






# using dfs for finding eulerian path traversal



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]      ,					__lowercase :      Tuple      ,					__lowercase :      Optional[int]      ,					__lowercase :      Dict=None     )    ->   Union[str, Any]:





    """simple docstring"""

    __A       =						(path or []) + [u]
    for v in graph[u]:
        if visited_edge[u][v] is False:
            __A     ,						__A       =						True, True
            __A       =						dfs(__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase     )
    return path



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Optional[Any]     )    ->   int:





    """simple docstring"""

    __A       =						0
    __A       =						-1
    for i in range(__lowercase     ):
        if i not in graph.keys():
            continue
        if len(graph[i]     ) % 2 == 1:
            odd_degree_nodes += 1
            __A       =						i
    if odd_degree_nodes == 0:
        return 1, odd_node
    if odd_degree_nodes == 2:
        return 2, odd_node
    return 3, odd_node



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Any     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
    __A     ,						__A       =						check_circuit_or_path(__lowercase      ,					__lowercase     )
    if check == 3:
        print("""graph is not Eulerian"""     )
        print("""no path"""     )
        return
    __A       =						1
    if check == 2:
        __A       =						odd_node
        print("""graph has a Euler path"""     )
    if check == 1:
        print("""graph has a Euler cycle"""     )
    __A       =						dfs(__lowercase      ,					__lowercase      ,					__lowercase     )
    print(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Optional[Any]:





    """simple docstring"""

    __A       =						{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
    __A       =						{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
    __A       =						{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
    __A       =						{1: [2, 3], 2: [1, 3], 3: [1, 2]}
    __A       =						{
        1: [],
        2: []
        # all degree is zero
    }
    __A       =						1_0
    check_euler(__lowercase      ,					__lowercase     )
    check_euler(__lowercase      ,					__lowercase     )
    check_euler(__lowercase      ,					__lowercase     )
    check_euler(__lowercase      ,					__lowercase     )
    check_euler(__lowercase      ,					__lowercase     )


if __name__ == "__main__":
  main()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a   :							Optional[int]						= {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Tuple						= ["XLNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							List[str]						= ["XLNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Any						= [
      "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "XLNetForMultipleChoice",
      "XLNetForQuestionAnswering",
      "XLNetForQuestionAnsweringSimple",
      "XLNetForSequenceClassification",
      "XLNetForTokenClassification",
      "XLNetLMHeadModel",
      "XLNetModel",
      "XLNetPreTrainedModel",
      "load_tf_weights_in_xlnet",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFXLNetForMultipleChoice",
      "TFXLNetForQuestionAnsweringSimple",
      "TFXLNetForSequenceClassification",
      "TFXLNetForTokenClassification",
      "TFXLNetLMHeadModel",
      "TFXLNetMainLayer",
      "TFXLNetModel",
      "TFXLNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




