






from ..utils import DummyObject, requires_backends


class  SCREAMING_SNAKE_CASE			(			metaclass=lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	['''keras_nlp''']


     def __init__(				self       :	Dict					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	Dict      ):
            requires_backends(self					, ["keras_nlp"]      )














from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






