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

set_seed(7_7_0)


_lowercase			:  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			:  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",
    },
}

_lowercase			:  List[Any]   							=	os.path.dirname(os.path.abspath(__file__))
_lowercase			:  Union[str, Any]   							=	os.path.join(os.path.expanduser("~"), ".cache")
_lowercase			:  str   							=	os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			List[str]=False							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	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 :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





						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 :			Any				,				__SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			List[Any]=False				,				__SCREAMING_SNAKE_CASE :			List[str]="text"							):



						"""simple docstring"""





						if model_type == "text":
												lowercase_     :					Optional[Any]													=	BarkSemanticModel
												lowercase_     :					str													=	BarkSemanticConfig
												lowercase_     :					Union[str, Any]													=	BarkSemanticGenerationConfig
						elif model_type == "coarse":
												lowercase_     :					Optional[Any]													=	BarkCoarseModel
												lowercase_     :					List[str]													=	BarkCoarseConfig
												lowercase_     :					Dict													=	BarkCoarseGenerationConfig
						elif model_type == "fine":
												lowercase_     :					Tuple													=	BarkFineModel
												lowercase_     :					Tuple													=	BarkFineConfig
												lowercase_     :					Tuple													=	BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						lowercase_     :					Tuple													=	F'''{model_type}_small''' if use_small else model_type
						lowercase_     :					Optional[int]													=	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_     :					Dict													=	torch.load(__SCREAMING_SNAKE_CASE				,				map_location=__SCREAMING_SNAKE_CASE							)
						# this is a hack
						lowercase_     :					Optional[Any]													=	checkpoint["""model_args"""]
						if "input_vocab_size" not in model_args:
												lowercase_     :					List[Any]													=	model_args["""vocab_size"""]
												lowercase_     :					str													=	model_args["""vocab_size"""]
												del model_args["vocab_size"]

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

						lowercase_     :					int													=	ConfigClass(**checkpoint['''model_args''']							)
						lowercase_     :					int													=	ModelClass(config=__SCREAMING_SNAKE_CASE							)
						lowercase_     :					List[str]													=	GenerationConfigClass()

						lowercase_     :					List[str]													=	model_generation_config
						lowercase_     :					Dict													=	checkpoint["""model"""]
						# fixup checkpoint
						lowercase_     :					Union[str, Any]													=	"""_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_     :					Optional[Any]													=	k[len(__SCREAMING_SNAKE_CASE							) :]
																		for old_layer_name in new_layer_name_dict:
																								lowercase_     :					str													=	new_k.replace(__SCREAMING_SNAKE_CASE				,				new_layer_name_dict[old_layer_name]							)

																		lowercase_     :					Tuple													=	state_dict.pop(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					Union[str, Any]													=	set(state_dict.keys()							) - set(model.state_dict().keys()							)
						lowercase_     :					Optional[int]													=	{k for k in extra_keys if not k.endswith('''.attn.bias'''							)}
						lowercase_     :					Optional[int]													=	set(model.state_dict().keys()							) - set(state_dict.keys()							)
						lowercase_     :					int													=	{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_     :					Optional[int]													=	model.num_parameters(exclude_embeddings=__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Union[str, Any]													=	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 :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			List[str]=False				,				__SCREAMING_SNAKE_CASE :			Optional[int]="text"							):



						"""simple docstring"""





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

						lowercase_     :					Union[str, Any]													=	"""cpu"""  # do conversion on cpu

						lowercase_     :					Union[str, Any]													=	_get_ckpt_path(__SCREAMING_SNAKE_CASE				,				use_small=__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Any													=	_load_model(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				model_type=__SCREAMING_SNAKE_CASE				,				use_small=__SCREAMING_SNAKE_CASE							)

						# load bark initial model
						lowercase_     :					Optional[int]													=	_bark_load_model(__SCREAMING_SNAKE_CASE				,				'''cpu'''				,				model_type=__SCREAMING_SNAKE_CASE				,				use_small=__SCREAMING_SNAKE_CASE							)

						if model_type == "text":
												lowercase_     :					Tuple													=	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_     :					Dict													=	5
						lowercase_     :					str													=	10

						if model_type in ["text", "coarse"]:
												lowercase_     :					Optional[int]													=	torch.randint(256				,				(batch_size, sequence_length)				,				dtype=torch.int							)
												lowercase_     :					Tuple													=	bark_model(__SCREAMING_SNAKE_CASE							)[0]

												lowercase_     :					Dict													=	model(__SCREAMING_SNAKE_CASE							)

												# take last logits
												lowercase_     :					List[Any]													=	output_new_model_total.logits[:, [-1], :]

						else:
												lowercase_     :					Any													=	3
												lowercase_     :					Dict													=	8
												lowercase_     :					Any													=	torch.randint(256				,				(batch_size, sequence_length, n_codes_total)				,				dtype=torch.int							)

												lowercase_     :					List[str]													=	model(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
												lowercase_     :					Dict													=	bark_model(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

												lowercase_     :					Union[str, Any]													=	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 :			Dict				,				__SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Dict				,				):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	os.path.join(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

						lowercase_     :					Optional[Any]													=	BarkSemanticConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE				,				'''config.json'''							)							)
						lowercase_     :					Optional[Any]													=	BarkCoarseConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE				,				'''config.json'''							)							)
						lowercase_     :					Dict													=	BarkFineConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE				,				'''config.json'''							)							)
						lowercase_     :					Dict													=	EncodecConfig.from_pretrained('''facebook/encodec_24khz'''							)

						lowercase_     :					int													=	BarkSemanticModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	BarkCoarseModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	BarkFineModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					str													=	EncodecModel.from_pretrained('''facebook/encodec_24khz'''							)

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

						lowercase_     :					str													=	BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config				,				coarseAcoustic.generation_config				,				fineAcoustic.generation_config							)

						lowercase_     :					int													=	BarkModel(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					List[str]													=	semantic
						lowercase_     :					Any													=	coarseAcoustic
						lowercase_     :					Optional[Any]													=	fineAcoustic
						lowercase_     :					Optional[int]													=	codec

						lowercase_     :					str													=	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__":
							_lowercase			:  Tuple   							=	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			:  str   							=	parser.parse_args()

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





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						assert column_title.isupper()
						lowercase_     :					Dict													=	0
						lowercase_     :					Tuple													=	len(__SCREAMING_SNAKE_CASE							) - 1
						lowercase_     :					Optional[int]													=	0

						while index >= 0:
												lowercase_     :					Optional[Any]													=	(ord(column_title[index]							) - 64) * pow(26				,				__SCREAMING_SNAKE_CASE							)
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
							from doctest import testmod

							testmod()



