




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							     =					logging.get_logger(__name__)

set_seed(770)


__a							     =					{
    '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							     =					{
    '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							     =					os.path.dirname(os.path.abspath(__file__))
__a							     =					os.path.join(os.path.expanduser('~'), '.cache')
__a							     =					os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')







def lowerCamelCase__  (     _lowercase					,		_lowercase=False ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     model_type
     if use_small:
          key += "_small"
     return os.path.join(_lowercase					,		REMOTE_MODEL_PATHS[key]['''file_name'''] )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=False					,		_lowercase="text" ):

     '''simple docstring'''


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

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

     UpperCAmelCase_     :				List[str]  =     ConfigClass(**checkpoint['''model_args'''] )
     UpperCAmelCase_     :				Tuple  =     ModelClass(config=_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     GenerationConfigClass()

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

               UpperCAmelCase_     :				Dict  =     state_dict.pop(_lowercase )

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

     return model







def lowerCamelCase__  (     _lowercase					,		_lowercase=False					,		_lowercase="text" ):

     '''simple docstring'''


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

     UpperCAmelCase_     :				Tuple  =     '''cpu'''  # do conversion on cpu

     UpperCAmelCase_     :				Tuple  =     _get_ckpt_path(_lowercase					,		use_small=_lowercase )
     UpperCAmelCase_     :				Optional[int]  =     _load_model(_lowercase					,		_lowercase					,		model_type=_lowercase					,		use_small=_lowercase )

     # load bark initial model
     UpperCAmelCase_     :				Union[str, Any]  =     _bark_load_model(_lowercase					,		'''cpu'''					,		model_type=_lowercase					,		use_small=_lowercase )

     if model_type == "text":
          UpperCAmelCase_     :				List[str]  =     bark_model['''model''']

     if model.num_parameters(exclude_embeddings=_lowercase ) != 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_     :				str  =     5
     UpperCAmelCase_     :				List[str]  =     10

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

          UpperCAmelCase_     :				int  =     model(_lowercase )

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

     else:
          UpperCAmelCase_     :				Union[str, Any]  =     3
          UpperCAmelCase_     :				Union[str, Any]  =     8
          UpperCAmelCase_     :				Optional[Any]  =     torch.randint(256					,		(batch_size, sequence_length, n_codes_total)					,		dtype=torch.int )

          UpperCAmelCase_     :				Tuple  =     model(_lowercase					,		_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     bark_model(_lowercase					,		_lowercase )

          UpperCAmelCase_     :				List[str]  =     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(_lowercase ).mkdir(exist_ok=_lowercase )
     model.save_pretrained(_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     os.path.join(_lowercase					,		_lowercase )

     UpperCAmelCase_     :				List[Any]  =     BarkSemanticConfig.from_pretrained(os.path.join(_lowercase					,		'''config.json''' ) )
     UpperCAmelCase_     :				Optional[int]  =     BarkCoarseConfig.from_pretrained(os.path.join(_lowercase					,		'''config.json''' ) )
     UpperCAmelCase_     :				Tuple  =     BarkFineConfig.from_pretrained(os.path.join(_lowercase					,		'''config.json''' ) )
     UpperCAmelCase_     :				Dict  =     EncodecConfig.from_pretrained('''facebook/encodec_24khz''' )

     UpperCAmelCase_     :				Tuple  =     BarkSemanticModel.from_pretrained(_lowercase )
     UpperCAmelCase_     :				Any  =     BarkCoarseModel.from_pretrained(_lowercase )
     UpperCAmelCase_     :				List[str]  =     BarkFineModel.from_pretrained(_lowercase )
     UpperCAmelCase_     :				Tuple  =     EncodecModel.from_pretrained('''facebook/encodec_24khz''' )

     UpperCAmelCase_     :				int  =     BarkConfig.from_sub_model_configs(
         _lowercase					,		_lowercase					,		_lowercase					,		_lowercase )

     UpperCAmelCase_     :				Union[str, Any]  =     BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config					,		coarseAcoustic.generation_config					,		fineAcoustic.generation_config )

     UpperCAmelCase_     :				Tuple  =     BarkModel(_lowercase )

     UpperCAmelCase_     :				Tuple  =     semantic
     UpperCAmelCase_     :				List[Any]  =     coarseAcoustic
     UpperCAmelCase_     :				Any  =     fineAcoustic
     UpperCAmelCase_     :				List[Any]  =     codec

     UpperCAmelCase_     :				int  =     bark_generation_config

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


if __name__ == "__main__":
     __a							     =					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							     =					parser.parse_args()

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





def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     0
     for i in range(1					,		1001 ):
          total += i**i
     return str(_lowercase )[-10:]


if __name__ == "__main__":
     print(solution())