



"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase  			=		logging.get_logger(__name__)
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase_      ,     config=UpperCamelCase_ )
      UpperCAmelCase_     					=				downstream_dict["projector.weight"]
      UpperCAmelCase_     					=				downstream_dict["projector.bias"]
      UpperCAmelCase_     					=				downstream_dict["model.post_net.linear.weight"]
      UpperCAmelCase_     					=				downstream_dict["model.post_net.linear.bias"]
      return model
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase_      ,     config=UpperCamelCase_ )
      UpperCAmelCase_     					=				downstream_dict["model.linear.weight"]
      UpperCAmelCase_     					=				downstream_dict["model.linear.bias"]
      return model
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				WavaVecaForXVector.from_pretrained(UpperCamelCase_      ,     config=UpperCamelCase_ )
      UpperCAmelCase_     					=				downstream_dict["connector.weight"]
      UpperCAmelCase_     					=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
            UpperCAmelCase_     					=				downstream_dict[
                f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
            ]
            UpperCAmelCase_     					=				downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

      UpperCAmelCase_     					=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCAmelCase_     					=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCAmelCase_     					=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCAmelCase_     					=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCAmelCase_     					=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				torch.load(UpperCamelCase_      ,     map_location="cpu" )

      UpperCAmelCase_     					=				checkpoint["Downstream"]

      UpperCAmelCase_     					=				WavaVecaConfig.from_pretrained(UpperCamelCase_ )
      UpperCAmelCase_     					=				WavaVecaFeatureExtractor.from_pretrained(
          UpperCamelCase_      ,     return_attention_mask=UpperCamelCase_      ,     do_normalize=UpperCamelCase_ )

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

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

      hf_feature_extractor.save_pretrained(UpperCamelCase_ )
      hf_model.save_pretrained(UpperCamelCase_ )


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








'''simple docstring'''
def       a_				(	UpperCamelCase_ ):

							if length <= 0 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise ValueError("Length must be a positive integer." )
							return [n * (2 * n - 1) for n in range(UpperCamelCase_ )]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=10))





