



"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : List[str]							=							{
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[Any]							=							["ConvBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : str							=							[
     "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConvBertForMaskedLM",
     "ConvBertForMultipleChoice",
     "ConvBertForQuestionAnswering",
     "ConvBertForSequenceClassification",
     "ConvBertForTokenClassification",
     "ConvBertLayer",
     "ConvBertModel",
     "ConvBertPreTrainedModel",
     "load_tf_weights_in_convbert",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFConvBertForMaskedLM",
     "TFConvBertForMultipleChoice",
     "TFConvBertForQuestionAnswering",
     "TFConvBertForSequenceClassification",
     "TFConvBertForTokenClassification",
     "TFConvBertLayer",
     "TFConvBertModel",
     "TFConvBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

 _lowerCAmelCase    : List[Any]							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










"""simple docstring"""




import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 						__snake_case						(				*SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Union[Dict, Any]] = None						,  SCREAMING_SNAKE_CASE__     :						int=True						,  SCREAMING_SNAKE_CASE__     :						Tuple=2    )  ->       Optional[Any]:





     '''simple docstring'''

     from .. import __version__

     _UpperCAmelCase      :     Tuple        =   take_from
     _UpperCAmelCase      :     Optional[int]        =   ()
     if not isinstance(args[0]						,  SCREAMING_SNAKE_CASE__    ):
          _UpperCAmelCase      :     int        =   (args,)

     for attribute, version_name, message in args:
          if version.parse(version.parse(SCREAMING_SNAKE_CASE__    ).base_version    ) >= version.parse(SCREAMING_SNAKE_CASE__    ):
               raise ValueError(
                   f'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                   f' version {__version__} is >= {version_name}'    )

          _UpperCAmelCase      :     Any        =   None
          if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ) and attribute in deprecated_kwargs:
               values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__    ),)
               _UpperCAmelCase      :     Tuple        =   f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
          elif hasattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
               values += (getattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ),)
               _UpperCAmelCase      :     str        =   f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
          elif deprecated_kwargs is None:
               _UpperCAmelCase      :     Tuple        =   f'`{attribute}` is deprecated and will be removed in version {version_name}.'

          if warning is not None:
               _UpperCAmelCase      :     Optional[int]        =   warning + " " if standard_warn else ""
               warnings.warn(warning + message						,  SCREAMING_SNAKE_CASE__						,  stacklevel=SCREAMING_SNAKE_CASE__    )

     if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ) and len(SCREAMING_SNAKE_CASE__    ) > 0:
          _UpperCAmelCase      :     List[Any]        =   inspect.getouterframes(inspect.currentframe()    )[1]
          _UpperCAmelCase      :     Optional[int]        =   call_frame.filename
          _UpperCAmelCase      :     Dict        =   call_frame.lineno
          _UpperCAmelCase      :     List[Any]        =   call_frame.function
          _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   next(iter(deprecated_kwargs.items()    )    )
          raise TypeError(f'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'    )

     if len(SCREAMING_SNAKE_CASE__    ) == 0:
          return
     elif len(SCREAMING_SNAKE_CASE__    ) == 1:
          return values[0]
     return values
