


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    					=      {
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """XLMRobertaXLForCausalLM""",
     """XLMRobertaXLForMaskedLM""",
     """XLMRobertaXLForMultipleChoice""",
     """XLMRobertaXLForQuestionAnswering""",
     """XLMRobertaXLForSequenceClassification""",
     """XLMRobertaXLForTokenClassification""",
     """XLMRobertaXLModel""",
     """XLMRobertaXLPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_xlm_roberta_xl import (
     XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XLMRobertaXLConfig,
     XLMRobertaXLOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_roberta_xl import (
      XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMRobertaXLForCausalLM,
      XLMRobertaXLForMaskedLM,
      XLMRobertaXLForMultipleChoice,
      XLMRobertaXLForQuestionAnswering,
      XLMRobertaXLForSequenceClassification,
      XLMRobertaXLForTokenClassification,
      XLMRobertaXLModel,
      XLMRobertaXLPreTrainedModel,
  )

else:
 import sys

 __lowercase    					=      _LazyModule(__name__, globals()["""__file__"""], _import_structure)










"""simple docstring"""







from typing import TYPE_CHECKING

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


__lowercase    					=      {
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """RagModel""",
     """RagPreTrainedModel""",
     """RagSequenceForGeneration""",
     """RagTokenForGeneration""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TFRagModel""",
     """TFRagPreTrainedModel""",
     """TFRagSequenceForGeneration""",
     """TFRagTokenForGeneration""",
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

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






