





"""simple docstring"""






from __future__ import annotations

from random import choice

def      __A   (      a_    :Tuple)		->		List[str]:
				return choice(a_)

def      __A   (      a_    :list[int] ,							a_    :int)		->		int:
				__a			:    Optional[int]      							=	random_pivot(a_)

				# partition based on pivot
				# linear time
				__a			:    Union[str, Any]      							=	[e for e in lst if e < pivot]
				__a			:    Any      							=	[e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(a_) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(a_) < k - 1:
								return kth_number(a_ ,							k - len(a_) - 1)
				# pivot is in elements smaller than k
				else:
								return kth_number(a_ ,							a_)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

'''simple docstring'''
from typing import TYPE_CHECKING

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


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


