
from queue import PriorityQueue
from typing import Any

import numpy as np




def   __UpperCamelCase	(							lowerCAmelCase__				:							Union[str, Any]    ,  lowerCAmelCase__				:							Optional[Any]    ,  lowerCAmelCase__				:							Any    ,  lowerCAmelCase__				:							Optional[int]    ,  lowerCAmelCase__				:							Any    ,  lowerCAmelCase__				:							Dict    ,  lowerCAmelCase__				:							Union[str, Any]    ,  lowerCAmelCase__				:							Any    ,  lowerCAmelCase__				:							Tuple    ,  ):
 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  __a					:    List[str]													=    cst_fwd.get(A_    ,  np.inf    )
  __a					:    Optional[int]													=    cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)    )
   __a					:    Any													=    new_cost_f
   __a					:    List[Any]													=    v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    __a					:    Optional[int]													=    cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance




def   __UpperCamelCase	(							lowerCAmelCase__				:							Tuple    ,  lowerCAmelCase__				:							Dict    ,  lowerCAmelCase__				:							str    ,  lowerCAmelCase__				:							Dict    ):
 __a					:    Optional[Any]													=    -1

 __a					:    Tuple													=    set()
 __a					:    List[Any]													=    set()
 __a					:    Optional[int]													=    {source: 0}
 __a					:    int													=    {destination: 0}
 __a					:    str													=    {source: None}
 __a					:    List[str]													=    {destination: None}
 __a					:    PriorityQueue[Any]													=    PriorityQueue()
 __a					:    PriorityQueue[Any]													=    PriorityQueue()

 __a					:    str													=    np.inf

 queue_forward.put((0, source)    )
 queue_backward.put((0, destination)    )

 if source == destination:
  return 0

 while not queue_forward.empty() and not queue_backward.empty():
  __a					:    int													=    queue_forward.get()
  visited_forward.add(A_    )

  __a					:    Optional[int]													=    queue_backward.get()
  visited_backward.add(A_    )

  __a					:    Any													=    pass_and_relaxation(
      A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  )

  __a					:    str													=    pass_and_relaxation(
      A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  )

  if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
   break

 if shortest_distance != np.inf:
  __a					:    Any													=    shortest_distance
 return shortest_path_distance


lowercase__         ={
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
lowercase__         ={
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

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)




