'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Optional[int]				)	->							Any:
      '''simple docstring'''


      lowercase_     =      WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      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 (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Optional[Any]				)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      lowercase_     =      downstream_dict["model.linear.weight"]
      lowercase_     =      downstream_dict["model.linear.bias"]
      return model


def __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Dict				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      WavaVecaForXVector.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      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 (	__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Union[str, Any]				)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      checkpoint["Downstream"]

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

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


if __name__ == "__main__":
       lowerCAmelCase_ : str						  =      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_ : Tuple						  =      parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						int				)	->							list[int]:
      '''simple docstring'''


      lowercase_     =      [True] * limit
      lowercase_     =      False
      lowercase_     =      False
      lowercase_     =      True

      for i in range(3 ,		int(limit**0.5 + 1				) ,		2				):
            lowercase_     =      i * 2
            while index < limit:
                  lowercase_     =      False
                  lowercase_     =      index + i

      lowercase_     =      [2]

      for i in range(3 ,		__lowerCamelCase ,		2				):
            if is_prime[i]:
                  primes.append(__lowerCamelCase				)

      return primes


def __a (	__lowerCamelCase			:						int = 1_000_000				)	->							int:
      '''simple docstring'''


      lowercase_     =      prime_sieve(__lowerCamelCase				)
      lowercase_     =      0
      lowercase_     =      0

      for i in range(len(__lowerCamelCase				)				):
            for j in range(i + length ,		len(__lowerCamelCase				)				):
                  lowercase_     =      sum(primes[i:j]				)
                  if sol >= ceiling:
                        break

                  if sol in primes:
                        lowercase_     =      j - i
                        lowercase_     =      sol

      return largest


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






