


from typing import TYPE_CHECKING

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


__UpperCamelCase    		=       {
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       ['MobileNetV2FeatureExtractor']
						__UpperCamelCase    		=       ['MobileNetV2ImageProcessor']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV2ForImageClassification',
						    'MobileNetV2ForSemanticSegmentation',
						    'MobileNetV2Model',
						    'MobileNetV2PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v2',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						__UpperCamelCase    		=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from collections import deque
def        UpperCamelCase_(				_A   :Union[str, Any]  )->		List[Any]:
			UpperCamelCase__					=       len(_A  )
			UpperCamelCase__					=       deque()
			UpperCamelCase__					=       [False for _ in range(_A  )]
			UpperCamelCase__					=       [-1 for _ in range(_A  )]
			UpperCamelCase__					=       index_of[:]

			def strong_connect(_A   :Optional[int]				,     _A   :Optional[int]				,     _A   :List[Any]  ):
						UpperCamelCase__					=       index  # the number when this node is seen
						UpperCamelCase__					=       index  # lowest rank node reachable from here
						index += 1
						stack.append(_A  )
						UpperCamelCase__					=       True

						for w in g[v]:
									if index_of[w] == -1:
												UpperCamelCase__					=       strong_connect(_A				,     _A				,     _A  )
												UpperCamelCase__					=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)
									elif on_stack[w]:
												UpperCamelCase__					=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)

						if lowlink_of[v] == index_of[v]:
									UpperCamelCase__					=       []
									UpperCamelCase__					=       stack.pop()
									UpperCamelCase__					=       False
									component.append(_A  )
									while w != v:
												UpperCamelCase__					=       stack.pop()
												UpperCamelCase__					=       False
												component.append(_A  )
									components.append(_A  )
						return index

			UpperCamelCase__					=       []
			for v in range(_A  ):
						if index_of[v] == -1:
									strong_connect(_A				,     0				,     _A  )

			return components
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Any  )->		Union[str, Any]:
			UpperCamelCase__					=       [[] for _ in range(_A  )]
			for u, v in edges:
						g[u].append(_A  )
			return g


if __name__ == "__main__":
						# Test
						__UpperCamelCase    		=       7
						__UpperCamelCase    		=       [0, 0, 1, 2, 3, 3, 4, 4, 6]
						__UpperCamelCase    		=       [1, 3, 2, 0, 1, 4, 5, 6, 5]
						__UpperCamelCase    		=       [(u, v) for u, v in zip(source, target)]
						__UpperCamelCase    		=       create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


