

'''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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[str]:
       A_						       =   WavaVecaForSequenceClassification.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__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[Any]:
       A_						       =   WavaVecaForAudioFrameClassification.from_pretrained(UpperCAmelCase__,							config=UpperCAmelCase__					)
       A_						       =   downstream_dict["""model.linear.weight"""]
       A_						       =   downstream_dict["""model.linear.bias"""]
       return model
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      str:
       A_						       =   WavaVecaForXVector.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__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Tuple:
       A_						       =   torch.load(UpperCAmelCase__,							map_location="""cpu"""					)

       A_						       =   checkpoint["""Downstream"""]

       A_						       =   WavaVecaConfig.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__":
      __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'''







from math import pow
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							)      ->      tuple[int, int]:
       if current_sum == needed_sum:
              # If the sum of the powers is equal to needed_sum, then we have a solution.
              solutions_count += 1
              return current_sum, solutions_count

       A_						       =   int(pow(UpperCAmelCase__,							UpperCAmelCase__					)					)
       if current_sum + i_to_n <= needed_sum:
              # If the sum of the powers is less than needed_sum, then continue adding powers.
              current_sum += i_to_n
              A_       ,      A_						       =   backtrack(
                  UpperCAmelCase__,							UpperCAmelCase__,							current_number + 1,							UpperCAmelCase__,							UpperCAmelCase__					)
              current_sum -= i_to_n
       if i_to_n < needed_sum:
              # If the power of i is less than needed_sum, then try with the next power.
              A_       ,      A_						       =   backtrack(
                  UpperCAmelCase__,							UpperCAmelCase__,							current_number + 1,							UpperCAmelCase__,							UpperCAmelCase__					)
       return current_sum, solutions_count
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  """Invalid input\n"""
                  """needed_sum must be between 1 and 1000, power between 2 and 10."""					)

       return backtrack(UpperCAmelCase__,							UpperCAmelCase__,							1,							0,							0					)[1]  # Return the solutions_count


if __name__ == "__main__":
      import doctest

      doctest.testmod()
