





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass



@dataclass
class 					A__   :
      _UpperCAmelCase	:int
      _UpperCAmelCase	:TreeNode | None						    =   None
      _UpperCAmelCase	:TreeNode | None						    =   None


__lowerCamelCase       :     Tuple   =     namedtuple("""CoinsDistribResult""", """moves excess""")



def 		A_				(     _lowerCAmelCase     )						-> int:

 if root is None:
  return 0

 # Validation
 def count_nodes(_lowerCAmelCase     ) -> int:
  if node is None:
   return 0

  return count_nodes(node.left     ) + count_nodes(node.right     ) + 1

 def count_coins(_lowerCAmelCase     ) -> int:
  if node is None:
   return 0

  return count_coins(node.left     ) + count_coins(node.right     ) + node.data

 if count_nodes(_lowerCAmelCase     ) != count_coins(_lowerCAmelCase     ):
  raise ValueError("The nodes number should be same as the number of coins"     )

 # Main calculation
 def get_distrib(_lowerCAmelCase     ) -> CoinsDistribResult:

  if node is None:
   return CoinsDistribResult(0							,   1     )

  UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   get_distrib(node.left     )
  UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   get_distrib(node.right     )

  UpperCamelCase	:					str       	=   1 - left_distrib_excess
  UpperCamelCase	:					Optional[Any]       	=   1 - right_distrib_excess

  UpperCamelCase	:					str       	=   (
      left_distrib_moves
      + right_distrib_moves
      + abs(_lowerCAmelCase     )
      + abs(_lowerCAmelCase     )
  )
  UpperCamelCase	:					str       	=   node.data - coins_to_left - coins_to_right

  return CoinsDistribResult(_lowerCAmelCase							,   _lowerCAmelCase     )

 return get_distrib(_lowerCAmelCase     )[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

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


if __name__ == "__main__":
				__lowerCamelCase							 =     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(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT 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							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





