





'''simple docstring'''

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

import torch_xla.distributed.xla_multiprocessing as xmp





def 		UpperCamelCase							(				):
    A__							=     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							(				):
    A__							=     parse_args()

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

    # Patch sys.argv
    A__							=     [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 Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :List[str]		,			lowercase_ :int = 7_68		,			)->				str:
     super().__init__()

     A__							=     nn.Parameter(torch.zeros(1		,			lowercase_				)				)
     A__							=     nn.Parameter(torch.ones(1		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Optional[Union[str, torch.device]] = None		,			lowercase_ :Optional[torch.dtype] = None		,			)->				Tuple:
     A__							=     nn.Parameter(self.mean.to(lowercase_				).to(lowercase_				)				)
     A__							=     nn.Parameter(self.std.to(lowercase_				).to(lowercase_				)				)
     return self
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :str				)->				Union[str, Any]:
     A__							=     (embeds - self.mean) * 1.0 / self.std
     return embeds



 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :List[Any]				)->				Tuple:
     A__							=     (embeds * self.std) + self.mean
     return embeds



