
import argparse

import torch

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


logging.set_verbosity_info()


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Optional[int]:
				# Initialise PyTorch model
				__a										=    RemBertConfig.from_json_file(a__  )
				print('''Building PyTorch model from configuration: {}'''.format(str(a__  )  )  )
				__a										=    RemBertModel(a__  )

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

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


if __name__ == "__main__":
					A     :	str    = 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.'
					)
					A     :	Tuple    = parser.parse_args()
					convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)

def     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    []
				__a										=    set({'''(''', '''[''', '''{'''}  )
				__a										=    set({''')''', ''']''', '''}'''}  )
				__a										=    {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

				for i in range(len(a__  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(a__  ) == 0 or (len(a__  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(a__  ) == 0


def     __lowerCAmelCase			(			)       ->  Dict:
				__a										=    input('''Enter sequence of brackets: '''  )
				if is_balanced(a__  ):
								print(a__						,    '''is balanced'''  )
				else:
								print(a__						,    '''is not balanced'''  )


if __name__ == "__main__":
					main()