"""simple docstring"""




import fcntl
import os
import socket

import torch
import torch.distributed as dist







def 							_UpperCAmelCase		(       *lowerCamelCase__     ):





		"""simple docstring"""




		with open(lowerCamelCase__			,  """r"""     ) as fh:
				fcntl.flock(lowerCamelCase__			,  fcntl.LOCK_EX     )
				try:
						print(*lowerCamelCase__     )
				finally:
						fcntl.flock(lowerCamelCase__			,  fcntl.LOCK_UN     )


__lowerCAmelCase    :							Optional[Any]													=						int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__lowerCAmelCase    :							Optional[Any]													=						torch.device("cuda", local_rank)
__lowerCAmelCase    :							List[str]													=						socket.gethostname()

__lowerCAmelCase    :							Dict													=						F"[{hostname}-{local_rank}]"

try:
			# test distributed
			dist.init_process_group("nccl")
			dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
			dist.barrier()

			# test cuda is available and can allocate memory
			torch.cuda.is_available()
			torch.ones(1).cuda(local_rank)

			# global rank
			__lowerCAmelCase    :							str													=						dist.get_rank()
			__lowerCAmelCase    :							List[Any]													=						dist.get_world_size()

			printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")

			dist.barrier()
			if rank == 0:
						printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")

except Exception:
			printflock(F"{gpu} is broken")
			raise





"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




