


import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase      			=  logging.get_logger(__name__)
def 	_lowerCamelCase  (		A_  :	Optional[Any]							,  A_  :	int							,  A_  :	List[Any]      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[int] 						=WavLMForSequenceClassification.from_pretrained(A_							,  config=A_      )
   UpperCamelCase__ :			Optional[int] 						=downstream_dict["projector.weight"]
   UpperCamelCase__ :			List[str] 						=downstream_dict["projector.bias"]
   UpperCamelCase__ :			int 						=downstream_dict["model.post_net.linear.weight"]
   UpperCamelCase__ :			Optional[int] 						=downstream_dict["model.post_net.linear.bias"]
   return model
def 	_lowerCamelCase  (		A_  :	Dict							,  A_  :	List[str]							,  A_  :	str      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=WavLMForAudioFrameClassification.from_pretrained(A_							,  config=A_      )
   UpperCamelCase__ :			List[Any] 						=downstream_dict["model.linear.weight"]
   UpperCamelCase__ :			Optional[int] 						=downstream_dict["model.linear.bias"]
   return model
def 	_lowerCamelCase  (		A_  :	List[str]							,  A_  :	List[Any]							,  A_  :	List[str]      )			->			Tuple:

   '''simple docstring'''






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

   UpperCamelCase__ :			List[Any] 						=downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCamelCase__ :			Optional[int] 						=downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCamelCase__ :			Union[str, Any] 						=downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCamelCase__ :			Any 						=downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCamelCase__ :			List[Any] 						=downstream_dict["objective.W"]
   return model
@torch.no_grad()
def 	_lowerCamelCase  (		A_  :	str							,  A_  :	str							,  A_  :	Optional[int]							,  A_  :	List[str]      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.load(A_							,  map_location="cpu"      )

   UpperCamelCase__ :			Any 						=checkpoint["Downstream"]

   UpperCamelCase__ :			Any 						=WavLMConfig.from_pretrained(A_      )
   UpperCamelCase__ :			Tuple 						=WavaVecaFeatureExtractor.from_pretrained(
       A_							,  return_attention_mask=A_							,  do_normalize=A_      )

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

   hf_feature_extractor.save_pretrained(A_      )
   hf_model.save_pretrained(A_      )


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





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	List[Any]=2_5_6      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=min(img.size      )

   if s < target_image_size:
      raise ValueError(f'''min dim for image {s} < {target_image_size}'''      )

   UpperCamelCase__ :			Optional[Any] 						=target_image_size / s
   UpperCamelCase__ :			Optional[Any] 						=(round(r * img.size[1]      ), round(r * img.size[0]      ))
   UpperCamelCase__ :			List[Any] 						=TF.resize(A_							,  A_							,  interpolation=PIL.Image.LANCZOS      )
   UpperCamelCase__ :			Optional[int] 						=TF.center_crop(A_							,  output_size=2 * [target_image_size]      )
   UpperCamelCase__ :			Union[str, Any] 						=torch.unsqueeze(T.ToTensor()(A_      )							,  0      )
   return img
def 	_lowerCamelCase  (		A_  :	Optional[int]      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=2.0 * x - 1.0
   return x
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Any=True							,  A_  :	int="RGB"      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=x.detach().cpu()
   if process:
      UpperCamelCase__ :			Optional[int] 						=post_process_tensor(A_      )
   UpperCamelCase__ :			Optional[int] 						=x.numpy()
   if process:
      UpperCamelCase__ :			Dict 						=(2_5_5 * x).astype(np.uinta      )
   UpperCamelCase__ :			int 						=Image.fromarray(A_      )
   if not x.mode == mode:
      UpperCamelCase__ :			Tuple 						=x.convert(A_      )
   return x
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.clamp(A_							,  -1.0							,  1.0      )
   UpperCamelCase__ :			Union[str, Any] 						=(x + 1.0) / 2.0
   UpperCamelCase__ :			Union[str, Any] 						=x.permute(1							,  2							,  0      )
   return x
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=post_process_tensor(x.squeeze()      )
   return x.permute(2							,  0							,  1      ).unsqueeze(0      )

