
import argparse

import torch

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


logging.set_verbosity_info()




def     a						(							snake_case__: str						,       snake_case__: int						,       snake_case__: List[Any] ):


				'''simple docstring'''
				# Initialise PyTorch model
				lowercase_					  = RoFormerConfig.from_json_file(snake_case__ )
				print(F'''Building PyTorch model from configuration: {config}''' )
				lowercase_					  = RoFormerForMaskedLM(snake_case__ )

				# Load weights from tf checkpoint
				load_tf_weights_in_roformer(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__						,       _use_new_zipfile_serialization=snake_case__ )


if __name__ == "__main__":
						__a        = 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.'
						)
						__a        = parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	)	->					None:
					lowercase_					  = [0, 1]




	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					list:
					if (difference := index - (len(self.sequence	) - 2)) >= 1:
									for _ in range(SCREAMING_SNAKE_CASE_	):
													self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
					return self.sequence[:index]




def     a						(							):


				'''simple docstring'''
				print(
				    '''Fibonacci Series Using Dynamic Programming\n'''						,       '''Enter the index of the Fibonacci number you want to calculate '''						,       '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''						,       sep=''''''						,       )
				lowercase_					  = Fibonacci()

				while True:
								lowercase_					  = input('''>> ''' )
								if prompt in {"exit", "quit"}:
												break

								try:
												lowercase_					  = int(snake_case__ )
								except ValueError:
												print('''Enter a number or \'exit\'''' )
												continue

								print(fibonacci.get(snake_case__ ) )


if __name__ == "__main__":
						main()




