




"""simple docstring"""



import argparse

import torch

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


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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Optional[int]:
 A__	=    UniSpeechSatForSequenceClassification.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__	=    UniSpeechSatForAudioFrameClassification.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_						)						->		Optional[Any]:
 A__	=    UniSpeechSatForXVector.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_						)						->		Any:
 A__	=    torch.load(lowercase_      ,			map_location="cpu"						)

 A__	=    checkpoint["Downstream"]

 A__	=    UniSpeechSatConfig.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"""



SCREAMING_SNAKE_CASE    =	"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE    =	[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE    =	{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





