





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		List[Any]		=							{
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		int		=							[
       'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'NllbMoeForConditionalGeneration',
       'NllbMoeModel',
       'NllbMoePreTrainedModel',
       'NllbMoeTop2Router',
       'NllbMoeSparseMLP',
   ]


if TYPE_CHECKING:
   from .configuration_nllb_moe import (
       NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
       NllbMoeConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_nllb_moe import (
          NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
          NllbMoeForConditionalGeneration,
          NllbMoeModel,
          NllbMoePreTrainedModel,
          NllbMoeSparseMLP,
          NllbMoeTopaRouter,
      )


else:
   import sys

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









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


