





'''simple docstring'''

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

import torch_xla.distributed.xla_multiprocessing as xmp




def  _a     (				):





      """simple docstring"""



      _snake_case		:						Tuple         =  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  _a     (				):





      """simple docstring"""



      _snake_case		:						List[str]         =  parse_args()

      # Import training_script as a module.
      _snake_case		:						Union[str, Any]         =  Path(args.training_script						)
      sys.path.append(str(script_fpath.parent.resolve()						)						)
      _snake_case		:						Any         =  script_fpath.stem
      _snake_case		:						Tuple         =  importlib.import_module(lowerCAmelCase_						)

      # Patch sys.argv
      _snake_case		:						Optional[int]         =  [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()









'''simple docstring'''

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,
)


UpperCAmelCase       :		Dict		=				{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Tuple		=				['MarianTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		List[str]		=				[
       'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MarianForCausalLM',
       'MarianModel',
       'MarianMTModel',
       'MarianPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Any		=				['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		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

   UpperCAmelCase       :		int		=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


