

"""simple docstring"""


from collections.abc import Iterable
from typing import Generic, TypeVar

__A						       =      TypeVar("_T")





class    snake_case     (    Generic[_T]  ):

				def __init__(     self							:    Dict					,  UpperCamelCase__							:    Iterable[_T] | None = None)->       None:



						'''simple docstring'''


						__lowerCAmelCase:	list[_T]   				=      list(iterable or [])
						__lowerCAmelCase:	list[_T]   				=      []

				def __len__(     self							:    Dict)->       int:



						'''simple docstring'''


						return len(self._stacka) + len(self._stacka)

				def __repr__(     self							:    List[Any])->       str:



						'''simple docstring'''


						return f"Queue({tuple(self._stacka[::-1] + self._stacka)})"

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    _T)->       None:



						'''simple docstring'''


						self._stacka.append(UpperCamelCase__)




				def 							lowercase_					(     self							:    List[Any])->       _T:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self._stacka.pop
						__lowerCAmelCase:	Any   				=      self._stacka.append

						if not self._stacka:
								while self._stacka:
										stacka_append(stacka_pop())

						if not self._stacka:
								raise IndexError("Queue is empty")
						return self._stacka.pop()


if __name__ == "__main__":
			from doctest import testmod

			testmod()





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						       =      {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Swinv2ForImageClassification",
			    "Swinv2ForMaskedImageModeling",
			    "Swinv2Model",
			    "Swinv2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


