


'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	RemBertConfig.from_json_file(_SCREAMING_SNAKE_CASE )
			print('Building PyTorch model from configuration: {}'.format(str(_SCREAMING_SNAKE_CASE ) ) )
			__A=	RemBertModel(_SCREAMING_SNAKE_CASE )

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

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


if __name__ == "__main__":
					UpperCAmelCase__  			=				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__  			=				parser.parse_args()
					convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)







'''simple docstring'''





import re

from filelock import FileLock


try:
					import nltk

					UpperCAmelCase__  			=				True
except (ImportError, ModuleNotFoundError):
					UpperCAmelCase__  			=				False

if NLTK_AVAILABLE:
					with FileLock('''.lock''') as lock:
										nltk.download('''punkt''', quiet=True)






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			re.sub('<n>','',_SCREAMING_SNAKE_CASE )  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )



