


'''simple docstring'''


from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__magic_name__			=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['GPTNeoXTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
         'GPTNeoXForCausalLM',
         'GPTNeoXForQuestionAnswering',
         'GPTNeoXForSequenceClassification',
         'GPTNeoXForTokenClassification',
         'GPTNeoXLayer',
         'GPTNeoXModel',
         'GPTNeoXPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neox import (
              GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoXForCausalLM,
              GPTNeoXForQuestionAnswering,
              GPTNeoXForSequenceClassification,
              GPTNeoXForTokenClassification,
              GPTNeoXLayer,
              GPTNeoXModel,
              GPTNeoXPreTrainedModel,
          )


else:
     import sys

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




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # 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(lowerCamelCase)
      return explored


__magic_name__			=      {
    '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'))
