





'''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()
lowerCAmelCase					:     int												=logging.get_logger(__name__)

set_seed(770)


lowerCAmelCase					:     Optional[int]												={
    '''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					:     Tuple												={
    '''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					:     Optional[Any]												=os.path.dirname(os.path.abspath(__file__))
lowerCAmelCase					:     List[str]												=os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowerCAmelCase					:     str												=os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					Optional[int]=False ):
   lowercase_			:Tuple    			= model_type
   if use_small:
      key += "_small"
   return os.path.join(__lowerCamelCase	,REMOTE_MODEL_PATHS[key]["file_name"] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Dict ):
   os.makedirs(__lowerCamelCase	,exist_ok=__lowerCamelCase )
   hf_hub_download(repo_id=__lowerCamelCase	,filename=__lowerCamelCase	,local_dir=__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any	,__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Dict=False	,__lowerCamelCase					:					Optional[int]="text" ):
   if model_type == "text":
      lowercase_			:Union[str, Any]    			= BarkSemanticModel
      lowercase_			:int    			= BarkSemanticConfig
      lowercase_			:List[Any]    			= BarkSemanticGenerationConfig
   elif model_type == "coarse":
      lowercase_			:List[Any]    			= BarkCoarseModel
      lowercase_			:List[Any]    			= BarkCoarseConfig
      lowercase_			:Dict    			= BarkCoarseGenerationConfig
   elif model_type == "fine":
      lowercase_			:List[str]    			= BarkFineModel
      lowercase_			:Tuple    			= BarkFineConfig
      lowercase_			:Optional[Any]    			= BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   lowercase_			:Any    			= F'{model_type}_small' if use_small else model_type
   lowercase_			:Optional[Any]    			= 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"] )
   lowercase_			:Dict    			= torch.load(__lowerCamelCase	,map_location=__lowerCamelCase )
   # this is a hack
   lowercase_			:List[Any]    			= checkpoint["model_args"]
   if "input_vocab_size" not in model_args:
      lowercase_			:int    			= model_args["vocab_size"]
      lowercase_			:List[str]    			= model_args["vocab_size"]
      del model_args["vocab_size"]

   # convert Bark model arguments to HF Bark model arguments
   lowercase_			:Any    			= model_args.pop("n_head" )
   lowercase_			:Optional[Any]    			= model_args.pop("n_embd" )
   lowercase_			:Dict    			= model_args.pop("n_layer" )

   lowercase_			:Dict    			= ConfigClass(**checkpoint["model_args"] )
   lowercase_			:Optional[int]    			= ModelClass(config=__lowerCamelCase )
   lowercase_			:Optional[int]    			= GenerationConfigClass()

   lowercase_			:Dict    			= model_generation_config
   lowercase_			:Dict    			= checkpoint["model"]
   # fixup checkpoint
   lowercase_			:Tuple    			= "_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
         lowercase_			:Tuple    			= k[len(__lowerCamelCase ) :]
         for old_layer_name in new_layer_name_dict:
            lowercase_			:List[Any]    			= new_k.replace(__lowerCamelCase	,new_layer_name_dict[old_layer_name] )

         lowercase_			:Dict    			= state_dict.pop(__lowerCamelCase )

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

   lowercase_			:Any    			= "cpu"  # do conversion on cpu

   lowercase_			:Tuple    			= _get_ckpt_path(__lowerCamelCase	,use_small=__lowerCamelCase )
   lowercase_			:Union[str, Any]    			= _load_model(__lowerCamelCase	,__lowerCamelCase	,model_type=__lowerCamelCase	,use_small=__lowerCamelCase )

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

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

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

      lowercase_			:List[Any]    			= model(__lowerCamelCase )

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

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

      lowercase_			:Union[str, Any]    			= model(__lowerCamelCase	,__lowerCamelCase )
      lowercase_			:Any    			= bark_model(__lowerCamelCase	,__lowerCamelCase )

      lowercase_			: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(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
   model.save_pretrained(__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Dict	,__lowerCamelCase					:					Dict	,__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Dict	,):
   lowercase_			:Any    			= os.path.join(__lowerCamelCase	,__lowerCamelCase )

   lowercase_			:Tuple    			= BarkSemanticConfig.from_pretrained(os.path.join(__lowerCamelCase	,"config.json" ) )
   lowercase_			:Union[str, Any]    			= BarkCoarseConfig.from_pretrained(os.path.join(__lowerCamelCase	,"config.json" ) )
   lowercase_			:Any    			= BarkFineConfig.from_pretrained(os.path.join(__lowerCamelCase	,"config.json" ) )
   lowercase_			:Optional[Any]    			= EncodecConfig.from_pretrained("facebook/encodec_24khz" )

   lowercase_			:int    			= BarkSemanticModel.from_pretrained(__lowerCamelCase )
   lowercase_			:List[Any]    			= BarkCoarseModel.from_pretrained(__lowerCamelCase )
   lowercase_			:Optional[int]    			= BarkFineModel.from_pretrained(__lowerCamelCase )
   lowercase_			:Tuple    			= EncodecModel.from_pretrained("facebook/encodec_24khz" )

   lowercase_			:Optional[int]    			= BarkConfig.from_sub_model_configs(
       __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase )

   lowercase_			:Optional[Any]    			= BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config	,coarseAcoustic.generation_config	,fineAcoustic.generation_config )

   lowercase_			:int    			= BarkModel(__lowerCamelCase )

   lowercase_			:Tuple    			= semantic
   lowercase_			:int    			= coarseAcoustic
   lowercase_			:Any    			= fineAcoustic
   lowercase_			:int    			= codec

   lowercase_			:Optional[Any]    			= 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					:     List[Any]												=parser.parse_args()

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









'''simple docstring'''


lowerCAmelCase					:     str												='''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase					:     int												=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase					:     List[str]												={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


