import json
import logging
import os
import subprocess
from argparse import ArgumentParser


lowerCAmelCase__  	=      logging.getLogger(__name__)

def 	__lowerCamelCase							(				)      ->       int:
	_lowercase   =ArgumentParser()
	_lowercase   ,  _lowercase   =parser.parse_known_args()
	for arg in unknown:
		if arg.startswith(("-", "--")		):
			parser.add_argument(arg.split("="		)[0]		)

	return parser.parse_args()

def 	__lowerCamelCase							(				)      ->       str:
	_lowercase   =parse_args()
	_lowercase   =8_888
	_lowercase   =int(os.environ["SM_NUM_GPUS"]		)
	_lowercase   =json.loads(os.environ["SM_HOSTS"]		)
	_lowercase   =len(__a		)
	_lowercase   =os.environ["SM_CURRENT_HOST"]
	_lowercase   =hosts.index(__a		)
	_lowercase   ="INFO"

	if num_nodes > 1:
		_lowercase   =f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]		)}'''
	else:
		_lowercase   =f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]		)}'''
	try:
		subprocess.run(__a      ,							shell=__a		)
	except Exception as e:
		logger.info(__a		)


if __name__ == "__main__":
						main()










import argparse

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


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				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(
		    """--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    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






