code: 
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{
      """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
      """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
      """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
      """google/electra-small-discriminator""": (
          """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
      ),
      """google/electra-base-discriminator""": (
          """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
      ),
      """google/electra-large-discriminator""": (
          """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
      ),
  }
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''electra'''
   def __init__(						self	,    a=3_0_5_2_2	,    a=1_2_8	,    a=2_5_6	,    a=1_2	,    a=4	,    a=1_0_2_4	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=2	,    a=0.02	,    a=1e-12	,    a="first"	,    a=True	,    a="gelu"	,    a=0.1	,    a=0	,    a="absolute"	,    a=True	,    a=None	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__(pad_token_id=a	,    **a					)
  
     _A           =    vocab_size
     _A           =    embedding_size
     _A           =    hidden_size
     _A           =    num_hidden_layers
     _A           =    num_attention_heads
     _A           =    intermediate_size
     _A           =    hidden_act
     _A           =    hidden_dropout_prob
     _A           =    attention_probs_dropout_prob
     _A           =    max_position_embeddings
     _A           =    type_vocab_size
     _A           =    initializer_range
     _A           =    layer_norm_eps
  
     _A           =    summary_type
     _A           =    summary_use_proj
     _A           =    summary_activation
     _A           =    summary_last_dropout
     _A           =    position_embedding_type
     _A           =    use_cache
     _A           =    classifier_dropout
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   @property
   def lowercase_		(						self					)	->							Mapping[str, Mapping[int, str]]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if self.task == "multiple-choice":
       _A           =    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
       _A           =    {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
             ('''token_type_ids''', dynamic_axis),
         ]					)
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_8_0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Dict:
    _A           =    k_size // 2
    _A    ,   _A           =    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _A           =    (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(__snake_case							) + np.square(__snake_case							)) / (2 * np.square(__snake_case							))							)
    )
    return g
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _A           =    np.zeros(__snake_case							)
  
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        _A           =    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           =    sobel_grad[row, col - 1]
          _A           =    sobel_grad[row, col + 1]
          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
            _A           =    sobel_grad[row, col]
  
        elif (
            PI / 8 <= direction < 3 * PI / 8
            or 9 * PI / 8 <= direction < 11 * PI / 8
        ):
          _A           =    sobel_grad[row + 1, col - 1]
          _A           =    sobel_grad[row - 1, col + 1]
          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
            _A           =    sobel_grad[row, col]
  
        elif (
            3 * PI / 8 <= direction < 5 * PI / 8
            or 11 * PI / 8 <= direction < 13 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col]
          _A           =    sobel_grad[row + 1, col]
          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
            _A           =    sobel_grad[row, col]
  
        elif (
            5 * PI / 8 <= direction < 7 * PI / 8
            or 13 * PI / 8 <= direction < 15 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col - 1]
          _A           =    sobel_grad[row + 1, col + 1]
          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
            _A           =    sobel_grad[row, col]
  
    return destination
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Optional[int]:
    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           =    strong
        elif destination[row, col] <= threshold_low:
          _A           =    0
        else:
          _A           =    weak
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> List[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           =    strong
          else:
            _A           =    0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=15						,				__snake_case=30						,				__snake_case=128						,				__snake_case=255							)   -> List[Any]:
    # gaussian_filter
    _A           =    img_convolve(__snake_case						,				gen_gaussian_kernel(9						,				sigma=1.4							)							)
    # get the gradient and degree by sobel_filter
    _A    ,   _A           =    sobel_filter(__snake_case							)
    _A           =    PI + np.radadeg(__snake_case							)
  
    _A           =    suppress_non_maximum(image.shape						,				__snake_case						,				__snake_case							)
  
    detect_high_low_threshold(
        image.shape						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    track_edge(image.shape						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    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)