





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	: int       =logging.get_logger(__name__)

set_seed(770)


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




def      a__			(       lowercase__	,     lowercase__=False	):






   '''simple docstring'''






   UpperCAmelCase_           =model_type
   if use_small:
      key += "_small"
   return os.path.join(lowercase__	,     REMOTE_MODEL_PATHS[key]["file_name"]	)




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   os.makedirs(lowercase__	,     exist_ok=lowercase__	)
   hf_hub_download(repo_id=lowercase__	,     filename=lowercase__	,     local_dir=lowercase__	)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__=False	,     lowercase__="text"	):






   '''simple docstring'''






   if model_type == "text":
      UpperCAmelCase_           =BarkSemanticModel
      UpperCAmelCase_           =BarkSemanticConfig
      UpperCAmelCase_           =BarkSemanticGenerationConfig
   elif model_type == "coarse":
      UpperCAmelCase_           =BarkCoarseModel
      UpperCAmelCase_           =BarkCoarseConfig
      UpperCAmelCase_           =BarkCoarseGenerationConfig
   elif model_type == "fine":
      UpperCAmelCase_           =BarkFineModel
      UpperCAmelCase_           =BarkFineConfig
      UpperCAmelCase_           =BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   UpperCAmelCase_           =F'{model_type}_small' if use_small else model_type
   UpperCAmelCase_           =REMOTE_MODEL_PATHS[model_key]
   if not os.path.exists(lowercase__	):
      logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.'	)
      _download(model_info["repo_id"]	,     model_info["file_name"]	)
   UpperCAmelCase_           =torch.load(lowercase__	,     map_location=lowercase__	)
   # this is a hack
   UpperCAmelCase_           =checkpoint["model_args"]
   if "input_vocab_size" not in model_args:
      UpperCAmelCase_           =model_args["vocab_size"]
      UpperCAmelCase_           =model_args["vocab_size"]
      del model_args["vocab_size"]

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

   UpperCAmelCase_           =ConfigClass(**checkpoint["model_args"]	)
   UpperCAmelCase_           =ModelClass(config=lowercase__	)
   UpperCAmelCase_           =GenerationConfigClass()

   UpperCAmelCase_           =model_generation_config
   UpperCAmelCase_           =checkpoint["model"]
   # fixup checkpoint
   UpperCAmelCase_           ="_orig_mod."
   for k, v in list(state_dict.items()	):
      if k.startswith(lowercase__	):
         # replace part of the key with corresponding layer name in HF implementation
         UpperCAmelCase_           =k[len(lowercase__	) :]
         for old_layer_name in new_layer_name_dict:
            UpperCAmelCase_           =new_k.replace(lowercase__	,     new_layer_name_dict[old_layer_name]	)

         UpperCAmelCase_           =state_dict.pop(lowercase__	)

   UpperCAmelCase_           =set(state_dict.keys()	) - set(model.state_dict().keys()	)
   UpperCAmelCase_           ={k for k in extra_keys if not k.endswith(".attn.bias"	)}
   UpperCAmelCase_           =set(model.state_dict().keys()	) - set(state_dict.keys()	)
   UpperCAmelCase_           ={k for k in missing_keys if not k.endswith(".attn.bias"	)}
   if len(lowercase__	) != 0:
      raise ValueError(F'extra keys found: {extra_keys}'	)
   if len(lowercase__	) != 0:
      raise ValueError(F'missing keys: {missing_keys}'	)
   model.load_state_dict(lowercase__	,     strict=lowercase__	)
   UpperCAmelCase_           =model.num_parameters(exclude_embeddings=lowercase__	)
   UpperCAmelCase_           =checkpoint["best_val_loss"].item()
   logger.info(F'model loaded: {round(n_params/1E6	,     1	)}M params, {round(lowercase__	,     3	)} loss'	)
   model.eval()
   model.to(lowercase__	)
   del checkpoint, state_dict

   return model




def      a__			(       lowercase__	,     lowercase__=False	,     lowercase__="text"	):






