




'''simple docstring'''





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      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						# Initialise PyTorch model
						_UpperCAmelCase      =			AlbertConfig.from_json_file(snake_case       )
						print(f"Building PyTorch model from configuration: {config}"       )
						_UpperCAmelCase      =			AlbertForPreTraining(snake_case       )

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

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


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





# flake8: noqa
# Lint as: python3
a													=			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental