import heapq as hq
import math
from collections.abc import Iterator


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Dict   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  str(id_   )
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  {}  # {vertex:distance}







  def __lt__(							self :       List[Any]  ,     __magic_name__ :       str   )  ->	List[Any]:
        return self.key < other.key







  def __repr__(							self :       Union[str, Any]   )  ->	List[Any]:
        return self.id







  def    __A     (							self :       int  ,     __magic_name__ :       List[Any]   )  ->	Any:
        self.neighbors.append(_A   )







  def    __A     (							self :       str  ,     __magic_name__ :       str  ,     __magic_name__ :       Any   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  weight



def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      # add the neighbors:
      graph[a - 1].add_neighbor(graph[b - 1]				)
      graph[b - 1].add_neighbor(graph[a - 1]				)
      # add the edges:
      graph[a - 1].add_edge(graph[b - 1]     ,      __UpperCamelCase				)
      graph[b - 1].add_edge(graph[a - 1]     ,      __UpperCamelCase				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  []
      for u in graph:
            SCREAMING_SNAKE_CASE_          =  math.inf
            SCREAMING_SNAKE_CASE_          =  None
      SCREAMING_SNAKE_CASE_          =  0
      SCREAMING_SNAKE_CASE_          =  graph[:]
      while q:
            SCREAMING_SNAKE_CASE_          =  min(__UpperCamelCase				)
            q.remove(__UpperCamelCase				)
            for v in u.neighbors:
                  if (v in q) and (u.edges[v.id] < v.key):
                        SCREAMING_SNAKE_CASE_          =  u
                        SCREAMING_SNAKE_CASE_          =  u.edges[v.id]
      for i in range(1     ,      len(__UpperCamelCase				)				):
            a.append((int(graph[i].id				) + 1, int(graph[i].pi.id				) + 1)				)
      return a


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      for u in graph:
            SCREAMING_SNAKE_CASE_          =  math.inf
            SCREAMING_SNAKE_CASE_          =  None
      SCREAMING_SNAKE_CASE_          =  0

      SCREAMING_SNAKE_CASE_          =  list(__UpperCamelCase				)
      hq.heapify(__UpperCamelCase				)

      while h:
            SCREAMING_SNAKE_CASE_          =  hq.heappop(__UpperCamelCase				)
            for v in u.neighbors:
                  if (v in h) and (u.edges[v.id] < v.key):
                        SCREAMING_SNAKE_CASE_          =  u
                        SCREAMING_SNAKE_CASE_          =  u.edges[v.id]
                        hq.heapify(__UpperCamelCase				)

      for i in range(1     ,      len(__UpperCamelCase				)				):
            yield (int(graph[i].id				) + 1, int(graph[i].pi.id				) + 1)


def      a__ (		):
      pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


