





import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()



def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					List[Any]												=      ConvBertConfig.from_json_file(UpperCamelCase_)
    UpperCamelCase__:					Optional[Any]												=      ConvBertModel(UpperCamelCase_)

    UpperCamelCase__:					str												=      load_tf_weights_in_convbert(UpperCamelCase_  ,UpperCamelCase_  ,UpperCamelCase_)
    model.save_pretrained(UpperCamelCase_)

    UpperCamelCase__:					Any												=      TFConvBertModel.from_pretrained(UpperCamelCase_  ,from_pt=UpperCamelCase_)
    tf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   A__:  int       					=					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(
       '''--convbert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained ConvBERT 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__:  Any       					=					parser.parse_args()
   convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

