



import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


UpperCAmelCase_									=							logging.get_logger(__name__)





class 		UpperCAmelCase						(						lowercase__				):





   def __init__(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         warnings.warn(
             '''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
             ''' Please use LayoutLMv2ImageProcessor instead.'''       ,     _lowerCAmelCase       ,     )
         super().__init__(*_lowerCAmelCase       ,     **_lowerCAmelCase			)




import argparse

import torch

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


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      # Initialise PyTorch model
      _lowerCAmelCase 			=  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
      print(f'''Building PyTorch model from configuration: {config}'''				)
      _lowerCAmelCase 			=  AlbertForPreTraining(_SCREAMING_SNAKE_CASE				)

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

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


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(
        "--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."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)