






from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()













from typing import TYPE_CHECKING

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


UpperCAmelCase__      			=						{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						[
			    '''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

			UpperCAmelCase__      			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)





