"""simple docstring"""



import random

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase = False							):
							A__       :     dict								=			{i: [] for i in range(A_							)}

							# if probability is greater or equal than 1, then generate a complete graph
							if probability >= 1:
														return complete_graph(A_							)
							# if probability is lower or equal than 0, then return a graph without edges
							if probability <= 0:
														return graph

							# for each couple of nodes, add an edge from u to v
							# if the number randomly generated is greater than probability probability
							for i in range(A_							):
														for j in range(i + 1			,  A_							):
																					if random.random() < probability:
																												graph[i].append(A_							)
																												if not directed:
																																			# if the graph is undirected, add an edge in from j to i, either
																																			graph[j].append(A_							)
							return graph

def  _A(		lowerCAmelCase							):
							return {
							    i: [j for j in range(A_							) if i != j] for i in range(A_							)
							}


if __name__ == "__main__":
				import doctest

				doctest.testmod()








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    : str				 =						{
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : List[Any]				 =						[
							    '''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

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




