




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		:				List[Any]       			=		logging.get_logger(__name__)

set_seed(770)


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




def 			__magic_name__				( A		:					Tuple,  A		:					Optional[int]=False   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					Optional[int],  A		:					Any   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					str,  A		:					str,  A		:					Any=False,  A		:					Tuple="text"   ):






			'''simple docstring'''







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

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

			a    =			ConfigClass(**checkpoint["model_args"]   )
			a    =			ModelClass(config=A   )
			a    =			GenerationConfigClass()

			a    =			model_generation_config
			a    =			checkpoint["model"]
			# fixup checkpoint
			a    =			"_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
									a    =			k[len(A   ) :]
									for old_layer_name in new_layer_name_dict:
												a    =			new_k.replace(A,  new_layer_name_dict[old_layer_name]   )

									a    =			state_dict.pop(A   )

			a    =			set(state_dict.keys()   ) - set(model.state_dict().keys()   )
			a    =			{k for k in extra_keys if not k.endswith(".attn.bias"   )}
			a    =			set(model.state_dict().keys()   ) - set(state_dict.keys()   )
			a    =			{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   )
			a    =			model.num_parameters(exclude_embeddings=A   )
			a    =			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 			__magic_name__				( A		:					Any,  A		:					List[str]=False,  A		:					Any="text"   ):






			'''simple docstring'''







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

			a    =			"cpu"  # do conversion on cpu

			a    =			_get_ckpt_path(A,  use_small=A   )
			a    =			_load_model(A,  A,  model_type=A,  use_small=A   )

			# load bark initial model
			a    =			_bark_load_model(A,  "cpu",  model_type=A,  use_small=A   )

			if model_type == "text":
						a    =			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
			a    =			5
			a    =			10

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

						a    =			model(A   )

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

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

						a    =			model(A,  A   )
						a    =			bark_model(A,  A   )

						a    =			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 			__magic_name__				( A		:					Union[str, Any],  A		:					Optional[Any],  A		:					Any,  A		:					Any,  A		:					Union[str, Any],  A		:					Union[str, Any],  ):






			'''simple docstring'''







			a    =			os.path.join(A,  A   )

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

			a    =			BarkSemanticModel.from_pretrained(A   )
			a    =			BarkCoarseModel.from_pretrained(A   )
			a    =			BarkFineModel.from_pretrained(A   )
			a    =			EncodecModel.from_pretrained("facebook/encodec_24khz"   )

			a    =			BarkConfig.from_sub_model_configs(
			    A,  A,  A,  A   )

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

			a    =			BarkModel(A   )

			a    =			semantic
			a    =			coarseAcoustic
			a    =			fineAcoustic
			a    =			codec

			a    =			bark_generation_config

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


if __name__ == "__main__":
		__lowerCAmelCase		:				Any       			=		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		:				str       			=		parser.parse_args()

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












from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






