





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__				:   str      ={
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__				:   Dict      =[
          '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''NezhaForNextSentencePrediction''',
          '''NezhaForMaskedLM''',
          '''NezhaForPreTraining''',
          '''NezhaForMultipleChoice''',
          '''NezhaForQuestionAnswering''',
          '''NezhaForSequenceClassification''',
          '''NezhaForTokenClassification''',
          '''NezhaModel''',
          '''NezhaPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nezha import (
                NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
                NezhaForMaskedLM,
                NezhaForMultipleChoice,
                NezhaForNextSentencePrediction,
                NezhaForPreTraining,
                NezhaForQuestionAnswering,
                NezhaForSequenceClassification,
                NezhaForTokenClassification,
                NezhaModel,
                NezhaPreTrainedModel,
            )


else:
      import sys

      A__				:   Optional[Any]      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''simple docstring'''

a_  :				Any						= """0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich



