'''simple docstring'''




from typing import TYPE_CHECKING

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


_UpperCamelCase					:		Optional[Any]							      =		{
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase					:		List[Any]							      =		[
         '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:
     _UpperCamelCase					:		str							      =		[
         '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

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







import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)

