
from typing import TYPE_CHECKING

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


a__				:      List[str]    		=     {'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      Tuple    		=     ['''ViltFeatureExtractor''']
      a__				:      Union[str, Any]    		=     ['''ViltImageProcessor''']
      a__				:      Optional[int]    		=     ['''ViltProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      Optional[int]    		=     [
          '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ViltForImageAndTextRetrieval''',
          '''ViltForImagesAndTextClassification''',
          '''ViltForTokenClassification''',
          '''ViltForMaskedLM''',
          '''ViltForQuestionAnswering''',
          '''ViltLayer''',
          '''ViltModel''',
          '''ViltPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_vilt import ViltFeatureExtractor
            from .image_processing_vilt import ViltImageProcessor
            from .processing_vilt import ViltProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vilt import (
                VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                ViltForImageAndTextRetrieval,
                ViltForImagesAndTextClassification,
                ViltForMaskedLM,
                ViltForQuestionAnswering,
                ViltForTokenClassification,
                ViltLayer,
                ViltModel,
                ViltPreTrainedModel,
            )


else:
      import sys

      a__				:      int    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






'''simple docstring'''






from typing import TYPE_CHECKING

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


lowercase						:   int      						=							{
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Any      						=							['''XLMProphetNetTokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Tuple      						=							[
          '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMProphetNetDecoder''',
          '''XLMProphetNetEncoder''',
          '''XLMProphetNetForCausalLM''',
          '''XLMProphetNetForConditionalGeneration''',
          '''XLMProphetNetModel''',
          '''XLMProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

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




