






from typing import TYPE_CHECKING

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


snake_case_   :	Dict							      ={
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''FlaubertForMultipleChoice''',
					    '''FlaubertForQuestionAnswering''',
					    '''FlaubertForQuestionAnsweringSimple''',
					    '''FlaubertForSequenceClassification''',
					    '''FlaubertForTokenClassification''',
					    '''FlaubertModel''',
					    '''FlaubertWithLMHeadModel''',
					    '''FlaubertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFFlaubertForMultipleChoice''',
					    '''TFFlaubertForQuestionAnsweringSimple''',
					    '''TFFlaubertForSequenceClassification''',
					    '''TFFlaubertForTokenClassification''',
					    '''TFFlaubertModel''',
					    '''TFFlaubertPreTrainedModel''',
					    '''TFFlaubertWithLMHeadModel''',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

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



'''simple docstring'''
import heapq







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Any:





  """simple docstring"""

  __A		:  Dict       				=		[(0, start)]  # cost from start node,end node
  __A		:  Union[str, Any]       				=		set()
  while heap:
    ((__A)				,       (__A))		:  int       				=		heapq.heappop(__SCREAMING_SNAKE_CASE			)
    if u in visited:
      continue
    visited.add(__SCREAMING_SNAKE_CASE			)
    if u == end:
      return cost
    for v, c in graph[u]:
      if v in visited:
        continue
      __A		:  List[str]       				=		cost + c
      heapq.heappush(__SCREAMING_SNAKE_CASE      ,  (next_item, v)			)
  return -1


A__ :		Any					={
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
A__ :		List[str]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
A__ :		List[Any]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

A__ :		List[str]					=dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A__ :		Optional[Any]					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

A__ :		Any					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
     import doctest

     doctest.testmod()



