




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 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A			):






  """simple docstring"""


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

  a_		    =  checkpoint['''Downstream''']

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

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

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

  hf_feature_extractor.save_pretrained(_A			)
  hf_model.save_pretrained(_A			)


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)






from __future__ import annotations

import sys
class 							__lowercase   :



     def __init__(  self   :							Any      ,		lowercase__   :							str      ,		lowercase__   :							int       ):
       a_		    =  letter
       a_		    =  freq
       a_		    =  {}



     def __repr__(  self   :							List[Any]       ):
       return f"{self.letter}:{self.freq}"



class 							__lowercase   :



     def __init__(  self   :							Optional[int]      ,		lowercase__   :							int      ,		lowercase__   :							Letter | TreeNode      ,		lowercase__   :							Letter | TreeNode       ):
       a_		    =  freq
       a_		    =  left
       a_		    =  right






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  {}
  with open(_A			) as f:
    while True:
      a_		    =  f.read(1			)
      if not c:
        break
      a_		    =  chars[c] + 1 if c in chars else 1
  return sorted((Letter(_A      ,      _A			) for c, f in chars.items())      ,      key=lambda _A			: x.freq			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  letters  # type: ignore
  while len(_A			) > 1:
    a_		    =  response.pop(0			)
    a_		    =  response.pop(0			)
    a_		    =  left.freq + right.freq
    a_		    =  TreeNode(_A      ,      _A      ,      _A			)
    response.append(_A			)
    response.sort(key=lambda _A			: x.freq			)
  return response[0]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if isinstance(_A      ,      _A			):
    a_		    =  bitstring
    return [root]
  a_		    =  root  # type: ignore
  a_		    =  []
  letters += traverse_tree(treenode.left      ,      bitstring + '''0'''			)
  letters += traverse_tree(treenode.right      ,      bitstring + '''1'''			)
  return letters






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  parse_file(_A			)
  a_		    =  build_tree(_A			)
  a_		    =  {
      k: v for letter in traverse_tree(_A      ,      ''''''			) for k, v in letter.bitstring.items()
  }
  print(f"Huffman Coding  of {file_path}: "			)
  with open(_A			) as f:
    while True:
      a_		    =  f.read(1			)
      if not c:
        break
      print(letters[c]      ,      end=''' '''			)
  print()


if __name__ == "__main__":
  # pass the file path to the huffman function
  huffman(sys.argv[1])
