"""
    #################################################################################################################################################################################################
    # 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 (EncodecConfig, EncodecFeatureExtractor, EncodecModel, logging)
logging.set_verbosity_info()
logger = logging.get_logger("sapiens_transformers.models.encodec")
MAPPING_QUANTIZER = {'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited', 'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed', 'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg'}
MAPPING_ENCODER = {'encoder.model.0.conv.conv': 'encoder.layers.0.conv', 'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv', 'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv', 'encoder.model.3.conv.conv': 'encoder.layers.3.conv', 'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv', 'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv', 'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv', 'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv', 'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv', 'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv', 'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv', 'encoder.model.12.conv.conv': 'encoder.layers.12.conv', 'encoder.model.13.lstm': 'encoder.layers.13.lstm', 'encoder.model.15.conv.conv': 'encoder.layers.15.conv'}
MAPPING_ENCODER_48K = {'encoder.model.0.conv.norm': 'encoder.layers.0.norm', 'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm', 'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm', 'encoder.model.3.conv.norm': 'encoder.layers.3.norm', 'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm', 'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm', 'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm', 'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm', 'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm', 'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm', 'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm', 'encoder.model.12.conv.norm': 'encoder.layers.12.norm', 'encoder.model.15.conv.norm': 'encoder.layers.15.norm'}
MAPPING_DECODER = {'decoder.model.0.conv.conv': 'decoder.layers.0.conv', 'decoder.model.1.lstm': 'decoder.layers.1.lstm', 'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv', 'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv', 'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv', 'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv', 'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv', 'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv', 'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv', 'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv', 'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv', 'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv', 'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv'}
MAPPING_DECODER_48K = {'decoder.model.0.conv.norm': 'decoder.layers.0.norm', 'decoder.model.3.convtr.norm': 'decoder.layers.3.norm', 'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm', 'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm', 'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm', 'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm', 'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm', 'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm', 'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm', 'decoder.model.12.convtr.norm': 'decoder.layers.12.norm', 'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm', 'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm', 'decoder.model.15.conv.norm': 'decoder.layers.15.norm'}
MAPPING_24K = {**MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_DECODER}
MAPPING_48K = {**MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_ENCODER_48K, **MAPPING_DECODER, **MAPPING_DECODER_48K}
TOP_LEVEL_KEYS = []
IGNORE_KEYS = []
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
    if hf_shape != value.shape: raise ValueError(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
    elif weight_type == "running_mean": hf_pointer.running_mean.data = value
    elif weight_type == "running_var": hf_pointer.running_var.data = value
    elif weight_type == "num_batches_tracked": hf_pointer.num_batches_tracked.data = value
    elif weight_type == "weight_ih_l0": hf_pointer.weight_ih_l0.data = value
    elif weight_type == "weight_hh_l0": hf_pointer.weight_hh_l0.data = value
    elif weight_type == "bias_ih_l0": hf_pointer.bias_ih_l0.data = value
    elif weight_type == "bias_hh_l0": hf_pointer.bias_hh_l0.data = value
    elif weight_type == "weight_ih_l1": hf_pointer.weight_ih_l1.data = value
    elif weight_type == "weight_hh_l1": hf_pointer.weight_hh_l1.data = value
    elif weight_type == "bias_ih_l1": hf_pointer.bias_ih_l1.data = value
    elif weight_type == "bias_hh_l1": hf_pointer.bias_hh_l1.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 should_ignore(name, ignore_keys):
    for key in ignore_keys:
        if key.endswith(".*"):
            if name.startswith(key[:-1]): return True
        elif ".*." in key:
            prefix, suffix = key.split(".*.")
            if prefix in name and suffix in name: return True
        elif key in name: return True
    return False
def recursively_load_weights(orig_dict, hf_model, model_name):
    unused_weights = []
    if model_name in ["encodec_24khz", "encodec_32khz"]: MAPPING = MAPPING_24K
    elif model_name == "encodec_48khz": MAPPING = MAPPING_48K
    else: raise ValueError(f"Unsupported model: {model_name}")
    for name, value in orig_dict.items():
        if should_ignore(name, IGNORE_KEYS):
            logger.info(f"{name} was ignored")
            continue
        is_used = False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                prefix, suffix = key.split(".*.")
                if prefix in name and suffix in name: key = suffix
            if key in name:
                if key.endswith("embed") and name.endswith("embed_avg"): continue
                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_ih_l0" in name: weight_type = "weight_ih_l0"
                elif "weight_hh_l0" in name: weight_type = "weight_hh_l0"
                elif "bias_ih_l0" in name: weight_type = "bias_ih_l0"
                elif "bias_hh_l0" in name: weight_type = "bias_hh_l0"
                elif "weight_ih_l1" in name: weight_type = "weight_ih_l1"
                elif "weight_hh_l1" in name: weight_type = "weight_hh_l1"
                elif "bias_ih_l1" in name: weight_type = "bias_ih_l1"
                elif "bias_hh_l1" in name: weight_type = "bias_hh_l1"
                elif "bias" in name: weight_type = "bias"
                elif "weight" in name: weight_type = "weight"
                elif "running_mean" in name: weight_type = "running_mean"
                elif "running_var" in name: weight_type = "running_var"
                elif "num_batches_tracked" in name: weight_type = "num_batches_tracked"
                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}")
@torch.no_grad()
def convert_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, config_path=None, repo_id=None):
    if config_path is not None: config = EncodecConfig.from_pretrained(config_path)
    else: config = EncodecConfig()
    if model_name == "encodec_24khz": pass
    elif model_name == "encodec_32khz":
        config.upsampling_ratios = [8, 5, 4, 4]
        config.target_bandwidths = [2.2]
        config.num_filters = 64
        config.sampling_rate = 32_000
        config.codebook_size = 2048
        config.use_causal_conv = False
        config.normalize = False
        config.use_conv_shortcut = False
    elif model_name == "encodec_48khz":
        config.upsampling_ratios = [8, 5, 4, 2]
        config.target_bandwidths = [3.0, 6.0, 12.0, 24.0]
        config.sampling_rate = 48_000
        config.audio_channels = 2
        config.use_causal_conv = False
        config.norm_type = "time_group_norm"
        config.normalize = True
        config.chunk_length_s = 1.0
        config.overlap = 0.01
    else: raise ValueError(f"Unknown model name: {model_name}")
    model = EncodecModel(config)
    feature_extractor = EncodecFeatureExtractor(feature_size=config.audio_channels, sampling_rate=config.sampling_rate, chunk_length_s=config.chunk_length_s, overlap=config.overlap)
    feature_extractor.save_pretrained(pytorch_dump_folder_path)
    original_checkpoint = torch.load(checkpoint_path)
    if "best_state" in original_checkpoint: original_checkpoint = original_checkpoint["best_state"]
    recursively_load_weights(original_checkpoint, model, model_name)
    model.save_pretrained(pytorch_dump_folder_path)
    if repo_id:
        print("Pushing to the hub...")
        feature_extractor.push_to_hub(repo_id)
        model.push_to_hub(repo_id)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", default="encodec_24khz", type=str, help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.")
    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument("--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--push_to_hub", default=None, type=str, help="Where to upload the converted model on the HF hub.")
    args = parser.parse_args()
    convert_checkpoint(args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
