"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
import torch
from sapiens_transformers import HubertConfig, HubertForSequenceClassification, Wav2Vec2FeatureExtractor, logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
SUPPORTED_MODELS = ["UtteranceLevel"]
@torch.no_grad()
def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, model_dump_path):
    checkpoint = torch.load(checkpoint_path, map_location="cpu")
    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS: raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}")
    downstream_dict = checkpoint["Downstream"]
    hf_congfig = HubertConfig.from_pretrained(config_path)
    hf_model = HubertForSequenceClassification.from_pretrained(base_model_name, config=hf_congfig)
    hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(base_model_name, return_attention_mask=True, do_normalize=False)
    if hf_congfig.use_weighted_layer_sum: hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"]
    hf_model.projector.weight.data = downstream_dict["projector.weight"]
    hf_model.projector.bias.data = downstream_dict["projector.bias"]
    hf_model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"]
    hf_model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"]
    hf_feature_extractor.save_pretrained(model_dump_path)
    hf_model.save_pretrained(model_dump_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--base_model_name", default=None, type=str, help="Name of the pretrained base model.")
    parser.add_argument("--config_path", default=None, type=str, help="Path to the 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.")
    args = parser.parse_args()
    convert_s3prl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
