






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 transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


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

set_seed(770)


UpperCamelCase    							=      {
    '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',
}


UpperCamelCase    							=      {
    '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',
    },
}

UpperCamelCase    							=      os.path.dirname(os.path.abspath(__file__))
UpperCamelCase    							=      os.path.join(os.path.expanduser('~'), '.cache')
UpperCamelCase    							=      os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]=False							)     ->  int:
   __UpperCamelCase	:			Optional[Any]					=					model_type
   if use_small:
      key += "_small"
   return os.path.join(__lowerCAmelCase					,							REMOTE_MODEL_PATHS[key]["""file_name"""]							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[Any]							)     ->  List[Any]:
   os.makedirs(__lowerCAmelCase					,							exist_ok=__lowerCAmelCase							)
   hf_hub_download(repo_id=__lowerCAmelCase					,							filename=__lowerCAmelCase					,							local_dir=__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					Union[str, Any]=False					,							__lowerCAmelCase							:					Tuple="text"							)     ->  Optional[Any]:
   if model_type == "text":
      __UpperCamelCase	:			List[str]					=					BarkSemanticModel
      __UpperCamelCase	:			Dict					=					BarkSemanticConfig
      __UpperCamelCase	:			str					=					BarkSemanticGenerationConfig
   elif model_type == "coarse":
      __UpperCamelCase	:			List[str]					=					BarkCoarseModel
      __UpperCamelCase	:			int					=					BarkCoarseConfig
      __UpperCamelCase	:			int					=					BarkCoarseGenerationConfig
   elif model_type == "fine":
      __UpperCamelCase	:			List[Any]					=					BarkFineModel
      __UpperCamelCase	:			Dict					=					BarkFineConfig
      __UpperCamelCase	:			int					=					BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   __UpperCamelCase	:			List[str]					=					f'{model_type}_small' if use_small else model_type
   __UpperCamelCase	:			Dict					=					REMOTE_MODEL_PATHS[model_key]
   if not os.path.exists(__lowerCAmelCase							):
      logger.info(f'{model_type} model not found, downloading into `{CACHE_DIR}`.'							)
      _download(model_info["""repo_id"""]					,							model_info["""file_name"""]							)
   __UpperCamelCase	:			Union[str, Any]					=					torch.load(__lowerCAmelCase					,							map_location=__lowerCAmelCase							)
   # this is a hack
   __UpperCamelCase	:			Any					=					checkpoint["""model_args"""]
   if "input_vocab_size" not in model_args:
      __UpperCamelCase	:			Tuple					=					model_args["""vocab_size"""]
      __UpperCamelCase	:			List[str]					=					model_args["""vocab_size"""]
      del model_args["vocab_size"]

   # convert Bark model arguments to HF Bark model arguments
   __UpperCamelCase	:			Tuple					=					model_args.pop("""n_head"""							)
   __UpperCamelCase	:			Tuple					=					model_args.pop("""n_embd"""							)
   __UpperCamelCase	:			Union[str, Any]					=					model_args.pop("""n_layer"""							)

   __UpperCamelCase	:			List[Any]					=					ConfigClass(**checkpoint["""model_args"""]							)
   __UpperCamelCase	:			Optional[int]					=					ModelClass(config=__lowerCAmelCase							)
   __UpperCamelCase	:			Dict					=					GenerationConfigClass()

   __UpperCamelCase	:			Any					=					model_generation_config
   __UpperCamelCase	:			str					=					checkpoint["""model"""]
   # fixup checkpoint
   __UpperCamelCase	:			Dict					=					"""_orig_mod."""
   for k, v in list(state_dict.items()							):
      if k.startswith(__lowerCAmelCase							):
         # replace part of the key with corresponding layer name in HF implementation
         __UpperCamelCase	:			List[str]					=					k[len(__lowerCAmelCase							) :]
         for old_layer_name in new_layer_name_dict:
            __UpperCamelCase	:			List[Any]					=					new_k.replace(__lowerCAmelCase					,							new_layer_name_dict[old_layer_name]							)

