





import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowercase :			Tuple     		=    [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]



def A_      (						A__							)    ->						Any:



       for file in FILES_TO_FIND:
              if not (transformers_path / file).exists():
                     return False
       return True


if __name__ == "__main__":
    lowercase :			Tuple     		=    argparse.ArgumentParser()
    parser.add_argument("""--check_lib""", action="""store_true""", help="""Whether to check the build or the actual package.""")
    lowercase :			List[str]     		=    parser.parse_args()
    if args.check_lib:
        lowercase :			Tuple     		=    importlib.import_module("""transformers""")
        lowercase :			List[Any]     		=    Path(transformers_module.__file__).parent
    else:
        lowercase :			Union[str, Any]     		=    Path.cwd() / 'build/lib/transformers'
    if not test_custom_files_are_present(transformers_path):
        raise ValueError("""The built release does not contain the custom files. Fix this before going further!""")







import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  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(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT 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_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)