





import argparse

import torch

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


logging.set_verbosity_info()


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       # Initialise PyTorch model
       _UpperCamelCase              =						AlbertConfig.from_json_file(lowerCAmelCase					)
       print(f'''Building PyTorch model from configuration: {config}'''					)
       _UpperCamelCase              =						AlbertForPreTraining(lowerCAmelCase					)

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

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


if __name__ == "__main__":
       lowercase    : 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(
           """--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."""
       )
       lowercase    : Any    	=  parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)













from __future__ import annotations

from collections.abc import Iterator

class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						value
            _UpperCamelCase              =						None
            _UpperCamelCase              =						None






class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						tree

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
            )



     def __iter__(      self      ):





            """simple docstring"""

            yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






