


import argparse

import torch

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


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

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[str]     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   List[Any]							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Optional[int]							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Tuple     ,	SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   str							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __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__":
   _A									=		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									=		parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   list     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=int((lower_bound + upper_bound) // 2							)
     if array[r] == value:
          return r
     if lower_bound >= upper_bound:
          return -1
     if array[r] < value:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	r + 1     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
     else:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	r - 1     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   list							):
     __UpperCamelCase       				=0
     if matrix[index][0] == value:
          return [index, 0]
     while index < len(SCREAMING_SNAKE_CASE__							) and matrix[index][0] < value:
          __UpperCamelCase       				=binary_search(matrix[index]     ,	0     ,	len(matrix[index]							) - 1     ,	SCREAMING_SNAKE_CASE__							)
          if r != -1:
               return [index, r]
          index += 1
     return [-1, -1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





