





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()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

set_seed(7_70)


_SCREAMING_SNAKE_CASE      	=		{
    """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""",
}


_SCREAMING_SNAKE_CASE      	=		{
    """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""",
    },
}

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


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


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


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

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

	snake_case_		:			List[Any]											=	ConfigClass(**checkpoint['model_args']   )
	snake_case_		:			Optional[Any]											=	ModelClass(config=snake_case_   )
	snake_case_		:			Optional[Any]											=	GenerationConfigClass()

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

			snake_case_		:			Tuple											=	state_dict.pop(snake_case_   )

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

	return model


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

	snake_case_		:			Tuple											=	"""cpu"""  # do conversion on cpu

	snake_case_		:			Tuple											=	_get_ckpt_path(snake_case_   , use_small=snake_case_   )
	snake_case_		:			List[str]											=	_load_model(snake_case_   , snake_case_   , model_type=snake_case_   , use_small=snake_case_   )

	# load bark initial model
	snake_case_		:			int											=	_bark_load_model(snake_case_   , 'cpu'   , model_type=snake_case_   , use_small=snake_case_   )

	if model_type == "text":
		snake_case_		:			List[Any]											=	bark_model["""model"""]

	if model.num_parameters(exclude_embeddings=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
	snake_case_		:			List[Any]											=	5
	snake_case_		:			List[Any]											=	10

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

		snake_case_		:			Union[str, Any]											=	model(snake_case_   )

		# take last logits
		snake_case_		:			Union[str, Any]											=	output_new_model_total.logits[:, [-1], :]

	else:
		snake_case_		:			Tuple											=	3
		snake_case_		:			Union[str, Any]											=	8
		snake_case_		:			int											=	torch.randint(2_56   , (batch_size, sequence_length, n_codes_total)   , dtype=torch.int   )

		snake_case_		:			Union[str, Any]											=	model(snake_case_   , snake_case_   )
		snake_case_		:			Dict											=	bark_model(snake_case_   , snake_case_   )

		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(snake_case_   ).mkdir(exist_ok=snake_case_   )
	model.save_pretrained(snake_case_   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a   , ):
	snake_case_		:			List[str]											=	os.path.join(snake_case_   , snake_case_   )

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

	snake_case_		:			Optional[Any]											=	BarkSemanticModel.from_pretrained(snake_case_   )
	snake_case_		:			List[str]											=	BarkCoarseModel.from_pretrained(snake_case_   )
	snake_case_		:			Optional[Any]											=	BarkFineModel.from_pretrained(snake_case_   )
	snake_case_		:			Union[str, Any]											=	EncodecModel.from_pretrained('facebook/encodec_24khz'   )

	snake_case_		:			List[Any]											=	BarkConfig.from_sub_model_configs(
	    snake_case_   , snake_case_   , snake_case_   , snake_case_   )

	snake_case_		:			Union[str, Any]											=	BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config   , coarseAcoustic.generation_config   , fineAcoustic.generation_config   )

	snake_case_		:			List[str]											=	BarkModel(snake_case_   )

	snake_case_		:			Tuple											=	semantic
	snake_case_		:			Optional[int]											=	coarseAcoustic
	snake_case_		:			List[str]											=	fineAcoustic
	snake_case_		:			Optional[int]											=	codec

	snake_case_		:			List[str]											=	bark_generation_config

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


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

						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()

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








def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ):
		raise ValueError('Input must be an integer'   )
	if input_num <= 0:
		raise ValueError('Input must be positive'   )
	return sum(
	    divisor for divisor in range(1   , input_num // 2 + 1   ) if input_num % divisor == 0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

