





import argparse

import torch

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


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






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  Tuple       ,      snake_case_	:  Optional[int]       )      ->				str:
   __snake_case							       =   WavaVecaForSequenceClassification.from_pretrained(snake_case_       ,      config=snake_case_       )
   __snake_case							       =   downstream_dict['''projector.weight''']
   __snake_case							       =   downstream_dict['''projector.bias''']
   __snake_case							       =   downstream_dict['''model.post_net.linear.weight''']
   __snake_case							       =   downstream_dict['''model.post_net.linear.bias''']
   return model






def lowerCamelCase__    (							snake_case_	:  Dict       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  Tuple       )      ->				str:
   __snake_case							       =   WavaVecaForAudioFrameClassification.from_pretrained(snake_case_       ,      config=snake_case_       )
   __snake_case							       =   downstream_dict['''model.linear.weight''']
   __snake_case							       =   downstream_dict['''model.linear.bias''']
   return model






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  str       ,      snake_case_	:  List[str]       )      ->				Dict:
   __snake_case							       =   WavaVecaForXVector.from_pretrained(snake_case_       ,      config=snake_case_       )
   __snake_case							       =   downstream_dict['''connector.weight''']
   __snake_case							       =   downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
      __snake_case							       =   downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      __snake_case							       =   downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   __snake_case							       =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   __snake_case							       =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   __snake_case							       =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   __snake_case							       =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   __snake_case							       =   downstream_dict['''objective.W''']
   return model






@torch.no_grad()
def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  List[Any]       ,      snake_case_	:  str       ,      snake_case_	:  List[str]       )      ->				Tuple:
   __snake_case							       =   torch.load(snake_case_       ,      map_location='''cpu'''       )

   __snake_case							       =   checkpoint['''Downstream''']

   __snake_case							       =   WavaVecaConfig.from_pretrained(snake_case_       )
   __snake_case							       =   WavaVecaFeatureExtractor.from_pretrained(
       snake_case_       ,      return_attention_mask=snake_case_       ,      do_normalize=snake_case_       )

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

   if hf_config.use_weighted_layer_sum:
      __snake_case							       =   checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(snake_case_       )
   hf_model.save_pretrained(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)








from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       )      ->				list[list[int]]:
   __snake_case							       =   []
   __snake_case							       =   []
   __snake_case							       =   0
   __snake_case							       =   sum(snake_case_       )
   create_state_space_tree(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
   return result






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       ,      snake_case_	:  int       ,      snake_case_	:  list[int]       ,      snake_case_	:  list[list[int]]       ,      snake_case_	:  int       ,      )      ->				None:
   if sum(snake_case_       ) > max_sum or (remaining_nums_sum + sum(snake_case_       )) < max_sum:
      return
   if sum(snake_case_       ) == max_sum:
      result.append(snake_case_       )
      return
   for index in range(snake_case_       ,      len(snake_case_       )       ):
      create_state_space_tree(
          snake_case_       ,      snake_case_       ,      index + 1       ,      [*path, nums[index]]       ,      snake_case_       ,      remaining_nums_sum - nums[index]       ,      )
snake_case_				=			[3, 34, 4, 12, 5, 2]
snake_case_				=			9
snake_case_				=			generate_sum_of_subsets_soln(nums, max_sum)







print(*result)

