





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()
SCREAMING_SNAKE_CASE__							:      str         =    logging.get_logger(__name__)

set_seed(7_7_0)


SCREAMING_SNAKE_CASE__							:      Dict         =    {
    """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""",
}


SCREAMING_SNAKE_CASE__							:      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""",
    },
}

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






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






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   os.makedirs(lowerCamelCase				,			exist_ok=lowerCamelCase )
   hf_hub_download(repo_id=lowerCamelCase				,			filename=lowerCamelCase				,			local_dir=lowerCamelCase )






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

   a__						:    List[str]   		=	ConfigClass(**checkpoint["model_args"] )
   a__						:    Optional[Any]   		=	ModelClass(config=lowerCamelCase )
   a__						:    Union[str, Any]   		=	GenerationConfigClass()

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

         a__						:    List[str]   		=	state_dict.pop(lowerCamelCase )

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

   return model






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

   a__						:    Optional[int]   		=	"cpu"  # do conversion on cpu

   a__						:    Tuple   		=	_get_ckpt_path(lowerCamelCase				,			use_small=lowerCamelCase )
   a__						:    str   		=	_load_model(lowerCamelCase				,			lowerCamelCase				,			model_type=lowerCamelCase				,			use_small=lowerCamelCase )

   # load bark initial model
   a__						:    Dict   		=	_bark_load_model(lowerCamelCase				,			"cpu"				,			model_type=lowerCamelCase				,			use_small=lowerCamelCase )

   if model_type == "text":
      a__						:    Dict   		=	bark_model["model"]

   if model.num_parameters(exclude_embeddings=lowerCamelCase ) != 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__						:    Optional[Any]   		=	5
   a__						:    Dict   		=	10

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

      a__						:    Optional[Any]   		=	model(lowerCamelCase )

      # take last logits
      a__						:    Optional[int]   		=	output_new_model_total.logits[:, [-1], :]

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

      a__						:    str   		=	model(lowerCamelCase				,			lowerCamelCase )
      a__						:    str   		=	bark_model(lowerCamelCase				,			lowerCamelCase )

      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(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
   model.save_pretrained(lowerCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			):
   a__						:    Optional[int]   		=	os.path.join(lowerCamelCase				,			lowerCamelCase )

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

   a__						:    Dict   		=	BarkSemanticModel.from_pretrained(lowerCamelCase )
   a__						:    List[Any]   		=	BarkCoarseModel.from_pretrained(lowerCamelCase )
   a__						:    Optional[Any]   		=	BarkFineModel.from_pretrained(lowerCamelCase )
   a__						:    Optional[Any]   		=	EncodecModel.from_pretrained("facebook/encodec_24khz" )

   a__						:    Any   		=	BarkConfig.from_sub_model_configs(
       lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )

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

   a__						:    Tuple   		=	BarkModel(lowerCamelCase )

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

   a__						:    Optional[Any]   		=	bark_generation_config

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Union[str, 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.""")

  SCREAMING_SNAKE_CASE__							:      Any         =    parser.parse_args()

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








def    _A	(   lowerCamelCase = 200 ):
   a__						:    List[str]   		=	[1, 2, 5, 10, 20, 50, 100, 200]
   a__						:    Dict   		=	[0] * (pence + 1)
   a__						:    int   		=	1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(lowerCamelCase				,			pence + 1				,			1 ):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
  assert solution(2_0_0) == 7_3_6_8_2

