
import argparse

import torch

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


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






def 						__magic_name__			(   __a      :	str	,		__a      :	int	,		__a      :	List[Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    WavaVecaForSequenceClassification.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 						__magic_name__			(   __a      :	Tuple	,		__a      :	Any	,		__a      :	List[str]						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Union[str, Any]	,		__a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    WavaVecaForXVector.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 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	Any						):



     '''simple docstring'''




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

     UpperCamelCase__				=    checkpoint["""Downstream"""]

     UpperCamelCase__				=    WavaVecaConfig.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__":
    lowerCamelCase_						     =					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.''')
    lowerCamelCase_						     =					parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_						     =					{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
    from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





