


'''simple docstring'''






from collections import deque







def 							UpperCamelCase_     (       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[Any]     =  len(A__   )
  lowerCAmelCase_     : List[Any]     =  deque()
  lowerCAmelCase_     : str     =  [False for _ in range(A__   )]
  lowerCAmelCase_     : Optional[int]     =  [-1 for _ in range(A__   )]
  lowerCAmelCase_     : List[Any]     =  index_of[:]

  def strong_connect(A__   :  Any				,       A__   :  Any				,       A__   :  str   ):
    lowerCAmelCase_     : Optional[int]     =  index  # the number when this node is seen
    lowerCAmelCase_     : Dict     =  index  # lowest rank node reachable from here
    index += 1
    stack.append(A__   )
    lowerCAmelCase_     : Dict     =  True

    for w in g[v]:
      if index_of[w] == -1:
        lowerCAmelCase_     : Optional[Any]     =  strong_connect(A__				,       A__				,       A__   )
        lowerCAmelCase_     : Optional[Any]     =  (
            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
        )
      elif on_stack[w]:
        lowerCAmelCase_     : Optional[int]     =  (
            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
        )

    if lowlink_of[v] == index_of[v]:
      lowerCAmelCase_     : Tuple     =  []
      lowerCAmelCase_     : int     =  stack.pop()
      lowerCAmelCase_     : Any     =  False
      component.append(A__   )
      while w != v:
        lowerCAmelCase_     : Optional[Any]     =  stack.pop()
        lowerCAmelCase_     : Optional[int]     =  False
        component.append(A__   )
      components.append(A__   )
    return index

  lowerCAmelCase_     : List[str]     =  []
  for v in range(A__   ):
    if index_of[v] == -1:
      strong_connect(A__				,       0				,       A__   )

  return components







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  str   ):
  '''simple docstring'''





  lowerCAmelCase_     : str     =  [[] for _ in range(A__   )]
  for u, v in edges:
    g[u].append(A__   )
  return g


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

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







'''simple docstring'''






from typing import TYPE_CHECKING

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


__A   :    str											= {
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Optional[int]											= [
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

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



