'''simple docstring'''


import argparse

import torch

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


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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_ ,	config=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       downstream_dict["""projector.weight"""]
  lowercase_     =       downstream_dict["""projector.bias"""]
  lowercase_     =       downstream_dict["""model.post_net.linear.weight"""]
  lowercase_     =       downstream_dict["""model.post_net.linear.bias"""]
  return model

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  lowercase_     =       WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_ ,	config=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       downstream_dict["""model.linear.weight"""]
  lowercase_     =       downstream_dict["""model.linear.bias"""]
  return model

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE_ ,	config=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       downstream_dict["""connector.weight"""]
  lowercase_     =       downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
    lowercase_     =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    lowercase_     =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

  lowercase_     =       downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowercase_     =       downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowercase_     =       downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowercase_     =       downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowercase_     =       downstream_dict["""objective.W"""]
  return model

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)

  lowercase_     =       checkpoint["""Downstream"""]

  lowercase_     =       WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       WavaVecaFeatureExtractor.from_pretrained(
      SCREAMING_SNAKE_CASE_ ,	return_attention_mask=SCREAMING_SNAKE_CASE_ ,	do_normalize=SCREAMING_SNAKE_CASE_						)

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

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

  hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)
  hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __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.""")
       __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 math

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       f"""Input value of [number={number}] must be an integer"""
    raise TypeError(SCREAMING_SNAKE_CASE_						)

  if number < 1:
    lowercase_     =       f"""Input value of [number={number}] must be > 0"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    lowercase_     =       int(math.log(number // 3 ,	2						)						) + 2

    lowercase_     =       [3, 5]
    lowercase_     =       2
    lowercase_     =       3
    for block in range(1 ,	SCREAMING_SNAKE_CASE_						):
      for _ in range(SCREAMING_SNAKE_CASE_						):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]						)
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for number in range(11):
              __snake_case						  =		0
              try:
                     __snake_case						  =		proth(number)
              except ValueError:
                     print(f'''ValueError: there is no {number}th Proth number''')
                     continue

              print(f'''The {number}th Proth number: {value}''')

