






'''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    	=   logging.get_logger(__name__)

set_seed(7_7_0)


__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        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Dict=False  )    -> Any:







   """simple docstring"""
   __snake_case				:      Any     							= model_type
   if use_small:
      key += "_small"
   return os.path.join(A  ,		REMOTE_MODEL_PATHS[key]['file_name']  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Any  )    -> List[Any]:







   """simple docstring"""
   os.makedirs(A  ,		exist_ok=A  )
   hf_hub_download(repo_id=A  ,		filename=A  ,		local_dir=A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       int  ,		A  :       Dict=False  ,		A  :       Dict="text"  )    -> str:







   """simple docstring"""
   if model_type == "text":
      __snake_case				:      List[Any]     							= BarkSemanticModel
      __snake_case				:      int     							= BarkSemanticConfig
      __snake_case				:      Any     							= BarkSemanticGenerationConfig
   elif model_type == "coarse":
      __snake_case				:      Union[str, Any]     							= BarkCoarseModel
      __snake_case				:      Optional[int]     							= BarkCoarseConfig
      __snake_case				:      str     							= BarkCoarseGenerationConfig
   elif model_type == "fine":
      __snake_case				:      Dict     							= BarkFineModel
      __snake_case				:      Optional[Any]     							= BarkFineConfig
      __snake_case				:      str     							= BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   __snake_case				:      Any     							= F"""{model_type}_small""" if use_small else model_type
   __snake_case				:      List[Any]     							= 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']  )
   __snake_case				:      List[Any]     							= torch.load(A  ,		map_location=A  )
   # this is a hack
   __snake_case				:      Optional[Any]     							= checkpoint['model_args']
   if "input_vocab_size" not in model_args:
      __snake_case				:      Optional[Any]     							= model_args['vocab_size']
      __snake_case				:      Optional[Any]     							= model_args['vocab_size']
      del model_args["vocab_size"]

   # convert Bark model arguments to HF Bark model arguments
   __snake_case				:      Any     							= model_args.pop('n_head'  )
   __snake_case				:      Tuple     							= model_args.pop('n_embd'  )
   __snake_case				:      int     							= model_args.pop('n_layer'  )

   __snake_case				:      str     							= ConfigClass(**checkpoint['model_args']  )
   __snake_case				:      List[str]     							= ModelClass(config=A  )
   __snake_case				:      List[Any]     							= GenerationConfigClass()

   __snake_case				:      Optional[Any]     							= model_generation_config
   __snake_case				:      int     							= checkpoint['model']
   # fixup checkpoint
   __snake_case				:      Optional[Any]     							= '_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
         __snake_case				:      Union[str, Any]     							= k[len(A  ) :]
         for old_layer_name in new_layer_name_dict:
            __snake_case				:      List[Any]     							= new_k.replace(A  ,		new_layer_name_dict[old_layer_name]  )

         __snake_case				:      Optional[int]     							= state_dict.pop(A  )

   __snake_case				:      Union[str, Any]     							= set(state_dict.keys()  ) - set(model.state_dict().keys()  )
   __snake_case				:      List[str]     							= {k for k in extra_keys if not k.endswith('.attn.bias'  )}
   __snake_case				:      Union[str, Any]     							= set(model.state_dict().keys()  ) - set(state_dict.keys()  )
   __snake_case				:      Optional[Any]     							= {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  )
   __snake_case				:      int     							= model.num_parameters(exclude_embeddings=A  )
   __snake_case				:      List[Any]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       Optional[int]=False  ,		A  :       Union[str, Any]="text"  )    -> Dict:







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

   __snake_case				:      List[str]     							= 'cpu'  # do conversion on cpu

   __snake_case				:      str     							= _get_ckpt_path(A  ,		use_small=A  )
   __snake_case				:      List[Any]     							= _load_model(A  ,		A  ,		model_type=A  ,		use_small=A  )

   # load bark initial model
   __snake_case				:      Optional[int]     							= _bark_load_model(A  ,		'cpu'  ,		model_type=A  ,		use_small=A  )

   if model_type == "text":
      __snake_case				:      str     							= 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
   __snake_case				:      Optional[Any]     							= 5
   __snake_case				:      Optional[Any]     							= 10

   if model_type in ["text", "coarse"]:
      __snake_case				:      Any     							= torch.randint(2_56  ,		(batch_size, sequence_length)  ,		dtype=torch.int  )
      __snake_case				:      Union[str, Any]     							= bark_model(A  )[0]

      __snake_case				:      Optional[int]     							= model(A  )

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

   else:
      __snake_case				:      Tuple     							= 3
      __snake_case				:      List[str]     							= 8
      __snake_case				:      str     							= torch.randint(2_56  ,		(batch_size, sequence_length, n_codes_total)  ,		dtype=torch.int  )

      __snake_case				:      Optional[Any]     							= model(A  ,		A  )
      __snake_case				:      Optional[Any]     							= bark_model(A  ,		A  )

      __snake_case				:      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(A  ).mkdir(exist_ok=A  )
   model.save_pretrained(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  ,		A  :       Dict  ,		A  :       Optional[int]  ,		A  :       int  ,		A  :       List[str]  ,		A  :       str  ,		)    -> int:







   """simple docstring"""
   __snake_case				:      Tuple     							= os.path.join(A  ,		A  )

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

   __snake_case				:      List[str]     							= BarkSemanticModel.from_pretrained(A  )
   __snake_case				:      Tuple     							= BarkCoarseModel.from_pretrained(A  )
   __snake_case				:      Optional[Any]     							= BarkFineModel.from_pretrained(A  )
   __snake_case				:      Optional[int]     							= EncodecModel.from_pretrained('facebook/encodec_24khz'  )

   __snake_case				:      List[Any]     							= BarkConfig.from_sub_model_configs(
       A  ,		A  ,		A  ,		A  )

   __snake_case				:      Optional[int]     							= BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config  ,		coarseAcoustic.generation_config  ,		fineAcoustic.generation_config  )

   __snake_case				:      Union[str, Any]     							= BarkModel(A  )

   __snake_case				:      List[Any]     							= semantic
   __snake_case				:      int     							= coarseAcoustic
   __snake_case				:      Union[str, Any]     							= fineAcoustic
   __snake_case				:      Any     							= codec

   __snake_case				:      Dict     							= bark_generation_config

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


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)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BioGptForCausalLM''',
          '''BioGptForTokenClassification''',
          '''BioGptForSequenceClassification''',
          '''BioGptModel''',
          '''BioGptPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)