




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()
_snake_case            = logging.get_logger(__name__)

set_seed(770)


_snake_case            = {
    "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",
}


_snake_case            = {
    "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",
    },
}

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







def        lowerCAmelCase_			(  snake_case_,snake_case_=False  ):
      _A							:  Tuple    =		model_type
      if use_small:
            key += "_small"
      return os.path.join(snake_case_,REMOTE_MODEL_PATHS[key]["""file_name"""]  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      os.makedirs(snake_case_,exist_ok=snake_case_  )
      hf_hub_download(repo_id=snake_case_,filename=snake_case_,local_dir=snake_case_  )







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

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

      _A							:  List[Any]    =		ConfigClass(**checkpoint["""model_args"""]  )
      _A							:  Optional[Any]    =		ModelClass(config=snake_case_  )
      _A							:  Optional[Any]    =		GenerationConfigClass()

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

                  _A							:  Tuple    =		state_dict.pop(snake_case_  )

      _A							:  Tuple    =		set(state_dict.keys()  ) - set(model.state_dict().keys()  )
      _A							:  str    =		{k for k in extra_keys if not k.endswith(""".attn.bias"""  )}
      _A							:  Any    =		set(model.state_dict().keys()  ) - set(state_dict.keys()  )
      _A							:  Any    =		{k for k in missing_keys if not k.endswith(""".attn.bias"""  )}
      if len(snake_case_  ) != 0:
            raise ValueError(f'''extra keys found: {extra_keys}'''  )
      if len(snake_case_  ) != 0:
            raise ValueError(f'''missing keys: {missing_keys}'''  )
      model.load_state_dict(snake_case_,strict=snake_case_  )
      _A							:  int    =		model.num_parameters(exclude_embeddings=snake_case_  )
      _A							:  Optional[Any]    =		checkpoint["""best_val_loss"""].item()
      logger.info(f'''model loaded: {round(n_params/1e6,1  )}M params, {round(snake_case_,3  )} loss'''  )
      model.eval()
      model.to(snake_case_  )
      del checkpoint, state_dict

      return model







def        lowerCAmelCase_			(  snake_case_,snake_case_=False,snake_case_="text"  ):
      if model_type not in ("text", "coarse", "fine"):
            raise NotImplementedError()

      _A							:  Tuple    =		"""cpu"""  # do conversion on cpu

      _A							:  Tuple    =		_get_ckpt_path(snake_case_,use_small=snake_case_  )
      _A							:  List[str]    =		_load_model(snake_case_,snake_case_,model_type=snake_case_,use_small=snake_case_  )

      # load bark initial model
      _A							:  int    =		_bark_load_model(snake_case_,"""cpu""",model_type=snake_case_,use_small=snake_case_  )

      if model_type == "text":
            _A							:  List[Any]    =		bark_model["""model"""]

      if model.num_parameters(exclude_embeddings=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
      _A							:  List[Any]    =		5
      _A							:  List[Any]    =		10

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

            _A							:  Union[str, Any]    =		model(snake_case_  )

            # take last logits
            _A							:  Union[str, Any]    =		output_new_model_total.logits[:, [-1], :]

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

            _A							:  Union[str, Any]    =		model(snake_case_,snake_case_  )
            _A							:  Dict    =		bark_model(snake_case_,snake_case_  )

            _A							:  List[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(snake_case_  ).mkdir(exist_ok=snake_case_  )
      model.save_pretrained(snake_case_  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,):
      _A							:  List[str]    =		os.path.join(snake_case_,snake_case_  )

      _A							:  List[str]    =		BarkSemanticConfig.from_pretrained(os.path.join(snake_case_,"""config.json"""  )  )
      _A							:  Union[str, Any]    =		BarkCoarseConfig.from_pretrained(os.path.join(snake_case_,"""config.json"""  )  )
      _A							:  str    =		BarkFineConfig.from_pretrained(os.path.join(snake_case_,"""config.json"""  )  )
      _A							:  Dict    =		EncodecConfig.from_pretrained("""facebook/encodec_24khz"""  )

      _A							:  Optional[Any]    =		BarkSemanticModel.from_pretrained(snake_case_  )
      _A							:  List[str]    =		BarkCoarseModel.from_pretrained(snake_case_  )
      _A							:  Optional[Any]    =		BarkFineModel.from_pretrained(snake_case_  )
      _A							:  Union[str, Any]    =		EncodecModel.from_pretrained("""facebook/encodec_24khz"""  )

      _A							:  List[Any]    =		BarkConfig.from_sub_model_configs(
          snake_case_,snake_case_,snake_case_,snake_case_  )

      _A							:  Union[str, Any]    =		BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config,coarseAcoustic.generation_config,fineAcoustic.generation_config  )

      _A							:  List[str]    =		BarkModel(snake_case_  )

      _A							:  Tuple    =		semantic
      _A							:  Optional[int]    =		coarseAcoustic
      _A							:  List[str]    =		fineAcoustic
      _A							:  Optional[int]    =		codec

      _A							:  List[str]    =		bark_generation_config

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


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

  _snake_case            = parser.parse_args()

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







from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_ = None  ):
      _A							:  Tuple    =		word_bank or []
      # create a table
      _A							:  int    =		len(snake_case_  ) + 1

      _A							:  list[list[list[str]]]    =		[]
      for _ in range(snake_case_  ):
            table.append([]  )
      # seed value
      _A							:  Dict    =		[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(snake_case_  ):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(snake_case_  )] == word:
                              _A							:  list[list[str]]    =		[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(snake_case_  )] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(snake_case_  )]:
            combination.reverse()

      return table[len(snake_case_  )]


if __name__ == "__main__":
  print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
  print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
  print(
      all_construct(
          "hexagonosaurus",
          ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
      )
  )

