





from typing import TYPE_CHECKING

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


__lowercase  :   Dict						      ={
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =["""LxmertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Optional[Any]						      =[
         """LxmertEncoder""",
         """LxmertForPreTraining""",
         """LxmertForQuestionAnswering""",
         """LxmertModel""",
         """LxmertPreTrainedModel""",
         """LxmertVisualFeatureEncoder""",
         """LxmertXLayer""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Any						      =[
         """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFLxmertForPreTraining""",
         """TFLxmertMainLayer""",
         """TFLxmertModel""",
         """TFLxmertPreTrainedModel""",
         """TFLxmertVisualFeatureEncoder""",
     ]


if TYPE_CHECKING:
     from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
     from .tokenization_lxmert import LxmertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_lxmert_fast import LxmertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_lxmert import (
              LxmertEncoder,
              LxmertForPreTraining,
              LxmertForQuestionAnswering,
              LxmertModel,
              LxmertPreTrainedModel,
              LxmertVisualFeatureEncoder,
              LxmertXLayer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_lxmert import (
              TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLxmertForPreTraining,
              TFLxmertMainLayer,
              TFLxmertModel,
              TFLxmertPreTrainedModel,
              TFLxmertVisualFeatureEncoder,
          )

else:
     import sys

     __lowercase  :   int						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











__lowercase  :   Dict						      ="""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




