






"""simple docstring"""





def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							str         =     sorted(__lowerCamelCase		,  key=lambda snake_case_						: edge[2]						)

       lowercase__:							Union[str, Any]         =     list(range(__lowerCamelCase						)						)

       def find_parent(snake_case_						):
              if i != parent[i]:
                     lowercase__:							str         =     find_parent(parent[i]						)
              return parent[i]

       lowercase__:							Union[str, Any]         =     0
       lowercase__:							Optional[Any]         =     []

       for edge in edges:
              lowercase__:							Optional[int]         =     find_parent(edge[0]						)
              lowercase__:							List[str]         =     find_parent(edge[1]						)
              if parent_a != parent_b:
                     minimum_spanning_tree_cost += edge[2]
                     minimum_spanning_tree.append(__lowerCamelCase						)
                     lowercase__:							Union[str, Any]         =     parent_b

       return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
  UpperCamelCase,     UpperCamelCase					=     list(map(int, input().strip().split()))
  UpperCamelCase					=     []

  for _ in range(num_edges):
    UpperCamelCase,     UpperCamelCase,     UpperCamelCase					=     (int(x) for x in input().strip().split())
    edges.append((nodea, nodea, cost))

  kruskal(num_nodes, edges)








"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   UpperCAmelCase_   :   Dict     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCAmelCase_   :   List[str]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCAmelCase_   :   Dict     	=       downstream_dict["objective.W"]
   return model







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

   UpperCAmelCase_   :   Any     	=       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"    ):
      UpperCAmelCase_   :   Optional[int]     	=       convert_classification(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForAudioFrameClassification"    ):
      UpperCAmelCase_   :   Dict     	=       convert_diarization(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForXVector"    ):
      UpperCAmelCase_   :   List[Any]     	=       convert_xvector(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   else:
      raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""    )

   if hf_config.use_weighted_layer_sum:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(__lowerCamelCase    )
   hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
    _a									=				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.')
    _a									=				parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


