






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__UpperCAmelCase      =				["UtteranceLevel"]





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







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE				    =		checkpoint['Downstream']

 SCREAMING_SNAKE_CASE				    =		HubertConfig.from_pretrained(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		HubertForSequenceClassification.from_pretrained(lowercase_	,  config=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		WavaVecaFeatureExtractor.from_pretrained(
     lowercase_	,  return_attention_mask=lowercase_	,  do_normalize=lowercase_			)

 if hf_congfig.use_weighted_layer_sum:
  SCREAMING_SNAKE_CASE				    =		checkpoint['Featurizer']['weights']

 SCREAMING_SNAKE_CASE				    =		downstream_dict['projector.weight']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['projector.bias']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['model.post_net.linear.weight']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(lowercase_			)
 hf_model.save_pretrained(lowercase_			)


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





"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



