



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()
snake_case__					:     Any    		=   logging.get_logger(__name__)

set_seed(7_70)


snake_case__					:     Tuple    		=   {
    """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""",
}


snake_case__					:     Optional[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""",
    },
}

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



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			model_type
	if use_small:
		key += "_small"
	return os.path.join(_SCREAMING_SNAKE_CASE  ,	REMOTE_MODEL_PATHS[key]["file_name"]   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	os.makedirs(_SCREAMING_SNAKE_CASE  ,	exist_ok=_SCREAMING_SNAKE_CASE   )
	hf_hub_download(repo_id=_SCREAMING_SNAKE_CASE  ,	filename=_SCREAMING_SNAKE_CASE  ,	local_dir=_SCREAMING_SNAKE_CASE   )



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

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

	__lowercase		       =			ConfigClass(**checkpoint["model_args"]   )
	__lowercase		       =			ModelClass(config=_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			GenerationConfigClass()

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

			__lowercase		       =			state_dict.pop(_SCREAMING_SNAKE_CASE   )

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

	return model



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE="text"   ):
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	__lowercase		       =			"cpu"  # do conversion on cpu

	__lowercase		       =			_get_ckpt_path(_SCREAMING_SNAKE_CASE  ,	use_small=_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_load_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	model_type=_SCREAMING_SNAKE_CASE  ,	use_small=_SCREAMING_SNAKE_CASE   )

	# load bark initial model
	__lowercase		       =			_bark_load_model(_SCREAMING_SNAKE_CASE  ,	"cpu"  ,	model_type=_SCREAMING_SNAKE_CASE  ,	use_small=_SCREAMING_SNAKE_CASE   )

	if model_type == "text":
		__lowercase		       =			bark_model["model"]

	if model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE   ) != 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
	__lowercase		       =			5
	__lowercase		       =			1_0

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

		__lowercase		       =			model(_SCREAMING_SNAKE_CASE   )

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

	else:
		__lowercase		       =			3
		__lowercase		       =			8
		__lowercase		       =			torch.randint(2_5_6  ,	(batch_size, sequence_length, n_codes_total)  ,	dtype=torch.int   )

		__lowercase		       =			model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			bark_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

		__lowercase		       =			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(_SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )
	model.save_pretrained(_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	):
	__lowercase		       =			os.path.join(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			BarkSemanticConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE  ,	"config.json"   )   )
	__lowercase		       =			BarkCoarseConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE  ,	"config.json"   )   )
	__lowercase		       =			BarkFineConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE  ,	"config.json"   )   )
	__lowercase		       =			EncodecConfig.from_pretrained("facebook/encodec_24khz"   )

	__lowercase		       =			BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			EncodecModel.from_pretrained("facebook/encodec_24khz"   )

	__lowercase		       =			BarkConfig.from_sub_model_configs(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

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

	__lowercase		       =			BarkModel(_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			semantic
	__lowercase		       =			coarseAcoustic
	__lowercase		       =			fineAcoustic
	__lowercase		       =			codec

	__lowercase		       =			bark_generation_config

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


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

	snake_case__					:     int    		=   parser.parse_args()

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








from ....utils import logging


snake_case__					:     List[Any]    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=2_048	):






							'''simple docstring'''



							__lowercase		       =			config.__dict__
							__lowercase		       =			modal_hidden_size
							if num_labels:
								__lowercase		       =			num_labels



