


'''simple docstring'''






from math import sqrt






def 	_A				(lowerCAmelCase__  :int = 1_00_00_00		)   ->  int:




     '''simple docstring'''



     _a			      =							0
     _a			      =							0
     _a			      =							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(lowerCAmelCase__				,						sum_shortest_sides // 2		)
                        - max(1				,						sum_shortest_sides - max_cuboid_size		)
                        + 1
                    )

     return max_cuboid_size


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





'''simple docstring'''






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     _a			      =							BertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							BertForPreTraining(lowerCAmelCase__		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'		)
     torch.save(model.state_dict()				,						lowerCAmelCase__		)


if __name__ == "__main__":
 a_					:    Optional[int]          =    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_					:    Optional[int]          =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

