code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCAmelCase					 =	{
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	['''DebertaV2TokenizerFast''']
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''TFDebertaV2ForMaskedLM''',
  	    '''TFDebertaV2ForQuestionAnswering''',
  	    '''TFDebertaV2ForSequenceClassification''',
  	    '''TFDebertaV2ForTokenClassification''',
  	    '''TFDebertaV2Model''',
  	    '''TFDebertaV2PreTrainedModel''',
  	]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''DebertaV2ForMaskedLM''',
  	    '''DebertaV2ForMultipleChoice''',
  	    '''DebertaV2ForQuestionAnswering''',
  	    '''DebertaV2ForSequenceClassification''',
  	    '''DebertaV2ForTokenClassification''',
  	    '''DebertaV2Model''',
  	    '''DebertaV2PreTrainedModel''',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_deberta_va import (
  	    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    DebertaVaConfig,
  	    DebertaVaOnnxConfig,
  	)
  	from .tokenization_deberta_va import DebertaVaTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_deberta_va import (
  		    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFDebertaVaForMaskedLM,
  		    TFDebertaVaForQuestionAnswering,
  		    TFDebertaVaForSequenceClassification,
  		    TFDebertaVaForTokenClassification,
  		    TFDebertaVaModel,
  		    TFDebertaVaPreTrainedModel,
  		)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_deberta_va import (
  		    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    DebertaVaForMaskedLM,
  		    DebertaVaForMultipleChoice,
  		    DebertaVaForQuestionAnswering,
  		    DebertaVaForSequenceClassification,
  		    DebertaVaForTokenClassification,
  		    DebertaVaModel,
  		    DebertaVaPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	lowerCAmelCase					 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return " ".join([word[::-1] for word in input_str.split()]			)
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  