





def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				return not any(
				    neighbour == 1 and colored_vertices[i] == color
				    for i, neighbour in enumerate(_lowerCamelCase						)						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:



				if index == len(_lowerCamelCase						):
								return True

				# Recursive Step
				for i in range(_lowerCamelCase						):
								if valid_coloring(graph[index]     ,    _lowerCamelCase     ,    _lowerCamelCase						):
												# Color current vertex
												UpperCamelCase_:							Any	=						i
												# Validate coloring
												if util_color(_lowerCamelCase     ,    _lowerCamelCase     ,    _lowerCamelCase     ,    index + 1						):
																return True
												# Backtrack
												UpperCamelCase_:							int	=						-1
				return False







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				UpperCamelCase_:							int	=						[-1] * len(_lowerCamelCase						)

				if util_color(_lowerCamelCase     ,    _lowerCamelCase     ,    _lowerCamelCase     ,    0						):
								return colored_vertices

				return []








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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 T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






