code: 
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  lowerCAmelCase_   =   logging.get_logger(__name__)
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict:
         _snake_case      :							List[Any]     				=						WavLMForSequenceClassification.from_pretrained(lowercase_      ,		config=lowercase_  )
         _snake_case      :							Optional[int]     				=						downstream_dict['''projector.weight''']
         _snake_case      :							Union[str, Any]     				=						downstream_dict['''projector.bias''']
         _snake_case      :							Union[str, Any]     				=						downstream_dict['''model.post_net.linear.weight''']
         _snake_case      :							Tuple     				=						downstream_dict['''model.post_net.linear.bias''']
         return model
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							Optional[Any]     				=						WavLMForAudioFrameClassification.from_pretrained(lowercase_      ,		config=lowercase_  )
         _snake_case      :							Optional[int]     				=						downstream_dict['''model.linear.weight''']
         _snake_case      :							Any     				=						downstream_dict['''model.linear.bias''']
         return model
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict:
         _snake_case      :							str     				=						WavLMForXVector.from_pretrained(lowercase_      ,		config=lowercase_  )
         _snake_case      :							Tuple     				=						downstream_dict['''connector.weight''']
         _snake_case      :							int     				=						downstream_dict['''connector.bias''']
         for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
                _snake_case      :							List[Any]     				=						downstream_dict[
                    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
                ]
                _snake_case      :							List[Any]     				=						downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
  
         _snake_case      :							str     				=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
         _snake_case      :							Dict     				=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
         _snake_case      :							Dict     				=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
         _snake_case      :							List[Any]     				=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
         _snake_case      :							Dict     				=						downstream_dict['''objective.W''']
         return model
  
  
  
  
  @torch.no_grad()
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         _snake_case      :							Union[str, Any]     				=						torch.load(lowercase_      ,		map_location='''cpu'''  )
  
         _snake_case      :							Any     				=						checkpoint['''Downstream''']
  
         _snake_case      :							int     				=						WavLMConfig.from_pretrained(lowercase_  )
         _snake_case      :							Union[str, Any]     				=						WavaVecaFeatureExtractor.from_pretrained(
             lowercase_      ,		return_attention_mask=lowercase_      ,		do_normalize=lowercase_  )
  
         _snake_case      :							Optional[int]     				=						hf_config.architectures[0]
         if arch.endswith('''ForSequenceClassification'''  ):
                _snake_case      :							Optional[int]     				=						convert_classification(lowercase_      ,		lowercase_      ,		lowercase_  )
         elif arch.endswith('''ForAudioFrameClassification'''  ):
                _snake_case      :							Any     				=						convert_diarization(lowercase_      ,		lowercase_      ,		lowercase_  )
         elif arch.endswith('''ForXVector'''  ):
                _snake_case      :							str     				=						convert_xvector(lowercase_      ,		lowercase_      ,		lowercase_  )
         else:
                raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''  )
  
         if hf_config.use_weighted_layer_sum:
                _snake_case      :							Any     				=						checkpoint['''Featurizer''']['''weights''']
  
         hf_feature_extractor.save_pretrained(lowercase_  )
         hf_model.save_pretrained(lowercase_  )
  
  
  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)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import cva
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
  
         try:
                return int(image[x_coordinate][y_coordinate] >= center  )
         except (IndexError, TypeError):
                return 0
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
         _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]
  
         # skip get_neighbors_pixel if center is null
         if center is None:
                return 0
  
         # Starting from the top right, assigning value to pixels clockwise
         _snake_case      :							Any     				=						[
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
         ]
  
         # Converting the binary value to decimal.
         return sum(
             binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )
  
  
  if __name__ == "__main__":
     # Reading the image and converting it to grayscale.
     lowerCAmelCase_   =   cva.imread(
         "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
     )
  
     # Create a numpy array as the same height and width of read image
     lowerCAmelCase_   =   np.zeros((image.shape[0], image.shape[1]))
  
     # Iterating through the image and calculating the
     # local binary pattern value for each pixel.
     for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
           lowerCAmelCase_   =   local_binary_value(image, i, j)
  
     cva.imshow("local binary pattern", lbp_image)
     cva.waitKey(0)
     cva.destroyAllWindows()
  
  
  
  
  
  
  