




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()
a_   		=     logging.get_logger(__name__)

set_seed(770)


a_   		=     {
    "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",
}


a_   		=     {
    "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",
    },
}

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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=False      )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						model_type
    if use_small:
        key += "_small"
    return os.path.join(lowercase_					,     REMOTE_MODEL_PATHS[key]['''file_name''']      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


    os.makedirs(lowercase_					,     exist_ok=lowercase_      )
    hf_hub_download(repo_id=lowercase_					,     filename=lowercase_					,     local_dir=lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=False					,     lowercase_="text"      )       ->	List[Any]:

    """simple docstring"""


    if model_type == "text":
        __UpperCamelCase  		=						BarkSemanticModel
        __UpperCamelCase  		=						BarkSemanticConfig
        __UpperCamelCase  		=						BarkSemanticGenerationConfig
    elif model_type == "coarse":
        __UpperCamelCase  		=						BarkCoarseModel
        __UpperCamelCase  		=						BarkCoarseConfig
        __UpperCamelCase  		=						BarkCoarseGenerationConfig
    elif model_type == "fine":
        __UpperCamelCase  		=						BarkFineModel
        __UpperCamelCase  		=						BarkFineConfig
        __UpperCamelCase  		=						BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    __UpperCamelCase  		=						F"{model_type}_small" if use_small else model_type
    __UpperCamelCase  		=						REMOTE_MODEL_PATHS[model_key]
    if not os.path.exists(lowercase_      ):
        logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."      )
        _download(model_info['''repo_id''']					,     model_info['''file_name''']      )
    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location=lowercase_      )
    # this is a hack
    __UpperCamelCase  		=						checkpoint['''model_args''']
    if "input_vocab_size" not in model_args:
        __UpperCamelCase  		=						model_args['''vocab_size''']
        __UpperCamelCase  		=						model_args['''vocab_size''']
        del model_args["vocab_size"]

    # convert Bark model arguments to HF Bark model arguments
    __UpperCamelCase  		=						model_args.pop('''n_head'''      )
    __UpperCamelCase  		=						model_args.pop('''n_embd'''      )
    __UpperCamelCase  		=						model_args.pop('''n_layer'''      )

    __UpperCamelCase  		=						ConfigClass(**checkpoint['''model_args''']      )
    __UpperCamelCase  		=						ModelClass(config=lowercase_      )
    __UpperCamelCase  		=						GenerationConfigClass()

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

            __UpperCamelCase  		=						state_dict.pop(lowercase_      )

    __UpperCamelCase  		=						set(state_dict.keys()      ) - set(model.state_dict().keys()      )
    __UpperCamelCase  		=						{k for k in extra_keys if not k.endswith('''.attn.bias'''      )}
    __UpperCamelCase  		=						set(model.state_dict().keys()      ) - set(state_dict.keys()      )
    __UpperCamelCase  		=						{k for k in missing_keys if not k.endswith('''.attn.bias'''      )}
    if len(lowercase_      ) != 0:
        raise ValueError(F"extra keys found: {extra_keys}"      )
    if len(lowercase_      ) != 0:
        raise ValueError(F"missing keys: {missing_keys}"      )
    model.load_state_dict(lowercase_					,     strict=lowercase_      )
    __UpperCamelCase  		=						model.num_parameters(exclude_embeddings=lowercase_      )
    __UpperCamelCase  		=						checkpoint['''best_val_loss'''].item()
    logger.info(F"model loaded: {round(n_params/1E6					,     1      )}M params, {round(lowercase_					,     3      )} loss"      )
    model.eval()
    model.to(lowercase_      )
    del checkpoint, state_dict

    return model




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=False					,     lowercase_="text"      )       ->	Any:

    """simple docstring"""


    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    __UpperCamelCase  		=						'''cpu'''  # do conversion on cpu

    __UpperCamelCase  		=						_get_ckpt_path(lowercase_					,     use_small=lowercase_      )
    __UpperCamelCase  		=						_load_model(lowercase_					,     lowercase_					,     model_type=lowercase_					,     use_small=lowercase_      )

    # load bark initial model
    __UpperCamelCase  		=						_bark_load_model(lowercase_					,     '''cpu'''					,     model_type=lowercase_					,     use_small=lowercase_      )

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

    if model.num_parameters(exclude_embeddings=lowercase_      ) != 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  		=						5
    __UpperCamelCase  		=						10

    if model_type in ["text", "coarse"]:
        __UpperCamelCase  		=						torch.randint(2_56					,     (batch_size, sequence_length)					,     dtype=torch.int      )
        __UpperCamelCase  		=						bark_model(lowercase_      )[0]

        __UpperCamelCase  		=						model(lowercase_      )

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

    else:
        __UpperCamelCase  		=						3
        __UpperCamelCase  		=						8
        __UpperCamelCase  		=						torch.randint(2_56					,     (batch_size, sequence_length, n_codes_total)					,     dtype=torch.int      )

        __UpperCamelCase  		=						model(lowercase_					,     lowercase_      )
        __UpperCamelCase  		=						bark_model(lowercase_					,     lowercase_      )

        __UpperCamelCase  		=						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(lowercase_      ).mkdir(exist_ok=lowercase_      )
    model.save_pretrained(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						os.path.join(lowercase_					,     lowercase_      )

    __UpperCamelCase  		=						BarkSemanticConfig.from_pretrained(os.path.join(lowercase_					,     '''config.json'''      )      )
    __UpperCamelCase  		=						BarkCoarseConfig.from_pretrained(os.path.join(lowercase_					,     '''config.json'''      )      )
    __UpperCamelCase  		=						BarkFineConfig.from_pretrained(os.path.join(lowercase_					,     '''config.json'''      )      )
    __UpperCamelCase  		=						EncodecConfig.from_pretrained('''facebook/encodec_24khz'''      )

    __UpperCamelCase  		=						BarkSemanticModel.from_pretrained(lowercase_      )
    __UpperCamelCase  		=						BarkCoarseModel.from_pretrained(lowercase_      )
    __UpperCamelCase  		=						BarkFineModel.from_pretrained(lowercase_      )
    __UpperCamelCase  		=						EncodecModel.from_pretrained('''facebook/encodec_24khz'''      )

    __UpperCamelCase  		=						BarkConfig.from_sub_model_configs(
        lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )

    __UpperCamelCase  		=						BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config					,     coarseAcoustic.generation_config					,     fineAcoustic.generation_config      )

    __UpperCamelCase  		=						BarkModel(lowercase_      )

    __UpperCamelCase  		=						semantic
    __UpperCamelCase  		=						coarseAcoustic
    __UpperCamelCase  		=						fineAcoustic
    __UpperCamelCase  		=						codec

    __UpperCamelCase  		=						bark_generation_config

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


if __name__ == "__main__":
 a_   		=     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.")

 a_   		=     parser.parse_args()

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












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @staticmethod
      @abstractmethod
      def       snake_case	(     snake_case					: ArgumentParser				):
          raise NotImplementedError()







      @abstractmethod
      def       snake_case	(     self					: Optional[Any]				):
          raise NotImplementedError()






