




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      (		)						->				Dict:
						_UpperCAmelCase      =			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=snake_case			,     default=1			,     help="""Number of TPU cores to use (1 or 8)."""       )

						# positional
						parser.add_argument(
						    """training_script"""			,     type=snake_case			,     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=snake_case       )

						return parser.parse_args()




def        _SCREAMING_SNAKE_CASE      (		)						->				Tuple:
						_UpperCAmelCase      =			parse_args()

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

						# Patch sys.argv
						_UpperCAmelCase      =			[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()





from typing import Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)