






from collections import deque
def 		_a       ( a					:str					)				->     List[Any]:
 a						    = len(a					)
 a						    = deque()
 a						    = [False for _ in range(a					)]
 a						    = [-1 for _ in range(a					)]
 a						    = index_of[:]

 def strong_connect(a					:str      ,				a					:Tuple      ,				a					:Dict					):
  a						    = index  # the number when this node is seen
  a						    = index  # lowest rank node reachable from here
  index += 1
  stack.append(a					)
  a						    = True

  for w in g[v]:
   if index_of[w] == -1:
    a						    = strong_connect(a      ,				a      ,				a					)
    a						    = (
        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
    )
   elif on_stack[w]:
    a						    = (
        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
    )

  if lowlink_of[v] == index_of[v]:
   a						    = []
   a						    = stack.pop()
   a						    = False
   component.append(a					)
   while w != v:
    a						    = stack.pop()
    a						    = False
    component.append(a					)
   components.append(a					)
  return index

 a						    = []
 for v in range(a					):
  if index_of[v] == -1:
   strong_connect(a      ,				0      ,				a					)

 return components
def 		_a       ( a					:List[Any]      ,				a					:Union[str, Any]					)				->     int:
 a						    = [[] for _ in range(a					)]
 for u, v in edges:
  g[u].append(a					)
 return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase__							  =						7
     UpperCAmelCase__							  =						[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase__							  =						[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase__							  =						[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase__							  =						create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "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

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


