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


lowerCAmelCase			:							Tuple =logging.get_logger(__name__)

# TODO: upload to AWS
lowerCAmelCase			:							Optional[Any] ={
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}




class 	__snake_case	( lowercase__		):





      '''simple docstring'''






      _snake_case    		=       'retribert'



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				List[Any]=3_0522    ,  _UpperCamelCase     :				Optional[int]=768    ,  _UpperCamelCase     :				Any=8    ,  _UpperCamelCase     :				List[str]=12    ,  _UpperCamelCase     :				Tuple=3072    ,  _UpperCamelCase     :				int="gelu"    ,  _UpperCamelCase     :				Union[str, Any]=0.1    ,  _UpperCamelCase     :				Any=0.1    ,  _UpperCamelCase     :				Optional[Any]=512    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				int=0.0_2    ,  _UpperCamelCase     :				Dict=1E-1_2    ,  _UpperCamelCase     :				Optional[int]=True    ,  _UpperCamelCase     :				str=128    ,  _UpperCamelCase     :				Optional[Any]=0    ,  **_UpperCamelCase     :				Tuple    ,  )				->Optional[int]:


          """simple docstring"""


          super().__init__(pad_token_id=_UpperCamelCase    ,  **_UpperCamelCase)

          _lowerCamelCase     :		int 						=						vocab_size
          _lowerCamelCase     :		Optional[int] 						=						hidden_size
          _lowerCamelCase     :		Tuple 						=						num_hidden_layers
          _lowerCamelCase     :		str 						=						num_attention_heads
          _lowerCamelCase     :		int 						=						hidden_act
          _lowerCamelCase     :		Tuple 						=						intermediate_size
          _lowerCamelCase     :		Tuple 						=						hidden_dropout_prob
          _lowerCamelCase     :		List[str] 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Any 						=						max_position_embeddings
          _lowerCamelCase     :		Tuple 						=						type_vocab_size
          _lowerCamelCase     :		Optional[int] 						=						initializer_range
          _lowerCamelCase     :		Tuple 						=						layer_norm_eps
          _lowerCamelCase     :		Tuple 						=						share_encoders
          _lowerCamelCase     :		List[Any] 						=						projection_dim





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()




