code: 
  
  
  
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    Optional[Any]   	=			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 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    List[str]   	=			parse_args()
  
  		# Import training_script as a module.
  		lowerCamelCase_  :    Optional[int]   	=			Path(args.training_script	)
  		sys.path.append(str(script_fpath.parent.resolve()	)	)
  		lowerCamelCase_  :    Union[str, Any]   	=			script_fpath.stem
  		lowerCamelCase_  :    Dict   	=			importlib.import_module(lowerCAmelCase__	)
  
  		# Patch sys.argv
  		lowerCamelCase_  :    Dict   	=			[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()
  
style_context: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _lowercase		:     int 					={
      """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=["""MarianTokenizer"""]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=[
  		    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
  		    """MarianForCausalLM""",
  		    """MarianModel""",
  		    """MarianMTModel""",
  		    """MarianPreTrainedModel""",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Dict 					=["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Optional[Any] 					=["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		_lowercase		:     List[str] 					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  