





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()
lowercase				:	str      						=  logging.get_logger(__name__)

set_seed(7_7_0)


lowercase				:	Optional[int]      						=  {
    """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""",
}


lowercase				:	Tuple      						=  {
    """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""",
    },
}

lowercase				:	Union[str, Any]      						=  os.path.dirname(os.path.abspath(__file__))
lowercase				:	Optional[Any]      						=  os.path.join(os.path.expanduser("""~"""), """.cache""")
lowercase				:	Any      						=  os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")



def A_     (		A__		,				A__=False    )  ->		Dict:
       a__						:     Optional[int]			=    model_type
       if use_small:
              key += "_small"
       return os.path.join(A__		,				REMOTE_MODEL_PATHS[key]['file_name']    )



def A_     (		A__		,				A__    )  ->		List[str]:
       os.makedirs(A__		,				exist_ok=A__    )
       hf_hub_download(repo_id=A__		,				filename=A__		,				local_dir=A__    )



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

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

       a__						:     Union[str, Any]			=    ConfigClass(**checkpoint['model_args']    )
       a__						:     List[str]			=    ModelClass(config=A__    )
       a__						:     int			=    GenerationConfigClass()

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

                     a__						:     Optional[int]			=    state_dict.pop(A__    )

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

       return model



def A_     (		A__		,				A__=False		,				A__="text"    )  ->		Tuple:
       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

       a__						:     Union[str, Any]			=    'cpu'  # do conversion on cpu

       a__						:     List[str]			=    _get_ckpt_path(A__		,				use_small=A__    )
       a__						:     Any			=    _load_model(A__		,				A__		,				model_type=A__		,				use_small=A__    )

       # load bark initial model
       a__						:     Any			=    _bark_load_model(A__		,				'cpu'		,				model_type=A__		,				use_small=A__    )

       if model_type == "text":
              a__						:     Optional[Any]			=    bark_model['model']

       if model.num_parameters(exclude_embeddings=A__    ) != 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
       a__						:     Any			=    5
       a__						:     str			=    10

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

              a__						:     Any			=    model(A__    )

              # take last logits
              a__						:     Optional[Any]			=    output_new_model_total.logits[:, [-1], :]

       else:
              a__						:     Optional[Any]			=    3
              a__						:     List[str]			=    8
              a__						:     Any			=    torch.randint(256		,				(batch_size, sequence_length, n_codes_total)		,				dtype=torch.int    )

              a__						:     Optional[int]			=    model(A__		,				A__    )
              a__						:     Union[str, Any]			=    bark_model(A__		,				A__    )

              a__						:     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(A__    ).mkdir(exist_ok=A__    )
       model.save_pretrained(A__    )



def A_     (		A__		,				A__		,				A__		,				A__		,				A__		,				A__		,				)  ->		int:
       a__						:     Optional[Any]			=    os.path.join(A__		,				A__    )

       a__						:     Optional[int]			=    BarkSemanticConfig.from_pretrained(os.path.join(A__		,				'config.json'    )    )
       a__						:     List[Any]			=    BarkCoarseConfig.from_pretrained(os.path.join(A__		,				'config.json'    )    )
       a__						:     Optional[int]			=    BarkFineConfig.from_pretrained(os.path.join(A__		,				'config.json'    )    )
       a__						:     Union[str, Any]			=    EncodecConfig.from_pretrained('facebook/encodec_24khz'    )

       a__						:     List[str]			=    BarkSemanticModel.from_pretrained(A__    )
       a__						:     Dict			=    BarkCoarseModel.from_pretrained(A__    )
       a__						:     Optional[Any]			=    BarkFineModel.from_pretrained(A__    )
       a__						:     Dict			=    EncodecModel.from_pretrained('facebook/encodec_24khz'    )

       a__						:     int			=    BarkConfig.from_sub_model_configs(
           A__		,				A__		,				A__		,				A__    )

       a__						:     List[str]			=    BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config		,				coarseAcoustic.generation_config		,				fineAcoustic.generation_config    )

       a__						:     Optional[int]			=    BarkModel(A__    )

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

       a__						:     str			=    bark_generation_config

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


if __name__ == "__main__":
    lowercase				:	Union[str, Any]      						=  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.""")

    lowercase				:	Dict      						=  parser.parse_args()

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









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase				:	Union[str, Any]      						=  data_utils.TransfoXLTokenizer
lowercase				:	Optional[int]      						=  data_utils.TransfoXLCorpus
lowercase				:	List[Any]      						=  data_utils
lowercase				:	Tuple      						=  data_utils



def A_     (		A__		,				A__		,				A__		,				A__    )  ->		Optional[Any]:
       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(A__		,				'rb'    ) as fp:
                     a__						:     int			=    pickle.load(A__		,				encoding='latin1'    )
              # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
              a__						:     int			=    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
              print(F'Save vocabulary to {pytorch_vocab_dump_path}'    )
              a__						:     List[Any]			=    corpus.vocab.__dict__
              torch.save(A__		,				A__    )

              a__						:     Dict			=    corpus.__dict__
              corpus_dict_no_vocab.pop('vocab'		,				A__    )
              a__						:     Optional[int]			=    pytorch_dump_folder_path + '/' + CORPUS_NAME
              print(F'Save dataset to {pytorch_dataset_dump_path}'    )
              torch.save(A__		,				A__    )

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              a__						:     Union[str, Any]			=    os.path.abspath(A__    )
              a__						:     Optional[Any]			=    os.path.abspath(A__    )

              print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'    )
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     a__						:     Dict			=    TransfoXLConfig()
              else:
                     a__						:     Dict			=    TransfoXLConfig.from_json_file(A__    )
              print(F'Building PyTorch model from configuration: {config}'    )
              a__						:     Optional[int]			=    TransfoXLLMHeadModel(A__    )

              a__						:     int			=    load_tf_weights_in_transfo_xl(A__		,				A__		,				A__    )
              # Save pytorch-model
              a__						:     Any			=    os.path.join(A__		,				A__    )
              a__						:     Dict			=    os.path.join(A__		,				A__    )
              print(F'Save PyTorch model to {os.path.abspath(A__    )}'    )
              torch.save(model.state_dict()		,				A__    )
              print(F'Save configuration file to {os.path.abspath(A__    )}'    )
              with open(A__		,				'w'		,				encoding='utf-8'    ) as f:
                     f.write(config.to_json_string()    )


if __name__ == "__main__":
    lowercase				:	List[Any]      						=  argparse.ArgumentParser()
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
    )
    parser.add_argument(
        """--tf_checkpoint_path""",
        default="""""",
        type=str,
        help="""An optional path to a TensorFlow checkpoint path to be converted.""",
    )
    parser.add_argument(
        """--transfo_xl_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained BERT model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--transfo_xl_dataset_file""",
        default="""""",
        type=str,
        help="""An optional dataset file to be converted in a vocabulary.""",
    )
    lowercase				:	Any      						=  parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )


