import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase													=		logging.get_logger(__name__)

def     lowercase_       (    _lowerCamelCase							: Any	,			_lowerCamelCase							: List[Any]	,			_lowerCamelCase							: Dict):
    lowercase__				:     Tuple					      =       WavLMForSequenceClassification.from_pretrained(_lowerCamelCase	,			config=_lowerCamelCase)
    lowercase__				:     Any					      =       downstream_dict["""projector.weight"""]
    lowercase__				:     List[str]					      =       downstream_dict["""projector.bias"""]
    lowercase__				:     int					      =       downstream_dict["""model.post_net.linear.weight"""]
    lowercase__				:     Union[str, Any]					      =       downstream_dict["""model.post_net.linear.bias"""]
    return model

def     lowercase_       (    _lowerCamelCase							: List[Any]	,			_lowerCamelCase							: List[Any]	,			_lowerCamelCase							: Tuple):
    lowercase__				:     str					      =       WavLMForAudioFrameClassification.from_pretrained(_lowerCamelCase	,			config=_lowerCamelCase)
    lowercase__				:     Optional[Any]					      =       downstream_dict["""model.linear.weight"""]
    lowercase__				:     Any					      =       downstream_dict["""model.linear.bias"""]
    return model

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: Optional[Any]	,			_lowerCamelCase							: Union[str, Any]):
    lowercase__				:     Tuple					      =       WavLMForXVector.from_pretrained(_lowerCamelCase	,			config=_lowerCamelCase)
    lowercase__				:     int					      =       downstream_dict["""connector.weight"""]
    lowercase__				:     Optional[int]					      =       downstream_dict["""connector.bias"""]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel):
        lowercase__				:     Tuple					      =       downstream_dict[
            f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
        ]
        lowercase__				:     Dict					      =       downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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

@torch.no_grad()
def     lowercase_       (    _lowerCamelCase							: Any	,			_lowerCamelCase							: List[str]	,			_lowerCamelCase							: int	,			_lowerCamelCase							: List[str]):
    lowercase__				:     Optional[int]					      =       torch.load(_lowerCamelCase	,			map_location="cpu")

    lowercase__				:     Optional[Any]					      =       checkpoint["""Downstream"""]

    lowercase__				:     Dict					      =       WavLMConfig.from_pretrained(_lowerCamelCase)
    lowercase__				:     int					      =       WavaVecaFeatureExtractor.from_pretrained(
        _lowerCamelCase	,			return_attention_mask=_lowerCamelCase	,			do_normalize=_lowerCamelCase)

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

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

    hf_feature_extractor.save_pretrained(_lowerCamelCase)
    hf_model.save_pretrained(_lowerCamelCase)


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)




from typing import Callable

def     lowercase_       (    _lowerCamelCase							: Callable):

    def wrapper(_lowerCamelCase							: str):
        lowercase__				:     Tuple					      =       function_with_docstring.__doc__
        return target_function

    return wrapper