         __UpperCamelCase	:			Optional[Any]					=					state_dict.pop(__lowerCAmelCase							)

   __UpperCamelCase	:			int					=					set(state_dict.keys()							) - set(model.state_dict().keys()							)
   __UpperCamelCase	:			List[Any]					=					{k for k in extra_keys if not k.endswith(""".attn.bias"""							)}
   __UpperCamelCase	:			Dict					=					set(model.state_dict().keys()							) - set(state_dict.keys()							)
   __UpperCamelCase	:			Optional[int]					=					{k for k in missing_keys if not k.endswith(""".attn.bias"""							)}
   if len(__lowerCAmelCase							) != 0:
      raise ValueError(f'extra keys found: {extra_keys}'							)
   if len(__lowerCAmelCase							) != 0:
      raise ValueError(f'missing keys: {missing_keys}'							)
   model.load_state_dict(__lowerCAmelCase					,							strict=__lowerCAmelCase							)
   __UpperCamelCase	:			Tuple					=					model.num_parameters(exclude_embeddings=__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					checkpoint["""best_val_loss"""].item()
   logger.info(f'model loaded: {round(n_params/1e6					,							1							)}M params, {round(__lowerCAmelCase					,							3							)} loss'							)
   model.eval()
   model.to(__lowerCAmelCase							)
   del checkpoint, state_dict

   return model


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					int=False					,							__lowerCAmelCase							:					Tuple="text"							)     ->  Tuple:
   if model_type not in ("text", "coarse", "fine"):
      raise NotImplementedError()

   __UpperCamelCase	:			str					=					"""cpu"""  # do conversion on cpu

   __UpperCamelCase	:			str					=					_get_ckpt_path(__lowerCAmelCase					,							use_small=__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					_load_model(__lowerCAmelCase					,							__lowerCAmelCase					,							model_type=__lowerCAmelCase					,							use_small=__lowerCAmelCase							)

   # load bark initial model
   __UpperCamelCase	:			Dict					=					_bark_load_model(__lowerCAmelCase					,							"""cpu"""					,							model_type=__lowerCAmelCase					,							use_small=__lowerCAmelCase							)

   if model_type == "text":
      __UpperCamelCase	:			Dict					=					bark_model["""model"""]

   if model.num_parameters(exclude_embeddings=__lowerCAmelCase							) != bark_model.get_num_params():
      raise ValueError("""initial and new models don't have the same number of parameters"""							)

   # check if same output as the bark model
   __UpperCamelCase	:			List[str]					=					5
   __UpperCamelCase	:			List[Any]					=					10

   if model_type in ["text", "coarse"]:
      __UpperCamelCase	:			List[Any]					=					torch.randint(256					,							(batch_size, sequence_length)					,							dtype=torch.int							)
      __UpperCamelCase	:			List[str]					=					bark_model(__lowerCAmelCase							)[0]

      __UpperCamelCase	:			Dict					=					model(__lowerCAmelCase							)

      # take last logits
      __UpperCamelCase	:			Union[str, Any]					=					output_new_model_total.logits[:, [-1], :]

   else:
      __UpperCamelCase	:			Optional[int]					=					3
      __UpperCamelCase	:			Tuple					=					8
      __UpperCamelCase	:			int					=					torch.randint(256					,							(batch_size, sequence_length, n_codes_total)					,							dtype=torch.int							)

      __UpperCamelCase	:			Union[str, Any]					=					model(__lowerCAmelCase					,							__lowerCAmelCase							)
      __UpperCamelCase	:			Tuple					=					bark_model(__lowerCAmelCase					,							__lowerCAmelCase							)

      __UpperCamelCase	:			Dict					=					output_new_model_total.logits

   # output difference should come from the difference of self-attention implementation design
   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(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
   model.save_pretrained(__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[Any]					,							)     ->  int:
   __UpperCamelCase	:			Optional[int]					=					os.path.join(__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			str					=					BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[str]					=					BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[Any]					=					BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[str]					=					EncodecConfig.from_pretrained("""facebook/encodec_24khz"""							)

   __UpperCamelCase	:			Optional[int]					=					BarkSemanticModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					BarkCoarseModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			Union[str, Any]					=					BarkFineModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			Union[str, Any]					=					EncodecModel.from_pretrained("""facebook/encodec_24khz"""							)

   __UpperCamelCase	:			Dict					=					BarkConfig.from_sub_model_configs(
       __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			List[Any]					=					BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config					,							coarseAcoustic.generation_config					,							fineAcoustic.generation_config							)

   __UpperCamelCase	:			Optional[int]					=					BarkModel(__lowerCAmelCase							)

   __UpperCamelCase	:			List[Any]					=					semantic
   __UpperCamelCase	:			Any					=					coarseAcoustic
   __UpperCamelCase	:			Dict					=					fineAcoustic
   __UpperCamelCase	:			List[str]					=					codec

   __UpperCamelCase	:			Optional[Any]					=					bark_generation_config

   Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
   bark.save_pretrained(__lowerCAmelCase					,							repo_id=__lowerCAmelCase					,							push_to_hub=__lowerCAmelCase							)


if __name__ == "__main__":
       UpperCamelCase    							=      argparse.ArgumentParser()
       # Required parameters

       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.')

       UpperCamelCase    							=      parser.parse_args()

       load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)











from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)



