


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()
__UpperCamelCase     		=							logging.get_logger(__name__)

set_seed(7_7_0)


__UpperCamelCase     		=							{
    "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",
}


__UpperCamelCase     		=							{
    "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",
    },
}

__UpperCamelCase     		=							os.path.dirname(os.path.abspath(__file__))
__UpperCamelCase     		=							os.path.join(os.path.expanduser('~'), '.cache')
__UpperCamelCase     		=							os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')
def 							UpperCamelCase_(					_A :Dict					,  _A :List[Any]=False	)->		Dict:
			UpperCamelCase__       =							model_type
			if use_small:
						key += "_small"
			return os.path.join(A__					,  REMOTE_MODEL_PATHS[key]["file_name"]	)
def 							UpperCamelCase_(					_A :List[Any]					,  _A :Union[str, Any]	)->		Optional[Any]:
			os.makedirs(A__					,  exist_ok=A__	)
			hf_hub_download(repo_id=A__					,  filename=A__					,  local_dir=A__	)
def 							UpperCamelCase_(					_A :Dict					,  _A :List[str]					,  _A :str=False					,  _A :Optional[Any]="text"	)->		List[str]:





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

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

			UpperCamelCase__       =							ConfigClass(**checkpoint["model_args"]	)
			UpperCamelCase__       =							ModelClass(config=A__	)
			UpperCamelCase__       =							GenerationConfigClass()

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

									UpperCamelCase__       =							state_dict.pop(A__	)

			UpperCamelCase__       =							set(state_dict.keys()	) - set(model.state_dict().keys()	)
			UpperCamelCase__       =							{k for k in extra_keys if not k.endswith(".attn.bias"	)}
			UpperCamelCase__       =							set(model.state_dict().keys()	) - set(state_dict.keys()	)
			UpperCamelCase__       =							{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__	)
			UpperCamelCase__       =							model.num_parameters(exclude_embeddings=A__	)
			UpperCamelCase__       =							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 							UpperCamelCase_(					_A :Union[str, Any]					,  _A :str=False					,  _A :Union[str, Any]="text"	)->		Union[str, Any]:





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

			UpperCamelCase__       =							"""cpu"""  # do conversion on cpu

			UpperCamelCase__       =							_get_ckpt_path(A__					,  use_small=A__	)
			UpperCamelCase__       =							_load_model(A__					,  A__					,  model_type=A__					,  use_small=A__	)

			# load bark initial model
			UpperCamelCase__       =							_bark_load_model(A__					,  "cpu"					,  model_type=A__					,  use_small=A__	)

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

			if model_type in ["text", "coarse"]:
						UpperCamelCase__       =							torch.randint(2_56					,  (batch_size, sequence_length)					,  dtype=torch.int	)
						UpperCamelCase__       =							bark_model(A__	)[0]

						UpperCamelCase__       =							model(A__	)

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

			else:
						UpperCamelCase__       =							3
						UpperCamelCase__       =							8
						UpperCamelCase__       =							torch.randint(2_56					,  (batch_size, sequence_length, n_codes_total)					,  dtype=torch.int	)

						UpperCamelCase__       =							model(A__					,  A__	)
						UpperCamelCase__       =							bark_model(A__					,  A__	)

						UpperCamelCase__       =							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 							UpperCamelCase_(					_A :int					,  _A :List[Any]					,  _A :Union[str, Any]					,  _A :Dict					,  _A :int					,  _A :Union[str, Any]					,  )->		Optional[Any]:
			UpperCamelCase__       =							os.path.join(A__					,  A__	)

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

			UpperCamelCase__       =							BarkSemanticModel.from_pretrained(A__	)
			UpperCamelCase__       =							BarkCoarseModel.from_pretrained(A__	)
			UpperCamelCase__       =							BarkFineModel.from_pretrained(A__	)
			UpperCamelCase__       =							EncodecModel.from_pretrained("facebook/encodec_24khz"	)

			UpperCamelCase__       =							BarkConfig.from_sub_model_configs(
			    A__					,  A__					,  A__					,  A__	)

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

			UpperCamelCase__       =							BarkModel(A__	)

			UpperCamelCase__       =							semantic
			UpperCamelCase__       =							coarseAcoustic
			UpperCamelCase__       =							fineAcoustic
			UpperCamelCase__       =							codec

			UpperCamelCase__       =							bark_generation_config

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


if __name__ == "__main__":
						__UpperCamelCase     		=							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.')

						__UpperCamelCase     		=							parser.parse_args()

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






'''simple docstring'''






from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



