




def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   Dict=None   )						->			str:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Any   )						->			List[str]:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   List[Any]   )						->			Optional[Any]:

     """simple docstring"""




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





def      __a     (       )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
     _snake_case							=				{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
     _snake_case							=				{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
     _snake_case							=				{1: [2, 3], 2: [1, 3], 3: [1, 2]}
     _snake_case							=				{
         1: [],
         2: []
         # all degree is zero
     }
     _snake_case							=				10
     check_euler(a__ ,						a__   )
     check_euler(a__ ,						a__   )
     check_euler(a__ ,						a__   )
     check_euler(a__ ,						a__   )
     check_euler(a__ ,						a__   )


if __name__ == "__main__":
   main()








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_						:      List[Any]       =						{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[Any]       =						['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						[
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Tuple       =						[
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      Tuple       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


