



import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()






def 			__lowercase     (				a__							,     a__							,     a__     )       ->				List[str]:
							# Initialise PyTorch model
							__SCREAMING_SNAKE_CASE								=      RoFormerConfig.from_json_file(a__     )
							print(f"""Building PyTorch model from configuration: {config}"""     )
							__SCREAMING_SNAKE_CASE								=      RoFormerForMaskedLM(a__     )

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

							# Save pytorch-model
							print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
							torch.save(model.state_dict()							,     a__							,     _use_new_zipfile_serialization=a__     )


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(
			    '''--bert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained BERT 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__	:  List[str] 		=parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











def 			__lowercase     (				a__     )       ->				list:
							__SCREAMING_SNAKE_CASE								=      1.3
							__SCREAMING_SNAKE_CASE								=      len(a__     )
							__SCREAMING_SNAKE_CASE								=      False

							while not completed:
														# Update the gap value for a next comb
														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
														if gap <= 1:
																					__SCREAMING_SNAKE_CASE								=      True

														__SCREAMING_SNAKE_CASE								=      0
														while index + gap < len(a__     ):
																					if data[index] > data[index + gap]:
																												# Swap values
																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
																												__SCREAMING_SNAKE_CASE								=      False
																					index += 1

							return data


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
			print(comb_sort(unsorted))






