


'''simple docstring'''



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()
A__		:					Tuple												=  logging.get_logger(__name__)

set_seed(770)


A__		:					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',
}


A__		:					Dict												=  {
    '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',
    },
}

A__		:					Dict												=  os.path.dirname(os.path.abspath(__file__))
A__		:					Tuple												=  os.path.join(os.path.expanduser("""~"""), """.cache""")
A__		:					Optional[Any]												=  os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")



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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Dict							)     ->		List[Any]:
     os.makedirs(__UpperCAmelCase      ,    exist_ok=__UpperCAmelCase							)
     hf_hub_download(repo_id=__UpperCAmelCase      ,    filename=__UpperCAmelCase      ,    local_dir=__UpperCAmelCase							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				Union[str, Any]=False      ,    UpperCAmelCase_    :				Optional[Any]="text"							)     ->		str:
     if model_type == "text":
          __lowerCamelCase   :	Optional[int]								=		BarkSemanticModel
          __lowerCamelCase   :	Optional[int]								=		BarkSemanticConfig
          __lowerCamelCase   :	List[str]								=		BarkSemanticGenerationConfig
     elif model_type == "coarse":
          __lowerCamelCase   :	int								=		BarkCoarseModel
          __lowerCamelCase   :	Optional[int]								=		BarkCoarseConfig
          __lowerCamelCase   :	Union[str, Any]								=		BarkCoarseGenerationConfig
     elif model_type == "fine":
          __lowerCamelCase   :	str								=		BarkFineModel
          __lowerCamelCase   :	List[Any]								=		BarkFineConfig
          __lowerCamelCase   :	Optional[Any]								=		BarkFineGenerationConfig
     else:
          raise NotImplementedError()
     __lowerCamelCase   :	Optional[Any]								=		F'{model_type}_small' if use_small else model_type
     __lowerCamelCase   :	Union[str, Any]								=		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']							)
     __lowerCamelCase   :	Tuple								=		torch.load(__UpperCAmelCase      ,    map_location=__UpperCAmelCase							)
     # this is a hack
     __lowerCamelCase   :	str								=		checkpoint['model_args']
     if "input_vocab_size" not in model_args:
          __lowerCamelCase   :	str								=		model_args['vocab_size']
          __lowerCamelCase   :	List[Any]								=		model_args['vocab_size']
          del model_args["vocab_size"]

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

     __lowerCamelCase   :	Optional[Any]								=		ConfigClass(**checkpoint['model_args']							)
     __lowerCamelCase   :	List[Any]								=		ModelClass(config=__UpperCAmelCase							)
     __lowerCamelCase   :	List[str]								=		GenerationConfigClass()

     __lowerCamelCase   :	Tuple								=		model_generation_config
     __lowerCamelCase   :	Any								=		checkpoint['model']
     # fixup checkpoint
     __lowerCamelCase   :	Union[str, Any]								=		'_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
               __lowerCamelCase   :	int								=		k[len(__UpperCAmelCase							) :]
               for old_layer_name in new_layer_name_dict:
                    __lowerCamelCase   :	int								=		new_k.replace(__UpperCAmelCase      ,    new_layer_name_dict[old_layer_name]							)

               __lowerCamelCase   :	Tuple								=		state_dict.pop(__UpperCAmelCase							)

     __lowerCamelCase   :	Optional[Any]								=		set(state_dict.keys()							) - set(model.state_dict().keys()							)
     __lowerCamelCase   :	Optional[int]								=		{k for k in extra_keys if not k.endswith('.attn.bias'							)}
     __lowerCamelCase   :	int								=		set(model.state_dict().keys()							) - set(state_dict.keys()							)
     __lowerCamelCase   :	Optional[int]								=		{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							)
     __lowerCamelCase   :	str								=		model.num_parameters(exclude_embeddings=__UpperCAmelCase							)
     __lowerCamelCase   :	Optional[Any]								=		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_    :				int      ,    UpperCAmelCase_    :				str=False      ,    UpperCAmelCase_    :				Optional[Any]="text"							)     ->		int:
     if model_type not in ("text", "coarse", "fine"):
          raise NotImplementedError()

