code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import argparse
  
  from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Tuple:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Dict             =    CanineConfig()
  		lowerCAmelCase_     :							Union[str, Any]             =    CanineModel(__UpperCamelCase )
  		model.eval()
  
  		print(f'''Building PyTorch model from configuration: {config}''' )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_canine(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )
  
  		# Save pytorch-model (weights and configuration)
  		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
  		model.save_pretrained(__UpperCamelCase )
  
  		# Save tokenizer files
  		lowerCAmelCase_     :							Any             =    CanineTokenizer()
  		print(f'''Save tokenizer files to {pytorch_dump_path}''' )
  		tokenizer.save_pretrained(__UpperCamelCase )
  
  
  if __name__ == "__main__":
  					lowercase__      =				argparse.ArgumentParser()
  					# Required parameters
  					parser.add_argument(
  					    """--tf_checkpoint_path""",
  					    default=None,
  					    type=str,
  					    required=True,
  					    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
  					)
  					parser.add_argument(
  					    """--pytorch_dump_path""",
  					    default=None,
  					    type=str,
  					    required=True,
  					    help="""Path to a folder where the PyTorch model will be placed.""",
  					)
  					lowercase__      =				parser.parse_args()
  					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from __future__ import annotations
  
  from PIL import Image
  
  # Define the first generation of cells
  # fmt: off
  lowercase__      =				[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					list[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		return [int(__UpperCamelCase ) for c in f'''{ruleset:08}'''[:8]]
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					list[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							int             =    len(cells[0] )  # 31
  		lowerCAmelCase_     :							List[Any]             =    []
  		for i in range(__UpperCamelCase ):
  				# Get the neighbors of each cell
  				# Handle neighbours outside bounds by using 0 as their value
  				lowerCAmelCase_     :							List[str]             =    0 if i == 0 else cells[time][i - 1]
  				lowerCAmelCase_     :							Optional[int]             =    0 if i == population - 1 else cells[time][i + 1]
  				# Define a new cell and add it to the new generation
  				lowerCAmelCase_     :							List[Any]             =    7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''   , 2 )
  				next_generation.append(rule[situation] )
  		return next_generation
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Image.Image:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[str]             =    Image.new("RGB"   , (len(cells[0] ), len(__UpperCamelCase )) )
  		lowerCAmelCase_     :							List[str]             =    img.load()
  		# Generates image
  		for w in range(img.width ):
  				for h in range(img.height ):
  						lowerCAmelCase_     :							Union[str, Any]             =    255 - int(255 * cells[h][w] )
  						lowerCAmelCase_     :							List[str]             =    (color, color, color)
  		return img
  
  
  if __name__ == "__main__":
  					lowercase__      =				bin(int(input("""Rule:\n""").strip()))[2:]
  					lowercase__      =				format_ruleset(int(rule_num))
  					for time in range(16):
  										CELLS.append(new_generation(CELLS, rule, time))
  					lowercase__      =				generate_image(CELLS)
  					# Uncomment to save the image
  					# img.save(f"rule_{rule_num}.png")
  					img.show()
  