






'''simple docstring'''







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_  :     Dict			,						a_  :     Tuple			,						a_  :     Tuple			)   -> Optional[int]:
       UpperCAmelCase__	:   List[Any]												=	WavLMForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE			,						config=__SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__	:   Optional[int]												=	downstream_dict["""projector.weight"""]
       UpperCAmelCase__	:   str												=	downstream_dict["""projector.bias"""]
       UpperCAmelCase__	:   str												=	downstream_dict["""model.post_net.linear.weight"""]
       UpperCAmelCase__	:   str												=	downstream_dict["""model.post_net.linear.bias"""]
       return model






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     Optional[int]			,						a_  :     Union[str, Any]			)   -> int:
       UpperCAmelCase__	:   List[str]												=	WavLMForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE			,						config=__SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__	:   Tuple												=	downstream_dict["""model.linear.weight"""]
       UpperCAmelCase__	:   int												=	downstream_dict["""model.linear.bias"""]
       return model






def  lowerCAmelCase__		(					a_  :     Dict			,						a_  :     int			,						a_  :     str			)   -> List[Any]:
       UpperCAmelCase__	:   Optional[int]												=	WavLMForXVector.from_pretrained(__SCREAMING_SNAKE_CASE			,						config=__SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__	:   Tuple												=	downstream_dict["""connector.weight"""]
       UpperCAmelCase__	:   int												=	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__	:   int												=	downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

       UpperCAmelCase__	:   Optional[int]												=	downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       UpperCAmelCase__	:   List[str]												=	downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       UpperCAmelCase__	:   List[Any]												=	downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       UpperCAmelCase__	:   str												=	downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       UpperCAmelCase__	:   Dict												=	downstream_dict["""objective.W"""]
       return model






@torch.no_grad()
def  lowerCAmelCase__		(					a_  :     Dict			,						a_  :     str			,						a_  :     List[Any]			,						a_  :     List[Any]			)   -> int:
       UpperCAmelCase__	:   Any												=	torch.load(__SCREAMING_SNAKE_CASE			,						map_location='''cpu'''			)

       UpperCAmelCase__	:   str												=	checkpoint["""Downstream"""]

       UpperCAmelCase__	:   int												=	WavLMConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__	:   Any												=	WavaVecaFeatureExtractor.from_pretrained(
           __SCREAMING_SNAKE_CASE			,						return_attention_mask=__SCREAMING_SNAKE_CASE			,						do_normalize=__SCREAMING_SNAKE_CASE			)

       UpperCAmelCase__	:   Any												=	hf_config.architectures[0]
       if arch.endswith('''ForSequenceClassification'''			):
              UpperCAmelCase__	:   List[str]												=	convert_classification(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			)
       elif arch.endswith('''ForAudioFrameClassification'''			):
              UpperCAmelCase__	:   int												=	convert_diarization(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			)
       elif arch.endswith('''ForXVector'''			):
              UpperCAmelCase__	:   int												=	convert_xvector(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			)
       else:
              raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""			)

       if hf_config.use_weighted_layer_sum:
              UpperCAmelCase__	:   Any												=	checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE			)
       hf_model.save_pretrained(__SCREAMING_SNAKE_CASE			)


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)

'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


A__ :		Optional[int]					=logging.get_logger(__name__)







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Union[str, Any]					, *lowerCamelCase      :     Dict					, **lowerCamelCase      :     Optional[Any]							):



   """simple docstring"""





   warnings.warn(
       """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use ViTImageProcessor instead."""					, lowerCamelCase					, )
   super().__init__(*lowerCamelCase					, **lowerCamelCase							)



