"""
    #################################################################################################################################################################################################
    # 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 s3prl.hub import distilhubert
from sapiens_transformers import HubertConfig, HubertModel, Wav2Vec2FeatureExtractor, logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
MAPPING = {'post_extract_proj': 'feature_projection.projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj', 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj', 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm', 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense', 'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm', 'encoder.layer_norm': 'encoder.layer_norm', 'mask_emb': 'masked_spec_embed'}
def set_recursively(hf_pointer, key, value, full_name, weight_type):
    for attribute in key.split("."): hf_pointer = getattr(hf_pointer, attribute)
    if weight_type is not None: hf_shape = getattr(hf_pointer, weight_type).shape
    else: hf_shape = hf_pointer.shape
    assert hf_shape == value.shape, (f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be {value.shape} for {full_name}")
    if weight_type == "weight": hf_pointer.weight.data = value
    elif weight_type == "weight_g": hf_pointer.weight_g.data = value
    elif weight_type == "weight_v": hf_pointer.weight_v.data = value
    elif weight_type == "bias": hf_pointer.bias.data = value
    else: hf_pointer.data = value
    logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")
def recursively_load_weights(fairseq_model, hf_model):
    unused_weights = []
    fairseq_dict = fairseq_model.state_dict()
    feature_extractor = hf_model.feature_extractor
    for name, value in fairseq_dict.items():
        is_used = False
        if "conv_layers" in name:
            load_conv_layer(name, value, feature_extractor, unused_weights, hf_model.config.feat_extract_norm == "group")
            is_used = True
        else:
            for key, mapped_key in MAPPING.items():
                mapped_key = mapped_key
                if key in name:
                    is_used = True
                    if "*" in mapped_key:
                        layer_index = name.split(key)[0].split(".")[-2]
                        mapped_key = mapped_key.replace("*", layer_index)
                    if "weight_g" in name: weight_type = "weight_g"
                    elif "weight_v" in name: weight_type = "weight_v"
                    elif "weight" in name: weight_type = "weight"
                    elif "bias" in name: weight_type = "bias"
                    else: weight_type = None
                    set_recursively(hf_model, mapped_key, value, name, weight_type)
                continue
        if not is_used: unused_weights.append(name)
    logger.warning(f"Unused weights: {unused_weights}")
def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
    name = full_name.split("conv_layers.")[-1]
    items = name.split(".")
    layer_id = int(items[0])
    type_id = int(items[1])
    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].conv.bias.data = value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].conv.weight.data = value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
    else: unused_weights.append(full_name)
def convert_config(model):
    config = HubertConfig()
    fs_config = model.config
    config.activation_dropout = fs_config.activation_dropout
    config.apply_spec_augment = False
    config.attention_dropout = fs_config.attention_dropout
    config.conv_bias = False
    conv_layers = eval(fs_config.extractor_conv_feature_layers)
    config.conv_dim = [x[0] for x in conv_layers]
    config.conv_kernel = [x[1] for x in conv_layers]
    config.conv_stride = [x[2] for x in conv_layers]
    config.feat_extract_activation = "gelu"
    config.feat_extract_norm = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
    config.feat_proj_layer_norm = False
    config.feat_proj_dropout = 0.0
    config.final_dropout = 0.0
    config.hidden_act = fs_config.activation_fn
    config.hidden_dropout = fs_config.dropout
    config.hidden_size = fs_config.encoder_embed_dim
    config.initializer_range = 0.02
    config.intermediate_size = fs_config.encoder_ffn_embed_dim
    config.layer_norm_eps = 1e-5
    config.layerdrop = 0.0
    config.num_attention_heads = fs_config.encoder_attention_heads
    config.num_conv_pos_embedding_groups = fs_config.conv_pos_groups
    config.num_conv_pos_embeddings = fs_config.conv_pos
    config.num_feat_extract_layers = len(conv_layers)
    config.num_hidden_layers = fs_config.encoder_layers
    return config
@torch.no_grad()
def convert_hubert_checkpoint(pytorch_dump_folder_path, config_path=None):
    model = distilhubert().model.model
    if config_path is not None: config = HubertConfig.from_pretrained(config_path)
    else: config = convert_config(model)
    model = model.eval()
    feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=False, return_attention_mask=False)
    hf_model = HubertModel(config)
    recursively_load_weights(model, hf_model)
    feature_extractor.save_pretrained(pytorch_dump_folder_path)
    hf_model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    args = parser.parse_args()
    convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_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.                                                                 #
    #################################################################################################################################################################################################
"""
