






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()
UpperCAmelCase__				   =		logging.get_logger(__name__)

set_seed(770)


UpperCAmelCase__				   =		{
    "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",
}


UpperCAmelCase__				   =		{
    "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",
    },
}

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

 # convert Bark model arguments to HF Bark model arguments
 a		  =      model_args.pop('''n_head''' )
 a		  =      model_args.pop('''n_embd''' )
 a		  =      model_args.pop('''n_layer''' )

 a		  =      ConfigClass(**checkpoint['''model_args'''] )
 a		  =      ModelClass(config=a )
 a		  =      GenerationConfigClass()

 a		  =      model_generation_config
 a		  =      checkpoint['''model''']
 # fixup checkpoint
 a		  =      '''_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
   a		  =      k[len(a ) :]
   for old_layer_name in new_layer_name_dict:
    a		  =      new_k.replace(a	,							new_layer_name_dict[old_layer_name] )

   a		  =      state_dict.pop(a )

 a		  =      set(state_dict.keys() ) - set(model.state_dict().keys() )
 a		  =      {k for k in extra_keys if not k.endswith('''.attn.bias''' )}
 a		  =      set(model.state_dict().keys() ) - set(state_dict.keys() )
 a		  =      {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 )
 a		  =      model.num_parameters(exclude_embeddings=a )
 a		  =      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 						_a    (      a :Any	,							a :List[str]=False	,							a :Dict="text" )							->			Any:
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 a		  =      '''cpu'''  # do conversion on cpu

 a		  =      _get_ckpt_path(a	,							use_small=a )
 a		  =      _load_model(a	,							a	,							model_type=a	,							use_small=a )

 # load bark initial model
 a		  =      _bark_load_model(a	,							'''cpu'''	,							model_type=a	,							use_small=a )

 if model_type == "text":
  a		  =      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
 a		  =      5
 a		  =      10

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

  a		  =      model(a )

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

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

  a		  =      model(a	,							a )
  a		  =      bark_model(a	,							a )

  a		  =      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 						_a    (      a :int	,							a :Optional[Any]	,							a :Dict	,							a :List[str]	,							a :Tuple	,							a :Optional[int]	,							)							->			Union[str, Any]:
 a		  =      os.path.join(a	,							a )

 a		  =      BarkSemanticConfig.from_pretrained(os.path.join(a	,							'''config.json''' ) )
 a		  =      BarkCoarseConfig.from_pretrained(os.path.join(a	,							'''config.json''' ) )
 a		  =      BarkFineConfig.from_pretrained(os.path.join(a	,							'''config.json''' ) )
 a		  =      EncodecConfig.from_pretrained('''facebook/encodec_24khz''' )

 a		  =      BarkSemanticModel.from_pretrained(a )
 a		  =      BarkCoarseModel.from_pretrained(a )
 a		  =      BarkFineModel.from_pretrained(a )
 a		  =      EncodecModel.from_pretrained('''facebook/encodec_24khz''' )

 a		  =      BarkConfig.from_sub_model_configs(
     a	,							a	,							a	,							a )

 a		  =      BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config	,							coarseAcoustic.generation_config	,							fineAcoustic.generation_config )

 a		  =      BarkModel(a )

 a		  =      semantic
 a		  =      coarseAcoustic
 a		  =      fineAcoustic
 a		  =      codec

 a		  =      bark_generation_config

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


if __name__ == "__main__":
     UpperCAmelCase__				   =		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.")

     UpperCAmelCase__				   =		parser.parse_args()

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










from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




