


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[Any]     						=     logging.get_logger(__name__)

set_seed(770)


A							:					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',
}


A							:					Any     						=     {
    '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							:					str     						=     os.path.dirname(os.path.abspath(__file__))
A							:					Optional[Any]     						=     os.path.join(os.path.expanduser('~'), '.cache')
A							:					int     						=     os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase=False )						->	int:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Any:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=False      ,	_lowerCAmelCase="text" )						->	Optional[Any]:


						'''simple docstring'''






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

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

						__snake_case				=		ConfigClass(**checkpoint["model_args"] )
						__snake_case				=		ModelClass(config=_lowerCAmelCase )
						__snake_case				=		GenerationConfigClass()

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

																		__snake_case				=		state_dict.pop(_lowerCAmelCase )

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

						return model





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase=False      ,	_lowerCAmelCase="text" )						->	int:


						'''simple docstring'''






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

						__snake_case				=		"cpu"  # do conversion on cpu

						__snake_case				=		_get_ckpt_path(_lowerCAmelCase      ,	use_small=_lowerCAmelCase )
						__snake_case				=		_load_model(_lowerCAmelCase      ,	_lowerCAmelCase      ,	model_type=_lowerCAmelCase      ,	use_small=_lowerCAmelCase )

						# load bark initial model
						__snake_case				=		_bark_load_model(_lowerCAmelCase      ,	"cpu"      ,	model_type=_lowerCAmelCase      ,	use_small=_lowerCAmelCase )

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

						if model.num_parameters(exclude_embeddings=_lowerCAmelCase ) != 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				=		5
						__snake_case				=		10

						if model_type in ["text", "coarse"]:
												__snake_case				=		torch.randint(256      ,	(batch_size, sequence_length)      ,	dtype=torch.int )
												__snake_case				=		bark_model(_lowerCAmelCase )[0]

												__snake_case				=		model(_lowerCAmelCase )

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

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

												__snake_case				=		model(_lowerCAmelCase      ,	_lowerCAmelCase )
												__snake_case				=		bark_model(_lowerCAmelCase      ,	_lowerCAmelCase )

												__snake_case				=		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(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
						model.save_pretrained(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	)						->	int:


						'''simple docstring'''






						__snake_case				=		os.path.join(_lowerCAmelCase      ,	_lowerCAmelCase )

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

						__snake_case				=		BarkSemanticModel.from_pretrained(_lowerCAmelCase )
						__snake_case				=		BarkCoarseModel.from_pretrained(_lowerCAmelCase )
						__snake_case				=		BarkFineModel.from_pretrained(_lowerCAmelCase )
						__snake_case				=		EncodecModel.from_pretrained("facebook/encodec_24khz" )

						__snake_case				=		BarkConfig.from_sub_model_configs(
						    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

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

						__snake_case				=		BarkModel(_lowerCAmelCase )

						__snake_case				=		semantic
						__snake_case				=		coarseAcoustic
						__snake_case				=		fineAcoustic
						__snake_case				=		codec

						__snake_case				=		bark_generation_config

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


if __name__ == "__main__":
	A							:					Optional[int]     						=     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							:					int     						=     parser.parse_args()

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









def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						__snake_case				=		0
						while n > 0:
												res += n % 10
												n //= 10
						return res





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						return n if n < 10 else n % 10 + sum_of_digits(n // 10 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase ) -> None:
												__snake_case				=		F'''{func.__name__}({value})'''
												__snake_case				=		timeit(F'''__main__.{call}'''      ,	setup="import __main__" )
												print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )

						for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
												for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
																		benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase )
												print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





