





'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    str					,       __lowerCamelCase :    List[Any]    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			UniSpeechSatForSequenceClassification.from_pretrained(__lowerCamelCase					,       config=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""projector.weight"""]
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""projector.bias"""]
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""model.post_net.linear.weight"""]
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""model.post_net.linear.bias"""]
    return model





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			UniSpeechSatForAudioFrameClassification.from_pretrained(__lowerCamelCase					,       config=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""model.linear.weight"""]
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""model.linear.bias"""]
    return model





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Dict    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			UniSpeechSatForXVector.from_pretrained(__lowerCamelCase					,       config=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""connector.weight"""]
    _SCREAMING_SNAKE_CASE									=			downstream_dict["""connector.bias"""]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
        _SCREAMING_SNAKE_CASE									=			downstream_dict[
            F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
        ]
        _SCREAMING_SNAKE_CASE									=			downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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





@torch.no_grad()
def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    int					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Tuple    )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       map_location="""cpu"""    )

    _SCREAMING_SNAKE_CASE									=			checkpoint["""Downstream"""]

    _SCREAMING_SNAKE_CASE									=			UniSpeechSatConfig.from_pretrained(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			WavaVecaFeatureExtractor.from_pretrained(
        __lowerCamelCase					,       return_attention_mask=__lowerCamelCase					,       do_normalize=__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			hf_config.architectures[0]
    if arch.endswith("""ForSequenceClassification"""    ):
        _SCREAMING_SNAKE_CASE									=			convert_classification(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    elif arch.endswith("""ForAudioFrameClassification"""    ):
        _SCREAMING_SNAKE_CASE									=			convert_diarization(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    elif arch.endswith("""ForXVector"""    ):
        _SCREAMING_SNAKE_CASE									=			convert_xvector(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    else:
        raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'    )

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

    hf_feature_extractor.save_pretrained(__lowerCamelCase    )
    hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
      lowercase_					= 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.""")
      lowercase_					= parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










'''simple docstring'''

from string import ascii_lowercase, ascii_uppercase





def lowerCamelCase						(     __lowerCamelCase :    str    )						->str:
    if not sentence:
        return ""
    _SCREAMING_SNAKE_CASE									=			dict(zip(__lowerCamelCase					,       __lowerCamelCase    )    )
    return lower_to_upper.get(sentence[0]					,       sentence[0]    ) + sentence[1:]


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

      testmod()