     __lowerCamelCase   :	Dict								=		'cpu'  # do conversion on cpu

     __lowerCamelCase   :	List[Any]								=		_get_ckpt_path(__UpperCAmelCase      ,    use_small=__UpperCAmelCase							)
     __lowerCamelCase   :	Dict								=		_load_model(__UpperCAmelCase      ,    __UpperCAmelCase      ,    model_type=__UpperCAmelCase      ,    use_small=__UpperCAmelCase							)

     # load bark initial model
     __lowerCamelCase   :	str								=		_bark_load_model(__UpperCAmelCase      ,    'cpu'      ,    model_type=__UpperCAmelCase      ,    use_small=__UpperCAmelCase							)

     if model_type == "text":
          __lowerCamelCase   :	Tuple								=		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
     __lowerCamelCase   :	List[Any]								=		5
     __lowerCamelCase   :	List[Any]								=		10

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

          __lowerCamelCase   :	int								=		model(__UpperCAmelCase							)

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

     else:
          __lowerCamelCase   :	int								=		3
          __lowerCamelCase   :	Optional[Any]								=		8
          __lowerCamelCase   :	Optional[int]								=		torch.randint(2_56      ,    (batch_size, sequence_length, n_codes_total)      ,    dtype=torch.int							)

          __lowerCamelCase   :	Dict								=		model(__UpperCAmelCase      ,    __UpperCAmelCase							)
          __lowerCamelCase   :	str								=		bark_model(__UpperCAmelCase      ,    __UpperCAmelCase							)

          __lowerCamelCase   :	Tuple								=		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_    :				Tuple      ,    UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Optional[int]      ,    )     ->		Optional[int]:
     __lowerCamelCase   :	Any								=		os.path.join(__UpperCAmelCase      ,    __UpperCAmelCase							)

     __lowerCamelCase   :	List[str]								=		BarkSemanticConfig.from_pretrained(os.path.join(__UpperCAmelCase      ,    'config.json'							)							)
     __lowerCamelCase   :	Any								=		BarkCoarseConfig.from_pretrained(os.path.join(__UpperCAmelCase      ,    'config.json'							)							)
     __lowerCamelCase   :	Tuple								=		BarkFineConfig.from_pretrained(os.path.join(__UpperCAmelCase      ,    'config.json'							)							)
     __lowerCamelCase   :	Tuple								=		EncodecConfig.from_pretrained('facebook/encodec_24khz'							)

     __lowerCamelCase   :	Optional[Any]								=		BarkSemanticModel.from_pretrained(__UpperCAmelCase							)
     __lowerCamelCase   :	Optional[int]								=		BarkCoarseModel.from_pretrained(__UpperCAmelCase							)
     __lowerCamelCase   :	Dict								=		BarkFineModel.from_pretrained(__UpperCAmelCase							)
     __lowerCamelCase   :	List[Any]								=		EncodecModel.from_pretrained('facebook/encodec_24khz'							)

     __lowerCamelCase   :	Tuple								=		BarkConfig.from_sub_model_configs(
         __UpperCAmelCase      ,    __UpperCAmelCase      ,    __UpperCAmelCase      ,    __UpperCAmelCase							)

     __lowerCamelCase   :	int								=		BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config      ,    coarseAcoustic.generation_config      ,    fineAcoustic.generation_config							)

     __lowerCamelCase   :	List[Any]								=		BarkModel(__UpperCAmelCase							)

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

     __lowerCamelCase   :	str								=		bark_generation_config

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


if __name__ == "__main__":
      A__		:					Dict												=  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.""")

      A__		:					Dict												=  parser.parse_args()

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












def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number | (1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number & ~(1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number ^ (1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> bool:
      return ((number >> position) & 1) == 1

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return int((number & (1 << position)) != 0							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()