code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{
      '''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:
  							snake_case_    						=				['''DebertaV2TokenizerFast''']
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							snake_case_    						=				[
  							    '''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:
  							snake_case_    						=				[
  							    '''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
  
  							snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  