





from __future__ import annotations

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

UpperCAmelCase_											=  TypeVar("T", bound=bool)




class  __UpperCamelCase						(       Generic[T]						):





     def __init__(    self							,       _UpperCamelCase				):
        _UpperCAmelCase											=      value
        _UpperCAmelCase											=      None
        _UpperCAmelCase											=      None





     @property
     def 		UpperCamelCase(    self				):
        return self._value





     @staticmethod
     def 		UpperCamelCase(    _UpperCamelCase							,       _UpperCamelCase				):
        if not roota:
           return roota

        if not roota:
           return roota

        if roota.value > roota.value:
           _UpperCAmelCase					,			_UpperCAmelCase											=      roota, roota

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

        _UpperCAmelCase											=      RandomizedHeapNode.merge(roota.left							,       _UpperCamelCase				)

        return roota




class  __UpperCamelCase						(       Generic[T]						):





     def __init__(    self							,       _UpperCamelCase = ()				):
        _UpperCAmelCase											=      None

        if data:
           for item in data:
              self.insert(_UpperCamelCase				)





     def 		UpperCamelCase(    self							,       _UpperCamelCase				):
        _UpperCAmelCase											=      RandomizedHeapNode.merge(self._root							,       RandomizedHeapNode(_UpperCamelCase				)				)





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      self.top()

        if self._root is None:
           return None

        _UpperCAmelCase											=      RandomizedHeapNode.merge(self._root.left							,       self._root.right				)

        return result





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





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      None





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      []
        while self:
           result.append(self.pop()				)

        return result





     def __bool__(    self				):
        return self._root is not None


if __name__ == "__main__":
   import doctest

   doctest.testmod()






import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_											=  logging.get_logger(__name__)
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			Dict	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''projector.weight''']
   _UpperCAmelCase											=      downstream_dict['''projector.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			List[Any]	)     ->     Optional[int]:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''model.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''connector.weight''']
   _UpperCAmelCase											=      downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
      _UpperCAmelCase											=      downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      _UpperCAmelCase											=      downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _UpperCAmelCase											=      downstream_dict['''objective.W''']
   return model
@torch.no_grad()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			List[str]							,  SCREAMING_SNAKE_CASE_      :			int							,  SCREAMING_SNAKE_CASE_      :			List[str]	)     ->     Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase											=      torch.load(SCREAMING_SNAKE_CASE_							,  map_location='''cpu'''	)

   _UpperCAmelCase											=      checkpoint['''Downstream''']

   _UpperCAmelCase											=      WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      WavaVecaFeatureExtractor.from_pretrained(
       SCREAMING_SNAKE_CASE_							,  return_attention_mask=SCREAMING_SNAKE_CASE_							,  do_normalize=SCREAMING_SNAKE_CASE_	)

   _UpperCAmelCase											=      hf_config.architectures[0]
   if arch.endswith('''ForSequenceClassification'''	):
      _UpperCAmelCase											=      convert_classification(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForAudioFrameClassification'''	):
      _UpperCAmelCase											=      convert_diarization(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForXVector'''	):
      _UpperCAmelCase											=      convert_xvector(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''	)

   if hf_config.use_weighted_layer_sum:
      _UpperCAmelCase											=      checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_	)
   hf_model.save_pretrained(SCREAMING_SNAKE_CASE_	)


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)