





'''simple docstring'''


def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->Optional[Any]:



	"""simple docstring"""







	print("""\nVertex Distance"""     )
	for i in range(UpperCAmelCase     ):
		if dist[i] != float("""inf"""     ):
			print(UpperCAmelCase							,      """\t"""							,      int(dist[i]     )							,      end="""\t"""     )
		else:
			print(UpperCAmelCase							,      """\t"""							,      """INF"""							,      end="""\t"""     )
		print()




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Dict:



	"""simple docstring"""







	A					=   float("""inf"""     )
	A					=   -1
	for i in range(UpperCAmelCase     ):
		if (not vset[i]) and mdist[i] < min_val:
			A					=   i
			A					=   mdist[i]
	return min_ind




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   [float("""inf"""     ) for _ in range(UpperCAmelCase     )]
	A					=   [False for _ in range(UpperCAmelCase     )]
	A					=   0.0

	for _ in range(v - 1     ):
		A					=   min_dist(UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )
		A					=   True

		for i in range(UpperCAmelCase     ):
			if (
			    (not vset[i])
			    and graph[u][i] != float("""inf"""     )
			    and mdist[u] + graph[u][i] < mdist[i]
			):
				A					=   mdist[u] + graph[u][i]

	print_dist(UpperCAmelCase							,      UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						Dict					     =							int(input('Enter number of vertices: ').strip())
				_lowerCamelCase					:						List[str]					     =							int(input('Enter number of edges: ').strip())

				_lowerCamelCase					:						List[Any]					     =							[[float('inf') for i in range(V)] for j in range(V)]

				for i in range(V):
								_lowerCamelCase					:						Dict					     =							0.0

				for i in range(E):
								print('\nEdge ', i + 1)
								_lowerCamelCase					:						List[str]					     =							int(input('Enter source:').strip())
								_lowerCamelCase					:						Tuple					     =							int(input('Enter destination:').strip())
								_lowerCamelCase					:						Tuple					     =							float(input('Enter weight:').strip())
								_lowerCamelCase					:						Optional[int]					     =							weight

				_lowerCamelCase					:						List[str]					     =							int(input('\nEnter shortest path source:').strip())
				dijkstra(graph, V, gsrc)








'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	A					=   downstream_dict["""projector.weight"""]
	A					=   downstream_dict["""projector.bias"""]
	A					=   downstream_dict["""model.post_net.linear.weight"""]
	A					=   downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

