




'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase_						:      List[str]       =						logging.get_logger(__name__)





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   List[Any]   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase ,						config=_UpperCamelCase   )
     _snake_case							=				downstream_dict["projector.weight"]
     _snake_case							=				downstream_dict["projector.bias"]
     _snake_case							=				downstream_dict["model.post_net.linear.weight"]
     _snake_case							=				downstream_dict["model.post_net.linear.bias"]
     return model





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   List[str] ,						_UpperCamelCase:   List[Any]   )						->			Dict:

     """simple docstring"""




     _snake_case							=				WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase ,						config=_UpperCamelCase   )
     _snake_case							=				downstream_dict["model.linear.weight"]
     _snake_case							=				downstream_dict["model.linear.bias"]
     return model





def      __a     (       _UpperCamelCase:   List[str] ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   str   )						->			List[Any]:

     """simple docstring"""




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

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





@torch.no_grad()
def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Any ,						_UpperCamelCase:   str ,						_UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     _snake_case							=				torch.load(_UpperCamelCase ,						map_location="cpu"   )

     _snake_case							=				checkpoint["Downstream"]

     _snake_case							=				WavaVecaConfig.from_pretrained(_UpperCamelCase   )
     _snake_case							=				WavaVecaFeatureExtractor.from_pretrained(
         _UpperCamelCase ,						return_attention_mask=_UpperCamelCase ,						do_normalize=_UpperCamelCase   )

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

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

     hf_feature_extractor.save_pretrained(_UpperCamelCase   )
     hf_model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCamelCase_						:      List[str]       =						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_						:      str       =						parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


