




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ={"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[Any]				      =[
          """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPTJForCausalLM""",
          """GPTJForQuestionAnswering""",
          """GPTJForSequenceClassification""",
          """GPTJModel""",
          """GPTJPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Dict				      =[
          """TFGPTJForCausalLM""",
          """TFGPTJForQuestionAnswering""",
          """TFGPTJForSequenceClassification""",
          """TFGPTJModel""",
          """TFGPTJPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Tuple				      =[
          """FlaxGPTJForCausalLM""",
          """FlaxGPTJModel""",
          """FlaxGPTJPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gptj import (
                GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTJForCausalLM,
                GPTJForQuestionAnswering,
                GPTJForSequenceClassification,
                GPTJModel,
                GPTJPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_gptj import (
                TFGPTJForCausalLM,
                TFGPTJForQuestionAnswering,
                TFGPTJForSequenceClassification,
                TFGPTJModel,
                TFGPTJPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Tuple				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NatForImageClassification""",
          """NatModel""",
          """NatPreTrainedModel""",
          """NatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)