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



class 						UpperCamelCase__    :
   def __init__(self					:   Dict   ,							snake_case_					:   Tuple  ):
    __a					:    Tuple													=    str(id_  )
    __a					:    Union[str, Any]													=    None
    __a					:    Union[str, Any]													=    None
    __a					:    Optional[int]													=    []
    __a					:    Optional[Any]													=    {}  # {vertex:distance}
   def __lt__(self					:   Dict   ,							snake_case_					:   Optional[int]  ):
    return self.key < other.key
   def __repr__(self					:   Optional[int]  ):
    return self.id
   def  lowerCAmelCase     (self					:   Any   ,							snake_case_					:   str  ):
    self.neighbors.append(lowercase_  )







   def  lowerCAmelCase     (self					:   List[Any]   ,							snake_case_					:   Optional[int]   ,							snake_case_					:   Any  ):
    __a					:    str													=    weight





def   __UpperCamelCase	(							lowerCAmelCase__				:							List[Any]    ,  lowerCAmelCase__				:							Tuple    ,  lowerCAmelCase__				:							List[Any]    ,  lowerCAmelCase__				:							Any    ):
 # 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]    ,  A_    )
 graph[b - 1].add_edge(graph[a - 1]    ,  A_    )




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




def   __UpperCamelCase	(							lowerCAmelCase__				:							Optional[Any]    ,  lowerCAmelCase__				:							Dict    ):
 for u in graph:
  __a					:    Tuple													=    math.inf
  __a					:    Optional[Any]													=    None
 __a					:    Tuple													=    0

 __a					:    Optional[int]													=    list(A_    )
 hq.heapify(A_    )

 while h:
  __a					:    int													=    hq.heappop(A_    )
  for v in u.neighbors:
   if (v in h) and (u.edges[v.id] < v.key):
    __a					:    Tuple													=    u
    __a					:    Any													=    u.edges[v.id]
    hq.heapify(A_    )

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




def   __UpperCamelCase	(							):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()



"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




