
'''simple docstring'''


import argparse

import torch

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


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



def  __snake_case     (    _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Optional[Any]):
   UpperCamelCase       =  WavaVecaForSequenceClassification.from_pretrained(_UpperCAmelCase,      config=_UpperCAmelCase)
   UpperCamelCase       =  downstream_dict['''projector.weight''']
   UpperCamelCase       =  downstream_dict['''projector.bias''']
   UpperCamelCase       =  downstream_dict['''model.post_net.linear.weight''']
   UpperCamelCase       =  downstream_dict['''model.post_net.linear.bias''']
   return model



def  __snake_case     (    _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Tuple):
   UpperCamelCase       =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCAmelCase,      config=_UpperCAmelCase)
   UpperCamelCase       =  downstream_dict['''model.linear.weight''']
   UpperCamelCase       =  downstream_dict['''model.linear.bias''']
   return model



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       List[Any]):
   UpperCamelCase       =  WavaVecaForXVector.from_pretrained(_UpperCAmelCase,      config=_UpperCAmelCase)
   UpperCamelCase       =  downstream_dict['''connector.weight''']
   UpperCamelCase       =  downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel):
      UpperCamelCase       =  downstream_dict[
          f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
      ]
      UpperCamelCase       =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Optional[Any]):
   UpperCamelCase       =  torch.load(_UpperCAmelCase,      map_location='''cpu''')

   UpperCamelCase       =  checkpoint['''Downstream''']

   UpperCamelCase       =  WavaVecaConfig.from_pretrained(_UpperCAmelCase)
   UpperCamelCase       =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCAmelCase,      return_attention_mask=_UpperCAmelCase,      do_normalize=_UpperCAmelCase)

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

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

   hf_feature_extractor.save_pretrained(_UpperCAmelCase)
   hf_model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
   snake_case_ :  List[Any]     =					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_ :  str     =					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  __snake_case     (    _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  len(_UpperCAmelCase) // 2

   # choose the middle 3 elements
   UpperCamelCase       =  lst[m - 1 : m + 2]

   # if middle element is peak
   if three[1] > three[0] and three[1] > three[2]:
      return three[1]

   # if increasing, recurse on right
   elif three[0] < three[2]:
      if len(lst[:m]) == 2:
         m -= 1
      return peak(lst[m:])

   # decreasing
   else:
      if len(lst[:m]) == 2:
         m += 1
      return peak(lst[:m])


if __name__ == "__main__":
   import doctest

   doctest.testmod()





