



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A       ={
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["LlamaTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["LlamaTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =[
				    "OpenLlamaForCausalLM",
				    "OpenLlamaModel",
				    "OpenLlamaPreTrainedModel",
				    "OpenLlamaForSequenceClassification",
				]


if TYPE_CHECKING:
				from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from transformers import LlamaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from transformers import LlamaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_open_llama import (
								    OpenLlamaForCausalLM,
								    OpenLlamaForSequenceClassification,
								    OpenLlamaModel,
								    OpenLlamaPreTrainedModel,
								)


else:
				import sys

				__A       =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










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


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





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

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      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       :	Dict						=      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()





