


'''simple docstring'''


import heapq as hq
import math
from collections.abc import Iterator



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      Tuple       	=					str(id_)
								UpperCAmelCase_		:      Any       	=					None
								UpperCAmelCase_		:      int       	=					None
								UpperCAmelCase_		:      Union[str, Any]       	=					[]
								UpperCAmelCase_		:      Optional[int]       	=					{}  # {vertex:distance}


							def __lt__(			self    ,			_snake_case)							->				Optional[Any]:
								return self.key < other.key


							def __repr__(			self)							->				Tuple:
								return self.id


							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								self.neighbors.append(UpperCamelCase__)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Any       	=					weight







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	# add the neighbors:
	graph[a - 1].add_neighbor(graph[b - 1]  )
	graph[b - 1].add_neighbor(graph[a - 1]  )
	# add the edges:
	graph[a - 1].add_edge(graph[b - 1]   ,UpperCamelCase  )
	graph[b - 1].add_edge(graph[a - 1]   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	UpperCAmelCase_		:      Union[str, Any]       	=					[]
	for u in graph:
		UpperCAmelCase_		:      Any       	=					math.inf
		UpperCAmelCase_		:      Any       	=					None
	UpperCAmelCase_		:      Optional[int]       	=					0
	UpperCAmelCase_		:      Dict       	=					graph[:]
	while q:
		UpperCAmelCase_		:      Union[str, Any]       	=					min(UpperCamelCase  )
		q.remove(UpperCamelCase  )
		for v in u.neighbors:
			if (v in q) and (u.edges[v.id] < v.key):
				UpperCAmelCase_		:      int       	=					u
				UpperCAmelCase_		:      List[str]       	=					u.edges[v.id]
	for i in range(1   ,len(UpperCamelCase  )  ):
		a.append((int(graph[i].id  ) + 1, int(graph[i].pi.id  ) + 1)  )
	return a







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	for u in graph:
		UpperCAmelCase_		:      int       	=					math.inf
		UpperCAmelCase_		:      Union[str, Any]       	=					None
	UpperCAmelCase_		:      Union[str, Any]       	=					0

	UpperCAmelCase_		:      int       	=					list(UpperCamelCase  )
	hq.heapify(UpperCamelCase  )

	while h:
		UpperCAmelCase_		:      str       	=					hq.heappop(UpperCamelCase  )
		for v in u.neighbors:
			if (v in h) and (u.edges[v.id] < v.key):
				UpperCAmelCase_		:      str       	=					u
				UpperCAmelCase_		:      Any       	=					u.edges[v.id]
				hq.heapify(UpperCamelCase  )

	for i in range(1   ,len(UpperCamelCase  )  ):
		yield (int(graph[i].id  ) + 1, int(graph[i].pi.id  ) + 1)







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__										= {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__										= [
						    "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ClapModel",
						    "ClapPreTrainedModel",
						    "ClapTextModel",
						    "ClapTextModelWithProjection",
						    "ClapAudioModel",
						    "ClapAudioModelWithProjection",
						]
						lowerCAmelCase__										= ["ClapFeatureExtractor"]

if TYPE_CHECKING:
						from .configuration_clap import (
						    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ClapAudioConfig,
						    ClapConfig,
						    ClapTextConfig,
						)
						from .processing_clap import ClapProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_clap import ClapFeatureExtractor
												from .modeling_clap import (
												    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ClapAudioModel,
												    ClapAudioModelWithProjection,
												    ClapModel,
												    ClapPreTrainedModel,
												    ClapTextModel,
												    ClapTextModelWithProjection,
												)


else:
						import sys

						lowerCAmelCase__										= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






