



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_   :List[str]        =      logging.get_logger(__name__)

set_seed(770)


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_   :Dict        =      {
    "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_   :List[str]        =      os.path.dirname(os.path.abspath(__file__))
a_   :Optional[Any]        =      os.path.join(os.path.expanduser("~"), ".cache")
a_   :Any        =      os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def        lowercase_							(A : int		,					A : Tuple=False      ):
       snake_case__							:	Tuple   	=	model_type
       if use_small:
              key += "_small"
       return os.path.join(A		,					REMOTE_MODEL_PATHS[key]['file_name']      )
def        lowercase_							(A : str		,					A : str      ):
       os.makedirs(A		,					exist_ok=A      )
       hf_hub_download(repo_id=A		,					filename=A		,					local_dir=A      )
def        lowercase_							(A : Dict		,					A : Union[str, Any]		,					A : Union[str, Any]=False		,					A : str="text"      ):
       if model_type == "text":
              snake_case__							:	Optional[int]   	=	BarkSemanticModel
              snake_case__							:	List[str]   	=	BarkSemanticConfig
              snake_case__							:	Union[str, Any]   	=	BarkSemanticGenerationConfig
       elif model_type == "coarse":
              snake_case__							:	int   	=	BarkCoarseModel
              snake_case__							:	Optional[int]   	=	BarkCoarseConfig
              snake_case__							:	str   	=	BarkCoarseGenerationConfig
       elif model_type == "fine":
              snake_case__							:	int   	=	BarkFineModel
              snake_case__							:	Dict   	=	BarkFineConfig
              snake_case__							:	Tuple   	=	BarkFineGenerationConfig
       else:
              raise NotImplementedError()
       snake_case__							:	int   	=	F'''{model_type}_small''' if use_small else model_type
       snake_case__							:	List[Any]   	=	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']      )
       snake_case__							:	int   	=	torch.load(A		,					map_location=A      )
       # this is a hack
       snake_case__							:	Dict   	=	checkpoint['model_args']
       if "input_vocab_size" not in model_args:
              snake_case__							:	Any   	=	model_args['vocab_size']
              snake_case__							:	List[Any]   	=	model_args['vocab_size']
              del model_args["vocab_size"]

       # convert Bark model arguments to HF Bark model arguments
       snake_case__							:	Optional[int]   	=	model_args.pop('n_head'      )
       snake_case__							:	Optional[int]   	=	model_args.pop('n_embd'      )
       snake_case__							:	Tuple   	=	model_args.pop('n_layer'      )

       snake_case__							:	str   	=	ConfigClass(**checkpoint['model_args']      )
       snake_case__							:	List[Any]   	=	ModelClass(config=A      )
       snake_case__							:	str   	=	GenerationConfigClass()

       snake_case__							:	List[str]   	=	model_generation_config
       snake_case__							:	Dict   	=	checkpoint['model']
       # fixup checkpoint
       snake_case__							:	List[Any]   	=	'_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
                     snake_case__							:	Optional[Any]   	=	k[len(A      ) :]
                     for old_layer_name in new_layer_name_dict:
                            snake_case__							:	List[Any]   	=	new_k.replace(A		,					new_layer_name_dict[old_layer_name]      )

                     snake_case__							:	List[str]   	=	state_dict.pop(A      )

       snake_case__							:	Dict   	=	set(state_dict.keys()      ) - set(model.state_dict().keys()      )
       snake_case__							:	Tuple   	=	{k for k in extra_keys if not k.endswith('.attn.bias'      )}
       snake_case__							:	Tuple   	=	set(model.state_dict().keys()      ) - set(state_dict.keys()      )
       snake_case__							:	List[str]   	=	{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      )
       snake_case__							:	Union[str, Any]   	=	model.num_parameters(exclude_embeddings=A      )
       snake_case__							:	Any   	=	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        lowercase_							(A : str		,					A : Union[str, Any]=False		,					A : str="text"      ):
       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

       snake_case__							:	List[Any]   	=	'cpu'  # do conversion on cpu

       snake_case__							:	Union[str, Any]   	=	_get_ckpt_path(A		,					use_small=A      )
       snake_case__							:	Tuple   	=	_load_model(A		,					A		,					model_type=A		,					use_small=A      )

