code: 
  
  
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  from numpy import divide, inta, multiply, ravel, sort, zeros_like
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase=3   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = int(mask / 2   )
       # copy image size
       __UpperCamelCase   = zeros_like(_lowercase   )
       for i in range(_lowercase				,     gray_img.shape[0] - bd   ):
            for j in range(_lowercase				,     gray_img.shape[1] - bd   ):
                 # get mask according with mask
                 __UpperCamelCase   = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]   )
                 # calculate mask median
                 __UpperCamelCase   = sort(_lowercase   )[inta(divide((multiply(_lowercase				,     _lowercase   ))				,     2   ) + 1   )]
                 __UpperCamelCase   = median
       return median_img
  
  
  if __name__ == "__main__":
      # read original image
      __snake_case             =	imread('''../image_data/lena.jpg''')
      # turn image in gray scale value
      __snake_case             =	cvtColor(img, COLOR_BGR2GRAY)
  
      # get values with two different mask size
      __snake_case             =	median_filter(gray, 3)
      __snake_case             =	median_filter(gray, 5)
  
      # show result images
      imshow('''median filter with 3x3 mask''', medianaxa)
      imshow('''median filter with 5x5 mask''', medianaxa)
      waitKey(0)
  
  
  
style_context: 
  
  
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
       __UpperCamelCase   = GPTNeoConfig(
           hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
       print(f'''Building PyTorch model from configuration: {config}'''   )
       __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )
  
       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
       model.save_pretrained(_lowercase   )
  
  
  if __name__ == "__main__":
      __snake_case             =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __snake_case             =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  