


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(770)


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     lowercase	(    a     , a=False ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						model_type
		if use_small:
				key += "_small"
		return os.path.join(a     , REMOTE_MODEL_PATHS[key]["file_name"] )


def     lowercase	(    a     , a ):



		'''simple docstring'''




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


def     lowercase	(    a     , a     , a=False     , a="text" ):



		'''simple docstring'''




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

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

		SCREAMING_SNAKE_CASE_				:Dict				     =						ConfigClass(**checkpoint["model_args"] )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						ModelClass(config=a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						GenerationConfigClass()

		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_generation_config
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						checkpoint["model"]
		# fixup checkpoint
		SCREAMING_SNAKE_CASE_				:int				     =						"_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
						SCREAMING_SNAKE_CASE_				:Dict				     =						k[len(a ) :]
						for old_layer_name in new_layer_name_dict:
								SCREAMING_SNAKE_CASE_				:Dict				     =						new_k.replace(a     , new_layer_name_dict[old_layer_name] )

						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						state_dict.pop(a )

		SCREAMING_SNAKE_CASE_				:Tuple				     =						set(state_dict.keys() ) - set(model.state_dict().keys() )
		SCREAMING_SNAKE_CASE_				:str				     =						{k for k in extra_keys if not k.endswith(".attn.bias" )}
		SCREAMING_SNAKE_CASE_				:Tuple				     =						set(model.state_dict().keys() ) - set(state_dict.keys() )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						{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 )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model.num_parameters(exclude_embeddings=a )
		SCREAMING_SNAKE_CASE_				:str				     =						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     , a=False     , a="text" ):



		'''simple docstring'''




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

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						"cpu"  # do conversion on cpu

		SCREAMING_SNAKE_CASE_				:str				     =						_get_ckpt_path(a     , use_small=a )
		SCREAMING_SNAKE_CASE_				:int				     =						_load_model(a     , a     , model_type=a     , use_small=a )

		# load bark initial model
		SCREAMING_SNAKE_CASE_				:int				     =						_bark_load_model(a     , "cpu"     , model_type=a     , use_small=a )

		if model_type == "text":
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						5
		SCREAMING_SNAKE_CASE_				:str				     =						10

		if model_type in ["text", "coarse"]:
				SCREAMING_SNAKE_CASE_				:Tuple				     =						torch.randint(256     , (batch_size, sequence_length)     , dtype=torch.int )
				SCREAMING_SNAKE_CASE_				:List[str]				     =						bark_model(a )[0]

				SCREAMING_SNAKE_CASE_				:Dict				     =						model(a )

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

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

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

				SCREAMING_SNAKE_CASE_				: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(a ).mkdir(exist_ok=a )
		model.save_pretrained(a )


def     lowercase	(    a     , a     , a     , a     , a     , a     , ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , a )

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

		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						BarkSemanticModel.from_pretrained(a )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						BarkCoarseModel.from_pretrained(a )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						BarkFineModel.from_pretrained(a )
		SCREAMING_SNAKE_CASE_				:Dict				     =						EncodecModel.from_pretrained("facebook/encodec_24khz" )

		SCREAMING_SNAKE_CASE_				:List[str]				     =						BarkConfig.from_sub_model_configs(
		    a     , a     , a     , a )

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

		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						BarkModel(a )

		SCREAMING_SNAKE_CASE_				:Any				     =						semantic
		SCREAMING_SNAKE_CASE_				:int				     =						coarseAcoustic
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						fineAcoustic
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						codec

		SCREAMING_SNAKE_CASE_				:List[str]				     =						bark_generation_config

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


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)










import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


SCREAMING_SNAKE_CASE__     					=			get_logger(__name__)


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		os.makedirs(a     , exist_ok=a )
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						SCREAMING_SNAKE_CASE_				:str				     =						os.path.join(a     , a )
						if accelerator.process_index == 0:
								logger.info(F"Saving model to {output_model_file}" )
								torch.save(a     , a )
								logger.info(F"Model saved to {output_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						(
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , a )
						logger.info(F"Saving model to {output_model_file}" )
						torch.save(a     , a )
						logger.info(F"Model saved to {output_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , F"{MODEL_NAME}_{model_index}" )
						os.makedirs(a     , exist_ok=a )
						logger.info(F"Saving model to {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:int				     =						{"model": state_dict}

						dist_cp.save_state_dict(
						    state_dict=a     , storage_writer=dist_cp.FileSystemWriter(a )     , planner=DefaultSavePlanner()     , )
						logger.info(F"Model saved to {ckpt_dir}" )


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(a ) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
										    "initializing FSDP object" )
								return
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:int				     =						(
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						(
						    os.path.join(a     , F"{MODEL_NAME}_{model_index}" )
						    if F"{MODEL_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading model from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Any				     =						{"model": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=a     , storage_reader=dist_cp.FileSystemReader(a )     , planner=DefaultLoadPlanner()     , )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						state_dict["model"]
						logger.info(F"Model loaded from {ckpt_dir}" )
				model.load_state_dict(a )


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		os.makedirs(a     , exist_ok=a )
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				SCREAMING_SNAKE_CASE_				:str				     =						FSDP.optim_state_dict(a     , a )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								SCREAMING_SNAKE_CASE_				:List[str]				     =						(
								    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
								)
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , a )
								logger.info(F"Saving Optimizer state to {output_optimizer_file}" )
								torch.save(a     , a )
								logger.info(F"Optimizer state saved in {output_optimizer_file}" )
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						os.path.join(a     , F"{OPTIMIZER_NAME}_{optimizer_index}" )
						os.makedirs(a     , exist_ok=a )
						logger.info(F"Saving Optimizer state to {ckpt_dir}" )
						dist_cp.save_state_dict(
						    state_dict={"optimizer": optim_state}     , storage_writer=dist_cp.FileSystemWriter(a )     , planner=DefaultSavePlanner()     , )
						logger.info(F"Optimizer state saved in {ckpt_dir}" )


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						(
						    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , a )
						logger.info(F"Loading Optimizer state from {input_optimizer_file}" )
						SCREAMING_SNAKE_CASE_				:str				     =						torch.load(a )
						logger.info(F"Optimizer state loaded from {input_optimizer_file}" )
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						(
						    os.path.join(a     , F"{OPTIMIZER_NAME}_{optimizer_index}" )
						    if F"{OPTIMIZER_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading Optimizer from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()     , optimizer_key="optimizer"     , storage_reader=dist_cp.FileSystemReader(a )     , )
						SCREAMING_SNAKE_CASE_				:List[str]				     =						optim_state["optimizer"]
						logger.info(F"Optimizer loaded from {ckpt_dir}" )
				SCREAMING_SNAKE_CASE_				:int				     =						FSDP.optim_state_dict_to_load(a     , a     , a )
				optimizer.load_state_dict(a )






