


'''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_      :		Any          =		logging.get_logger(__name__)

set_seed(770)


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


def    UpperCAmelCase_  (       A		,	A=False      ):
   '''simple docstring'''





   _a					:  Any						=  model_type
   if use_small:
      key += "_small"
   return os.path.join(A		,	REMOTE_MODEL_PATHS[key]['file_name']      )


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





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


def    UpperCAmelCase_  (       A		,	A		,	A=False		,	A="text"      ):
   '''simple docstring'''





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

   # convert Bark model arguments to HF Bark model arguments
   _a					:  List[Any]						=  model_args.pop('n_head'      )
   _a					:  int						=  model_args.pop('n_embd'      )
   _a					:  Tuple						=  model_args.pop('n_layer'      )

   _a					:  Tuple						=  ConfigClass(**checkpoint['model_args']      )
   _a					:  str						=  ModelClass(config=A      )
   _a					:  str						=  GenerationConfigClass()

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

         _a					:  List[Any]						=  state_dict.pop(A      )

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

   return model


def    UpperCAmelCase_  (       A		,	A=False		,	A="text"      ):
   '''simple docstring'''





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

   _a					:  Dict						=  'cpu'  # do conversion on cpu

   _a					:  Dict						=  _get_ckpt_path(A		,	use_small=A      )
   _a					:  Any						=  _load_model(A		,	A		,	model_type=A		,	use_small=A      )

   # load bark initial model
   _a					:  Optional[Any]						=  _bark_load_model(A		,	'cpu'		,	model_type=A		,	use_small=A      )

   if model_type == "text":
      _a					:  Optional[Any]						=  bark_model['model']

   if model.num_parameters(exclude_embeddings=A      ) != 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					:  Optional[int]						=  1_0

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

      _a					:  Dict						=  model(A      )

      # take last logits
      _a					:  List[Any]						=  output_new_model_total.logits[:, [-1], :]

   else:
      _a					:  int						=  3
      _a					:  str						=  8
      _a					:  Optional[int]						=  torch.randint(2_5_6		,	(batch_size, sequence_length, n_codes_total)		,	dtype=torch.int      )

      _a					:  str						=  model(A		,	A      )
      _a					:  Optional[int]						=  bark_model(A		,	A      )

      _a					:  int						=  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(A      ).mkdir(exist_ok=A      )
   model.save_pretrained(A      )


def    UpperCAmelCase_  (       A		,	A		,	A		,	A		,	A		,	A		,	):
   '''simple docstring'''





   _a					:  Optional[int]						=  os.path.join(A		,	A      )

   _a					:  str						=  BarkSemanticConfig.from_pretrained(os.path.join(A		,	'config.json'      )      )
   _a					:  Union[str, Any]						=  BarkCoarseConfig.from_pretrained(os.path.join(A		,	'config.json'      )      )
   _a					:  int						=  BarkFineConfig.from_pretrained(os.path.join(A		,	'config.json'      )      )
   _a					:  Optional[int]						=  EncodecConfig.from_pretrained('facebook/encodec_24khz'      )

   _a					:  str						=  BarkSemanticModel.from_pretrained(A      )
   _a					:  Optional[Any]						=  BarkCoarseModel.from_pretrained(A      )
   _a					:  Optional[Any]						=  BarkFineModel.from_pretrained(A      )
   _a					:  List[Any]						=  EncodecModel.from_pretrained('facebook/encodec_24khz'      )

   _a					:  Tuple						=  BarkConfig.from_sub_model_configs(
       A		,	A		,	A		,	A      )

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

   _a					:  Tuple						=  BarkModel(A      )

   _a					:  int						=  semantic
   _a					:  Union[str, Any]						=  coarseAcoustic
   _a					:  Tuple						=  fineAcoustic
   _a					:  Dict						=  codec

   _a					:  Dict						=  bark_generation_config

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


if __name__ == "__main__":
 UpperCAmelCase_      :		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.")

 UpperCAmelCase_      :		Union[str, Any]          =		parser.parse_args()

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






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      :		Dict          =		logging.get_logger(__name__)

UpperCAmelCase_      :		List[str]          =		{
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		"""visual_bert"""




 def __init__(    self		,      lowerCamelCase_=3_0_5_2_2		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=2		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-12		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      lowerCamelCase_=1		,      lowerCamelCase_=0		,      lowerCamelCase_=2		,      **lowerCamelCase_		,      )      ->    Optional[Any]:
    super().__init__(pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_		,      **lowerCamelCase_      )

    _a					:  List[str]						=  vocab_size
    _a					:  Union[str, Any]						=  max_position_embeddings
    _a					:  Tuple						=  hidden_size
    _a					:  Tuple						=  visual_embedding_dim
    _a					:  Optional[Any]						=  num_hidden_layers
    _a					:  str						=  num_attention_heads
    _a					:  Union[str, Any]						=  intermediate_size
    _a					:  List[str]						=  hidden_act
    _a					:  str						=  hidden_dropout_prob
    _a					:  List[Any]						=  attention_probs_dropout_prob
    _a					:  Optional[Any]						=  initializer_range
    _a					:  List[Any]						=  type_vocab_size
    _a					:  Any						=  layer_norm_eps
    _a					:  Tuple						=  bypass_transformer
    _a					:  str						=  special_visual_initialize