   '''simple docstring'''






   if model_type not in ("text", "coarse", "fine"):
      raise NotImplementedError()

   UpperCAmelCase_           ="cpu"  # do conversion on cpu

   UpperCAmelCase_           =_get_ckpt_path(lowercase__	,     use_small=lowercase__	)
   UpperCAmelCase_           =_load_model(lowercase__	,     lowercase__	,     model_type=lowercase__	,     use_small=lowercase__	)

   # load bark initial model
   UpperCAmelCase_           =_bark_load_model(lowercase__	,     "cpu"	,     model_type=lowercase__	,     use_small=lowercase__	)

   if model_type == "text":
      UpperCAmelCase_           =bark_model["model"]

   if model.num_parameters(exclude_embeddings=lowercase__	) != 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
   UpperCAmelCase_           =5
   UpperCAmelCase_           =1_0

   if model_type in ["text", "coarse"]:
      UpperCAmelCase_           =torch.randint(2_5_6	,     (batch_size, sequence_length)	,     dtype=torch.int	)
      UpperCAmelCase_           =bark_model(lowercase__	)[0]

      UpperCAmelCase_           =model(lowercase__	)

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

   else:
      UpperCAmelCase_           =3
      UpperCAmelCase_           =8
      UpperCAmelCase_           =torch.randint(2_5_6	,     (batch_size, sequence_length, n_codes_total)	,     dtype=torch.int	)

      UpperCAmelCase_           =model(lowercase__	,     lowercase__	)
      UpperCAmelCase_           =bark_model(lowercase__	,     lowercase__	)

      UpperCAmelCase_           =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(lowercase__	).mkdir(exist_ok=lowercase__	)
   model.save_pretrained(lowercase__	)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	,     ):






   '''simple docstring'''






   UpperCAmelCase_           =os.path.join(lowercase__	,     lowercase__	)

   UpperCAmelCase_           =BarkSemanticConfig.from_pretrained(os.path.join(lowercase__	,     "config.json"	)	)
   UpperCAmelCase_           =BarkCoarseConfig.from_pretrained(os.path.join(lowercase__	,     "config.json"	)	)
   UpperCAmelCase_           =BarkFineConfig.from_pretrained(os.path.join(lowercase__	,     "config.json"	)	)
   UpperCAmelCase_           =EncodecConfig.from_pretrained("facebook/encodec_24khz"	)

   UpperCAmelCase_           =BarkSemanticModel.from_pretrained(lowercase__	)
   UpperCAmelCase_           =BarkCoarseModel.from_pretrained(lowercase__	)
   UpperCAmelCase_           =BarkFineModel.from_pretrained(lowercase__	)
   UpperCAmelCase_           =EncodecModel.from_pretrained("facebook/encodec_24khz"	)

   UpperCAmelCase_           =BarkConfig.from_sub_model_configs(
       lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	)

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

   UpperCAmelCase_           =BarkModel(lowercase__	)

   UpperCAmelCase_           =semantic
   UpperCAmelCase_           =coarseAcoustic
   UpperCAmelCase_           =fineAcoustic
   UpperCAmelCase_           =codec

   UpperCAmelCase_           =bark_generation_config

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


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)












'''simple docstring'''



from __future__ import annotations


def 				lowercase_					(     __A			:							list[list[int]]      )							->   int:



 """simple docstring"""




 for i in range(1       ,   len(matrix[0]      )      ):
  matrix[0][i] += matrix[0][i - 1]

 # preprocessing the first column
 for i in range(1       ,   len(__A      )      ):
  matrix[i][0] += matrix[i - 1][0]

 # updating the path cost for current position
 for i in range(1       ,   len(__A      )      ):
  for j in range(1       ,   len(matrix[0]      )      ):
   matrix[i][j] += min(matrix[i - 1][j]       ,   matrix[i][j - 1]      )

 return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()



