from queue import PriorityQueue
from typing import Any

import numpy as np






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  ):
       for nxt, d in graph[v]:
              if nxt in visited_forward:
                     continue
              lowerCAmelCase__					     = cst_fwd.get(UpperCAmelCase__     ,  np.inf     )
              lowerCAmelCase__					     = cst_fwd[v] + d
              if new_cost_f < old_cost_f:
                     queue.put((new_cost_f, nxt)     )
                     lowerCAmelCase__					     = new_cost_f
                     lowerCAmelCase__					     = v
              if nxt in visited_backward:
                     if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                            lowerCAmelCase__					     = cst_fwd[v] + d + cst_bwd[nxt]
       return shortest_distance






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = -1

       lowerCAmelCase__					     = set()
       lowerCAmelCase__					     = set()
       lowerCAmelCase__					     = {source: 0}
       lowerCAmelCase__					     = {destination: 0}
       lowerCAmelCase__					     = {source: None}
       lowerCAmelCase__					     = {destination: None}
       lowerCAmelCase__					     = PriorityQueue()
       lowerCAmelCase__					     = PriorityQueue()

       lowerCAmelCase__					     = 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():
              lowerCAmelCase__     , lowerCAmelCase__					     = queue_forward.get()
              visited_forward.add(UpperCAmelCase__     )

              lowerCAmelCase__     , lowerCAmelCase__					     = queue_backward.get()
              visited_backward.add(UpperCAmelCase__     )

              lowerCAmelCase__					     = pass_and_relaxation(
                  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  )

              lowerCAmelCase__					     = pass_and_relaxation(
                  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  )

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

       if shortest_distance != np.inf:
              lowerCAmelCase__					     = shortest_distance
       return shortest_path_distance


lowerCAmelCase__     						=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase__     						=			{
    '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()

from typing import TYPE_CHECKING

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


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

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