



"""simple docstring"""

def __lowercase    (							_a					):  # noqa: E741
    snake_case_				:  Optional[int]       =			len(_a					)
    snake_case_				:  Union[str, Any]       =			0
    snake_case_				:  List[Any]       =			[0] * n
    snake_case_				:  Dict       =			[False] * n
    snake_case_				:  Dict       =			[False] * n

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

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                snake_case_				:  Optional[Any]       =			dfs(_a	,    _a	,    _a	,    _a					)
                snake_case_				:  Dict       =			min(low[at]	,    low[to]					)

                # AP found via bridge
                if at < low[to]:
                    snake_case_				:  Optional[Any]       =			True
                # AP found via cycle
                if at == low[to]:
                    snake_case_				:  Optional[Any]       =			True
            else:
                snake_case_				:  Any       =			min(low[at]	,    _a					)
        return out_edge_count

    for i in range(_a					):
        if not visited[i]:
            snake_case_				:  List[Any]       =			0
            snake_case_				:  Optional[Any]       =			dfs(_a	,    _a	,    -1	,    _a					)
            snake_case_				:  str       =			out_edge_count > 1

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


# Adjacency list of graph
lowercase__					:						Optional[Any]												=			{
    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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





