


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :      List[Any]												= {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      List[Any]												= ["XLNetTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      List[Any]												= ["XLNetTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= [
     "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLNetForMultipleChoice",
     "XLNetForQuestionAnswering",
     "XLNetForQuestionAnsweringSimple",
     "XLNetForSequenceClassification",
     "XLNetForTokenClassification",
     "XLNetLMHeadModel",
     "XLNetModel",
     "XLNetPreTrainedModel",
     "load_tf_weights_in_xlnet",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLNetForMultipleChoice",
     "TFXLNetForQuestionAnsweringSimple",
     "TFXLNetForSequenceClassification",
     "TFXLNetForTokenClassification",
     "TFXLNetLMHeadModel",
     "TFXLNetMainLayer",
     "TFXLNetModel",
     "TFXLNetPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlnet import XLNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlnet_fast import XLNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlnet import (
      XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLNetForMultipleChoice,
      XLNetForQuestionAnswering,
      XLNetForQuestionAnsweringSimple,
      XLNetForSequenceClassification,
      XLNetForTokenClassification,
      XLNetLMHeadModel,
      XLNetModel,
      XLNetPreTrainedModel,
      load_tf_weights_in_xlnet,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlnet import (
      TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLNetForMultipleChoice,
      TFXLNetForQuestionAnsweringSimple,
      TFXLNetForSequenceClassification,
      TFXLNetForTokenClassification,
      TFXLNetLMHeadModel,
      TFXLNetMainLayer,
      TFXLNetModel,
      TFXLNetPreTrainedModel,
  )

else:
 import sys

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






from __future__ import annotations



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						bool:
    if len(lowerCamelCase_ ) < 2:
        raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
    if any(i <= 0 for i in nums ):
        raise ValueError('All values must be greater than 0' )
    _lowercase       :							Tuple      		=		nums.copy()
    copy_nums.sort()
    return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


