






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def 						_a    (      a :Optional[int]	,							a :List[Any]	,							a :Union[str, Any] )							->			Tuple:
 # Initialise PyTorch model
 a		  =      TaConfig.from_json_file(a )
 print(F"""Building PyTorch model from configuration: {config}""" )
 a		  =      TaForConditionalGeneration(a )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(a	,							a	,							a )

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}""" )
 model.save_pretrained(a )


if __name__ == "__main__":
     UpperCAmelCase__				   =		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(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help=(
             "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
         ),
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     UpperCAmelCase__				   =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.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()
