from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase										= {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''DebertaTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DebertaForMaskedLM''',
        '''DebertaForQuestionAnswering''',
        '''DebertaForSequenceClassification''',
        '''DebertaForTokenClassification''',
        '''DebertaModel''',
        '''DebertaPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFDebertaForMaskedLM''',
        '''TFDebertaForQuestionAnswering''',
        '''TFDebertaForSequenceClassification''',
        '''TFDebertaForTokenClassification''',
        '''TFDebertaModel''',
        '''TFDebertaPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    __lowercase										= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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

import torch_xla.distributed.xla_multiprocessing as xmp



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					: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=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_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=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

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

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[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()





