


'''simple docstring'''

import argparse

import torch

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


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






def __UpperCamelCase (      __lowerCamelCase		:			Union[str, Any]   ,	__lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			str					)   ->  str:







     '''simple docstring'''






     _a            =		WavLMForSequenceClassification.from_pretrained(lowerCAmelCase_   ,	config=lowerCAmelCase_					)
     _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''']
     return model






def __UpperCamelCase (      __lowerCamelCase		:			str   ,	__lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			List[Any]					)   ->  Optional[int]:







     '''simple docstring'''






     _a            =		WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase_   ,	config=lowerCAmelCase_					)
     _a            =		downstream_dict['''model.linear.weight''']
     _a            =		downstream_dict['''model.linear.bias''']
     return model






def __UpperCamelCase (      __lowerCamelCase		:			List[Any]   ,	__lowerCamelCase		:			Any   ,	__lowerCamelCase		:			Any					)   ->  Optional[int]:







     '''simple docstring'''






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

     _a            =		downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
     _a            =		downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
     _a            =		downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
     _a            =		downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
     _a            =		downstream_dict['''objective.W''']
     return model






@torch.no_grad()
def __UpperCamelCase (      __lowerCamelCase		:			Any   ,	__lowerCamelCase		:			Tuple   ,	__lowerCamelCase		:			str   ,	__lowerCamelCase		:			Tuple					)   ->  str:







     '''simple docstring'''






     _a            =		torch.load(lowerCAmelCase_   ,	map_location="cpu"					)

     _a            =		checkpoint['''Downstream''']

     _a            =		WavLMConfig.from_pretrained(lowerCAmelCase_					)
     _a            =		WavaVecaFeatureExtractor.from_pretrained(
         lowerCAmelCase_   ,	return_attention_mask=lowerCAmelCase_   ,	do_normalize=lowerCAmelCase_					)

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

     if hf_config.use_weighted_layer_sum:
          _a            =		checkpoint['''Featurizer''']['''weights''']

     hf_feature_extractor.save_pretrained(lowerCAmelCase_					)
     hf_model.save_pretrained(lowerCAmelCase_					)


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








'''simple docstring'''

def  _a     (				lowerCAmelCase_ = 4_000_000						):





      """simple docstring"""



      _snake_case		:						Optional[int]         =  1
      _snake_case		:						Tuple         =  2
      _snake_case		:						Optional[Any]         =  0
      while j <= n:
            if j % 2 == 0:
                  total += j
            _snake_case  ,				_snake_case		:						str         =  j, i + j

      return total


if __name__ == "__main__":
   print(F"""{solution() = }""")


