


'''simple docstring'''







import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a__    :   str								=			logging.get_logger(__name__)
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      'hybrid-clip'
    __UpperCAmelCase       =      True


    def __init__(	self    ,   a=5_12    ,   **a       ):
          super().__init__(**a       )

          if "text_config" not in kwargs:
                raise ValueError("`text_config` can not be `None`."       )

          if "vision_config" not in kwargs:
                raise ValueError("`vision_config` can not be `None`."       )

          UpperCamelCase__      =							kwargs.pop("text_config"       )
          UpperCamelCase__      =							kwargs.pop("vision_config"       )

          UpperCamelCase__      =							text_config.pop("model_type"       )
          UpperCamelCase__      =							vision_config.pop("model_type"       )

          from transformers import AutoConfig

          UpperCamelCase__      =							AutoConfig.for_model(a    ,   **a       )

          if vision_model_type == "clip":
                UpperCamelCase__      =							AutoConfig.for_model(a    ,   **a       ).vision_config
          elif vision_model_type == "clip_vision_model":
                from transformers import CLIPVisionConfig

                UpperCamelCase__      =							CLIPVisionConfig(**a       )
          else:
                UpperCamelCase__      =							AutoConfig.for_model(a    ,   **a       )

          UpperCamelCase__      =							projection_dim
          UpperCamelCase__      =							1.0


    @classmethod
    def   __a				(	cls    ,   a    ,   a    ,   **a       ):
          return cls(text_config=text_config.to_dict()    ,   vision_config=vision_config.to_dict()    ,   **a       )


    def   __a				(	self       ):
          UpperCamelCase__      =							copy.deepcopy(self.__dict__       )
          UpperCamelCase__      =							self.text_config.to_dict()
          UpperCamelCase__      =							self.vision_config.to_dict()
          UpperCamelCase__      =							self.__class__.model_type
          return output









'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
a__    :   Optional[Any]								=			logging.get_logger(__name__)







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["projector.weight"]
      UpperCamelCase__      =							downstream_dict["projector.bias"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForXVector.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["connector.weight"]
      UpperCamelCase__      =							downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
            UpperCamelCase__      =							downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCamelCase__      =							downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCamelCase__      =							downstream_dict["objective.W"]
      return model







@torch.no_grad()
def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

      UpperCamelCase__      =							hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"       ):
            UpperCamelCase__      =							convert_classification(__A ,     __A ,     __A       )
      elif arch.endswith("ForAudioFrameClassification"       ):
            UpperCamelCase__      =							convert_diarization(__A ,     __A ,     __A       )
      elif arch.endswith("ForXVector"       ):
            UpperCamelCase__      =							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__      =							checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, 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.')
  a__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





