
import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				int  ):
 __a :  List[Any]						=  UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase__  ,   config=lowerCAmelCase__  )
 __a :  Union[str, Any]						=  downstream_dict['''projector.weight''']
 __a :  Union[str, Any]						=  downstream_dict['''projector.bias''']
 __a :  Optional[Any]						=  downstream_dict['''model.post_net.linear.weight''']
 __a :  List[Any]						=  downstream_dict['''model.post_net.linear.bias''']
 return model




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Optional[int]  ):
 __a :  List[str]						=  UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase__  ,   config=lowerCAmelCase__  )
 __a :  Tuple						=  downstream_dict['''model.linear.weight''']
 __a :  Optional[Any]						=  downstream_dict['''model.linear.bias''']
 return model




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

 __a :  List[str]						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
 __a :  Union[str, Any]						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
 __a :  int						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
 __a :  Optional[Any]						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
 __a :  Union[str, Any]						=  downstream_dict['''objective.W''']
 return model




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Union[str, Any]  ):
 __a :  Optional[Any]						=  torch.load(lowerCAmelCase__  ,   map_location='''cpu'''  )

 __a :  Any						=  checkpoint['''Downstream''']

 __a :  Any						=  UniSpeechSatConfig.from_pretrained(lowerCAmelCase__  )
 __a :  List[Any]						=  WavaVecaFeatureExtractor.from_pretrained(
     lowerCAmelCase__  ,   return_attention_mask=lowerCAmelCase__  ,   do_normalize=lowerCAmelCase__  )

 __a :  Dict						=  hf_config.architectures[0]
 if arch.endswith('''ForSequenceClassification'''  ):
  __a :  Tuple						=  convert_classification(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
 elif arch.endswith('''ForAudioFrameClassification'''  ):
  __a :  Union[str, Any]						=  convert_diarization(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
 elif arch.endswith('''ForXVector'''  ):
  __a :  Tuple						=  convert_xvector(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
 else:
  raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"  )

 if hf_config.use_weighted_layer_sum:
  __a :  List[str]						=  checkpoint['''Featurizer''']['''weights''']

 hf_feature_extractor.save_pretrained(lowerCAmelCase__  )
 hf_model.save_pretrained(lowerCAmelCase__  )


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


from __future__ import annotations

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  0

    __a :  list[Any]						=  [-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes   ):
     self.m_component.update({node: node}   )
     component_size.append(1   )

    __a :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[Any]						=  self.m_component[v]

      if u_component != v_component:

       for component in (u_component, v_component):
        if (
            minimum_weight_edge[component] == -1
            or minimum_weight_edge[component][2] > w
        ):
         __a :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  self.m_component[v]

       if u_component != v_component:
        mst_weight += w
        self.union(snake_case_					,				snake_case_					,				snake_case_   )
        print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"   )
        num_of_components -= 1

     __a :  Optional[int]						=  [-1] * self.m_num_of_nodes
    print(f"The total weight of the minimal spanning tree is: {mst_weight}"   )





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
