
'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np



def 			_a		(	_SCREAMING_SNAKE_CASE		:   dict	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   set	,			_SCREAMING_SNAKE_CASE		:   set	,			_SCREAMING_SNAKE_CASE		:   dict	,			_SCREAMING_SNAKE_CASE		:   dict	,			_SCREAMING_SNAKE_CASE		:   PriorityQueue	,			_SCREAMING_SNAKE_CASE		:   dict	,			_SCREAMING_SNAKE_CASE		:   float | int	,			):
						for nxt, d in graph[v]:
												if nxt in visited_forward:
																		continue
												_SCREAMING_SNAKE_CASE				= cst_fwd.get(_SCREAMING_SNAKE_CASE	,			np.inf  )
												_SCREAMING_SNAKE_CASE				= cst_fwd[v] + d
												if new_cost_f < old_cost_f:
																		queue.put((new_cost_f, nxt)  )
																		_SCREAMING_SNAKE_CASE				= new_cost_f
																		_SCREAMING_SNAKE_CASE				= v
												if nxt in visited_backward:
																		if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																								_SCREAMING_SNAKE_CASE				= cst_fwd[v] + d + cst_bwd[nxt]
						return shortest_distance



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   dict	,			_SCREAMING_SNAKE_CASE		:   dict  ):
						_SCREAMING_SNAKE_CASE				= -1

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

						_SCREAMING_SNAKE_CASE				= 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():
												_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= queue_forward.get()
												visited_forward.add(_SCREAMING_SNAKE_CASE  )

												_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= queue_backward.get()
												visited_backward.add(_SCREAMING_SNAKE_CASE  )

												_SCREAMING_SNAKE_CASE				= pass_and_relaxation(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			)

												_SCREAMING_SNAKE_CASE				= pass_and_relaxation(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			)

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

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


_snake_case				:   Tuple     =				{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
_snake_case				:   str     =				{
    """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_tf_available, is_torch_available


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """TFRagModel""",
						    """TFRagPreTrainedModel""",
						    """TFRagSequenceForGeneration""",
						    """TFRagTokenForGeneration""",
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

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