






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_   :	Union[str, Any]							      ={'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	int							      =[
					    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''GPTJForCausalLM''',
					    '''GPTJForQuestionAnswering''',
					    '''GPTJForSequenceClassification''',
					    '''GPTJModel''',
					    '''GPTJPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''TFGPTJForCausalLM''',
					    '''TFGPTJForQuestionAnswering''',
					    '''TFGPTJForSequenceClassification''',
					    '''TFGPTJModel''',
					    '''TFGPTJPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	List[Any]							      =[
					    '''FlaxGPTJForCausalLM''',
					    '''FlaxGPTJModel''',
					    '''FlaxGPTJPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					snake_case_   :	List[str]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   ArgumentParser(
           description=(
               'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
           )      )

       # Optional arguments for the launch helper
       parser.add_argument('--num_cores'      ,    type=lowerCAmelCase__      ,    default=1      ,    help='Number of TPU cores to use (1 or 8).'      )

       # positional
       parser.add_argument(
           'training_script'      ,    type=lowerCAmelCase__      ,    help=(
               'The full path to the single TPU training '
               'program/script to be launched in parallel, '
               'followed by all the arguments for the '
               'training script'
           )      ,    )

       # rest from the training program
       parser.add_argument('training_script_args'      ,    nargs=lowerCAmelCase__      )

       return parser.parse_args()






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   parse_args()

       # Import training_script as a module.
       lowerCAmelCase__	      =   Path(args.training_script      )
       sys.path.append(str(script_fpath.parent.resolve()      )      )
       lowerCAmelCase__	      =   script_fpath.stem
       lowerCAmelCase__	      =   importlib.import_module(lowerCAmelCase__      )

       # Patch sys.argv
       lowerCAmelCase__	      =   [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

       xmp.spawn(mod._mp_fn      ,    args=()      ,    nprocs=args.num_cores      )


if __name__ == "__main__":
    main()
