
"""simple docstring"""

from __future__ import annotations






def 							A		(       snake_case   :dict       ,      snake_case   :str	)   ->			set[str]:
 __UpperCamelCase						,			__UpperCamelCase       						= set(snake_case	), [start]

 while stack:
  __UpperCamelCase       						= stack.pop()
  explored.add(snake_case	)
  # Differences from BFS:
  # 1) pop last element instead of first one
  # 2) add adjacent elements to stack without exploring them
  for adj in reversed(graph[v]	):
   if adj not in explored:
    stack.append(snake_case	)
 return explored


UpperCamelCase			:       Any									=				{
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(depth_first_search(G, "A"))





"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase			:       List[str]									=				{
    "configuration_autoformer": [
        "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AutoformerConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Dict									=				[
          "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AutoformerForPrediction",
          "AutoformerModel",
          "AutoformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       int									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



