



"""simple docstring"""
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     =			logging.get_logger(__name__)

set_seed(7_7_0)


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





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





def      __magic_name__				(							lowercase							,			lowercase ):
   os.makedirs(lowercase							,			exist_ok=lowercase )
   hf_hub_download(repo_id=lowercase							,			filename=lowercase							,			local_dir=lowercase )





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

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

   SCREAMING_SNAKE_CASE_: str     			=ConfigClass(**checkpoint["""model_args"""] )
   SCREAMING_SNAKE_CASE_: Any     			=ModelClass(config=lowercase )
   SCREAMING_SNAKE_CASE_: str     			=GenerationConfigClass()

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

         SCREAMING_SNAKE_CASE_: Optional[int]     			=state_dict.pop(lowercase )

   SCREAMING_SNAKE_CASE_: Dict     			=set(state_dict.keys() ) - set(model.state_dict().keys() )
   SCREAMING_SNAKE_CASE_: List[str]     			={k for k in extra_keys if not k.endswith(""".attn.bias""" )}
   SCREAMING_SNAKE_CASE_: str     			=set(model.state_dict().keys() ) - set(state_dict.keys() )
   SCREAMING_SNAKE_CASE_: Tuple     			={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 )
   SCREAMING_SNAKE_CASE_: List[Any]     			=model.num_parameters(exclude_embeddings=lowercase )
   SCREAMING_SNAKE_CASE_: Tuple     			=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      __magic_name__				(							lowercase							,			lowercase=False							,			lowercase="text" ):
   if model_type not in ("text", "coarse", "fine"):
      raise NotImplementedError()

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

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_get_ckpt_path(lowercase							,			use_small=lowercase )
   SCREAMING_SNAKE_CASE_: Dict     			=_load_model(lowercase							,			lowercase							,			model_type=lowercase							,			use_small=lowercase )

   # load bark initial model
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_bark_load_model(lowercase							,			"""cpu"""							,			model_type=lowercase							,			use_small=lowercase )

   if model_type == "text":
      SCREAMING_SNAKE_CASE_: Any     			=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
   SCREAMING_SNAKE_CASE_: List[str]     			=5
   SCREAMING_SNAKE_CASE_: Any     			=10

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

      SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(lowercase )

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

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

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

      SCREAMING_SNAKE_CASE_: Optional[Any]     			=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      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			):
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=os.path.join(lowercase							,			lowercase )

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

   SCREAMING_SNAKE_CASE_: int     			=BarkSemanticModel.from_pretrained(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=BarkCoarseModel.from_pretrained(lowercase )
   SCREAMING_SNAKE_CASE_: int     			=BarkFineModel.from_pretrained(lowercase )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

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

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

   SCREAMING_SNAKE_CASE_: List[Any]     			=BarkModel(lowercase )

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

   SCREAMING_SNAKE_CASE_: str     			=bark_generation_config

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


if __name__ == "__main__":
   _UpperCAmelCase     =			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     =			parser.parse_args()

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








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   if upper_limit < 0:
      raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )

   SCREAMING_SNAKE_CASE_: Tuple     			=[0] * (upper_limit + 1)

   # Base case: C(0) = C(1) = 1
   SCREAMING_SNAKE_CASE_: Any     			=1
   if upper_limit > 0:
      SCREAMING_SNAKE_CASE_: List[str]     			=1

   # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
   for i in range(2							,			upper_limit + 1 ):
      for j in range(lowercase ):
         catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

   return catalan_list


if __name__ == "__main__":
   print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
   print("""\n*** Enter -1 at any time to quit ***""")
   print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
   try:
      while True:
         _UpperCAmelCase     =			int(input().strip())
         if N < 0:
            print("""\n********* Goodbye!! ************""")
            break
         else:
            print(f"""The Catalan numbers from 0 through {N} are:""")
            print(catalan_numbers(N))
            print("""Try another upper limit for the sequence: """, end="""""")
   except (NameError, ValueError):
      print("""\n********* Invalid input, goodbye! ************\n""")

   import doctest

   doctest.testmod()



