



'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase: List[Any]							=						{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase: Optional[int]							=						['NllbTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase: Dict							=						['NllbTokenizerFast']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb import NllbTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb_fast import NllbTokenizerFast

else:
    import sys

    lowerCAmelCase: Tuple							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase: Optional[int]							=						{
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: int							=						[
         'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'GraphormerForGraphClassification',
         'GraphormerModel',
         'GraphormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_graphormer import (
              GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              GraphormerForGraphClassification,
              GraphormerModel,
              GraphormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: str							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)