



from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__magic_name__       =     {
    '''A''': ['''B''', '''C'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E'''],
}





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       {start}
					lowercase						   =       [start]
					lowercase						   =       Queue()
					queue.put(_UpperCAmelCase				)
					while not queue.empty():
										lowercase						   =       queue.get()
										for w in graph[v]:
															if w not in explored:
																				explored.add(_UpperCAmelCase				)
																				result.append(_UpperCAmelCase				)
																				queue.put(_UpperCAmelCase				)
					return result





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       {start}
					lowercase						   =       [start]
					lowercase						   =       deque([start]				)
					while queue:
										lowercase						   =       queue.popleft()
										for child in graph[v]:
															if child not in visited:
																				visited.add(_UpperCAmelCase				)
																				result.append(_UpperCAmelCase				)
																				queue.append(_UpperCAmelCase				)
					return result





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       f"""from __main__ import G, {name}"""
					lowercase						   =       1_00_00
					lowercase						   =       timeit(f"""{name}(G, 'A')"""       ,			setup=_UpperCAmelCase       ,			number=_UpperCAmelCase				)
					print(f"""{name:<35} finished {number} runs in {res:.5f} seconds"""				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		benchmark_function('''breadth_first_search''')
		benchmark_function('''breadth_first_search_with_deque''')
		# breadth_first_search                finished 10000 runs in 0.20999 seconds
		# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds











import argparse

import torch

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


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

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

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


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






