
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  : Dict						   =							logging.get_logger(__name__)

set_seed(770)


lowercase  : 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""",
}


lowercase  : int						   =							{
    """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  : Optional[Any]						   =							os.path.dirname(os.path.abspath(__file__))
lowercase  : Optional[Any]						   =							os.path.join(os.path.expanduser("""~"""), """.cache""")
lowercase  : Tuple						   =							os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False    )			->   Optional[Any]:
       lowercase							:					Any	     =				model_type
       if use_small:
              key += "_small"
       return os.path.join(SCREAMING_SNAKE_CASE__ ,     REMOTE_MODEL_PATHS[key]["""file_name"""]    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False ,     SCREAMING_SNAKE_CASE__="text"    )			->   Union[str, Any]:
       if model_type == "text":
              lowercase							:					List[Any]	     =				BarkSemanticModel
              lowercase							:					int	     =				BarkSemanticConfig
              lowercase							:					Optional[int]	     =				BarkSemanticGenerationConfig
       elif model_type == "coarse":
              lowercase							:					Union[str, Any]	     =				BarkCoarseModel
              lowercase							:					Optional[Any]	     =				BarkCoarseConfig
              lowercase							:					str	     =				BarkCoarseGenerationConfig
       elif model_type == "fine":
              lowercase							:					Optional[int]	     =				BarkFineModel
              lowercase							:					int	     =				BarkFineConfig
              lowercase							:					List[Any]	     =				BarkFineGenerationConfig
       else:
              raise NotImplementedError()
       lowercase							:					Optional[Any]	     =				f"{model_type}_small" if use_small else model_type
       lowercase							:					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"""]    )
       lowercase							:					Optional[Any]	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location=SCREAMING_SNAKE_CASE__    )
       # this is a hack
       lowercase							:					Any	     =				checkpoint["""model_args"""]
       if "input_vocab_size" not in model_args:
              lowercase							:					Dict	     =				model_args["""vocab_size"""]
              lowercase							:					str	     =				model_args["""vocab_size"""]
              del model_args["vocab_size"]

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

       lowercase							:					Tuple	     =				ConfigClass(**checkpoint["""model_args"""]    )
       lowercase							:					Any	     =				ModelClass(config=SCREAMING_SNAKE_CASE__    )
       lowercase							:					Dict	     =				GenerationConfigClass()

       lowercase							:					Optional[int]	     =				model_generation_config
       lowercase							:					Dict	     =				checkpoint["""model"""]
       # fixup checkpoint
       lowercase							:					str	     =				"""_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
                     lowercase							:					Optional[int]	     =				k[len(SCREAMING_SNAKE_CASE__    ) :]
                     for old_layer_name in new_layer_name_dict:
                            lowercase							:					int	     =				new_k.replace(SCREAMING_SNAKE_CASE__ ,     new_layer_name_dict[old_layer_name]    )

                     lowercase							:					Dict	     =				state_dict.pop(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Optional[int]	     =				set(state_dict.keys()    ) - set(model.state_dict().keys()    )
       lowercase							:					Any	     =				{k for k in extra_keys if not k.endswith(""".attn.bias"""    )}
       lowercase							:					List[str]	     =				set(model.state_dict().keys()    ) - set(state_dict.keys()    )
       lowercase							:					Dict	     =				{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__    )
       lowercase							:					Tuple	     =				model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE__    )
       lowercase							:					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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False ,     SCREAMING_SNAKE_CASE__="text"    )			->   Optional[Any]:
       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

       lowercase							:					List[Any]	     =				"""cpu"""  # do conversion on cpu

       lowercase							:					Optional[int]	     =				_get_ckpt_path(SCREAMING_SNAKE_CASE__ ,     use_small=SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				_load_model(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     model_type=SCREAMING_SNAKE_CASE__ ,     use_small=SCREAMING_SNAKE_CASE__    )

       # load bark initial model
       lowercase							:					List[str]	     =				_bark_load_model(SCREAMING_SNAKE_CASE__ ,     """cpu""" ,     model_type=SCREAMING_SNAKE_CASE__ ,     use_small=SCREAMING_SNAKE_CASE__    )

       if model_type == "text":
              lowercase							:					Dict	     =				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
       lowercase							:					List[Any]	     =				5
       lowercase							:					Dict	     =				10

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

              lowercase							:					Tuple	     =				model(SCREAMING_SNAKE_CASE__    )

