 9 * PI / 8
                       ):
                              _A   : Dict       					=				sobel_grad[row, col - 1]
                              _A   : Any       					=				sobel_grad[row, col + 1]
                              if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           PI / 8 <= direction < 3 * PI / 8
                           or 9 * PI / 8 <= direction < 11 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row + 1, col - 1]
                              _A   : str       					=				sobel_grad[row - 1, col + 1]
                              if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           3 * PI / 8 <= direction < 5 * PI / 8
                           or 11 * PI / 8 <= direction < 13 * PI / 8
                       ):
                              _A   : Any       					=				sobel_grad[row - 1, col]
                              _A   : int       					=				sobel_grad[row + 1, col]
                              if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                     _A   : Any       					=				sobel_grad[row, col]
  
                       elif (
                           5 * PI / 8 <= direction < 7 * PI / 8
                           or 13 * PI / 8 <= direction < 15 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row - 1, col - 1]
                              _A   : Optional[Any]       					=				sobel_grad[row + 1, col + 1]
                              if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
         return destination
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						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:
                              _A   : Optional[int]       					=				strong
                       elif destination[row, col] <= threshold_low:
                              _A   : Optional[int]       					=				0
                       else:
                              _A   : Union[str, Any]       					=				weak
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
         for row in range(1       ,							image_shape[0]		):
                for col in range(1       ,							image_shape[1]		):
                       if destination[row, col] == weak:
                              if 255 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],
                              ):
                                     _A   : str       					=				strong
                              else:
                                     _A   : Any       					=				0
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
         # gaussian_filter
         _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
         # get the gradient and degree by sobel_filter
         _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
         _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)
  
         _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         detect_high_low_threshold(
             image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      lowerCAmelCase__   	=				cva.imread(R'../image_data/lena.jpg', 0)
      # canny edge detection
      lowerCAmelCase__   	=				canny(lena)
      cva.imshow('canny', canny_destination)
      cva.waitKey(0)
  