'''simple docstring'''




import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__snake_case   :      Union[str, Any]						    =  logging.get_logger(__name__)





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	int,   _UpperCamelCase    :	Any   )	->      Optional[int]:
    A_     =   WavLMForSequenceClassification.from_pretrained(_UpperCamelCase,   config=_UpperCamelCase   )
    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      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	str   )	->      int:
    A_     =   WavLMForAudioFrameClassification.from_pretrained(_UpperCamelCase,   config=_UpperCamelCase   )
    A_     =   downstream_dict['''model.linear.weight''']
    A_     =   downstream_dict['''model.linear.bias''']
    return model





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Any,   _UpperCamelCase    :	str   )	->      Dict:
    A_     =   WavLMForXVector.from_pretrained(_UpperCamelCase,   config=_UpperCamelCase   )
    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      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	str   )	->      Dict:
    A_     =   torch.load(_UpperCamelCase,   map_location='''cpu'''   )

    A_     =   checkpoint['''Downstream''']

    A_     =   WavLMConfig.from_pretrained(_UpperCamelCase   )
    A_     =   WavaVecaFeatureExtractor.from_pretrained(
        _UpperCamelCase,   return_attention_mask=_UpperCamelCase,   do_normalize=_UpperCamelCase   )

    A_     =   hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''   ):
        A_     =   convert_classification(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
    elif arch.endswith('''ForAudioFrameClassification'''   ):
        A_     =   convert_diarization(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
    elif arch.endswith('''ForXVector'''   ):
        A_     =   convert_xvector(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
    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(_UpperCamelCase   )
    hf_model.save_pretrained(_UpperCamelCase   )


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













"""simple docstring"""




def        a_				(							_lowercase = 10**6   ):

						if n <= 0:
												raise ValueError('''Please enter an integer greater than 0'''   )

						_UpperCamelCase			:					Any     =    list(range(n + 1   )   )
						for number in range(2       ,						n + 1   ):
												if phi[number] == number:
																		phi[number] -= 1
																		for multiple in range(number * 2       ,						n + 1       ,						_lowercase   ):
																								_UpperCamelCase			:					List[str]     =    (phi[multiple] // number) * (number - 1)

						_UpperCamelCase			:					Union[str, Any]     =    1
						for number in range(1       ,						n + 1   ):
												if (answer / phi[answer]) < (number / phi[number]):
																		_UpperCamelCase			:					List[str]     =    number

						return answer


if __name__ == "__main__":
						print(solution())



