

import argparse

import torch

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


logging.set_verbosity_info()




def 			UpperCAmelCase__				(			_A      : str	,      _A      : Dict	,      _A      : Optional[Any]							):





  '''simple docstring'''







  a__  =AlbertConfig.from_json_file(_A							)
  print(F"""Building PyTorch model from configuration: {config}"""							)
  a__  =AlbertForPreTraining(_A							)

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

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


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)



from __future__ import annotations




def 			UpperCAmelCase__				(			_A      : float	,      _A      : float	,      _A      : float	,      ):





  '''simple docstring'''







  if (electron_conc, hole_conc, intrinsic_conc).count(0							) != 1:
    raise ValueError('''You cannot supply more or less than 2 values'''							)
  elif electron_conc < 0:
    raise ValueError('''Electron concentration cannot be negative in a semiconductor'''							)
  elif hole_conc < 0:
    raise ValueError('''Hole concentration cannot be negative in a semiconductor'''							)
  elif intrinsic_conc < 0:
    raise ValueError(
        '''Intrinsic concentration cannot be negative in a semiconductor'''							)
  elif electron_conc == 0:
    return (
        "electron_conc",
        intrinsic_conc**2 / hole_conc,
    )
  elif hole_conc == 0:
    return (
        "hole_conc",
        intrinsic_conc**2 / electron_conc,
    )
  elif intrinsic_conc == 0:
    return (
        "intrinsic_conc",
        (electron_conc * hole_conc) ** 0.5,
    )
  else:
    return (-1, -1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
