def 					__lowercase						(    lowerCamelCase__ = 10_00       )					->int:

  '''simple docstring'''




  a__  		=  0
  a__  		=  (n - 1) // 3
  total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
  a__  		=  (n - 1) // 5
  total += ((terms) * (10 + (terms - 1) * 5)) // 2
  a__  		=  (n - 1) // 15
  total -= ((terms) * (30 + (terms - 1) * 15)) // 2
  return total


if __name__ == "__main__":
     print(F"""{solution() = }""")


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  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"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


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)

