





from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_  ,snake_case_						:     Dict     =		9, 14  # noqa: F841
	snake_case_						:     Union[str, Any]     =		[
	    [0, 1, 4],
	    [0, 7, 8],
	    [1, 2, 8],
	    [7, 8, 7],
	    [7, 6, 1],
	    [2, 8, 2],
	    [8, 6, 6],
	    [2, 3, 7],
	    [2, 5, 4],
	    [6, 5, 2],
	    [3, 5, 14],
	    [3, 4, 9],
	    [5, 4, 10],
	    [1, 7, 11],
	]

	snake_case_						:     Any     =		defaultdict(__a				)
	for nodea, nodea, cost in edges:
		adjancency[nodea].append([nodea, cost]				)
		adjancency[nodea].append([nodea, cost]				)

	snake_case_						:     Any     =		mst(__a				)

	snake_case_						:     List[Any]     =		[
	    [7, 6, 1],
	    [2, 8, 2],
	    [6, 5, 2],
	    [0, 1, 4],
	    [2, 5, 4],
	    [2, 3, 7],
	    [0, 7, 8],
	    [3, 4, 9],
	]

	for answer in expected:
		snake_case_						:     Tuple     =		tuple(answer[:2]				)
		snake_case_						:     Optional[int]     =		tuple(edge[::-1]				)
		assert edge in result or reverse in result








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

