





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:  # noqa: E741




    """simple docstring"""




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

    def dfs(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        if parent == root:
            out_edge_count += 1
        _A     =							True
        _A     =							at

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

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

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

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


# Adjacency list of graph
__A							:			str         =			{
    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)












from typing import TYPE_CHECKING

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


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

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





