



import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 	a						(				_UpperCAmelCase							: int			,       _UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     json.load(open(_UpperCAmelCase			,       '''r'''						)						)
		__UpperCAmelCase   :					int													=     GPTNeoConfig(
		    hidden_size=config_json['''n_embd''']			,       num_layers=config_json['''n_layer''']			,       num_heads=config_json['''n_head''']			,       attention_types=config_json['''attention_types''']			,       max_position_embeddings=config_json['''n_positions''']			,       resid_dropout=config_json['''res_dropout''']			,       embed_dropout=config_json['''embed_dropout''']			,       attention_dropout=config_json['''attn_dropout''']			,       )
		print(f'Building PyTorch model from configuration: {config}'						)
		__UpperCAmelCase   :					Any													=     GPTNeoForCausalLM(_UpperCAmelCase						)

		# Load weights from tf checkpoint
		load_tf_weights_in_gpt_neo(_UpperCAmelCase			,       _UpperCAmelCase			,       _UpperCAmelCase						)

		# Save pytorch-model
		print(f'Save PyTorch model to {pytorch_dump_path}'						)
		model.save_pretrained(_UpperCAmelCase						)


if __name__ == "__main__":
				__A             =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(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained mesh-tf 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             =parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










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


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

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


if __name__ == "__main__":
				main()





