




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  :			str        =       {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			Optional[Any]        =       ['RobertaTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			List[Any]        =       [
		    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RobertaForCausalLM',
		    'RobertaForMaskedLM',
		    'RobertaForMultipleChoice',
		    'RobertaForQuestionAnswering',
		    'RobertaForSequenceClassification',
		    'RobertaForTokenClassification',
		    'RobertaModel',
		    'RobertaPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			str        =       [
		    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFRobertaForCausalLM',
		    'TFRobertaForMaskedLM',
		    'TFRobertaForMultipleChoice',
		    'TFRobertaForQuestionAnswering',
		    'TFRobertaForSequenceClassification',
		    'TFRobertaForTokenClassification',
		    'TFRobertaMainLayer',
		    'TFRobertaModel',
		    'TFRobertaPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			Union[str, Any]        =       [
		    'FlaxRobertaForCausalLM',
		    'FlaxRobertaForMaskedLM',
		    'FlaxRobertaForMultipleChoice',
		    'FlaxRobertaForQuestionAnswering',
		    'FlaxRobertaForSequenceClassification',
		    'FlaxRobertaForTokenClassification',
		    'FlaxRobertaModel',
		    'FlaxRobertaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
		from .tokenization_roberta import RobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roberta_fast import RobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta import (
				    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaForCausalLM,
				    RobertaForMaskedLM,
				    RobertaForMultipleChoice,
				    RobertaForQuestionAnswering,
				    RobertaForSequenceClassification,
				    RobertaForTokenClassification,
				    RobertaModel,
				    RobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta import (
				    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaForCausalLM,
				    TFRobertaForMaskedLM,
				    TFRobertaForMultipleChoice,
				    TFRobertaForQuestionAnswering,
				    TFRobertaForSequenceClassification,
				    TFRobertaForTokenClassification,
				    TFRobertaMainLayer,
				    TFRobertaModel,
				    TFRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				    FlaxRobertaPreTrainedModel,
				)

else:
		import sys

		_lowerCamelCase  :			Any        =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











"""simple docstring"""

from queue import PriorityQueue
from typing import Any

import numpy as np

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      ):




	"""simple docstring"""






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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		-1

	A_  :				Any	   =		set()
	A_  :				Tuple	   =		set()
	A_  :				Optional[int]	   =		{source: 0}
	A_  :				Dict	   =		{destination: 0}
	A_  :				Any	   =		{source: None}
	A_  :				Optional[Any]	   =		{destination: None}
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()

	A_  :				Union[str, 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():
		A_   , A_  :				Union[str, Any]	   =		queue_forward.get()
		visited_forward.add(_UpperCAmelCase	)

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

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

		A_  :				Dict	   =		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_  :				Optional[Any]	   =		shortest_distance
	return shortest_path_distance


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





