"""simple docstring"""




from typing import TYPE_CHECKING

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


_a				:		Union[str, 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:
     _a				:		Optional[Any]	   =       ['FunnelTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Dict	   =       [
         '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:
     _a				:		int	   =       [
         '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

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





"""simple docstring"""




from __future__ import annotations





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	list     )	->				float:
  if not nums:
    raise ValueError("""List is empty"""     )
  return sum(_lowerCamelCase     ) / len(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




