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
  
  A__	:      List[str]				=						180
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       Tuple							)  ->	Union[str, Any]:
       __lowerCamelCase     :			Union[str, Any]              =	k_size // 2
       __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       __lowerCamelCase     :			Optional[int]              =	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(UpperCAmelCase_							) + np.square(UpperCAmelCase_							)) / (2 * np.square(UpperCAmelCase_							))							)
       )
       return g
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Union[str, Any]   ,					UpperCAmelCase_					:       List[str]							)  ->	Any:
       __lowerCamelCase     :			List[Any]              =	np.zeros(UpperCAmelCase_							)
  
       for row in range(1   ,					image_shape[0] - 1							):
            for col in range(1   ,					image_shape[1] - 1							):
                 __lowerCamelCase     :			Union[str, Any]              =	gradient_direction[row, col]
  
                 if (
                     0 <= direction < PI / 8
                     or 15 * PI / 8 <= direction <= 2 * PI
                     or 7 * PI / 8 <= direction <= 9 * PI / 8
                 ):
                      __lowerCamelCase     :			List[Any]              =	sobel_grad[row, col - 1]
                      __lowerCamelCase     :			str              =	sobel_grad[row, col + 1]
                      if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                           __lowerCamelCase     :			List[Any]              =	sobel_grad[row, col]
  
                 elif (
                     PI / 8 <= direction < 3 * PI / 8
                     or 9 * PI / 8 <= direction < 11 * PI / 8
                 ):
                      __lowerCamelCase     :			Union[str, Any]              =	sobel_grad[row + 1, col - 1]
                      __lowerCamelCase     :			Dict              =	sobel_grad[row - 1, col + 1]
                      if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                           __lowerCamelCase     :			Optional[int]              =	sobel_grad[row, col]
  
                 elif (
                     3 * PI / 8 <= direction < 5 * PI / 8
                     or 11 * PI / 8 <= direction < 13 * PI / 8
                 ):
                      __lowerCamelCase     :			str              =	sobel_grad[row - 1, col]
                      __lowerCamelCase     :			Union[str, Any]              =	sobel_grad[row + 1, col]
                      if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                           __lowerCamelCase     :			List[Any]              =	sobel_grad[row, col]
  
                 elif (
                     5 * PI / 8 <= direction < 7 * PI / 8
                     or 13 * PI / 8 <= direction < 15 * PI / 8
                 ):
                      __lowerCamelCase     :			Optional[Any]              =	sobel_grad[row - 1, col - 1]
                      __lowerCamelCase     :			Dict              =	sobel_grad[row + 1, col + 1]
                      if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                           __lowerCamelCase     :			Union[str, Any]              =	sobel_grad[row, col]
  
       return destination
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Union[str, Any]   ,					UpperCAmelCase_					:       Union[str, Any]   ,					UpperCAmelCase_					:       Dict   ,					UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       Union[str, Any]   ,					UpperCAmelCase_					:       List[str]							)  ->	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:
                      __lowerCamelCase     :			Optional[Any]              =	strong
                 elif destination[row, col] <= threshold_low:
                      __lowerCamelCase     :			List[Any]              =	0
                 else:
                      __lowerCamelCase     :			Optional[int]              =	weak
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Dict							)  ->	Any:
       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],
                      ):
                           __lowerCamelCase     :			Optional[int]              =	strong
                      else:
                           __lowerCamelCase     :			Union[str, Any]              =	0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       int=15   ,					UpperCAmelCase_					:       List[Any]=30   ,					UpperCAmelCase_					:       Tuple=1_28   ,					UpperCAmelCase_					:       Any=2_55							)  ->	int:
       # gaussian_filter
       __lowerCamelCase     :			Union[str, Any]              =	img_convolve(UpperCAmelCase_   ,					gen_gaussian_kernel(9   ,					sigma=1.4							)							)
       # get the gradient and degree by sobel_filter
       __lowerCamelCase       , __lowerCamelCase     :			str              =	sobel_filter(UpperCAmelCase_							)
       __lowerCamelCase     :			Tuple              =	PI + np.radadeg(UpperCAmelCase_							)
  
       __lowerCamelCase     :			List[Any]              =	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
        A__	:      List[str]				=						cva.imread(R"""../image_data/lena.jpg""", 0)
        # canny edge detection
        A__	:      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_tf_available, is_torch_available
  
  
  A__	:      List[str]				=						{
      """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
      """tokenization_flaubert""": ["""FlaubertTokenizer"""],
  }
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """FlaubertForMultipleChoice""",
            """FlaubertForQuestionAnswering""",
            """FlaubertForQuestionAnsweringSimple""",
            """FlaubertForSequenceClassification""",
            """FlaubertForTokenClassification""",
            """FlaubertModel""",
            """FlaubertWithLMHeadModel""",
            """FlaubertPreTrainedModel""",
        ]
  
  try:
        if not is_tf_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """TFFlaubertForMultipleChoice""",
            """TFFlaubertForQuestionAnsweringSimple""",
            """TFFlaubertForSequenceClassification""",
            """TFFlaubertForTokenClassification""",
            """TFFlaubertModel""",
            """TFFlaubertPreTrainedModel""",
            """TFFlaubertWithLMHeadModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
        from .tokenization_flaubert import FlaubertTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )
  
        try:
              if not is_tf_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )
  
  else:
        import sys
  
        A__	:      Tuple				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  