





import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase :  Tuple										=  logging.get_logger(__name__)



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Union[str, Any]:
 UpperCamelCase			:      List[Any] 			=     UniSpeechSatForSequenceClassification.from_pretrained(__UpperCAmelCase							,     config=__UpperCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     downstream_dict["projector.weight"]
 UpperCamelCase			:      Union[str, Any] 			=     downstream_dict["projector.bias"]
 UpperCamelCase			:      str 			=     downstream_dict["model.post_net.linear.weight"]
 UpperCamelCase			:      int 			=     downstream_dict["model.post_net.linear.bias"]
 return model



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      str 			=     UniSpeechSatForAudioFrameClassification.from_pretrained(__UpperCAmelCase							,     config=__UpperCAmelCase  )
 UpperCamelCase			:      Any 			=     downstream_dict["model.linear.weight"]
 UpperCamelCase			:      Union[str, Any] 			=     downstream_dict["model.linear.bias"]
 return model



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				int:
 UpperCamelCase			:      Optional[Any] 			=     UniSpeechSatForXVector.from_pretrained(__UpperCAmelCase							,     config=__UpperCAmelCase  )
 UpperCamelCase			:      Union[str, Any] 			=     downstream_dict["connector.weight"]
 UpperCamelCase			:      Any 			=     downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
  UpperCamelCase			:      Union[str, Any] 			=     downstream_dict[
      F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  UpperCamelCase			:      Union[str, Any] 			=     downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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



@torch.no_grad()
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Dict 			=     torch.load(__UpperCAmelCase							,     map_location="cpu"  )

 UpperCamelCase			:      Tuple 			=     checkpoint["Downstream"]

 UpperCamelCase			:      Optional[Any] 			=     UniSpeechSatConfig.from_pretrained(__UpperCAmelCase  )
 UpperCamelCase			:      List[str] 			=     WavaVecaFeatureExtractor.from_pretrained(
     __UpperCAmelCase							,     return_attention_mask=__UpperCAmelCase							,     do_normalize=__UpperCAmelCase  )

 UpperCamelCase			:      Any 			=     hf_config.architectures[0]
 if arch.endswith("ForSequenceClassification"  ):
  UpperCamelCase			:      List[str] 			=     convert_classification(__UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase  )
 elif arch.endswith("ForAudioFrameClassification"  ):
  UpperCamelCase			:      Optional[Any] 			=     convert_diarization(__UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase  )
 elif arch.endswith("ForXVector"  ):
  UpperCamelCase			:      Optional[int] 			=     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			:      Tuple 			=     checkpoint["Featurizer"]["weights"]

 hf_feature_extractor.save_pretrained(__UpperCAmelCase  )
 hf_model.save_pretrained(__UpperCAmelCase  )


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 :  Union[str, Any]										=  parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







from PIL import Image



def 	A_       (  _lowerCAmelCase  )		->				Image:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     image.size
 UpperCamelCase			:      Union[str, Any] 			=     0
 UpperCamelCase			:      List[str] 			=     image.load()
 for i in range(_lowerCAmelCase  ):
  for j in range(_lowerCAmelCase  ):
   UpperCamelCase			:      List[Any] 			=     pixels[j, i]
   mean += pixel
 mean //= width * height

 for j in range(_lowerCAmelCase  ):
  for i in range(_lowerCAmelCase  ):
   UpperCamelCase			:      Union[str, Any] 			=     255 if pixels[i, j] > mean else 0
 return image


if __name__ == "__main__":
    __lowerCamelCase :  Union[str, Any]										=  mean_threshold(Image.open("""path_to_image""").convert("""L"""))
    image.save("""output_image_path""")
