






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__				   =		logging.get_logger(__name__)
def 						_a    (      a :Union[str, Any]	,							a :List[str]	,							a :List[str] )							->			int:
 a		  =      UniSpeechSatForSequenceClassification.from_pretrained(a	,							config=a )
 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 						_a    (      a :Any	,							a :str	,							a :List[Any] )							->			Tuple:
 a		  =      UniSpeechSatForAudioFrameClassification.from_pretrained(a	,							config=a )
 a		  =      downstream_dict['''model.linear.weight''']
 a		  =      downstream_dict['''model.linear.bias''']
 return model
def 						_a    (      a :Dict	,							a :int	,							a :Union[str, Any] )							->			Dict:
 a		  =      UniSpeechSatForXVector.from_pretrained(a	,							config=a )
 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 						_a    (      a :str	,							a :List[Any]	,							a :Optional[int]	,							a :Any )							->			str:
 a		  =      torch.load(a	,							map_location='''cpu''' )

 a		  =      checkpoint['''Downstream''']

 a		  =      UniSpeechSatConfig.from_pretrained(a )
 a		  =      WavaVecaFeatureExtractor.from_pretrained(
     a	,							return_attention_mask=a	,							do_normalize=a )

 a		  =      hf_config.architectures[0]
 if arch.endswith('''ForSequenceClassification''' ):
  a		  =      convert_classification(a	,							a	,							a )
 elif arch.endswith('''ForAudioFrameClassification''' ):
  a		  =      convert_diarization(a	,							a	,							a )
 elif arch.endswith('''ForXVector''' ):
  a		  =      convert_xvector(a	,							a	,							a )
 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(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)






import unittest

from knapsack import knapsack as k





class   __a			(    unittest.TestCase      ):
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   0
        lowercase__:   List[Any]  			=   [0]
        lowercase__:   str  			=   [0]
        lowercase__:   Tuple  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 0 )

        lowercase__:   Optional[Any]  			=   [60]
        lowercase__:   Dict  			=   [10]
        lowercase__:   str  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 0 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   3
        lowercase__:   List[str]  			=   [1, 2, 3]
        lowercase__:   Union[str, Any]  			=   [3, 2, 1]
        lowercase__:   Union[str, Any]  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 5 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   50
        lowercase__:   str  			=   [60, 100, 120]
        lowercase__:   Any  			=   [10, 20, 30]
        lowercase__:   List[Any]  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 220 )


if __name__ == "__main__":
 unittest.main()


