


'''simple docstring'''






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_					:    Any          =    logging.get_logger(__name__)

set_seed(7_7_0)


a_					:    List[Any]          =    {
    "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_					:    int          =    {
    "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_					:    Any          =    os.path.dirname(os.path.abspath(__file__))
a_					:    Tuple          =    os.path.join(os.path.expanduser("~"), ".cache")
a_					:    Tuple          =    os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")






def 	_A				(lowerCAmelCase__  :Any				,						lowerCAmelCase__  :Tuple=False		)   ->  List[str]:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :int		)   ->  List[str]:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Optional[int]=False				,						lowerCAmelCase__  :Dict="text"		)   ->  Dict:




     '''simple docstring'''



     if model_type == "text":
          _a			      =							BarkSemanticModel
          _a			      =							BarkSemanticConfig
          _a			      =							BarkSemanticGenerationConfig
     elif model_type == "coarse":
          _a			      =							BarkCoarseModel
          _a			      =							BarkCoarseConfig
          _a			      =							BarkCoarseGenerationConfig
     elif model_type == "fine":
          _a			      =							BarkFineModel
          _a			      =							BarkFineConfig
          _a			      =							BarkFineGenerationConfig
     else:
          raise NotImplementedError()
     _a			      =							f'{model_type}_small' if use_small else model_type
     _a			      =							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']		)
     _a			      =							torch.load(lowerCAmelCase__				,						map_location=lowerCAmelCase__		)
     # this is a hack
     _a			      =							checkpoint['model_args']
     if "input_vocab_size" not in model_args:
          _a			      =							model_args['vocab_size']
          _a			      =							model_args['vocab_size']
          del model_args["vocab_size"]

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

     _a			      =							ConfigClass(**checkpoint['model_args']		)
     _a			      =							ModelClass(config=lowerCAmelCase__		)
     _a			      =							GenerationConfigClass()

     _a			      =							model_generation_config
     _a			      =							checkpoint['model']
     # fixup checkpoint
     _a			      =							'_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
               _a			      =							k[len(lowerCAmelCase__		) :]
               for old_layer_name in new_layer_name_dict:
                    _a			      =							new_k.replace(lowerCAmelCase__				,						new_layer_name_dict[old_layer_name]		)

               _a			      =							state_dict.pop(lowerCAmelCase__		)

     _a			      =							set(state_dict.keys()		) - set(model.state_dict().keys()		)
     _a			      =							{k for k in extra_keys if not k.endswith('.attn.bias'		)}
     _a			      =							set(model.state_dict().keys()		) - set(state_dict.keys()		)
     _a			      =							{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__		)
     _a			      =							model.num_parameters(exclude_embeddings=lowerCAmelCase__		)
     _a			      =							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 	_A				(lowerCAmelCase__  :Union[str, Any]				,						lowerCAmelCase__  :int=False				,						lowerCAmelCase__  :int="text"		)   ->  str:




     '''simple docstring'''



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

     _a			      =							'cpu'  # do conversion on cpu

     _a			      =							_get_ckpt_path(lowerCAmelCase__				,						use_small=lowerCAmelCase__		)
     _a			      =							_load_model(lowerCAmelCase__				,						lowerCAmelCase__				,						model_type=lowerCAmelCase__				,						use_small=lowerCAmelCase__		)

     # load bark initial model
     _a			      =							_bark_load_model(lowerCAmelCase__				,						'cpu'				,						model_type=lowerCAmelCase__				,						use_small=lowerCAmelCase__		)

     if model_type == "text":
          _a			      =							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
     _a			      =							5
     _a			      =							10

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

          _a			      =							model(lowerCAmelCase__		)

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

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

          _a			      =							model(lowerCAmelCase__				,						lowerCAmelCase__		)
          _a			      =							bark_model(lowerCAmelCase__				,						lowerCAmelCase__		)

          _a			      =							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 	_A				(lowerCAmelCase__  :Union[str, Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Dict				,						)   ->  List[Any]:




     '''simple docstring'''



     _a			      =							os.path.join(lowerCAmelCase__				,						lowerCAmelCase__		)

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

     _a			      =							BarkSemanticModel.from_pretrained(lowerCAmelCase__		)
     _a			      =							BarkCoarseModel.from_pretrained(lowerCAmelCase__		)
     _a			      =							BarkFineModel.from_pretrained(lowerCAmelCase__		)
     _a			      =							EncodecModel.from_pretrained('facebook/encodec_24khz'		)

     _a			      =							BarkConfig.from_sub_model_configs(
         lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

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

     _a			      =							BarkModel(lowerCAmelCase__		)

     _a			      =							semantic
     _a			      =							coarseAcoustic
     _a			      =							fineAcoustic
     _a			      =							codec

     _a			      =							bark_generation_config

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


if __name__ == "__main__":
       a_					:    str          =    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_					:    Optional[Any]          =    parser.parse_args()

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





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

