


"""simple docstring"""






from ..utils import DummyObject, requires_backends



class     A__     (						metaclass=lowercase__							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			["""note_seq"""]

	def __init__(  self:	int ,    *_SCREAMING_SNAKE_CASE:	Optional[int] ,    **_SCREAMING_SNAKE_CASE:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				requires_backends(self ,    ["note_seq"])

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Union[str, Any] ,    *_SCREAMING_SNAKE_CASE:	List[str] ,    **_SCREAMING_SNAKE_CASE:	List[Any])       -> Optional[int]:

				"""simple docstring"""

				requires_backends(cls ,    ["note_seq"])







	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Optional[Any] ,    *_SCREAMING_SNAKE_CASE:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	Tuple)       -> List[str]:

				"""simple docstring"""

				requires_backends(cls ,    ["note_seq"])




"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= 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__":
	__snake_case							:     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.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)