




"""simple docstring"""



import argparse

import torch

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


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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
 A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
 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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
 A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
 A__ =       downstream_dict["model.linear.weight"]
 A__ =       downstream_dict["model.linear.bias"]
 return model







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
 A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
 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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
 A__ =       torch.load(lowercase_    ,				map_location="cpu"		)

 A__ =       checkpoint["Downstream"]

 A__ =       WavLMConfig.from_pretrained(lowercase_		)
 A__ =       WavaVecaFeatureExtractor.from_pretrained(
     lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)

 A__ =       hf_config.architectures[0]
 if arch.endswith("ForSequenceClassification"		):
  A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
 elif arch.endswith("ForAudioFrameClassification"		):
  A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
 elif arch.endswith("ForXVector"		):
  A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
 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(lowercase_		)
 hf_model.save_pretrained(lowercase_		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE       = 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.")
      SCREAMING_SNAKE_CASE       = parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











"""simple docstring"""



import argparse

import torch

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


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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
 A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
 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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
 A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
 A__ =       downstream_dict["model.linear.weight"]
 A__ =       downstream_dict["model.linear.bias"]
 return model







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
 A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
 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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
 A__ =       torch.load(lowercase_    ,				map_location="cpu"		)

 A__ =       checkpoint["Downstream"]

 A__ =       WavLMConfig.from_pretrained(lowercase_		)
 A__ =       WavaVecaFeatureExtractor.from_pretrained(
     lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)

 A__ =       hf_config.architectures[0]
 if arch.endswith("ForSequenceClassification"		):
  A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
 elif arch.endswith("ForAudioFrameClassification"		):
  A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
 elif arch.endswith("ForXVector"		):
  A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
 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(lowercase_		)
 hf_model.save_pretrained(lowercase_		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE       = 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.")
      SCREAMING_SNAKE_CASE       = parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





