code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import numpy as np
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  
  from digital_image_processing.filters.convolve import img_convolve
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]   )
  			__A=  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]   )
  
  			__A=  np.abs(img_convolve(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )   )
  			__A=  np.abs(img_convolve(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )   )
  			# modify the pix within [0, 255]
  			__A=  dst_x * 255 / np.max(_SCREAMING_SNAKE_CASE   )
  			__A=  dst_y * 255 / np.max(_SCREAMING_SNAKE_CASE   )
  
  			__A=  np.sqrt((np.square(_SCREAMING_SNAKE_CASE   )) + (np.square(_SCREAMING_SNAKE_CASE   ))   )
  			__A=  dst_xy * 255 / np.max(_SCREAMING_SNAKE_CASE   )
  			__A=  dst_xy.astype(np.uinta   )
  
  			__A=  np.arctana(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  			return dst, theta
  
  
  if __name__ == "__main__":
  					# read original image
  					UpperCAmelCase__      =							imread('''../image_data/lena.jpg''')
  					# turn image in gray scale value
  					UpperCAmelCase__      =							cvtColor(img, COLOR_BGR2GRAY)
  
  					UpperCAmelCase__ ,     UpperCAmelCase__      =							sobel_filter(gray)
  
  					# show result images
  					imshow('''sobel filter''', sobel_grad)
  					imshow('''sobel theta''', sobel_theta)
  					waitKey(0)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  			A	:							Optional[int]							= '''mobilebert'''
  			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  hidden_act
  						__A=  intermediate_size
  						__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=  embedding_size
  						__A=  trigram_input
  						__A=  use_bottleneck
  						__A=  intra_bottleneck_size
  						__A=  use_bottleneck_attention
  						__A=  key_query_shared_bottleneck
  						__A=  num_feedforward_networks
  						__A=  normalization_type
  						__A=  classifier_activation
  
  						if self.use_bottleneck:
  									__A=  intra_bottleneck_size
  						else:
  									__A=  hidden_size
  
  						__A=  classifier_dropout
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
  						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),
  						    ]  )
  
  
  
  