






'''simple docstring'''



def 							lowercase_				(    __A :       Optional[Any]			)       ->      int:



 """simple docstring"""




 lowercase					:   Optional[int]				  =[0] * len(__A			)
 lowercase					:   List[str]				  =[]
 lowercase					:   List[str]				  =[1] * len(__A			)

 for values in graph.values():
  for i in values:
   indegree[i] += 1

 for i in range(len(__A			)			):
  if indegree[i] == 0:
   queue.append(__A			)

 while queue:
  lowercase					:   List[Any]				  =queue.pop(0			)
  for x in graph[vertex]:
   indegree[x] -= 1

   if long_dist[vertex] + 1 > long_dist[x]:
    lowercase					:   Dict				  =long_dist[vertex] + 1

   if indegree[x] == 0:
    queue.append(__A			)

 print(max(__A			)			)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE						=	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}





longest_distance(graph)











'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RemBertForCausalLM',
          'RemBertForMaskedLM',
          'RemBertForMultipleChoice',
          'RemBertForQuestionAnswering',
          'RemBertForSequenceClassification',
          'RemBertForTokenClassification',
          'RemBertLayer',
          'RemBertModel',
          'RemBertPreTrainedModel',
          'load_tf_weights_in_rembert',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRemBertForCausalLM',
          'TFRemBertForMaskedLM',
          'TFRemBertForMultipleChoice',
          'TFRemBertForQuestionAnswering',
          'TFRemBertForSequenceClassification',
          'TFRemBertForTokenClassification',
          'TFRemBertLayer',
          'TFRemBertModel',
          'TFRemBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



