

import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase     :   List[str]						     =  logging.get_logger(__name__)

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  str           =     WavaVecaForSequenceClassification.from_pretrained(a					,   config=a  )
      SCREAMING_SNAKE_CASE_				:  str           =     downstream_dict['projector.weight']
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     downstream_dict['projector.bias']
      SCREAMING_SNAKE_CASE_				:  List[str]           =     downstream_dict['model.post_net.linear.weight']
      SCREAMING_SNAKE_CASE_				:  Dict           =     downstream_dict['model.post_net.linear.bias']
      return model

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     WavaVecaForAudioFrameClassification.from_pretrained(a					,   config=a  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     downstream_dict['model.linear.weight']
      SCREAMING_SNAKE_CASE_				:  str           =     downstream_dict['model.linear.bias']
      return model

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Any           =     WavaVecaForXVector.from_pretrained(a					,   config=a  )
      SCREAMING_SNAKE_CASE_				:  int           =     downstream_dict['connector.weight']
      SCREAMING_SNAKE_CASE_				:  Any           =     downstream_dict['connector.bias']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     downstream_dict[
                f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
            ]
            SCREAMING_SNAKE_CASE_				:  str           =     downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
      SCREAMING_SNAKE_CASE_				:  List[str]           =     downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
      SCREAMING_SNAKE_CASE_				:  Dict           =     downstream_dict['objective.W']
      return model

@torch.no_grad()
def    A_  (							a					,   a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  str           =     torch.load(a					,   map_location='cpu'  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     checkpoint['Downstream']

      SCREAMING_SNAKE_CASE_				:  str           =     WavaVecaConfig.from_pretrained(a  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     WavaVecaFeatureExtractor.from_pretrained(
          a					,   return_attention_mask=a					,   do_normalize=a  )

      SCREAMING_SNAKE_CASE_				:  int           =     hf_config.architectures[0]
      if arch.endswith('ForSequenceClassification'  ):
            SCREAMING_SNAKE_CASE_				:  List[str]           =     convert_classification(a					,   a					,   a  )
      elif arch.endswith('ForAudioFrameClassification'  ):
            SCREAMING_SNAKE_CASE_				:  Tuple           =     convert_diarization(a					,   a					,   a  )
      elif arch.endswith('ForXVector'  ):
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     convert_xvector(a					,   a					,   a  )
      else:
            raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"  )

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

      hf_feature_extractor.save_pretrained(a  )
      hf_model.save_pretrained(a  )


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









def    A_  (							a					,   a  ):

      """simple docstring"""



      return int((input_a, input_a).count(0  ) != 0  )

def    A_  (							):

      """simple docstring"""



      assert nand_gate(0					,   0  ) == 1
      assert nand_gate(0					,   1  ) == 1
      assert nand_gate(1					,   0  ) == 1
      assert nand_gate(1					,   1  ) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






