"""simple docstring"""






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = 42






class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	Optional[Any]    ,lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							[[] for _ in range(lowerCamelCase__			)]
									UpperCAmelCase__             =							size







					def __getitem__(				self   :	int    ,lowerCamelCase__   :	int			):
									return iter(self._graph[vertex]			)







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									return self._size







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	int			):
									if weight not in (0, 1):
													raise ValueError('Edge weight must be either 0 or 1.'			)

									if to_vertex < 0 or to_vertex >= self.size:
													raise ValueError('Vertex indexes must be in [0; size).'			)

									self._graph[from_vertex].append(Edge(lowerCamelCase__    ,lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							deque([start_vertex]			)
									UpperCAmelCase__             =							[None] * self.size
									UpperCAmelCase__             =							0

									while queue:
													UpperCAmelCase__             =							queue.popleft()
													UpperCAmelCase__             =							distances[current_vertex]
													if current_distance is None:
																	continue

													for edge in self[current_vertex]:
																	UpperCAmelCase__             =							current_distance + edge.weight
																	UpperCAmelCase__             =							distances[edge.destination_vertex]
																	if (
																	    isinstance(lowerCamelCase__    ,lowerCamelCase__			)
																	    and new_distance >= dest_vertex_distance
																	):
																					continue
																	UpperCAmelCase__             =							new_distance
																	if edge.weight == 0:
																					queue.appendleft(edge.destination_vertex			)
																	else:
																					queue.append(edge.destination_vertex			)

									if distances[finish_vertex] is None:
													raise ValueError('No path from start_vertex to finish_vertex.'			)

									return distances[finish_vertex]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__  :  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:
						lowerCAmelCase__  :  List[str]						     =	[
						    '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

						lowerCAmelCase__  :  str						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



