
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  :					Any						    =							logging.get_logger(__name__)

set_seed(770)


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







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

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

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

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

                     lowercase							:     Dict       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      set(state_dict.keys()				) - set(model.state_dict().keys()				)
       lowercase							:     Tuple       	=      {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							:     str       	=      {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							:     Any       	=      model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      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"				) ->    Any:
       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

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

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

       # load bark initial model
       lowercase							:     Dict       	=      _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							:     Optional[int]       	=      5
       lowercase							:     Any       	=      10

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

              lowercase							:     Any       	=      model(SCREAMING_SNAKE_CASE__				)

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

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

              lowercase							:     int       	=      model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[Any]       	=      bark_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

              lowercase							:     Optional[int]       	=      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__					,	) ->    Optional[Any]:
       lowercase							:     Any       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

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

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

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

       lowercase							:     List[str]       	=      BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config					,	coarseAcoustic.generation_config					,	fineAcoustic.generation_config				)

       lowercase							:     Optional[int]       	=      BarkModel(SCREAMING_SNAKE_CASE__				)

       lowercase							:     int       	=      semantic
       lowercase							:     Union[str, Any]       	=      coarseAcoustic
       lowercase							:     List[str]       	=      fineAcoustic
       lowercase							:     Dict       	=      codec

       lowercase							:     Optional[int]       	=      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  :					Dict						    =							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)


import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						_snake_case(  *SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=2				) ->    Optional[Any]:
       from .. import __version__

       lowercase							:     int       	=      take_from
       lowercase							:     Tuple       	=      ()
       if not isinstance(args[0]					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      (args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(SCREAMING_SNAKE_CASE__				).base_version				) >= version.parse(SCREAMING_SNAKE_CASE__				):
                     raise ValueError(
                         f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                         f" version {__version__} is >= {version_name}"				)

              lowercase							:     int       	=      None
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     Union[str, Any]       	=      f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
              elif hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     values += (getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     int       	=      f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
              elif deprecated_kwargs is None:
                     lowercase							:     Dict       	=      f"`{attribute}` is deprecated and will be removed in version {version_name}."

              if warning is not None:
                     lowercase							:     Dict       	=      warning + """ """ if standard_warn else """"""
                     warnings.warn(warning + message					,	SCREAMING_SNAKE_CASE__					,	stacklevel=SCREAMING_SNAKE_CASE__				)

       if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
              lowercase							:     str       	=      inspect.getouterframes(inspect.currentframe()				)[1]
              lowercase							:     List[str]       	=      call_frame.filename
              lowercase							:     Tuple       	=      call_frame.lineno
              lowercase							:     List[str]       	=      call_frame.function
              lowercase	,			lowercase							:     Optional[Any]       	=      next(iter(deprecated_kwargs.items()				)				)
              raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"				)

       if len(SCREAMING_SNAKE_CASE__				) == 0:
              return
       elif len(SCREAMING_SNAKE_CASE__				) == 1:
              return values[0]
       return values
