



"""simple docstring"""
from typing import TYPE_CHECKING

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


A:     Any										=	{"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Tuple										=	[
          "OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OPTForCausalLM",
          "OPTModel",
          "OPTPreTrainedModel",
          "OPTForSequenceClassification",
          "OPTForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     List[str]										=	["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Union[str, Any]										=	[
          "FlaxOPTForCausalLM",
          "FlaxOPTModel",
          "FlaxOPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_opt import (
                OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                OPTForCausalLM,
                OPTForQuestionAnswering,
                OPTForSequenceClassification,
                OPTModel,
                OPTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
      import sys

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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


A:     Dict										=	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

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