
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()
lowerCamelCase__          =		logging.get_logger(__name__)

set_seed(770)


lowerCamelCase__          =		{
    """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""",
}


lowerCamelCase__          =		{
    """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""",
    },
}

lowerCamelCase__          =		os.path.dirname(os.path.abspath(__file__))
lowerCamelCase__          =		os.path.join(os.path.expanduser("""~"""), """.cache""")
lowerCamelCase__          =		os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=False     )				->		Tuple:
			lowerCAmelCase__      :      Union[str, Any]         =		model_type
			if use_small:
						key += "_small"
			return os.path.join(SCREAMING_SNAKE_CASE_       ,							REMOTE_MODEL_PATHS[key]['file_name']     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		str:
			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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=False       ,							SCREAMING_SNAKE_CASE_="text"     )				->		List[Any]:
			if model_type == "text":
						lowerCAmelCase__      :      Tuple         =		BarkSemanticModel
						lowerCAmelCase__      :      Dict         =		BarkSemanticConfig
						lowerCAmelCase__      :      Optional[Any]         =		BarkSemanticGenerationConfig
			elif model_type == "coarse":
						lowerCAmelCase__      :      int         =		BarkCoarseModel
						lowerCAmelCase__      :      List[str]         =		BarkCoarseConfig
						lowerCAmelCase__      :      List[Any]         =		BarkCoarseGenerationConfig
			elif model_type == "fine":
						lowerCAmelCase__      :      Tuple         =		BarkFineModel
						lowerCAmelCase__      :      Optional[Any]         =		BarkFineConfig
						lowerCAmelCase__      :      Dict         =		BarkFineGenerationConfig
			else:
						raise NotImplementedError()
			lowerCAmelCase__      :      str         =		F'''{model_type}_small''' if use_small else model_type
			lowerCAmelCase__      :      Optional[Any]         =		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']     )
			lowerCAmelCase__      :      Any         =		torch.load(SCREAMING_SNAKE_CASE_       ,							map_location=SCREAMING_SNAKE_CASE_     )
			# this is a hack
			lowerCAmelCase__      :      int         =		checkpoint['model_args']
			if "input_vocab_size" not in model_args:
						lowerCAmelCase__      :      Dict         =		model_args['vocab_size']
						lowerCAmelCase__      :      str         =		model_args['vocab_size']
						del model_args["vocab_size"]

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

			lowerCAmelCase__      :      Tuple         =		ConfigClass(**checkpoint['model_args']     )
			lowerCAmelCase__      :      Optional[int]         =		ModelClass(config=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Any         =		GenerationConfigClass()

			lowerCAmelCase__      :      Dict         =		model_generation_config
			lowerCAmelCase__      :      str         =		checkpoint['model']
			# fixup checkpoint
			lowerCAmelCase__      :      Dict         =		'_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
									lowerCAmelCase__      :      Tuple         =		k[len(SCREAMING_SNAKE_CASE_     ) :]
									for old_layer_name in new_layer_name_dict:
												lowerCAmelCase__      :      List[Any]         =		new_k.replace(SCREAMING_SNAKE_CASE_       ,							new_layer_name_dict[old_layer_name]     )

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

			lowerCAmelCase__      :      Tuple         =		set(state_dict.keys()     ) - set(model.state_dict().keys()     )
			lowerCAmelCase__      :      Dict         =		{k for k in extra_keys if not k.endswith('.attn.bias'     )}
			lowerCAmelCase__      :      Any         =		set(model.state_dict().keys()     ) - set(state_dict.keys()     )
			lowerCAmelCase__      :      List[Any]         =		{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_     )
			lowerCAmelCase__      :      str         =		model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      str         =		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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=False       ,							SCREAMING_SNAKE_CASE_="text"     )				->		List[str]:
			if model_type not in ("text", "coarse", "fine"):
						raise NotImplementedError()

			lowerCAmelCase__      :      Union[str, Any]         =		'cpu'  # do conversion on cpu

			lowerCAmelCase__      :      Dict         =		_get_ckpt_path(SCREAMING_SNAKE_CASE_       ,							use_small=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[int]         =		_load_model(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							model_type=SCREAMING_SNAKE_CASE_       ,							use_small=SCREAMING_SNAKE_CASE_     )

			# load bark initial model
			lowerCAmelCase__      :      List[str]         =		_bark_load_model(SCREAMING_SNAKE_CASE_       ,							'cpu'       ,							model_type=SCREAMING_SNAKE_CASE_       ,							use_small=SCREAMING_SNAKE_CASE_     )

			if model_type == "text":
						lowerCAmelCase__      :      Dict         =		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
			lowerCAmelCase__      :      Optional[Any]         =		5
			lowerCAmelCase__      :      Any         =		10

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

						lowerCAmelCase__      :      int         =		model(SCREAMING_SNAKE_CASE_     )

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

			else:
						lowerCAmelCase__      :      Any         =		3
						lowerCAmelCase__      :      List[Any]         =		8
						lowerCAmelCase__      :      List[str]         =		torch.randint(256       ,							(batch_size, sequence_length, n_codes_total)       ,							dtype=torch.int     )

						lowerCAmelCase__      :      Any         =		model(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[Any]         =		bark_model(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )

						lowerCAmelCase__      :      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(SCREAMING_SNAKE_CASE_     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )
			model.save_pretrained(SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							)				->		Optional[Any]:
			lowerCAmelCase__      :      Optional[Any]         =		os.path.join(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )

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

			lowerCAmelCase__      :      List[str]         =		BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      List[str]         =		BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[Any]         =		BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Dict         =		EncodecModel.from_pretrained('facebook/encodec_24khz'     )

			lowerCAmelCase__      :      Dict         =		BarkConfig.from_sub_model_configs(
			    SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      Tuple         =		BarkGenerationConfig.from_sub_model_configs(
			    semantic.generation_config       ,							coarseAcoustic.generation_config       ,							fineAcoustic.generation_config     )

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

			lowerCAmelCase__      :      Union[str, Any]         =		semantic
			lowerCAmelCase__      :      Optional[Any]         =		coarseAcoustic
			lowerCAmelCase__      :      int         =		fineAcoustic
			lowerCAmelCase__      :      Optional[Any]         =		codec

			lowerCAmelCase__      :      Union[str, Any]         =		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__":
					lowerCamelCase__          =		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.""")

					lowerCamelCase__          =		parser.parse_args()

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

class      A__							:






				def __init__(						self					:      Dict    ,     a					:      list[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		len(a   )
							lowerCAmelCase__      :      Optional[Any]         =		[0] * len_array

							if len_array > 0:
										lowerCAmelCase__      :      List[Any]         =		array[0]

							for i in range(1    ,     a   ):
										lowerCAmelCase__      :      Optional[int]         =		self.prefix_sum[i - 1] + array[i]






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							if start == 0:
										return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{0}
							for sum_item in self.prefix_sum:
										if sum_item - target_sum in sums:
													return True

										sums.add(a   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()