
'''simple docstring'''
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):  # noqa: E741
    """simple docstring"""






    snake_case_ :    Dict     			=						len(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Dict     			=						0
    snake_case_ :    Dict     			=						[0] * n
    snake_case_ :    Optional[int]     			=						[False] * n
    snake_case_ :    List[Any]     			=						[False] * n

    def dfs(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
        if parent == root:
            out_edge_count += 1
        snake_case_ :    int     			=						True
        snake_case_ :    int     			=						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                snake_case_ :    str     			=						dfs(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                snake_case_ :    Tuple     			=						min(low[at],		low[to]       )

                # AP found via bridge
                if at < low[to]:
                    snake_case_ :    str     			=						True
                # AP found via cycle
                if at == low[to]:
                    snake_case_ :    int     			=						True
            else:
                snake_case_ :    Tuple     			=						min(low[at],		__SCREAMING_SNAKE_CASE       )
        return out_edge_count

    for i in range(__SCREAMING_SNAKE_CASE       ):
        if not visited[i]:
            snake_case_ :    Optional[int]     			=						0
            snake_case_ :    Optional[int]     			=						dfs(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		-1,		__SCREAMING_SNAKE_CASE       )
            snake_case_ :    int     			=						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_											=						{
    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_tokenizers_available, is_torch_available


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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




