





from __future__ import annotations

import sys




class  __UpperCamelCase						:





     def __init__(    self							,       _UpperCamelCase							,       _UpperCamelCase				):
        _UpperCAmelCase											=      letter
        _UpperCAmelCase											=      freq
        _UpperCAmelCase											=      {}





     def __repr__(    self				):
        return f'''{self.letter}:{self.freq}'''




class  __UpperCamelCase						:





     def __init__(    self							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				):
        _UpperCAmelCase											=      freq
        _UpperCAmelCase											=      left
        _UpperCAmelCase											=      right




def     A__					(						SCREAMING_SNAKE_CASE_      :			str	)     ->     list[Letter]:






   """simple docstring"""







   _UpperCAmelCase											=      {}
   with open(SCREAMING_SNAKE_CASE_	) as f:
      while True:
         _UpperCAmelCase											=      f.read(1	)
         if not c:
            break
         _UpperCAmelCase											=      chars[c] + 1 if c in chars else 1
   return sorted((Letter(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	) for c, f in chars.items())							,  key=lambda SCREAMING_SNAKE_CASE_	: x.freq	)
def     A__					(						SCREAMING_SNAKE_CASE_      :			list[Letter]	)     ->     Letter | TreeNode:






   """simple docstring"""







   _UpperCAmelCase											=      letters  # type: ignore
   while len(SCREAMING_SNAKE_CASE_	) > 1:
      _UpperCAmelCase											=      response.pop(0	)
      _UpperCAmelCase											=      response.pop(0	)
      _UpperCAmelCase											=      left.freq + right.freq
      _UpperCAmelCase											=      TreeNode(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
      response.append(SCREAMING_SNAKE_CASE_	)
      response.sort(key=lambda SCREAMING_SNAKE_CASE_	: x.freq	)
   return response[0]
def     A__					(						SCREAMING_SNAKE_CASE_      :			Letter | TreeNode							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     list[Letter]:






   """simple docstring"""







   if isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      _UpperCAmelCase											=      bitstring
      return [root]
   _UpperCAmelCase											=      root  # type: ignore
   _UpperCAmelCase											=      []
   letters += traverse_tree(treenode.left							,  bitstring + '''0'''	)
   letters += traverse_tree(treenode.right							,  bitstring + '''1'''	)
   return letters
def     A__					(						SCREAMING_SNAKE_CASE_      :			str	)     ->     None:






   """simple docstring"""







   _UpperCAmelCase											=      parse_file(SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      build_tree(SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      {
       k: v for letter in traverse_tree(SCREAMING_SNAKE_CASE_							,  ''''''	) for k, v in letter.bitstring.items()
   }
   print(F'''Huffman Coding  of {file_path}: '''	)
   with open(SCREAMING_SNAKE_CASE_	) as f:
      while True:
         _UpperCAmelCase											=      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])






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)