code: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
  }
  
  try:
         if not is_sentencepiece_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __snake_case							      =						["""CamembertTokenizer"""]
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __snake_case							      =						["""CamembertTokenizerFast"""]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __snake_case							      =						[
             """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """CamembertForCausalLM""",
             """CamembertForMaskedLM""",
             """CamembertForMultipleChoice""",
             """CamembertForQuestionAnswering""",
             """CamembertForSequenceClassification""",
             """CamembertForTokenClassification""",
             """CamembertModel""",
             """CamembertPreTrainedModel""",
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __snake_case							      =						[
             """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """TFCamembertForCausalLM""",
             """TFCamembertForMaskedLM""",
             """TFCamembertForMultipleChoice""",
             """TFCamembertForQuestionAnswering""",
             """TFCamembertForSequenceClassification""",
             """TFCamembertForTokenClassification""",
             """TFCamembertModel""",
             """TFCamembertPreTrainedModel""",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
         try:
                if not is_sentencepiece_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_camembert import CamembertTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_camembert_fast import CamembertTokenizerFast
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_camembert import (
                    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                    CamembertForCausalLM,
                    CamembertForMaskedLM,
                    CamembertForMultipleChoice,
                    CamembertForQuestionAnswering,
                    CamembertForSequenceClassification,
                    CamembertForTokenClassification,
                    CamembertModel,
                    CamembertPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_camembert import (
                    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFCamembertForCausalLM,
                    TFCamembertForMaskedLM,
                    TFCamembertForMultipleChoice,
                    TFCamembertForQuestionAnswering,
                    TFCamembertForSequenceClassification,
                    TFCamembertForTokenClassification,
                    TFCamembertModel,
                    TFCamembertPreTrainedModel,
                )
  
  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_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  