



from typing import TYPE_CHECKING

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


__snake_case		:Tuple   	=  {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

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

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:int   	=  ['''XLNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:str   	=  [
     '''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:
 __snake_case		:Tuple   	=  [
     '''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

 __snake_case		:Union[str, Any]   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase = False       ):

    if not isinstance(_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       f'Expected string as input, found {type(_UpperCAmelCase       )}'
        raise ValueError(_UpperCAmelCase       )
    if not isinstance(_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       f'Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase       )}'
        raise ValueError(_UpperCAmelCase       )

    __a		 =       input_str.split('''_'''       )

    __a		 =       0 if use_pascal else 1

    __a		 =       words[start_index:]

    __a		 =       [word[0].upper() + word[1:] for word in words_to_capitalize]

    __a		 =       '''''' if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]       )


if __name__ == "__main__":
 from doctest import testmod

 testmod()





