






def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[int]					,  snake_case_ :							list[int]					,  snake_case_ :							int       ):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case_       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[list[int]]					,  snake_case_ :							int					,  snake_case_ :							list[int]					,  snake_case_ :							int       ):

	# Base Case
	if index == len(snake_case_       ):
		return True

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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[list[int]]					,  snake_case_ :							int       ):
	snake_case__			:				int       =  [-1] * len(snake_case_       )

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

	return []











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				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(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT 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."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



