"""
    #################################################################################################################################################################################################
    # 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 os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from sapiens_transformers import EncodecConfig, EncodecModel, set_seed
from sapiens_transformers.models.bark.configuration_bark import (BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig)
from sapiens_transformers.models.bark.generation_configuration_bark import (BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkGenerationConfig, BarkSemanticGenerationConfig)
from sapiens_transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
set_seed(770)
new_layer_name_dict = {'c_attn': 'att_proj', 'c_proj': 'out_proj', 'c_fc': 'in_proj', 'transformer.': '', 'h.': 'layers.', 'ln_1': 'layernorm_1',
'ln_2': 'layernorm_2', 'ln_f': 'layernorm_final', 'wpe': 'position_embeds_layer', 'wte': 'input_embeds_layer'}
REMOTE_MODEL_PATHS = {"text_small": {'repo_id': 'suno/bark', 'file_name': 'text.pt'}, "coarse_small": {'repo_id': 'suno/bark', 'file_name': 'coarse.pt'},
"fine_small": {'repo_id': 'suno/bark', 'file_name': 'fine.pt'}, "text": {'repo_id': 'suno/bark', 'file_name': 'text_2.pt'},
"coarse": {'repo_id': 'suno/bark', 'file_name': 'coarse_2.pt'}, "fine": {'repo_id': 'suno/bark', 'file_name': 'fine_2.pt'}}
CUR_PATH = os.path.dirname(os.path.abspath(__file__))
default_cache_dir = os.path.join(os.path.expanduser("~"), ".cache")
CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def _get_ckpt_path(model_type, use_small=False):
    key = model_type
    if use_small: key += "_small"
    return os.path.join(CACHE_DIR, REMOTE_MODEL_PATHS[key]["file_name"])
def _download(from_hf_path, file_name):
    os.makedirs(CACHE_DIR, exist_ok=True)
    hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR)
def _load_model(ckpt_path, device, use_small=False, model_type="text"):
    if model_type == "text":
        ModelClass = BarkSemanticModel
        ConfigClass = BarkSemanticConfig
        GenerationConfigClass = BarkSemanticGenerationConfig
    elif model_type == "coarse":
        ModelClass = BarkCoarseModel
        ConfigClass = BarkCoarseConfig
        GenerationConfigClass = BarkCoarseGenerationConfig
    elif model_type == "fine":
        ModelClass = BarkFineModel
        ConfigClass = BarkFineConfig
        GenerationConfigClass = BarkFineGenerationConfig
    else: raise NotImplementedError()
    model_key = f"{model_type}_small" if use_small else model_type
    model_info = REMOTE_MODEL_PATHS[model_key]
    if not os.path.exists(ckpt_path):
        logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.")
        _download(model_info["repo_id"], model_info["file_name"])
    checkpoint = torch.load(ckpt_path, map_location=device)
    model_args = checkpoint["model_args"]
    if "input_vocab_size" not in model_args:
        model_args["input_vocab_size"] = model_args["vocab_size"]
        model_args["output_vocab_size"] = model_args["vocab_size"]
        del model_args["vocab_size"]
    model_args["num_heads"] = model_args.pop("n_head")
    model_args["hidden_size"] = model_args.pop("n_embd")
    model_args["num_layers"] = model_args.pop("n_layer")
    model_config = ConfigClass(**checkpoint["model_args"])
    model = ModelClass(config=model_config)
    model_generation_config = GenerationConfigClass()
    model.generation_config = model_generation_config
    state_dict = checkpoint["model"]
    unwanted_prefix = "_orig_mod."