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

       else:
              lowercase							:					Union[str, Any]	     =				3
              lowercase							:					List[str]	     =				8
              lowercase							:					List[Any]	     =				torch.randint(256 ,     (batch_size, sequence_length, n_codes_total) ,     dtype=torch.int    )

              lowercase							:					Optional[Any]	     =				model(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              lowercase							:					Tuple	     =				bark_model(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

              lowercase							:					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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     )			->   str:
       lowercase							:					Dict	     =				os.path.join(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       lowercase							:					Dict	     =				BarkSemanticConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__ ,     """config.json"""    )    )
       lowercase							:					Optional[int]	     =				BarkCoarseConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__ ,     """config.json"""    )    )
       lowercase							:					Any	     =				BarkFineConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__ ,     """config.json"""    )    )
       lowercase							:					Tuple	     =				EncodecConfig.from_pretrained("""facebook/encodec_24khz"""    )

       lowercase							:					int	     =				BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Any	     =				BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				EncodecModel.from_pretrained("""facebook/encodec_24khz"""    )

       lowercase							:					List[Any]	     =				BarkConfig.from_sub_model_configs(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       lowercase							:					Tuple	     =				BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config ,     coarseAcoustic.generation_config ,     fineAcoustic.generation_config    )

       lowercase							:					Tuple	     =				BarkModel(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Tuple	     =				semantic
       lowercase							:					Optional[int]	     =				coarseAcoustic
       lowercase							:					List[Any]	     =				fineAcoustic
       lowercase							:					Dict	     =				codec

       lowercase							:					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__":
  lowercase  : 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.""")

  lowercase  : str						   =							parser.parse_args()

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


class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=None		,snake_case=None				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				data
           lowercase							:					List[Any]	     =				previous
           lowercase							:					List[str]	     =				next_node
    def __str__(  self				):



           '''simple docstring'''







           return f"{self.data}"
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.data
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.next
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.previous






class __snake_case		:
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				head
    def __iter__(  self				):



           '''simple docstring'''







           return self
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if not self.current:
                  raise StopIteration
           else:
                  lowercase							:					Union[str, Any]	     =				self.current.get_data()
                  lowercase							:					Optional[Any]	     =				self.current.get_next()
                  return value






class __snake_case		:
    def __init__(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				None  # First node in list
           lowercase							:					str	     =				None  # Last node in list
    def __str__(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.head
           lowercase							:					str	     =				[]
           while current is not None:
                  nodes.append(current.get_data()				)
                  lowercase							:					Dict	     =				current.get_next()
           return " ".join(str(snake_case				) for node in nodes				)
    def __contains__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.head
           while current:
                  if current.get_data() == value:
                         return True
                  lowercase							:					Any	     =				current.get_next()
           return False
    def __iter__(  self				):



           '''simple docstring'''







           return LinkedListIterator(self.head				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.head:
                  return self.head.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.tail:
                  return self.tail.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  lowercase							:					Any	     =				node
                  lowercase							:					Dict	     =				node
           else:
                  self.insert_before_node(self.head		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				Node(snake_case				)
           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.set_tail(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				node
           lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous() is None:
                  lowercase							:					Optional[int]	     =				node_to_insert
           else:
                  lowercase							:					Optional[int]	     =				node_to_insert

           lowercase							:					List[Any]	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				node
           lowercase							:					List[str]	     =				node.next

           if node.get_next() is None:
                  lowercase							:					Union[str, Any]	     =				node_to_insert
           else:
                  lowercase							:					List[str]	     =				node_to_insert

           lowercase							:					Dict	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				1
           lowercase							:					List[str]	     =				Node(snake_case				)
           lowercase							:					Any	     =				self.head
           while node:
                  if current_position == position:
                         self.insert_before_node(snake_case		,snake_case				)
                         return
                  current_position += 1
                  lowercase							:					List[Any]	     =				node.next
           self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.head
           while node:
                  if node.get_data() == item:
                         return node
                  lowercase							:					Any	     =				node.get_next()
           raise Exception("""Node not found"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if (node := self.get_node(snake_case				)) is not None:
                  if node == self.head:
                         lowercase							:					Optional[Any]	     =				self.head.get_next()

                  if node == self.tail:
                         lowercase							:					List[str]	     =				self.tail.get_previous()

                  self.remove_node_pointers(snake_case				)
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           if node.get_next():
                  lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous():
                  lowercase							:					Union[str, Any]	     =				node.next

           lowercase							:					Union[str, Any]	     =				None
           lowercase							:					Optional[Any]	     =				None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.head is None







def 						_snake_case(  )			->   None:
       pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
