'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : Tuple					,    snake_case_       : int			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   WavaVecaForSequenceClassification.from_pretrained(snake_case_					,    config=snake_case_			)
     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 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Dict					,    snake_case_       : List[str]			)    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   WavaVecaForAudioFrameClassification.from_pretrained(snake_case_					,    config=snake_case_			)
     UpperCAmelCase_           =   downstream_dict["model.linear.weight"]
     UpperCAmelCase_           =   downstream_dict["model.linear.bias"]
     return model





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : List[str]					,    snake_case_       : Union[str, Any]			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   WavaVecaForXVector.from_pretrained(snake_case_					,    config=snake_case_			)
     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 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Tuple					,    snake_case_       : List[Any]					,    snake_case_       : Dict			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   torch.load(snake_case_					,    map_location="cpu"			)

     UpperCAmelCase_           =   checkpoint["Downstream"]

     UpperCAmelCase_           =   WavaVecaConfig.from_pretrained(snake_case_			)
     UpperCAmelCase_           =   WavaVecaFeatureExtractor.from_pretrained(
         snake_case_					,    return_attention_mask=snake_case_					,    do_normalize=snake_case_			)

     UpperCAmelCase_           =   hf_config.architectures[0]
     if arch.endswith("ForSequenceClassification"			):
          UpperCAmelCase_           =   convert_classification(snake_case_					,    snake_case_					,    snake_case_			)
     elif arch.endswith("ForAudioFrameClassification"			):
          UpperCAmelCase_           =   convert_diarization(snake_case_					,    snake_case_					,    snake_case_			)
     elif arch.endswith("ForXVector"			):
          UpperCAmelCase_           =   convert_xvector(snake_case_					,    snake_case_					,    snake_case_			)
     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(snake_case_			)
     hf_model.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     int  =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.')
    SCREAMING_SNAKE_CASE_:     List[str]  =parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





'''simple docstring'''





from __future__ import annotations





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : int | None = None					,    snake_case_       : int | None = None			)    ->      None:


     '''simple docstring'''




     if start is None:
          UpperCAmelCase_           =   0

     if end is None:
          UpperCAmelCase_           =   len(snake_case_			) - 1

     if start >= end:
          return

     UpperCAmelCase_           =   (start + end) // 2

     slowsort(snake_case_					,    snake_case_					,    snake_case_			)
     slowsort(snake_case_					,    mid + 1					,    snake_case_			)

     if sequence[end] < sequence[mid]:
          UpperCAmelCase_    , UpperCAmelCase_           =   sequence[mid], sequence[end]

     slowsort(snake_case_					,    snake_case_					,    end - 1			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()




