




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







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


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







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

     '''simple docstring'''


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

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

     UpperCAmelCase_      :							int       =      ConfigClass(**checkpoint['''model_args''']				)
     UpperCAmelCase_      :							int       =      ModelClass(config=__SCREAMING_SNAKE_CASE				)
     UpperCAmelCase_      :							List[str]       =      GenerationConfigClass()

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

               UpperCAmelCase_      :							Tuple       =      state_dict.pop(__SCREAMING_SNAKE_CASE				)

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

     UpperCAmelCase_      :							Union[str, Any]       =      _get_ckpt_path(__SCREAMING_SNAKE_CASE			,						use_small=__SCREAMING_SNAKE_CASE				)
     UpperCAmelCase_      :							Any       =      _load_model(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						model_type=__SCREAMING_SNAKE_CASE			,						use_small=__SCREAMING_SNAKE_CASE				)

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

     if model_type == "text":
          UpperCAmelCase_      :							Tuple       =      bark_model["""model"""]

     if model.num_parameters(exclude_embeddings=__SCREAMING_SNAKE_CASE				) != 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_      :							Dict       =      5
     UpperCAmelCase_      :							str       =      10

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

          UpperCAmelCase_      :							Dict       =      model(__SCREAMING_SNAKE_CASE				)

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

     else:
          UpperCAmelCase_      :							Any       =      3
          UpperCAmelCase_      :							Dict       =      8
          UpperCAmelCase_      :							Any       =      torch.randint(256			,						(batch_size, sequence_length, n_codes_total)			,						dtype=torch.int				)

          UpperCAmelCase_      :							List[str]       =      model(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE				)
          UpperCAmelCase_      :							Dict       =      bark_model(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE				)

          UpperCAmelCase_      :							Union[str, Any]       =      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(__SCREAMING_SNAKE_CASE				).mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)
     model.save_pretrained(__SCREAMING_SNAKE_CASE				)







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

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      os.path.join(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE				)

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

     UpperCAmelCase_      :							int       =      BarkSemanticModel.from_pretrained(__SCREAMING_SNAKE_CASE				)
     UpperCAmelCase_      :							Dict       =      BarkCoarseModel.from_pretrained(__SCREAMING_SNAKE_CASE				)
     UpperCAmelCase_      :							Dict       =      BarkFineModel.from_pretrained(__SCREAMING_SNAKE_CASE				)
     UpperCAmelCase_      :							str       =      EncodecModel.from_pretrained('''facebook/encodec_24khz'''				)

     UpperCAmelCase_      :							List[Any]       =      BarkConfig.from_sub_model_configs(
         __SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE				)

     UpperCAmelCase_      :							str       =      BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config			,						coarseAcoustic.generation_config			,						fineAcoustic.generation_config				)

     UpperCAmelCase_      :							int       =      BarkModel(__SCREAMING_SNAKE_CASE				)

     UpperCAmelCase_      :							List[str]       =      semantic
     UpperCAmelCase_      :							Any       =      coarseAcoustic
     UpperCAmelCase_      :							Optional[Any]       =      fineAcoustic
     UpperCAmelCase_      :							Optional[int]       =      codec

     UpperCAmelCase_      :							str       =      bark_generation_config

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


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)





from ...processing_utils import ProcessorMixin

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''SpeechT5FeatureExtractor'''
    lowerCAmelCase        =				'''SpeechT5Tokenizer'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         super().__init__(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def __call__(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''audio'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      kwargs.pop('''text'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''text_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''audio_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''sampling_rate'''       ,_SCREAMING_SNAKE_CASE  )

         if audio is not None and text is not None:
              raise ValueError(
                  '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''  )
         if audio_target is not None and text_target is not None:
              raise ValueError(
                  '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''  )
         if audio is None and audio_target is None and text is None and text_target is None:
              raise ValueError(
                  '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''  )

         if audio is not None:
              UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif text is not None:
              UpperCAmelCase_      :							Dict       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if audio_target is not None:
              UpperCAmelCase_      :							List[Any]       =      self.feature_extractor(audio_target=_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      targets['''input_values''']
         elif text_target is not None:
              UpperCAmelCase_      :							Optional[int]       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[Any]       =      targets['''input_ids''']
         else:
              UpperCAmelCase_      :							Tuple       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Dict       =      labels

              UpperCAmelCase_      :							Optional[int]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Dict       =      kwargs.pop('''input_values'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      kwargs.pop('''input_ids'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''labels'''       ,_SCREAMING_SNAKE_CASE  )

         if input_values is not None and input_ids is not None:
              raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''  )
         if input_values is None and input_ids is None and labels is None:
              raise ValueError(
                  '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''  )

         if input_values is not None:
              UpperCAmelCase_      :							Tuple       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif input_ids is not None:
              UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if labels is not None:
              if "input_ids" in labels or (isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) and "input_ids" in labels[0]):
                   UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Any       =      targets['''input_ids''']
              else:
                   UpperCAmelCase_      :							int       =      self.feature_extractor.feature_size
                   UpperCAmelCase_      :							List[str]       =      self.feature_extractor.num_mel_bins
                   UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      feature_size_hack
                   UpperCAmelCase_      :							List[Any]       =      targets['''input_values''']
         else:
              UpperCAmelCase_      :							Optional[int]       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Optional[int]       =      labels

              UpperCAmelCase_      :							Union[str, Any]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )