


import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()




def    a   (							A__      ,    A__      ,    A__							)	-> Optional[int]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		LxmertConfig.from_json_file(A__							)
      print(f"""Building PyTorch model from configuration: {config}"""							)
      SCREAMING_SNAKE_CASE__       :  str						     =		LxmertForPreTraining(A__							)

      # Load weights from tf checkpoint
      load_tf_weights_in_lxmert(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_ :List[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(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    a_ :Optional[int]  							=			parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_      :  Dict				     =  {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  [
     'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GraphormerForGraphClassification',
     'GraphormerModel',
     'GraphormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  Tuple				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
