





from typing import TYPE_CHECKING

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


snake_case 				=       {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RobertaPreLayerNormForCausalLM""",
          """RobertaPreLayerNormForMaskedLM""",
          """RobertaPreLayerNormForMultipleChoice""",
          """RobertaPreLayerNormForQuestionAnswering""",
          """RobertaPreLayerNormForSequenceClassification""",
          """RobertaPreLayerNormForTokenClassification""",
          """RobertaPreLayerNormModel""",
          """RobertaPreLayerNormPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFRobertaPreLayerNormForCausalLM""",
          """TFRobertaPreLayerNormForMaskedLM""",
          """TFRobertaPreLayerNormForMultipleChoice""",
          """TFRobertaPreLayerNormForQuestionAnswering""",
          """TFRobertaPreLayerNormForSequenceClassification""",
          """TFRobertaPreLayerNormForTokenClassification""",
          """TFRobertaPreLayerNormMainLayer""",
          """TFRobertaPreLayerNormModel""",
          """TFRobertaPreLayerNormPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxRobertaPreLayerNormForCausalLM""",
          """FlaxRobertaPreLayerNormForMaskedLM""",
          """FlaxRobertaPreLayerNormForMultipleChoice""",
          """FlaxRobertaPreLayerNormForQuestionAnswering""",
          """FlaxRobertaPreLayerNormForSequenceClassification""",
          """FlaxRobertaPreLayerNormForTokenClassification""",
          """FlaxRobertaPreLayerNormModel""",
          """FlaxRobertaPreLayerNormPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RobertaPreLayerNormConfig,
          RobertaPreLayerNormOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta_prelayernorm import (
                ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaPreLayerNormForCausalLM,
                RobertaPreLayerNormForMaskedLM,
                RobertaPreLayerNormForMultipleChoice,
                RobertaPreLayerNormForQuestionAnswering,
                RobertaPreLayerNormForSequenceClassification,
                RobertaPreLayerNormForTokenClassification,
                RobertaPreLayerNormModel,
                RobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta_prelayernorm import (
                TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaPreLayerNormForCausalLM,
                TFRobertaPreLayerNormForMaskedLM,
                TFRobertaPreLayerNormForMultipleChoice,
                TFRobertaPreLayerNormForQuestionAnswering,
                TFRobertaPreLayerNormForSequenceClassification,
                TFRobertaPreLayerNormForTokenClassification,
                TFRobertaPreLayerNormMainLayer,
                TFRobertaPreLayerNormModel,
                TFRobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta_prelayernorm import (
                FlaxRobertaPreLayerNormForCausalLM,
                FlaxRobertaPreLayerNormForMaskedLM,
                FlaxRobertaPreLayerNormForMultipleChoice,
                FlaxRobertaPreLayerNormForQuestionAnswering,
                FlaxRobertaPreLayerNormForSequenceClassification,
                FlaxRobertaPreLayerNormForTokenClassification,
                FlaxRobertaPreLayerNormModel,
                FlaxRobertaPreLayerNormPreTrainedModel,
            )

else:
      import sys

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






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Dict						,*__A	:	Optional[int]						,**__A	:	List[Any]  )						->					None:
         warnings.warn(
             'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DonutImageProcessor instead.'						,__A						,)
         super().__init__(*__A						,**__A  )