





def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      return [
          {
              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],
          },
          {
              0: [6],
              1: [9],
              2: [4, 5],
              3: [4],
              4: [2, 3],
              5: [2],
              6: [0, 7],
              7: [6],
              8: [],
              9: [1],
          },
          {
              0: [4],
              1: [6],
              2: [],
              3: [5, 6, 7],
              4: [0, 6],
              5: [3, 8, 9],
              6: [1, 3, 4, 7],
              7: [3, 6, 8, 9],
              8: [5, 7],
              9: [5, 7],
          },
          {
              0: [1, 3],
              1: [0, 2, 4],
              2: [1, 3, 4],
              3: [0, 2, 4],
              4: [1, 2, 3],
          },
      ][index]



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   list[tuple[int, int]]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE	)  # No of vertices in graph
      SCREAMING_SNAKE_CASE		=						[0] * n
      SCREAMING_SNAKE_CASE		=						[False] * n

      def dfs(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	):
            SCREAMING_SNAKE_CASE		=						True
            SCREAMING_SNAKE_CASE		=						id_
            id_ += 1
            for to in graph[at]:
                  if to == parent:
                        pass
                  elif not visited[to]:
                        dfs(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						id_	)
                        SCREAMING_SNAKE_CASE		=						min(low[at] ,						low[to]	)
                        if id_ <= low[to]:
                              bridges.append((at, to) if at < to else (to, at)	)
                  else:
                        # This edge is a back edge and cannot be a bridge
                        SCREAMING_SNAKE_CASE		=						min(low[at] ,						low[to]	)

      SCREAMING_SNAKE_CASE		=						[]
      for i in range(_SCREAMING_SNAKE_CASE	):
            if not visited[i]:
                  dfs(_SCREAMING_SNAKE_CASE ,						-1 ,						_SCREAMING_SNAKE_CASE ,						id_	)
      return bridges


if __name__ == "__main__":
      import doctest

      doctest.testmod()









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

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


