"""simple docstring"""





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

from packaging import version





def        a_  (   *_lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Tuple = None     ,				_lowerCAmelCase						:				str=True     ,				_lowerCAmelCase						:				Dict=2  ):





       '''simple docstring'''

       from .. import __version__

       lowercase__    :	Optional[Any]     					=			take_from
       lowercase__    :	Any     					=			()
       if not isinstance(args[0]     ,				lowerCamelCase__  ):
              lowercase__    :	Optional[int]     					=			(args,)

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

              lowercase__    :	List[Any]     					=			None
              if isinstance(lowerCamelCase__     ,				lowerCamelCase__  ) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(lowerCamelCase__  ),)
                     lowercase__    :	List[Any]     					=			f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
              elif hasattr(lowerCamelCase__     ,				lowerCamelCase__  ):
                     values += (getattr(lowerCamelCase__     ,				lowerCamelCase__  ),)
                     lowercase__    :	Any     					=			f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
              elif deprecated_kwargs is None:
                     lowercase__    :	Optional[int]     					=			f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

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

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

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


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

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

