



"""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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		# Initialize PyTorch model
		_lowercase				: Union[str, Any]     =   CanineConfig()
		_lowercase				: 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
		_lowercase				: Optional[Any]     =   CanineTokenizer()
		print(F"""Save tokenizer files to {pytorch_dump_path}"""      )
		tokenizer.save_pretrained(__UpperCAmelCase      )


if __name__ == "__main__":
		UpperCAmelCase:  int			     =				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.""",
		)
		UpperCAmelCase:  Any			     =				parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)











"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
UpperCamelCase_						:					int      				=			[[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      __lowercase       (     a			:					int			)       ->       list[int]:
      return [int(a			) for c in f'''{ruleset:08}'''[:8]]



def      __lowercase       (     a			:					list[list[int]]							,		a			:					list[int]							,		a			:					int			)       ->       list[int]:
      __snake_case   :     Optional[Any]	      =len(cells[0]			)  # 31
      __snake_case   :     List[str]	      =[]
      for i in range(a			):
            # Get the neighbors of each cell
            # Handle neighbours outside bounds by using 0 as their value
            __snake_case   :     Optional[Any]	      =0 if i == 0 else cells[time][i - 1]
            __snake_case   :     Dict	      =0 if i == population - 1 else cells[time][i + 1]
            # Define a new cell and add it to the new generation
            __snake_case   :     Optional[int]	      =7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''							,		2			)
            next_generation.append(rule[situation]			)
      return next_generation



def      __lowercase       (     a			:					list[list[int]]			)       ->       Image.Image:
      __snake_case   :     int	      =Image.new('''RGB'''							,		(len(cells[0]			), len(a			))			)
      __snake_case   :     List[Any]	      =img.load()
      # Generates image
      for w in range(img.width			):
            for h in range(img.height			):
                  __snake_case   :     int	      =255 - int(255 * cells[h][w]			)
                  __snake_case   :     List[str]	      =(color, color, color)
      return img


if __name__ == "__main__":
    UpperCamelCase_						:					Optional[Any]      				=			bin(int(input("""Rule:\n""").strip()))[2:]
    UpperCamelCase_						:					List[str]      				=			format_ruleset(int(rule_num))
    for time in range(16):
        CELLS.append(new_generation(CELLS, rule, time))
    UpperCamelCase_						:					List[Any]      				=			generate_image(CELLS)
    # Uncomment to save the image
    # img.save(f"rule_{rule_num}.png")
    img.show()


