

'''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()
_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  _A		(     snake_case		,							snake_case=False					)      ->     Optional[int]:
   _lowercase      :				List[Any]      					=       model_type
   if use_small:
      key += "_small"
   return os.path.join(_lowerCAmelCase		,							REMOTE_MODEL_PATHS[key]["file_name"]					)







def  _A		(     snake_case		,							snake_case					)      ->     Dict:
   os.makedirs(_lowerCAmelCase		,							exist_ok=_lowerCAmelCase					)
   hf_hub_download(repo_id=_lowerCAmelCase		,							filename=_lowerCAmelCase		,							local_dir=_lowerCAmelCase					)







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

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

   _lowercase      :				Union[str, Any]      					=       ConfigClass(**checkpoint["model_args"]					)
   _lowercase      :				Tuple      					=       ModelClass(config=_lowerCAmelCase					)
   _lowercase      :				str      					=       GenerationConfigClass()

   _lowercase      :				Tuple      					=       model_generation_config
   _lowercase      :				Dict      					=       checkpoint["""model"""]
   # fixup checkpoint
   _lowercase      :				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
         _lowercase      :				Optional[int]      					=       k[len(_lowerCAmelCase					) :]
         for old_layer_name in new_layer_name_dict:
            _lowercase      :				Tuple      					=       new_k.replace(_lowerCAmelCase		,							new_layer_name_dict[old_layer_name]					)

         _lowercase      :				List[Any]      					=       state_dict.pop(_lowerCAmelCase					)

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

   _lowercase      :				str      					=       """cpu"""  # do conversion on cpu

   _lowercase      :				Union[str, Any]      					=       _get_ckpt_path(_lowerCAmelCase		,							use_small=_lowerCAmelCase					)
   _lowercase      :				Union[str, Any]      					=       _load_model(_lowerCAmelCase		,							_lowerCAmelCase		,							model_type=_lowerCAmelCase		,							use_small=_lowerCAmelCase					)

   # load bark initial model
   _lowercase      :				Optional[int]      					=       _bark_load_model(_lowerCAmelCase		,							"cpu"		,							model_type=_lowerCAmelCase		,							use_small=_lowerCAmelCase					)

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

   if model_type in ["text", "coarse"]:
      _lowercase      :				Optional[int]      					=       torch.randint(2_56		,							(batch_size, sequence_length)		,							dtype=torch.int					)
      _lowercase      :				Optional[int]      					=       bark_model(_lowerCAmelCase					)[0]

      _lowercase      :				Any      					=       model(_lowerCAmelCase					)

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

   else:
      _lowercase      :				str      					=       3
      _lowercase      :				Union[str, Any]      					=       8
      _lowercase      :				Any      					=       torch.randint(2_56		,							(batch_size, sequence_length, n_codes_total)		,							dtype=torch.int					)

      _lowercase      :				int      					=       model(_lowerCAmelCase		,							_lowerCAmelCase					)
      _lowercase      :				Optional[Any]      					=       bark_model(_lowerCAmelCase		,							_lowerCAmelCase					)

      _lowercase      :				Tuple      					=       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		(     snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							)      ->     List[str]:
   _lowercase      :				List[str]      					=       os.path.join(_lowerCAmelCase		,							_lowerCAmelCase					)

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

   _lowercase      :				int      					=       BarkSemanticModel.from_pretrained(_lowerCAmelCase					)
   _lowercase      :				str      					=       BarkCoarseModel.from_pretrained(_lowerCAmelCase					)
   _lowercase      :				Tuple      					=       BarkFineModel.from_pretrained(_lowerCAmelCase					)
   _lowercase      :				int      					=       EncodecModel.from_pretrained("facebook/encodec_24khz"					)

   _lowercase      :				List[Any]      					=       BarkConfig.from_sub_model_configs(
       _lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase					)

   _lowercase      :				int      					=       BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config		,							coarseAcoustic.generation_config		,							fineAcoustic.generation_config					)

   _lowercase      :				Tuple      					=       BarkModel(_lowerCAmelCase					)

   _lowercase      :				Optional[Any]      					=       semantic
   _lowercase      :				List[Any]      					=       coarseAcoustic
   _lowercase      :				List[str]      					=       fineAcoustic
   _lowercase      :				List[str]      					=       codec

   _lowercase      :				Dict      					=       bark_generation_config

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


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)






'''simple docstring'''
from timeit import timeit







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				Union[str, Any]      					=       0
   while number:
      number &= number - 1
      result += 1
   return result







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				int      					=       0
   while number:
      if number % 2 == 1:
         result += 1
      number >>= 1
   return result







def  _A		(     )      ->     None:

   def do_benchmark(snake_case					) -> None:
      _lowercase      :				Optional[int]      					=       "import __main__ as z"
      print(F'''Benchmark when {number = }:'''					)
      print(F'''{get_set_bits_count_using_modulo_operator(snake_case					) = }'''					)
      _lowercase      :				int      					=       timeit("z.get_set_bits_count_using_modulo_operator(25)"		,							setup=snake_case					)
      print(F'''timeit() runs in {timing} seconds'''					)
      print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(snake_case					) = }'''					)
      _lowercase      :				Optional[int]      					=       timeit(
          "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"		,							setup=snake_case		,							)
      print(F'''timeit() runs in {timing} seconds'''					)

   for number in (25, 37, 58, 0):
      do_benchmark(snake_case					)
      print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()






