




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__			:       Optional[int]         =							logging.get_logger(__name__)

set_seed(770)


lowerCamelCase__			:       List[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__			:       Union[str, 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',
    },
}

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Optional[int]=False							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      model_type
      if use_small:
            key += "_small"
      return os.path.join(__UpperCAmelCase      ,      REMOTE_MODEL_PATHS[key]['file_name']							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     Any							) ->			Optional[Any]:
      os.makedirs(__UpperCAmelCase      ,      exist_ok=__UpperCAmelCase							)
      hf_hub_download(repo_id=__UpperCAmelCase      ,      filename=__UpperCAmelCase      ,      local_dir=__UpperCAmelCase							)

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

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

      SCREAMING_SNAKE_CASE_     						=      ConfigClass(**checkpoint['model_args']							)
      SCREAMING_SNAKE_CASE_     						=      ModelClass(config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      GenerationConfigClass()

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

                  SCREAMING_SNAKE_CASE_     						=      state_dict.pop(__UpperCAmelCase							)

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

      return model

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Union[str, Any]=False      ,      __UpperCAmelCase						:     int="text"							) ->			str:
      if model_type not in ("text", "coarse", "fine"):
            raise NotImplementedError()

      SCREAMING_SNAKE_CASE_     						=      'cpu'  # do conversion on cpu

      SCREAMING_SNAKE_CASE_     						=      _get_ckpt_path(__UpperCAmelCase      ,      use_small=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      _load_model(__UpperCAmelCase      ,      __UpperCAmelCase      ,      model_type=__UpperCAmelCase      ,      use_small=__UpperCAmelCase							)

      # load bark initial model
      SCREAMING_SNAKE_CASE_     						=      _bark_load_model(__UpperCAmelCase      ,      'cpu'      ,      model_type=__UpperCAmelCase      ,      use_small=__UpperCAmelCase							)

      if model_type == "text":
            SCREAMING_SNAKE_CASE_     						=      bark_model['model']

      if model.num_parameters(exclude_embeddings=__UpperCAmelCase							) != 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
      SCREAMING_SNAKE_CASE_     						=      5
      SCREAMING_SNAKE_CASE_     						=      10

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

            SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase							)

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

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

            SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase      ,      __UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      bark_model(__UpperCAmelCase      ,      __UpperCAmelCase							)

            SCREAMING_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(__UpperCAmelCase							).mkdir(exist_ok=__UpperCAmelCase							)
      model.save_pretrained(__UpperCAmelCase							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Tuple      ,      ) ->			Optional[Any]:
      SCREAMING_SNAKE_CASE_     						=      os.path.join(__UpperCAmelCase      ,      __UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      BarkSemanticConfig.from_pretrained(os.path.join(__UpperCAmelCase      ,      'config.json'							)							)
      SCREAMING_SNAKE_CASE_     						=      BarkCoarseConfig.from_pretrained(os.path.join(__UpperCAmelCase      ,      'config.json'							)							)
      SCREAMING_SNAKE_CASE_     						=      BarkFineConfig.from_pretrained(os.path.join(__UpperCAmelCase      ,      'config.json'							)							)
      SCREAMING_SNAKE_CASE_     						=      EncodecConfig.from_pretrained('facebook/encodec_24khz'							)

      SCREAMING_SNAKE_CASE_     						=      BarkSemanticModel.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      BarkCoarseModel.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      BarkFineModel.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      EncodecModel.from_pretrained('facebook/encodec_24khz'							)

      SCREAMING_SNAKE_CASE_     						=      BarkConfig.from_sub_model_configs(
          __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_     						=      BarkModel(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      semantic
      SCREAMING_SNAKE_CASE_     						=      coarseAcoustic
      SCREAMING_SNAKE_CASE_     						=      fineAcoustic
      SCREAMING_SNAKE_CASE_     						=      codec

      SCREAMING_SNAKE_CASE_     						=      bark_generation_config

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


if __name__ == "__main__":
   lowerCamelCase__			:       Tuple         =							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)





def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(__UpperCAmelCase							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
   __import__('doctest').testmod()