



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase			:			Union[str, Any]										=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			Dict										=						[
		    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """RobertaPreLayerNormForCausalLM""",
		    """RobertaPreLayerNormForMaskedLM""",
		    """RobertaPreLayerNormForMultipleChoice""",
		    """RobertaPreLayerNormForQuestionAnswering""",
		    """RobertaPreLayerNormForSequenceClassification""",
		    """RobertaPreLayerNormForTokenClassification""",
		    """RobertaPreLayerNormModel""",
		    """RobertaPreLayerNormPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			List[str]										=						[
		    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFRobertaPreLayerNormForCausalLM""",
		    """TFRobertaPreLayerNormForMaskedLM""",
		    """TFRobertaPreLayerNormForMultipleChoice""",
		    """TFRobertaPreLayerNormForQuestionAnswering""",
		    """TFRobertaPreLayerNormForSequenceClassification""",
		    """TFRobertaPreLayerNormForTokenClassification""",
		    """TFRobertaPreLayerNormMainLayer""",
		    """TFRobertaPreLayerNormModel""",
		    """TFRobertaPreLayerNormPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			Dict										=						[
		    """FlaxRobertaPreLayerNormForCausalLM""",
		    """FlaxRobertaPreLayerNormForMaskedLM""",
		    """FlaxRobertaPreLayerNormForMultipleChoice""",
		    """FlaxRobertaPreLayerNormForQuestionAnswering""",
		    """FlaxRobertaPreLayerNormForSequenceClassification""",
		    """FlaxRobertaPreLayerNormForTokenClassification""",
		    """FlaxRobertaPreLayerNormModel""",
		    """FlaxRobertaPreLayerNormPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    RobertaPreLayerNormConfig,
		    RobertaPreLayerNormOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaPreLayerNormForCausalLM,
				    RobertaPreLayerNormForMaskedLM,
				    RobertaPreLayerNormForMultipleChoice,
				    RobertaPreLayerNormForQuestionAnswering,
				    RobertaPreLayerNormForSequenceClassification,
				    RobertaPreLayerNormForTokenClassification,
				    RobertaPreLayerNormModel,
				    RobertaPreLayerNormPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta_prelayernorm import (
				    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaPreLayerNormForCausalLM,
				    TFRobertaPreLayerNormForMaskedLM,
				    TFRobertaPreLayerNormForMultipleChoice,
				    TFRobertaPreLayerNormForQuestionAnswering,
				    TFRobertaPreLayerNormForSequenceClassification,
				    TFRobertaPreLayerNormForTokenClassification,
				    TFRobertaPreLayerNormMainLayer,
				    TFRobertaPreLayerNormModel,
				    TFRobertaPreLayerNormPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				    FlaxRobertaPreLayerNormPreTrainedModel,
				)

else:
		import sys

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









"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =[0] * len(__lowerCamelCase     )
			lowerCamelCase__		:		List[Any]		     =[]
			lowerCamelCase__		:		List[Any]		     =[1] * len(__lowerCamelCase     )

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

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

			while queue:
						lowerCamelCase__		:		Tuple		     =queue.pop(0     )
						for x in graph[vertex]:
									indegree[x] -= 1

									if long_dist[vertex] + 1 > long_dist[x]:
												lowerCamelCase__		:		Optional[Any]		     =long_dist[vertex] + 1

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

			print(max(__lowerCamelCase     )     )


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

longest_distance(graph)




