

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__ :     List[str]				    =    logging.get_logger(__name__)

set_seed(770)


UpperCAmelCase__ :     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""",
}


UpperCAmelCase__ :     Optional[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""",
    },
}

UpperCAmelCase__ :     Any				    =    os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase__ :     Any				    =    os.path.join(os.path.expanduser("""~"""), """.cache""")
UpperCAmelCase__ :     List[Any]				    =    os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")






def 					__lowercase      (     _A						,    _A=False			)     ->						List[str]:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   model_type
      if use_small:
            key += "_small"
      return os.path.join(_A						,    REMOTE_MODEL_PATHS[key]["""file_name"""]			)






def 					__lowercase      (     _A						,    _A			)     ->						str:
      os.makedirs(_A						,    exist_ok=_A			)
      hf_hub_download(repo_id=_A						,    filename=_A						,    local_dir=_A			)






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

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

      SCREAMING_SNAKE_CASE :      List[str]       =   ConfigClass(**checkpoint["""model_args"""]			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ModelClass(config=_A			)
      SCREAMING_SNAKE_CASE :      Dict       =   GenerationConfigClass()

      SCREAMING_SNAKE_CASE :      Optional[Any]       =   model_generation_config
      SCREAMING_SNAKE_CASE :      List[Any]       =   checkpoint["""model"""]
      # fixup checkpoint
      SCREAMING_SNAKE_CASE :      List[str]       =   """_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
                  SCREAMING_SNAKE_CASE :      Dict       =   k[len(_A			) :]
                  for old_layer_name in new_layer_name_dict:
                        SCREAMING_SNAKE_CASE :      Tuple       =   new_k.replace(_A						,    new_layer_name_dict[old_layer_name]			)

                  SCREAMING_SNAKE_CASE :      str       =   state_dict.pop(_A			)

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

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

      SCREAMING_SNAKE_CASE :      Optional[Any]       =   _get_ckpt_path(_A						,    use_small=_A			)
      SCREAMING_SNAKE_CASE :      Any       =   _load_model(_A						,    _A						,    model_type=_A						,    use_small=_A			)

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

      if model_type == "text":
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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
      SCREAMING_SNAKE_CASE :      Tuple       =   5
      SCREAMING_SNAKE_CASE :      int       =   10

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

            SCREAMING_SNAKE_CASE :      Any       =   model(_A			)

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

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

            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(_A						,    _A			)
            SCREAMING_SNAKE_CASE :      List[str]       =   bark_model(_A						,    _A			)

            SCREAMING_SNAKE_CASE :      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(_A			).mkdir(exist_ok=_A			)
      model.save_pretrained(_A			)






def 					__lowercase      (     _A						,    _A						,    _A						,    _A						,    _A						,    _A						,    )     ->						Any:
      SCREAMING_SNAKE_CASE :      int       =   os.path.join(_A						,    _A			)

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

      SCREAMING_SNAKE_CASE :      Any       =   BarkSemanticModel.from_pretrained(_A			)
      SCREAMING_SNAKE_CASE :      Any       =   BarkCoarseModel.from_pretrained(_A			)
      SCREAMING_SNAKE_CASE :      Tuple       =   BarkFineModel.from_pretrained(_A			)
      SCREAMING_SNAKE_CASE :      int       =   EncodecModel.from_pretrained("""facebook/encodec_24khz"""			)

      SCREAMING_SNAKE_CASE :      int       =   BarkConfig.from_sub_model_configs(
          _A						,    _A						,    _A						,    _A			)

      SCREAMING_SNAKE_CASE :      Optional[int]       =   BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config						,    coarseAcoustic.generation_config						,    fineAcoustic.generation_config			)

      SCREAMING_SNAKE_CASE :      Tuple       =   BarkModel(_A			)

      SCREAMING_SNAKE_CASE :      Dict       =   semantic
      SCREAMING_SNAKE_CASE :      Any       =   coarseAcoustic
      SCREAMING_SNAKE_CASE :      Any       =   fineAcoustic
      SCREAMING_SNAKE_CASE :      Dict       =   codec

      SCREAMING_SNAKE_CASE :      int       =   bark_generation_config

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


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

  UpperCAmelCase__ :     Tuple				    =    parser.parse_args()

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







import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase__ :     Optional[Any]				    =    3






def 					__lowercase      (     _A			)     ->						int:
      print("""Generating primitive root of p"""			)
      while True:
            SCREAMING_SNAKE_CASE :      int       =   random.randrange(3						,    _A			)
            if pow(_A						,    2						,    _A			) == 1:
                  continue
            if pow(_A						,    _A						,    _A			) == 1:
                  continue
            return g






def 					__lowercase      (     _A			)     ->						tuple[tuple[int, int, int, int], tuple[int, int]]:
      print("""Generating prime p..."""			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   rabin_miller.generate_large_prime(_A			)  # select large prime number.
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   primitive_root(_A			)  # one primitive root on modulo p.
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   random.randrange(3						,    _A			)  # private_key -> have to be greater than 2 for safety.
      SCREAMING_SNAKE_CASE :      List[str]       =   cryptomath.find_mod_inverse(pow(_A						,    _A						,    _A			)						,    _A			)

      SCREAMING_SNAKE_CASE :      str       =   (key_size, e_a, e_a, p)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (key_size, d)

      return public_key, private_key






def 					__lowercase      (     _A						,    _A			)     ->						None:
      if os.path.exists(F"{name}_pubkey.txt"			) or os.path.exists(F"{name}_privkey.txt"			):
            print("""\nWARNING:"""			)
            print(
                F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                """Use a different name or delete these files and re-run this program."""			)
            sys.exit()

      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[str]       =   generate_key(_A			)
      print(F"\nWriting public key to file {name}_pubkey.txt..."			)
      with open(F"{name}_pubkey.txt"						,    """w"""			) as fo:
            fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"			)

      print(F"Writing private key to file {name}_privkey.txt..."			)
      with open(F"{name}_privkey.txt"						,    """w"""			) as fo:
            fo.write(F"{private_key[0]},{private_key[1]}"			)






def 					__lowercase      (     )     ->						None:
      print("""Making key files..."""			)
      make_key_files("""elgamal"""						,    2048			)
      print("""Key files generation successful"""			)


if __name__ == "__main__":
  main()




