


"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						_a				(      *_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = None				, _SCREAMING_SNAKE_CASE=True				, _SCREAMING_SNAKE_CASE=2				)    ->				List[str]:
							from .. import __version__

							snake_case_           =		take_from
							snake_case_           =		()
							if not isinstance(args[0]				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		(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}"""				)

														snake_case_           =		None
														if isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				) and attribute in deprecated_kwargs:
																					values += (deprecated_kwargs.pop(_SCREAMING_SNAKE_CASE				),)
																					snake_case_           =		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				),)
																					snake_case_           =		f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
														elif deprecated_kwargs is None:
																					snake_case_           =		f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

														if warning is not None:
																					snake_case_           =		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:
														snake_case_           =		inspect.getouterframes(inspect.currentframe()				)[1]
														snake_case_           =		call_frame.filename
														snake_case_           =		call_frame.lineno
														snake_case_           =		call_frame.function
														snake_case_       ,						snake_case_           =		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_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    [
				    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BioGptForCausalLM',
				    'BioGptForTokenClassification',
				    'BioGptForSequenceClassification',
				    'BioGptModel',
				    'BioGptPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
				from .tokenization_biogpt import BioGptTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_biogpt import (
								    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BioGptForCausalLM,
								    BioGptForSequenceClassification,
								    BioGptForTokenClassification,
								    BioGptModel,
								    BioGptPreTrainedModel,
								)


else:
				import sys

				__SCREAMING_SNAKE_CASE							:    Dict		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



