code: 
  
  
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  from numpy import array, dot, pad, ravel, uinta, zeros
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   Any:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = image.shape
       __UpperCamelCase   = cols - block_size[1] + 1
       __UpperCamelCase   = rows - block_size[0] + 1
       __UpperCamelCase   = zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __UpperCamelCase   = 0
       for i in range(0				,     _lowercase   ):
            for j in range(0				,     _lowercase   ):
                 __UpperCamelCase   = ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                 __UpperCamelCase   = window
                 row += 1
  
       return image_array
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   Dict:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = image.shape[0], image.shape[1]
       __UpperCamelCase   = filter_kernel.shape[0]
       __UpperCamelCase   = k_size // 2
       # Pads image with the edge values of array.
       __UpperCamelCase   = pad(_lowercase				,     _lowercase				,     mode='edge'   )
  
       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __UpperCamelCase   = imacol(_lowercase				,     (k_size, k_size)   )
  
       #  turn the kernel into shape(k*k, 1)
       __UpperCamelCase   = ravel(_lowercase   )
       # reshape and get the dst image
       __UpperCamelCase   = dot(_lowercase				,     _lowercase   ).reshape(_lowercase				,     _lowercase   )
       return dst
  
  
  if __name__ == "__main__":
      # read original image
      __snake_case             =	imread(r'''../image_data/lena.jpg''')
      # turn image in gray scale value
      __snake_case             =	cvtColor(img, COLOR_BGR2GRAY)
      # Laplace operator
      __snake_case             =	array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
      __snake_case             =	img_convolve(gray, Laplace_kernel).astype(uinta)
      imshow('''Laplacian''', out)
      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)
  
  
  