

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCAmelCase		:   List[str]										=       {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:   int										=       [
					    """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CTRLForSequenceClassification""",
					    """CTRLLMHeadModel""",
					    """CTRLModel""",
					    """CTRLPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:   Dict										=       [
					    """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFCTRLForSequenceClassification""",
					    """TFCTRLLMHeadModel""",
					    """TFCTRLModel""",
					    """TFCTRLPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase		:   Dict										=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case             =     {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _snake_case             =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



