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						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[str]			)							->      Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   AlbertConfig.from_json_file(__UpperCamelCase			)
  print(f"""Building PyTorch model from configuration: {config}"""			)
  SCREAMING_SNAKE_CASE__    							=   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    :				Optional[Any]							     =	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    :				List[str]							     =	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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list			)							->      float:







  """simple docstring"""





  if not nums:
    raise ValueError("""List is empty"""			)
  return sum(__UpperCamelCase			) / len(__UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
