






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)









'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    'split_dict'				,  [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42				,  dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]				,  )
def 				lowercase__   (   __lowercase						:  SplitDict     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  split_dict._to_yaml_list()
       assert len(__lowercase     ) == len(__lowercase     )
       __UpperCamelCase       =  SplitDict._from_yaml_list(__lowercase     )
       for split_name, split_info in split_dict.items():
              # dataset_name field is deprecated, and is therefore not part of the YAML dump
              __UpperCamelCase       =  None
              # the split name of split_dict takes over the name of the split info object
              __UpperCamelCase       =  split_name
       assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'				,  [SplitInfo(), SplitInfo(dataset_name=__lowercase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  asdict(SplitDict({'train': split_info}     )     )
       assert "dataset_name" in split_dict_asdict["train"]
       assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
