






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]					)		->			Optional[int]:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	RemBertConfig.from_json_file(__magic_name__					)
	print("""Building PyTorch model from configuration: {}""".format(str(__magic_name__					)					)					)
	UpperCamelCase     :Optional[Any]          =	RemBertModel(__magic_name__					)

	# Load weights from tf checkpoint
	load_tf_weights_in_rembert(__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Save pytorch-model
	print("""Save PyTorch model to {}""".format(__magic_name__					)					)
	torch.save(model.state_dict()	,			__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						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(
	    '''--rembert_config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained RemBERT 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.'''
	)
	UpperCAmelCase_ :						List[Any]      =      parser.parse_args()
	convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)








def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  list[int]					)		->			bool:






	"""simple docstring"""



	if graph[path[curr_ind - 1]][next_ver] == 0:
		return False

	# 2. Validate that next vertex is not already in path
	return not any(vertex == next_ver for vertex in path					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  list[int]	,			__magic_name__					:  int					)		->			bool:






	"""simple docstring"""



	if curr_ind == len(__magic_name__					):
		# return whether path exists between current and starting vertices
		return graph[path[curr_ind - 1]][path[0]] == 1

	# Recursive Step
	for next_ver in range(0	,			len(__magic_name__					)					):
		if valid_connection(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					):
			# Insert current vertex  into path as next transition
			UpperCamelCase     :str          =	next_ver
			# Validate created path
			if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			curr_ind + 1					):
				return True
			# Backtrack
			UpperCamelCase     :Union[str, Any]          =	-1
	return False

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int = 0					)		->			list[int]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[-1] * (len(__magic_name__					) + 1)
	# initialize start and end of path with starting index
	UpperCamelCase     :Any          =	start_index
	# evaluate and if we find answer return path either return empty array
	return path if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			1					) else []
