





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)













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()






