

from queue import PriorityQueue
from typing import Any

import numpy as np
def 							__SCREAMING_SNAKE_CASE     (   a__    :   dict			,a__    :   str			,a__    :   set			,a__    :   set			,a__    :   dict			,a__    :   dict			,a__    :   PriorityQueue			,a__    :   dict			,a__    :   float | int			,)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		__A :			Tuple		    =			cst_fwd.get(a__			,np.inf       )
		__A :			int		    =			cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt)       )
			__A :			List[Any]		    =			new_cost_f
			__A :			Any		    =			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 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   dict			,a__    :   dict       )					->				int:
	__A :			int		    =			-1

	__A :			List[str]		    =			set()
	__A :			List[Any]		    =			set()
	__A :			str		    =			{source: 0}
	__A :			List[str]		    =			{destination: 0}
	__A :			int		    =			{source: None}
	__A :			Tuple		    =			{destination: None}
	__A :			PriorityQueue[Any]		    =			PriorityQueue()
	__A :			PriorityQueue[Any]		    =			PriorityQueue()

	__A :			str		    =			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(a__       )

		__A    ,				__A :			Optional[Any]		    =			queue_backward.get()
		visited_backward.add(a__       )

		__A :			int		    =			pass_and_relaxation(
		    a__			,a__			,a__			,a__			,a__			,a__			,a__			,a__			,a__			,)

		__A :			List[Any]		    =			pass_and_relaxation(
		    a__			,a__			,a__			,a__			,a__			,a__			,a__			,a__			,a__			,)

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

	if shortest_distance != np.inf:
		__A :			Optional[int]		    =			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_							:					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()








from typing import TYPE_CHECKING

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


UpperCAmelCase_							:					Optional[Any]		 =   {
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Optional[Any]		 =   [
			    '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Wav2Vec2ForAudioFrameClassification''',
			    '''Wav2Vec2ForCTC''',
			    '''Wav2Vec2ForMaskedLM''',
			    '''Wav2Vec2ForPreTraining''',
			    '''Wav2Vec2ForSequenceClassification''',
			    '''Wav2Vec2ForXVector''',
			    '''Wav2Vec2Model''',
			    '''Wav2Vec2PreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[Any]		 =   [
			    '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFWav2Vec2ForCTC''',
			    '''TFWav2Vec2Model''',
			    '''TFWav2Vec2PreTrainedModel''',
			    '''TFWav2Vec2ForSequenceClassification''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Dict		 =   [
			    '''FlaxWav2Vec2ForCTC''',
			    '''FlaxWav2Vec2ForPreTraining''',
			    '''FlaxWav2Vec2Model''',
			    '''FlaxWav2Vec2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

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





