

'''simple docstring'''
def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			int:  # noqa: E741
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =len(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =[0] * n
     _SCREAMING_SNAKE_CASE							    =[False] * n
     _SCREAMING_SNAKE_CASE							    =[False] * n

     def dfs(_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Any							, _UpperCamelCase						:     List[str]				):
          if parent == root:
               out_edge_count += 1
          _SCREAMING_SNAKE_CASE							    =True
          _SCREAMING_SNAKE_CASE							    =at

          for to in l[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    _SCREAMING_SNAKE_CASE							    =dfs(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)
                    _SCREAMING_SNAKE_CASE							    =min(low[at]							, low[to]				)

                    # AP found via bridge
                    if at < low[to]:
                         _SCREAMING_SNAKE_CASE							    =True
                    # AP found via cycle
                    if at == low[to]:
                         _SCREAMING_SNAKE_CASE							    =True
               else:
                    _SCREAMING_SNAKE_CASE							    =min(low[at]							, _UpperCamelCase				)
          return out_edge_count

     for i in range(_UpperCamelCase				):
          if not visited[i]:
               _SCREAMING_SNAKE_CASE							    =0
               _SCREAMING_SNAKE_CASE							    =dfs(_UpperCamelCase							, _UpperCamelCase							, -1							, _UpperCamelCase				)
               _SCREAMING_SNAKE_CASE							    =out_edge_count > 1

     for x in range(len(_UpperCamelCase				)				):
          if is_art[x] is True:
               print(_UpperCamelCase				)


# Adjacency list of graph
lowerCamelCase :      int					 =     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase :      Any					 =     {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     ["FunnelTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[str]					 =     [
         "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FunnelBaseModel",
         "FunnelForMaskedLM",
         "FunnelForMultipleChoice",
         "FunnelForPreTraining",
         "FunnelForQuestionAnswering",
         "FunnelForSequenceClassification",
         "FunnelForTokenClassification",
         "FunnelModel",
         "FunnelPreTrainedModel",
         "load_tf_weights_in_funnel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFunnelBaseModel",
         "TFFunnelForMaskedLM",
         "TFFunnelForMultipleChoice",
         "TFFunnelForPreTraining",
         "TFFunnelForQuestionAnswering",
         "TFFunnelForSequenceClassification",
         "TFFunnelForTokenClassification",
         "TFFunnelModel",
         "TFFunnelPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_funnel_fast import FunnelTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_funnel import (
              FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              FunnelBaseModel,
              FunnelForMaskedLM,
              FunnelForMultipleChoice,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForSequenceClassification,
              FunnelForTokenClassification,
              FunnelModel,
              FunnelPreTrainedModel,
              load_tf_weights_in_funnel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_funnel import (
              TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFunnelBaseModel,
              TFFunnelForMaskedLM,
              TFFunnelForMultipleChoice,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForSequenceClassification,
              TFFunnelForTokenClassification,
              TFFunnelModel,
              TFFunnelPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      Optional[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
