

from typing import TYPE_CHECKING

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


a_     =			{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			['RoFormerTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'RoFormerForCausalLM',
			    'RoFormerForMaskedLM',
			    'RoFormerForMultipleChoice',
			    'RoFormerForQuestionAnswering',
			    'RoFormerForSequenceClassification',
			    'RoFormerForTokenClassification',
			    'RoFormerLayer',
			    'RoFormerModel',
			    'RoFormerPreTrainedModel',
			    'load_tf_weights_in_roformer',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFRoFormerForCausalLM',
			    'TFRoFormerForMaskedLM',
			    'TFRoFormerForMultipleChoice',
			    'TFRoFormerForQuestionAnswering',
			    'TFRoFormerForSequenceClassification',
			    'TFRoFormerForTokenClassification',
			    'TFRoFormerLayer',
			    'TFRoFormerModel',
			    'TFRoFormerPreTrainedModel',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaxRoFormerForMaskedLM',
			    'FlaxRoFormerForMultipleChoice',
			    'FlaxRoFormerForQuestionAnswering',
			    'FlaxRoFormerForSequenceClassification',
			    'FlaxRoFormerForTokenClassification',
			    'FlaxRoFormerModel',
			    'FlaxRoFormerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
			from .tokenization_roformer import RoFormerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roformer_fast import RoFormerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roformer import (
						    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoFormerForCausalLM,
						    RoFormerForMaskedLM,
						    RoFormerForMultipleChoice,
						    RoFormerForQuestionAnswering,
						    RoFormerForSequenceClassification,
						    RoFormerForTokenClassification,
						    RoFormerLayer,
						    RoFormerModel,
						    RoFormerPreTrainedModel,
						    load_tf_weights_in_roformer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roformer import (
						    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerLayer,
						    TFRoFormerModel,
						    TFRoFormerPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roformer import (
						    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						    FlaxRoFormerPreTrainedModel,
						)


else:
			import sys

			a_     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



'''simple docstring'''



from typing import TYPE_CHECKING

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


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

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






