code: 
  
  
  A__	:      List[str]				=						{
      0: """underwear""",
      1: """pants""",
      2: """belt""",
      3: """suit""",
      4: """shoe""",
      5: """socks""",
      6: """shirt""",
      7: """tie""",
      8: """watch""",
  }
  
  A__	:      Optional[Any]				=						[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  A__	:      Optional[Any]				=						[0 for x in range(len(graph))]
  A__	:      str				=						[]
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       Union[str, Any]							)  ->	int:
       __lowerCamelCase     :			Optional[int]              =	1
       while stack:
            __lowerCamelCase     :			List[Any]              =	stack.pop()
            print(UpperCAmelCase_   ,					clothes[current_clothing]							)
            order += 1
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Union[str, Any]							)  ->	Union[str, Any]:
       __lowerCamelCase     :			List[Any]              =	1
       for v in graph[u]:
            if not visited[v]:
                 depth_first_search(UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_							)
  
       stack.append(UpperCAmelCase_							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Union[str, Any]							)  ->	Optional[int]:
       for v in range(len(UpperCAmelCase_							)							):
            if not visited[v]:
                 depth_first_search(UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_							)
  
  
  if __name__ == "__main__":
        topological_sort(graph, visited)
        print(stack)
        print_stack(stack, clothes)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  A__	:      Optional[Any]				=						{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      Optional[Any]				=						[
            """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """XLMProphetNetDecoder""",
            """XLMProphetNetEncoder""",
            """XLMProphetNetForCausalLM""",
            """XLMProphetNetForConditionalGeneration""",
            """XLMProphetNetModel""",
            """XLMProphetNetPreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )
  
  else:
        import sys
  
        A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  