
import argparse

import torch

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


logging.set_verbosity_info()


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Optional[int]:
				# Initialise PyTorch model
				__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__":
					A     :	Union[str, 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.'
					)
					A     :	List[str]    = parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





from torch import nn


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(f"Unsupported activation function: {act_fn}"							)
