code: 
  
  
  import functools
  import operator
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				{
      'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
      # See all Hubert models at https://huggingface.co/models?filter=hubert
  }
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				"hubert"
  
  
       def __init__(		self				,		__lowerCamelCase=3_2				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=3_0_7_2				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=1e-5				,		__lowerCamelCase="group"				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)				,		__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)				,		__lowerCamelCase=(1_0, 3, 3, 3, 3, 2, 2)				,		__lowerCamelCase=False				,		__lowerCamelCase=1_2_8				,		__lowerCamelCase=1_6				,		__lowerCamelCase=False				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0_5				,		__lowerCamelCase=1_0				,		__lowerCamelCase=2				,		__lowerCamelCase=0.0				,		__lowerCamelCase=1_0				,		__lowerCamelCase=0				,		__lowerCamelCase="sum"				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		__lowerCamelCase=2_5_6				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=2				,		**__lowerCamelCase				,		)    ->       List[Any]:
              super().__init__(**__lowerCamelCase				,		pad_token_id=__lowerCamelCase				,		bos_token_id=__lowerCamelCase				,		eos_token_id=__lowerCamelCase)
              _A   : Tuple       					=				hidden_size
              _A   : Tuple       					=				feat_extract_norm
              _A   : Tuple       					=				feat_extract_activation
              _A   : Tuple       					=				list(__lowerCamelCase)
              _A   : List[str]       					=				list(__lowerCamelCase)
              _A   : List[str]       					=				list(__lowerCamelCase)
              _A   : str       					=				conv_bias
              _A   : Dict       					=				num_conv_pos_embeddings
              _A   : Dict       					=				num_conv_pos_embedding_groups
              _A   : List[str]       					=				len(self.conv_dim)
              _A   : Any       					=				num_hidden_layers
              _A   : Dict       					=				intermediate_size
              _A   : str       					=				hidden_act
              _A   : Optional[int]       					=				num_attention_heads
              _A   : Optional[Any]       					=				hidden_dropout
              _A   : int       					=				attention_dropout
              _A   : Optional[Any]       					=				activation_dropout
              _A   : int       					=				feat_proj_layer_norm
              _A   : Union[str, Any]       					=				feat_proj_dropout
              _A   : Union[str, Any]       					=				final_dropout
              _A   : str       					=				layerdrop
              _A   : Tuple       					=				layer_norm_eps
              _A   : Dict       					=				initializer_range
              _A   : Optional[int]       					=				vocab_size
              _A   : List[str]       					=				do_stable_layer_norm
              _A   : Optional[int]       					=				use_weighted_layer_sum
              _A   : Tuple       					=				classifier_proj_size
  
              if (
                  (len(self.conv_stride) != self.num_feat_extract_layers)
                  or (len(self.conv_kernel) != self.num_feat_extract_layers)
                  or (len(self.conv_dim) != self.num_feat_extract_layers)
              ):
                     raise ValueError(
                         "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                         " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                         F" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
                         F" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")
  
              # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
              _A   : Optional[Any]       					=				apply_spec_augment
              _A   : Optional[Any]       					=				mask_time_prob
              _A   : Tuple       					=				mask_time_length
              _A   : Optional[Any]       					=				mask_time_min_masks
              _A   : List[str]       					=				mask_feature_prob
              _A   : Dict       					=				mask_feature_length
              _A   : Optional[Any]       					=				mask_feature_min_masks
  
              # ctc loss
              _A   : Optional[Any]       					=				ctc_loss_reduction
              _A   : Any       					=				ctc_zero_infinity
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       int:
              return functools.reduce(operator.mul				,		self.conv_stride				,		1)
  
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)
  