





'''simple docstring'''






def       lowercase_      (		lowercase__						)   ->Tuple:  # noqa: E741
						_snake_case:   Optional[Any]  					=		len(lowerCAmelCase_						)
						_snake_case:   Optional[Any]  					=		0
						_snake_case:   Union[str, Any]  					=		[0] * n
						_snake_case:   List[Any]  					=		[False] * n
						_snake_case:   List[str]  					=		[False] * n

						def dfs(lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						):
												if parent == root:
																		out_edge_count += 1
												_snake_case:   Union[str, Any]  					=		True
												_snake_case:   Any  					=		at

												for to in l[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								_snake_case:   Optional[Any]  					=		dfs(lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_						)
																								_snake_case:   Optional[Any]  					=		min(low[at]				,  low[to]						)

																								# AP found via bridge
																								if at < low[to]:
																														_snake_case:   Optional[Any]  					=		True
																								# AP found via cycle
																								if at == low[to]:
																														_snake_case:   List[str]  					=		True
																		else:
																								_snake_case:   Dict  					=		min(low[at]				,  lowerCAmelCase_						)
												return out_edge_count

						for i in range(lowerCAmelCase_						):
												if not visited[i]:
																		_snake_case:   List[Any]  					=		0
																		_snake_case:   Optional[int]  					=		dfs(lowerCAmelCase_				,  lowerCAmelCase_				,  -1				,  lowerCAmelCase_						)
																		_snake_case:   str  					=		out_edge_count > 1

						for x in range(len(lowerCAmelCase_						)						):
												if is_art[x] is True:
																		print(lowerCAmelCase_						)


# Adjacency list of graph
A   : Dict             =			{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)












'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		A   : Union[str, Any]             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





