

"""simple docstring"""


from queue import PriorityQueue
from typing import Any

import numpy as np







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			)      ->		float | int:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A					       = -1

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

		A					       = 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       , A					       = queue_forward.get()
				visited_forward.add(UpperCamelCase__	)

				A       , A					       = queue_backward.get()
				visited_backward.add(UpperCamelCase__	)

				A					       = pass_and_relaxation(
				    UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			)

				A					       = 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:
				A					       = shortest_distance
		return shortest_path_distance


UpperCamelCase						:				Union[str, 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()









"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase						:				Dict             =      {
    "configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Any             =      [
	    "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MegatronBertForCausalLM",
	    "MegatronBertForMaskedLM",
	    "MegatronBertForMultipleChoice",
	    "MegatronBertForNextSentencePrediction",
	    "MegatronBertForPreTraining",
	    "MegatronBertForQuestionAnswering",
	    "MegatronBertForSequenceClassification",
	    "MegatronBertForTokenClassification",
	    "MegatronBertModel",
	    "MegatronBertPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_megatron_bert import (
		    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegatronBertForCausalLM,
		    MegatronBertForMaskedLM,
		    MegatronBertForMultipleChoice,
		    MegatronBertForNextSentencePrediction,
		    MegatronBertForPreTraining,
		    MegatronBertForQuestionAnswering,
		    MegatronBertForSequenceClassification,
		    MegatronBertForTokenClassification,
		    MegatronBertModel,
		    MegatronBertPreTrainedModel,
		)

else:
	import sys

	UpperCamelCase						:				Dict             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





