

'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase :      List[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_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      Any					 =     {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     ["FunnelTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[str]					 =     [
         "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FunnelBaseModel",
         "FunnelForMaskedLM",
         "FunnelForMultipleChoice",
         "FunnelForPreTraining",
         "FunnelForQuestionAnswering",
         "FunnelForSequenceClassification",
         "FunnelForTokenClassification",
         "FunnelModel",
         "FunnelPreTrainedModel",
         "load_tf_weights_in_funnel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFunnelBaseModel",
         "TFFunnelForMaskedLM",
         "TFFunnelForMultipleChoice",
         "TFFunnelForPreTraining",
         "TFFunnelForQuestionAnswering",
         "TFFunnelForSequenceClassification",
         "TFFunnelForTokenClassification",
         "TFFunnelModel",
         "TFFunnelPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_funnel_fast import FunnelTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_funnel import (
              FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              FunnelBaseModel,
              FunnelForMaskedLM,
              FunnelForMultipleChoice,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForSequenceClassification,
              FunnelForTokenClassification,
              FunnelModel,
              FunnelPreTrainedModel,
              load_tf_weights_in_funnel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_funnel import (
              TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFunnelBaseModel,
              TFFunnelForMaskedLM,
              TFFunnelForMultipleChoice,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForSequenceClassification,
              TFFunnelForTokenClassification,
              TFFunnelModel,
              TFFunnelPreTrainedModel,
          )

else:
     import sys

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