






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCamelCase			:							Union[str, Any]       =logging.get_logger(__name__)

lowerCamelCase			:							List[str]       =['''UtteranceLevel''']







@torch.no_grad()
def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			Union[str, Any]:
     UpperCamelCase__    :     List[Any]        =						torch.load(__lowerCAmelCase      ,					map_location="cpu"    )
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'The supported s3prl models are {SUPPORTED_MODELS}'    )

     UpperCamelCase__    :     Tuple        =						checkpoint["Downstream"]

     UpperCamelCase__    :     Dict        =						HubertConfig.from_pretrained(__lowerCAmelCase    )
     UpperCamelCase__    :     Union[str, Any]        =						HubertForSequenceClassification.from_pretrained(__lowerCAmelCase      ,					config=__lowerCAmelCase    )
     UpperCamelCase__    :     List[Any]        =						WavaVecaFeatureExtractor.from_pretrained(
         __lowerCAmelCase      ,					return_attention_mask=__lowerCAmelCase      ,					do_normalize=__lowerCAmelCase    )

     if hf_congfig.use_weighted_layer_sum:
          UpperCamelCase__    :     List[Any]        =						checkpoint["Featurizer"]["weights"]

     UpperCamelCase__    :     Optional[int]        =						downstream_dict["projector.weight"]
     UpperCamelCase__    :     Union[str, Any]        =						downstream_dict["projector.bias"]
     UpperCamelCase__    :     List[Any]        =						downstream_dict["model.post_net.linear.weight"]
     UpperCamelCase__    :     Optional[Any]        =						downstream_dict["model.post_net.linear.bias"]

     hf_feature_extractor.save_pretrained(__lowerCAmelCase    )
     hf_model.save_pretrained(__lowerCAmelCase    )


if __name__ == "__main__":
 lowerCamelCase			:							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			:							Any       =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 collections.abc import Iterator
from typing import Any





class     __a						:







 def __init__(	self      :     str							,     SCREAMING_SNAKE_CASE      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						data
      UpperCamelCase__    :     Node | None        =						None







 def __str__(	self      :     Union[str, Any]		):


      '''simple docstring'''






      return F'{self.data}'







class     __a						:







 def __init__(	self      :     Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__    :     Node | None        =						None
      UpperCamelCase__    :     Node | None        =						None







 def __iter__(	self      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     int        =						self.front
      while node:
           yield node.data
           UpperCamelCase__    :     Any        =						node.next







 def __len__(	self      :     Tuple		):


      '''simple docstring'''






      return len(tuple(iter(self		)		)		)







 def __str__(	self      :     Union[str, Any]		):


      '''simple docstring'''






      return " <- ".join(str(SCREAMING_SNAKE_CASE		) for item in self		)







 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      return len(self		) == 0







 def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     int        =						Node(SCREAMING_SNAKE_CASE		)
      if self.is_empty():
           UpperCamelCase__    :     int        =						node
      else:
           assert isinstance(self.rear							,     SCREAMING_SNAKE_CASE		)
           UpperCamelCase__    :     Dict        =						node
           UpperCamelCase__    :     str        =						node







 def       __lowercase      (	self      :     Dict		):


      '''simple docstring'''






      if self.is_empty():
           raise IndexError("dequeue from empty queue"		)
      assert isinstance(self.front							,     SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						self.front
      UpperCamelCase__    :     List[str]        =						self.front.next
      if self.front is None:
           UpperCamelCase__    :     List[Any]        =						None
      return node.data







 def       __lowercase      (	self      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[int]        =						None


if __name__ == "__main__":
 from doctest import testmod

 testmod()