


'''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()
lowercase				=logging.get_logger(__name__)

set_seed(770)


lowercase				={
    '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',
}


lowercase				={
    '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',
    },
}

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





def lowerCamelCase__     (		__lowerCamelCase			:				Optional[int]			,       __lowerCamelCase			:				Tuple=False				):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =model_type
   if use_small:
      key += "_small"
   return os.path.join(__lowerCamelCase			,       REMOTE_MODEL_PATHS[key]['file_name']				)





def lowerCamelCase__     (		__lowerCamelCase			:				int			,       __lowerCamelCase			:				Any				):







   '''simple docstring'''




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





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Optional[int]=False			,       __lowerCamelCase			:				Optional[int]="text"				):







   '''simple docstring'''




   if model_type == "text":
      _UpperCAmelCase  :  List[str]				       =BarkSemanticModel
      _UpperCAmelCase  :  Optional[int]				       =BarkSemanticConfig
      _UpperCAmelCase  :  int				       =BarkSemanticGenerationConfig
   elif model_type == "coarse":
      _UpperCAmelCase  :  List[Any]				       =BarkCoarseModel
      _UpperCAmelCase  :  str				       =BarkCoarseConfig
      _UpperCAmelCase  :  Union[str, Any]				       =BarkCoarseGenerationConfig
   elif model_type == "fine":
      _UpperCAmelCase  :  List[Any]				       =BarkFineModel
      _UpperCAmelCase  :  List[str]				       =BarkFineConfig
      _UpperCAmelCase  :  Tuple				       =BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   _UpperCAmelCase  :  Any				       =f"{model_type}_small" if use_small else model_type
   _UpperCAmelCase  :  Optional[int]				       =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']				)
   _UpperCAmelCase  :  Optional[int]				       =torch.load(__lowerCamelCase			,       map_location=__lowerCamelCase				)
   # this is a hack
   _UpperCAmelCase  :  int				       =checkpoint['model_args']
   if "input_vocab_size" not in model_args:
      _UpperCAmelCase  :  Any				       =model_args['vocab_size']
      _UpperCAmelCase  :  Any				       =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  :  Union[str, Any]				       =model_args.pop('n_embd'				)
   _UpperCAmelCase  :  List[Any]				       =model_args.pop('n_layer'				)

   _UpperCAmelCase  :  Union[str, Any]				       =ConfigClass(**checkpoint['model_args']				)
   _UpperCAmelCase  :  Union[str, Any]				       =ModelClass(config=__lowerCamelCase				)
   _UpperCAmelCase  :  List[Any]				       =GenerationConfigClass()

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

         _UpperCAmelCase  :  Union[str, Any]				       =state_dict.pop(__lowerCamelCase				)

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







   '''simple docstring'''




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

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

   _UpperCAmelCase  :  Any				       =_get_ckpt_path(__lowerCamelCase			,       use_small=__lowerCamelCase				)
   _UpperCAmelCase  :  Any				       =_load_model(__lowerCamelCase			,       __lowerCamelCase			,       model_type=__lowerCamelCase			,       use_small=__lowerCamelCase				)

   # load bark initial model
   _UpperCAmelCase  :  List[str]				       =_bark_load_model(__lowerCamelCase			,       'cpu'			,       model_type=__lowerCamelCase			,       use_small=__lowerCamelCase				)

   if model_type == "text":
      _UpperCAmelCase  :  Dict				       =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
   _UpperCAmelCase  :  Union[str, Any]				       =5
   _UpperCAmelCase  :  List[Any]				       =1_0

   if model_type in ["text", "coarse"]:
      _UpperCAmelCase  :  Dict				       =torch.randint(2_5_6			,       (batch_size, sequence_length)			,       dtype=torch.int				)
      _UpperCAmelCase  :  Tuple				       =bark_model(__lowerCamelCase				)[0]

      _UpperCAmelCase  :  Tuple				       =model(__lowerCamelCase				)

      # take last logits
      _UpperCAmelCase  :  Optional[int]				       =output_new_model_total.logits[:, [-1], :]

   else:
      _UpperCAmelCase  :  List[str]				       =3
      _UpperCAmelCase  :  Optional[int]				       =8
      _UpperCAmelCase  :  Optional[Any]				       =torch.randint(2_5_6			,       (batch_size, sequence_length, n_codes_total)			,       dtype=torch.int				)

      _UpperCAmelCase  :  List[Any]				       =model(__lowerCamelCase			,       __lowerCamelCase				)
      _UpperCAmelCase  :  List[Any]				       =bark_model(__lowerCamelCase			,       __lowerCamelCase				)

      _UpperCAmelCase  :  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 lowerCamelCase__     (		__lowerCamelCase			:				int			,       __lowerCamelCase			:				int			,       __lowerCamelCase			:				Any			,       __lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Any			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =os.path.join(__lowerCamelCase			,       __lowerCamelCase				)

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

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

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

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

   _UpperCAmelCase  :  List[Any]				       =BarkModel(__lowerCamelCase				)

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

   _UpperCAmelCase  :  List[Any]				       =bark_generation_config

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


if __name__ == "__main__":
 lowercase				=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.')

 lowercase				=parser.parse_args()

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





'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =''
   for ch in key:
      if ch == " " or ch not in key_no_dups and ch.isalpha():
         key_no_dups += ch
   return key_no_dups





def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =[chr(i + 6_5				) for i in range(2_6				)]
   # Remove duplicate characters from key
   _UpperCAmelCase  :  Any				       =remove_duplicates(key.upper()				)
   _UpperCAmelCase  :  Optional[Any]				       =len(__lowerCamelCase				)
   # First fill cipher with key characters
   _UpperCAmelCase  :  List[Any]				       ={alphabet[i]: char for i, char in enumerate(__lowerCamelCase				)}
   # Then map remaining characters in alphabet to
   # the alphabet from the beginning
   for i in range(len(__lowerCamelCase				)			,       2_6				):
      _UpperCAmelCase  :  Union[str, Any]				       =alphabet[i - offset]
      # Ensure we are not mapping letters to letters previously mapped
      while char in key:
         offset -= 1
         _UpperCAmelCase  :  str				       =alphabet[i - offset]
      _UpperCAmelCase  :  Any				       =char
   return cipher_alphabet





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   return "".join(cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       ={v: k for k, v in cipher_map.items()}
   return "".join(rev_cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =input('Enter message to encode or decode: '				).strip()
   _UpperCAmelCase  :  int				       =input('Enter keyword: '				).strip()
   _UpperCAmelCase  :  int				       =input('Encipher or decipher? E/D:'				).strip()[0].lower()
   try:
      _UpperCAmelCase  :  Tuple				       ={'e': encipher, 'd': decipher}[option]
   except KeyError:
      raise KeyError('invalid input option'				)
   _UpperCAmelCase  :  List[str]				       =create_cipher_map(__lowerCamelCase				)
   print(func(__lowerCamelCase			,       __lowerCamelCase				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

