



'''simple docstring'''






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  # Initialise PyTorch model
  a						: str					       =					RemBertConfig.from_json_file(_A    )
  print('Building PyTorch model from configuration: {}'.format(str(_A    )    )    )
  a						: Dict					       =					RemBertModel(_A    )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(_A			,      _A			,      _A    )

  # Save pytorch-model
  print('Save PyTorch model to {}'.format(_A    )    )
  torch.save(model.state_dict()			,      _A    )


if __name__ == "__main__":
    lowerCAmelCase: Dict							=						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(
        '--rembert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained RemBERT 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.'
    )
    lowerCAmelCase: Union[str, Any]							=						parser.parse_args()
    convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_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: Optional[int]							=						{
    '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: str							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)