
_lowerCamelCase           ={
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

_lowerCamelCase           =[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase           =[0 for x in range(len(graph))]
_lowerCamelCase           =[]





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Optional[int]           =   1
 while stack:
  lowerCamelCase	:			List[str]           =   stack.pop()
  print(lowerCamelCase,			clothes[current_clothing]							)
  order += 1





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			List[Any]           =   1
 for v in graph[u]:
  if not visited[v]:
   depth_first_search(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

 stack.append(lowerCamelCase							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 for v in range(len(lowerCamelCase							)							):
  if not visited[v]:
   depth_first_search(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)


if __name__ == "__main__":
      topological_sort(graph, visited)
      print(stack)
      print_stack(stack, clothes)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase           ={
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =["""GPT2TokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =[
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =[
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]

try:
      if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =["""TFGPT2Tokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_fast import GPTaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpta import (
                GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTaDoubleHeadsModel,
                GPTaForQuestionAnswering,
                GPTaForSequenceClassification,
                GPTaForTokenClassification,
                GPTaLMHeadModel,
                GPTaModel,
                GPTaPreTrainedModel,
                load_tf_weights_in_gpta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_gpta import (
                TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGPTaDoubleHeadsModel,
                TFGPTaForSequenceClassification,
                TFGPTaLMHeadModel,
                TFGPTaMainLayer,
                TFGPTaModel,
                TFGPTaPreTrainedModel,
            )

      try:
            if not is_keras_nlp_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_tf import TFGPTaTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
      import sys

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




