


'''simple docstring'''







from __future__ import annotations

import sys
class    lowercase_	:


    def __init__(	self    ,   a    ,   a       ):
          UpperCamelCase__      =							letter
          UpperCamelCase__      =							freq
          UpperCamelCase__      =							{}


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

class    lowercase_	:


    def __init__(	self    ,   a    ,   a    ,   a       ):
          UpperCamelCase__      =							freq
          UpperCamelCase__      =							left
          UpperCamelCase__      =							right







def _UpperCamelCase			(	__A       )				->						list[Letter]:


      '''simple docstring'''



      UpperCamelCase__      =							{}
      with open(__A       ) 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(__A ,     __A       ) for c, f in chars.items()) ,     key=lambda __A       : x.freq       )







def _UpperCamelCase			(	__A       )				->						Letter | TreeNode:


      '''simple docstring'''



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







def _UpperCamelCase			(	__A ,     __A       )				->						list[Letter]:


      '''simple docstring'''



      if isinstance(__A ,     __A       ):
            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 _UpperCamelCase			(	__A       )				->						None:


      '''simple docstring'''



      UpperCamelCase__      =							parse_file(__A       )
      UpperCamelCase__      =							build_tree(__A       )
      UpperCamelCase__      =							{
          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:
                  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])









'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
a__    :   Optional[Any]								=			logging.get_logger(__name__)







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      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 _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForXVector.from_pretrained(__A ,     config=__A       )
      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 _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

      UpperCamelCase__      =							hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"       ):
            UpperCamelCase__      =							convert_classification(__A ,     __A ,     __A       )
      elif arch.endswith("ForAudioFrameClassification"       ):
            UpperCamelCase__      =							convert_diarization(__A ,     __A ,     __A       )
      elif arch.endswith("ForXVector"       ):
            UpperCamelCase__      =							convert_xvector(__A ,     __A ,     __A       )
      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(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, Any]								=			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.')
  a__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





