
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						: List[str]         =  {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: str         =  [
							    """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FocalNetForImageClassification""",
							    """FocalNetForMaskedImageModeling""",
							    """FocalNetBackbone""",
							    """FocalNetModel""",
							    """FocalNetPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_focalnet import (
														    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FocalNetBackbone,
														    FocalNetForImageClassification,
														    FocalNetForMaskedImageModeling,
														    FocalNetModel,
														    FocalNetPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase_						: str         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	dict:
				UpperCamelCase_:							dict	=						{i: [] for i in range(lowerCamelCase						)}

				# if probability is greater or equal than 1, then generate a complete graph
				if probability >= 1:
								return complete_graph(lowerCamelCase						)
				# 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(lowerCamelCase						):
								for j in range(i + 1     ,    lowerCamelCase						):
												if random.random() < probability:
																graph[i].append(lowerCamelCase						)
																if not directed:
																				# if the graph is undirected, add an edge in from j to i, either
																				graph[j].append(lowerCamelCase						)
				return graph







def        A__					(						lowerCamelCase						)  ->	dict:
				return {
				    i: [j for j in range(lowerCamelCase						) if i != j] for i in range(lowerCamelCase						)
				}


if __name__ == "__main__":
							import doctest

							doctest.testmod()






