


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)










'''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__)






