
'''simple docstring'''






from queue import PriorityQueue
from typing import Any

import numpy as np







def 		A_							(						_lowerCAmelCase :     dict					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     set					,						_lowerCAmelCase :     set					,						_lowerCAmelCase :     dict					,						_lowerCAmelCase :     dict					,						_lowerCAmelCase :     PriorityQueue					,						_lowerCAmelCase :     dict					,						_lowerCAmelCase :     float | int					,						):



      """simple docstring"""




      for nxt, d in graph[v]:
            if nxt in visited_forward:
                  continue
            _lowerCamelCase					: str		    = cst_fwd.get(_lowerCAmelCase					,						np.inf				)
            _lowerCamelCase					: Any		    = cst_fwd[v] + d
            if new_cost_f < old_cost_f:
                  queue.put((new_cost_f, nxt)				)
                  _lowerCamelCase					: Dict		    = new_cost_f
                  _lowerCamelCase					: str		    = v
            if nxt in visited_backward:
                  if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                        _lowerCamelCase					: List[Any]		    = cst_fwd[v] + d + cst_bwd[nxt]
      return shortest_distance







def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     dict					,						_lowerCAmelCase :     dict				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = -1

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

      _lowerCamelCase					: List[Any]		    = 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					: List[str]		    = queue_forward.get()
            visited_forward.add(_lowerCAmelCase				)

            _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = queue_backward.get()
            visited_backward.add(_lowerCAmelCase				)

            _lowerCamelCase					: List[Any]		    = pass_and_relaxation(
                _lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						)

            _lowerCamelCase					: List[str]		    = pass_and_relaxation(
                _lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						)

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

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


UpperCAmelCase_							: Optional[Any] =							{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
UpperCAmelCase_							: Dict =							{
    '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_torch_available, is_vision_available


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


