


'''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()
__magic_name__			=      logging.get_logger(__name__)

set_seed(770)


__magic_name__			=      {
    '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',
}


__magic_name__			=      {
    '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',
    },
}

__magic_name__			=      os.path.dirname(os.path.abspath(__file__))
__magic_name__			=      os.path.join(os.path.expanduser('~'), '.cache')
__magic_name__			=      os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')




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




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




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

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

      A_						:  Optional[int]          =				ConfigClass(**checkpoint["""model_args"""])
      A_						:  List[str]          =				ModelClass(config=lowerCamelCase)
      A_						:  int          =				GenerationConfigClass()

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

                  A_						:  Union[str, Any]          =				state_dict.pop(lowerCamelCase)

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

      A_						:  Tuple          =				"""cpu"""  # do conversion on cpu

      A_						:  str          =				_get_ckpt_path(lowerCamelCase							,						use_small=lowerCamelCase)
      A_						:  Optional[Any]          =				_load_model(lowerCamelCase							,						lowerCamelCase							,						model_type=lowerCamelCase							,						use_small=lowerCamelCase)

      # load bark initial model
      A_						:  Optional[Any]          =				_bark_load_model(lowerCamelCase							,						"""cpu"""							,						model_type=lowerCamelCase							,						use_small=lowerCamelCase)

      if model_type == "text":
            A_						:  Optional[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
      A_						:  Dict          =				5
      A_						:  Dict          =				10

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

            A_						:  str          =				model(lowerCamelCase)

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

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

            A_						:  int          =				model(lowerCamelCase							,						lowerCamelCase)
            A_						:  Any          =				bark_model(lowerCamelCase							,						lowerCamelCase)

            A_						:  Dict          =				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      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Dict							,						):
      A_						:  List[str]          =				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_						:  Any          =				BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase							,						"""config.json"""))
      A_						:  Optional[Any]          =				EncodecConfig.from_pretrained("""facebook/encodec_24khz""")

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

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

      A_						:  List[Any]          =				BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config							,						coarseAcoustic.generation_config							,						fineAcoustic.generation_config)

      A_						:  str          =				BarkModel(lowerCamelCase)

      A_						:  int          =				semantic
      A_						:  Tuple          =				coarseAcoustic
      A_						:  Any          =				fineAcoustic
      A_						:  Tuple          =				codec

      A_						:  List[Any]          =				bark_generation_config

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


if __name__ == "__main__":
     __magic_name__			=      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.')

     __magic_name__			=      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      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]):
                  if adj not in explored:
                        stack.append(lowerCamelCase)
      return explored


__magic_name__			=      {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, 'A'))
