



'''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








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