       # load bark initial model
       snake_case__							:	Dict   	=	_bark_load_model(A		,					'cpu'		,					model_type=A		,					use_small=A      )

       if model_type == "text":
              snake_case__							:	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
       snake_case__							:	Tuple   	=	5
       snake_case__							:	Any   	=	1_0

       if model_type in ["text", "coarse"]:
              snake_case__							:	Any   	=	torch.randint(2_5_6		,					(batch_size, sequence_length)		,					dtype=torch.int      )
              snake_case__							:	List[Any]   	=	bark_model(A      )[0]

              snake_case__							:	str   	=	model(A      )

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

       else:
              snake_case__							:	Tuple   	=	3
              snake_case__							:	Any   	=	8
              snake_case__							:	str   	=	torch.randint(2_5_6		,					(batch_size, sequence_length, n_codes_total)		,					dtype=torch.int      )

              snake_case__							:	List[Any]   	=	model(A		,					A      )
              snake_case__							:	Dict   	=	bark_model(A		,					A      )

              snake_case__							:	List[str]   	=	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        lowercase_							(A : Union[str, Any]		,					A : int		,					A : Union[str, Any]		,					A : Optional[Any]		,					A : int		,					A : Union[str, Any]		,					):
       snake_case__							:	Union[str, Any]   	=	os.path.join(A		,					A      )

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

       snake_case__							:	Optional[int]   	=	BarkSemanticModel.from_pretrained(A      )
       snake_case__							:	List[Any]   	=	BarkCoarseModel.from_pretrained(A      )
       snake_case__							:	Optional[int]   	=	BarkFineModel.from_pretrained(A      )
       snake_case__							:	str   	=	EncodecModel.from_pretrained('facebook/encodec_24khz'      )

       snake_case__							:	str   	=	BarkConfig.from_sub_model_configs(
           A		,					A		,					A		,					A      )

       snake_case__							:	int   	=	BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config		,					coarseAcoustic.generation_config		,					fineAcoustic.generation_config      )

       snake_case__							:	List[Any]   	=	BarkModel(A      )

       snake_case__							:	Any   	=	semantic
       snake_case__							:	Dict   	=	coarseAcoustic
       snake_case__							:	Optional[int]   	=	fineAcoustic
       snake_case__							:	Any   	=	codec

       snake_case__							:	str   	=	bark_generation_config

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


if __name__ == "__main__":
 a_   :Dict        =      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_   :Any        =      parser.parse_args()

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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   :Optional[int]        =      logging.get_logger(__name__)

a_   :Dict        =      {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""openai-gpt"""
     _SCREAMING_SNAKE_CASE						=	{
         """max_position_embeddings""": """n_positions""",
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }






     def __init__(       self  :  Optional[int], _snake_case  :  Dict=4_0_4_7_8, _snake_case  :  str=5_1_2, _snake_case  :  int=7_6_8, _snake_case  :  Tuple=1_2, _snake_case  :  Any=1_2, _snake_case  :  str="gelu", _snake_case  :  List[str]=0.1, _snake_case  :  Any=0.1, _snake_case  :  Dict=0.1, _snake_case  :  int=1e-5, _snake_case  :  Optional[Any]=0.0_2, _snake_case  :  List[Any]="cls_index", _snake_case  :  Any=True, _snake_case  :  Any=None, _snake_case  :  int=True, _snake_case  :  Optional[Any]=0.1, **_snake_case  :  List[Any], )   ->Optional[int]:
            snake_case__							:	int   	=	vocab_size
            snake_case__							:	Dict   	=	n_positions
            snake_case__							:	str   	=	n_embd
            snake_case__							:	str   	=	n_layer
            snake_case__							:	List[Any]   	=	n_head
            snake_case__							:	List[Any]   	=	afn
            snake_case__							:	Optional[Any]   	=	resid_pdrop
            snake_case__							:	List[str]   	=	embd_pdrop
            snake_case__							:	List[Any]   	=	attn_pdrop
            snake_case__							:	Optional[int]   	=	layer_norm_epsilon
            snake_case__							:	str   	=	initializer_range
            snake_case__							:	List[str]   	=	summary_type
            snake_case__							:	Optional[int]   	=	summary_use_proj
            snake_case__							:	List[str]   	=	summary_activation
            snake_case__							:	Optional[Any]   	=	summary_first_dropout
            snake_case__							:	int   	=	summary_proj_to_labels
            super().__init__(**_snake_case					)






