from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase			:							Dict =logging.get_logger(__name__)

lowerCAmelCase			:							Dict ={
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class 	__snake_case	( __UpperCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       "xglm"
      _snake_case    		=       ["past_key_values"]

      _snake_case    		=       {
          "num_attention_heads": "attention_heads",
          "hidden_size": "d_model",
          "num_hidden_layers": "num_layers",
      }



      def __init__(					self     :				int    ,  _UpperCamelCase     :				List[str]=25_6008    ,  _UpperCamelCase     :				Any=2048    ,  _UpperCamelCase     :				List[str]=1024    ,  _UpperCamelCase     :				List[str]=4096    ,  _UpperCamelCase     :				List[str]=24    ,  _UpperCamelCase     :				List[Any]=16    ,  _UpperCamelCase     :				int="gelu"    ,  _UpperCamelCase     :				List[str]=0.1    ,  _UpperCamelCase     :				List[Any]=0.1    ,  _UpperCamelCase     :				Tuple=0.0    ,  _UpperCamelCase     :				List[Any]=0.0    ,  _UpperCamelCase     :				Union[str, Any]=0.0_2    ,  _UpperCamelCase     :				Union[str, Any]=True    ,  _UpperCamelCase     :				int=True    ,  _UpperCamelCase     :				List[str]=2    ,  _UpperCamelCase     :				List[str]=1    ,  _UpperCamelCase     :				Optional[int]=0    ,  _UpperCamelCase     :				int=2    ,  **_UpperCamelCase     :				Tuple    ,  )				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						vocab_size
          _lowerCamelCase     :		Tuple 						=						max_position_embeddings
          _lowerCamelCase     :		str 						=						d_model
          _lowerCamelCase     :		Any 						=						ffn_dim
          _lowerCamelCase     :		Optional[Any] 						=						num_layers
          _lowerCamelCase     :		List[Any] 						=						attention_heads
          _lowerCamelCase     :		Optional[Any] 						=						activation_function
          _lowerCamelCase     :		Dict 						=						dropout
          _lowerCamelCase     :		int 						=						attention_dropout
          _lowerCamelCase     :		List[str] 						=						activation_dropout
          _lowerCamelCase     :		Optional[int] 						=						layerdrop
          _lowerCamelCase     :		List[str] 						=						init_std
          _lowerCamelCase     :		Tuple 						=						scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowerCamelCase     :		Union[str, Any] 						=						use_cache

          super().__init__(
              pad_token_id=_lowerCamelCase    ,  bos_token_id=_lowerCamelCase    ,  eos_token_id=_lowerCamelCase    ,  decoder_start_token_id=_lowerCamelCase    ,  **_lowerCamelCase    ,  )





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'''
    _lowerCamelCase     :		Optional[int] 						=						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=__A   , default=1   , help="""Number of TPU cores to use (1 or 8)."""	)

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

    return parser.parse_args()







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						parse_args()

    # Import training_script as a module.
    _lowerCamelCase     :		List[Any] 						=						Path(args.training_script	)
    sys.path.append(str(script_fpath.parent.resolve()	)	)
    _lowerCamelCase     :		Optional[Any] 						=						script_fpath.stem
    _lowerCamelCase     :		Dict 						=						importlib.import_module(__A	)

    # Patch sys.argv
    _lowerCamelCase     :		Union[str, 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()