    for k, v in list(state_dict.items()):
        if k.startswith(unwanted_prefix):
            new_k = k[len(unwanted_prefix) :]
            for old_layer_name in new_layer_name_dict: new_k = new_k.replace(old_layer_name, new_layer_name_dict[old_layer_name])
            state_dict[new_k] = state_dict.pop(k)
    extra_keys = set(state_dict.keys()) - set(model.state_dict().keys())
    extra_keys = {k for k in extra_keys if not k.endswith(".attn.bias")}
    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
    missing_keys = {k for k in missing_keys if not k.endswith(".attn.bias")}
    if len(extra_keys) != 0: raise ValueError(f"extra keys found: {extra_keys}")
    if len(missing_keys) != 0: raise ValueError(f"missing keys: {missing_keys}")
    model.load_state_dict(state_dict, strict=False)
    n_params = model.num_parameters(exclude_embeddings=True)
    val_loss = checkpoint["best_val_loss"].item()
    logger.info(f"model loaded: {round(n_params/1e6,1)}M params, {round(val_loss,3)} loss")
    model.eval()
    model.to(device)
    del checkpoint, state_dict
    return model
def load_model(pytorch_dump_folder_path, use_small=False, model_type="text"):
    if model_type not in ("text", "coarse", "fine"): raise NotImplementedError()
    device = "cpu"
    ckpt_path = _get_ckpt_path(model_type, use_small=use_small)
    model = _load_model(ckpt_path, device, model_type=model_type, use_small=use_small)
    bark_model = _bark_load_model(ckpt_path, "cpu", model_type=model_type, use_small=use_small)
    if model_type == "text": bark_model = bark_model["model"]
    if model.num_parameters(exclude_embeddings=True) != bark_model.get_num_params(): raise ValueError("initial and new models don't have the same number of parameters")
    batch_size = 5
    sequence_length = 10
    if model_type in ["text", "coarse"]:
        vec = torch.randint(256, (batch_size, sequence_length), dtype=torch.int)
        output_old_model = bark_model(vec)[0]
        output_new_model_total = model(vec)
        output_new_model = output_new_model_total.logits[:, [-1], :]
    else:
        prediction_codeboook_channel = 3
        n_codes_total = 8
        vec = torch.randint(256, (batch_size, sequence_length, n_codes_total), dtype=torch.int)
        output_new_model_total = model(prediction_codeboook_channel, vec)
        output_old_model = bark_model(prediction_codeboook_channel, vec)
        output_new_model = output_new_model_total.logits
    if output_new_model.shape != output_old_model.shape: raise ValueError("initial and new outputs don't have the same shape")
    if (output_new_model - output_old_model).abs().max().item() > 1e-3: raise ValueError("initial and new outputs are not equal")
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    model.save_pretrained(pytorch_dump_folder_path)
def load_whole_bark_model(semantic_path, coarse_path, fine_path, append_text, hub_path, folder_path):
    pytorch_dump_folder_path = os.path.join(folder_path, append_text)
    semanticConfig = BarkSemanticConfig.from_pretrained(os.path.join(semantic_path, "config.json"))
    coarseAcousticConfig = BarkCoarseConfig.from_pretrained(os.path.join(coarse_path, "config.json"))
    fineAcousticConfig = BarkFineConfig.from_pretrained(os.path.join(fine_path, "config.json"))
    codecConfig = EncodecConfig.from_pretrained("facebook/encodec_24khz")
    semantic = BarkSemanticModel.from_pretrained(semantic_path)
    coarseAcoustic = BarkCoarseModel.from_pretrained(coarse_path)
    fineAcoustic = BarkFineModel.from_pretrained(fine_path)
    codec = EncodecModel.from_pretrained("facebook/encodec_24khz")
    bark_config = BarkConfig.from_sub_model_configs(semanticConfig, coarseAcousticConfig, fineAcousticConfig, codecConfig)
    bark_generation_config = BarkGenerationConfig.from_sub_model_configs(semantic.generation_config, coarseAcoustic.generation_config, fineAcoustic.generation_config)
    bark = BarkModel(bark_config)
    bark.semantic = semantic
    bark.coarse_acoustics = coarseAcoustic
    bark.fine_acoustics = fineAcoustic
    bark.codec_model = codec
    bark.generation_config = bark_generation_config
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    bark.save_pretrained(pytorch_dump_folder_path, repo_id=hub_path, push_to_hub=True)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("model_type", type=str, help="text, coarse or fine.")
    parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
    args = parser.parse_args()
    load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
