



"""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





"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RoFormerForCausalLM",
     "RoFormerForMaskedLM",
     "RoFormerForMultipleChoice",
     "RoFormerForQuestionAnswering",
     "RoFormerForSequenceClassification",
     "RoFormerForTokenClassification",
     "RoFormerLayer",
     "RoFormerModel",
     "RoFormerPreTrainedModel",
     "load_tf_weights_in_roformer",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[int]							=							[
     "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRoFormerForCausalLM",
     "TFRoFormerForMaskedLM",
     "TFRoFormerForMultipleChoice",
     "TFRoFormerForQuestionAnswering",
     "TFRoFormerForSequenceClassification",
     "TFRoFormerForTokenClassification",
     "TFRoFormerLayer",
     "TFRoFormerModel",
     "TFRoFormerPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : List[str]							=							[
     "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxRoFormerForMaskedLM",
     "FlaxRoFormerForMultipleChoice",
     "FlaxRoFormerForQuestionAnswering",
     "FlaxRoFormerForSequenceClassification",
     "FlaxRoFormerForTokenClassification",
     "FlaxRoFormerModel",
     "FlaxRoFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

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