




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(7_7_0)


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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Any=False	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Union[str, Any]=False						,							_lowerCAmelCase							:   Union[str, Any]="text"	):




 """simple docstring"""







 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(_lowerCAmelCase	):
  logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''	)
  _download(model_info["repo_id"]						,							model_info["file_name"]	)
 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase						,							map_location=_lowerCAmelCase	)
 # 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=_lowerCAmelCase	)
 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(_lowerCAmelCase	):
   # replace part of the key with corresponding layer name in HF implementation
   UpperCAmelCase__							 =						k[len(_lowerCAmelCase	) :]
   for old_layer_name in new_layer_name_dict:
    UpperCAmelCase__							 =						new_k.replace(_lowerCAmelCase						,							new_layer_name_dict[old_layer_name]	)

   UpperCAmelCase__							 =						state_dict.pop(_lowerCAmelCase	)

 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(_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	)
 UpperCAmelCase__							 =						model.num_parameters(exclude_embeddings=_lowerCAmelCase	)
 UpperCAmelCase__							 =						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							:   str						,							_lowerCAmelCase							:   Tuple=False						,							_lowerCAmelCase							:   str="text"	):




 """simple docstring"""







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

 UpperCAmelCase__							 =						"cpu"  # do conversion on cpu

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

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

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

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

  UpperCAmelCase__							 =						model(_lowerCAmelCase	)

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

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

  UpperCAmelCase__							 =						model(_lowerCAmelCase						,							_lowerCAmelCase	)
  UpperCAmelCase__							 =						bark_model(_lowerCAmelCase						,							_lowerCAmelCase	)

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   int						,							):




 """simple docstring"""







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

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

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

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

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

 UpperCAmelCase__							 =						BarkModel(_lowerCAmelCase	)

 UpperCAmelCase__							 =						semantic
 UpperCAmelCase__							 =						coarseAcoustic
 UpperCAmelCase__							 =						fineAcoustic
 UpperCAmelCase__							 =						codec

 UpperCAmelCase__							 =						bark_generation_config

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


if __name__ == "__main__":
  _lowerCAmelCase						:   Union[str, 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						:   Tuple		=   parser.parse_args()

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











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   Union[str, Any]		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   Union[str, Any]		=   os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
    _lowerCAmelCase						:   List[str]		=   F'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )





