"""simple docstring"""






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase					: Any        ={
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: List[str]        =[
           """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XmodForCausalLM""",
           """XmodForMaskedLM""",
           """XmodForMultipleChoice""",
           """XmodForQuestionAnswering""",
           """XmodForSequenceClassification""",
           """XmodForTokenClassification""",
           """XmodModel""",
           """XmodPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xmod import (
                  XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XmodForCausalLM,
                  XmodForMaskedLM,
                  XmodForMultipleChoice,
                  XmodForQuestionAnswering,
                  XmodForSequenceClassification,
                  XmodForTokenClassification,
                  XmodModel,
                  XmodPreTrainedModel,
              )

else:
       import sys

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







"""simple docstring"""






from typing import TYPE_CHECKING

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


__lowerCAmelCase					: Dict        ={
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: int        =[
           """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XLMForMultipleChoice""",
           """XLMForQuestionAnswering""",
           """XLMForQuestionAnsweringSimple""",
           """XLMForSequenceClassification""",
           """XLMForTokenClassification""",
           """XLMModel""",
           """XLMPreTrainedModel""",
           """XLMWithLMHeadModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: Union[str, Any]        =[
           """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFXLMForMultipleChoice""",
           """TFXLMForQuestionAnsweringSimple""",
           """TFXLMForSequenceClassification""",
           """TFXLMForTokenClassification""",
           """TFXLMMainLayer""",
           """TFXLMModel""",
           """TFXLMPreTrainedModel""",
           """TFXLMWithLMHeadModel""",
       ]


if TYPE_CHECKING:
       from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
       from .tokenization_xlm import XLMTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm import (
                  XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMForMultipleChoice,
                  XLMForQuestionAnswering,
                  XLMForQuestionAnsweringSimple,
                  XLMForSequenceClassification,
                  XLMForTokenClassification,
                  XLMModel,
                  XLMPreTrainedModel,
                  XLMWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm import (
                  TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMForMultipleChoice,
                  TFXLMForQuestionAnsweringSimple,
                  TFXLMForSequenceClassification,
                  TFXLMForTokenClassification,
                  TFXLMMainLayer,
                  TFXLMModel,
                  TFXLMPreTrainedModel,
                  TFXLMWithLMHeadModel,
              )

else:
       import sys

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



