





import importlib.metadata
import subprocess
import sys





def   _lowercase       (    _UpperCAmelCase = False					)    ->	Union[str, Any]:
				lowerCamelCase									=False
				if "IPython" in sys.modules:
								lowerCamelCase									="""google.colab""" in str(sys.modules["""IPython"""].get_ipython()					)

				if in_colab:
								if upgrade:
												lowerCamelCase									=["""pip""", """install""", """-U""", """torch"""]
												subprocess.run(_UpperCAmelCase      ,   check=_UpperCAmelCase					)
								# get the current version of torch
								lowerCamelCase									=importlib.metadata.version("""torch"""					)
								lowerCamelCase									=torch_version[: torch_version.rindex("""."""					)]
								lowerCamelCase									=F"""https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl"""
								lowerCamelCase									=["""pip""", """install""", xla_wheel]
								subprocess.run(_UpperCAmelCase      ,   check=_UpperCAmelCase					)
				else:
								raise RuntimeError("""`install_xla` utility works only on google colab."""					)







'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

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


if __name__ == "__main__":
 a__		: Tuple  							=							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(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT 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__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






