



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

import torch_xla.distributed.xla_multiprocessing as xmp




def 	__UpperCamelCase	(  )       ->  List[str]:



    """simple docstring"""






    A :       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 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Union[str, Any]          =   parse_args()

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

    # Patch sys.argv
    A :       Any          =   [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 __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


