





"""simple docstring"""


import argparse

import torch

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


logging.set_verbosity_info()
a__					:     Optional[int]							=				logging.get_logger(__name__)





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				WavLMForSequenceClassification.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	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Any:

 '''simple docstring'''






 snake_case__    							=				WavLMForAudioFrameClassification.from_pretrained(_UpperCAmelCase	,     config=_UpperCAmelCase	)
 snake_case__    							=				downstream_dict["model.linear.weight"]
 snake_case__    							=				downstream_dict["model.linear.bias"]
 return model





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  List[str]:

 '''simple docstring'''






 snake_case__    							=				WavLMForXVector.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	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Tuple:

 '''simple docstring'''






 snake_case__    							=				torch.load(_UpperCAmelCase	,     map_location="cpu"	)

 snake_case__    							=				checkpoint["Downstream"]

 snake_case__    							=				WavLMConfig.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__":
   a__					:     Optional[int]							=				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__					:     Any							=				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


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

