'''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 				snake_case__							(			_A:					List[str]      ,   _A:					Any      ,   _A:					Tuple )    ->	Any:

		'''simple docstring'''


		lowerCAmelCase   					=  RemBertConfig.from_json_file(_A )
		print("""Building PyTorch model from configuration: {}""".format(str(_A ) ) )
		lowerCAmelCase   					=  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__":
						__lowercase          =  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.'''
						)
						__lowercase          =  parser.parse_args()
						convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





'''simple docstring'''




from math import sqrt






def 				snake_case__							(			_A:					int = 1000000 )    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  42

		while num_cuboids <= limit:
				max_cuboid_size += 1
				for sum_shortest_sides in range(2      ,   2 * max_cuboid_size + 1 ):
						if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
								num_cuboids += (
								    min(_A      ,   sum_shortest_sides // 2 )
								    - max(1      ,   sum_shortest_sides - max_cuboid_size )
								    + 1
								)

		return max_cuboid_size


if __name__ == "__main__":
						print(f'{solution() = }')




