






def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	index
    _lowercase     =	2 * index + 1
    _lowercase     =	2 * index + 2
    if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
        _lowercase     =	left_index

    if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
        _lowercase     =	right_index

    if largest != index:
        _lowercase ,    _lowercase     =	unsorted[index], unsorted[largest]
        heapify(_lowercase,      _lowercase,      _lowercase      )
def        UpperCAmelCase      (   _lowercase      ):


    '''simple docstring'''



    _lowercase     =	len(_lowercase      )
    for i in range(n // 2 - 1,      -1,      -1      ):
        heapify(_lowercase,      _lowercase,      _lowercase      )
    for i in range(n - 1,      0,      -1      ):
        _lowercase ,    _lowercase     =	unsorted[i], unsorted[0]
        heapify(_lowercase,      0,      _lowercase      )
    return unsorted


if __name__ == "__main__":
   lowerCamelCase										=  input('''Enter numbers separated by a comma:\n''').strip()
   lowerCamelCase										=  [int(item) for item in user_input.split(''',''')]
   print(heap_sort(unsorted))








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowerCamelCase										=  ['''UtteranceLevel''']
@torch.no_grad()
def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	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}'''      )

    _lowercase     =	checkpoint['Downstream']

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

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

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

    hf_feature_extractor.save_pretrained(_lowercase      )
    hf_model.save_pretrained(_lowercase      )


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