




from __future__ import annotations

import sys





class        lowerCamelCase__:
      def __init__(							self:					int       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					int  ):
             __lowerCamelCase  =		letter
             __lowerCamelCase  =		freq
             __lowerCamelCase  =		{}




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





class        lowerCamelCase__:
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					Letter | TreeNode       ,			UpperCamelCase_:					Letter | TreeNode  ):
             __lowerCamelCase  =		freq
             __lowerCamelCase  =		left
             __lowerCamelCase  =		right




def    lowerCamelCase__			(		A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		{}
       with open(A__      ) as f:
              while True:
                     __lowerCamelCase  =		f.read(1      )
                     if not c:
                            break
                     __lowerCamelCase  =		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    lowerCamelCase__			(		A__			:   list[Letter]      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Letter | TreeNode    ,     A__			:   str      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		parse_file(A__      )
       __lowerCamelCase  =		build_tree(A__      )
       __lowerCamelCase  =		{
           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:
                     __lowerCamelCase  =		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    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



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

       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase  =		downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

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

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase  =		checkpoint["""Featurizer"""]["""weights"""]

       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)

