





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def     lowerCamelCase__			(__lowerCamelCase	):






     if "img_encoder.pos_embed" in name:
          _SCREAMING_SNAKE_CASE       :	Any						    =							name.replace("img_encoder.pos_embed",       "vision_model.embeddings.position_embeddings"	)
     if "img_encoder.patch_embed.proj" in name:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							name.replace("img_encoder.patch_embed.proj",       "vision_model.embeddings.patch_embeddings.projection"	)
     if "img_encoder.patch_embed.norm" in name:
          _SCREAMING_SNAKE_CASE       :	Any						    =							name.replace("img_encoder.patch_embed.norm",       "vision_model.embeddings.layernorm"	)
     if "img_encoder.layers" in name:
          _SCREAMING_SNAKE_CASE       :	Dict						    =							name.replace("img_encoder.layers",       "vision_model.encoder.stages"	)
     if "blocks" in name and "res" not in name:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							name.replace("blocks",       "layers"	)
     if "attn" in name and "pre_assign" not in name:
          _SCREAMING_SNAKE_CASE       :	Dict						    =							name.replace("attn",       "self_attn"	)
     if "proj" in name and "self_attn" in name and "text" not in name:
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							name.replace("proj",       "out_proj"	)
     if "pre_assign_attn.attn.proj" in name:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							name.replace("pre_assign_attn.attn.proj",       "pre_assign_attn.attn.out_proj"	)
     if "norm1" in name:
          _SCREAMING_SNAKE_CASE       :	str						    =							name.replace("norm1",       "layer_norm1"	)
     if "norm2" in name and "pre_assign" not in name:
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							name.replace("norm2",       "layer_norm2"	)
     if "img_encoder.norm" in name:
          _SCREAMING_SNAKE_CASE       :	Any						    =							name.replace("img_encoder.norm",       "vision_model.layernorm"	)
     # text encoder
     if "text_encoder.token_embedding" in name:
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							name.replace("text_encoder.token_embedding",       "text_model.embeddings.token_embedding"	)
     if "text_encoder.positional_embedding" in name:
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							name.replace("text_encoder.positional_embedding",       "text_model.embeddings.position_embedding.weight"	)
     if "text_encoder.transformer.resblocks." in name:
          _SCREAMING_SNAKE_CASE       :	str						    =							name.replace("text_encoder.transformer.resblocks.",       "text_model.encoder.layers."	)
     if "ln_1" in name:
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							name.replace("ln_1",       "layer_norm1"	)
     if "ln_2" in name:
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							name.replace("ln_2",       "layer_norm2"	)
     if "c_fc" in name:
          _SCREAMING_SNAKE_CASE       :	Dict						    =							name.replace("c_fc",       "fc1"	)
     if "c_proj" in name:
          _SCREAMING_SNAKE_CASE       :	int						    =							name.replace("c_proj",       "fc2"	)
     if "text_encoder" in name:
          _SCREAMING_SNAKE_CASE       :	int						    =							name.replace("text_encoder",       "text_model"	)
     if "ln_final" in name:
          _SCREAMING_SNAKE_CASE       :	int						    =							name.replace("ln_final",       "final_layer_norm"	)
     # projection layers
     if "img_projector.linear_hidden." in name:
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							name.replace("img_projector.linear_hidden.",       "visual_projection."	)
     if "img_projector.linear_out." in name:
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							name.replace("img_projector.linear_out.",       "visual_projection.3."	)
     if "text_projector.linear_hidden" in name:
          _SCREAMING_SNAKE_CASE       :	Dict						    =							name.replace("text_projector.linear_hidden",       "text_projection"	)
     if "text_projector.linear_out" in name:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							name.replace("text_projector.linear_out",       "text_projection.3"	)

     return name






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):






     for key in orig_state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							orig_state_dict.pop(a__	)

          if "qkv" in key:
               # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _SCREAMING_SNAKE_CASE       :	Dict						    =							key.split("."	)
               _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							int(key_split[2]	), int(key_split[4]	)
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							config.vision_config.hidden_size
               if "weight" in key:
                    _SCREAMING_SNAKE_CASE       :	int						    =							val[:dim, :]
                    _SCREAMING_SNAKE_CASE       :	Dict						    =							val[dim : dim * 2, :]
                    _SCREAMING_SNAKE_CASE       :	Tuple						    =							val[-dim:, :]
               else:
                    _SCREAMING_SNAKE_CASE       :	Tuple						    =							val[:dim]
                    _SCREAMING_SNAKE_CASE       :	Dict						    =							val[dim : dim * 2]
                    _SCREAMING_SNAKE_CASE       :	List[Any]						    =							val[-dim:]
          elif "in_proj" in key:
               # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							key.split("."	)
               _SCREAMING_SNAKE_CASE       :	List[str]						    =							int(key_split[3]	)
               _SCREAMING_SNAKE_CASE       :	str						    =							config.text_config.hidden_size
               if "weight" in key:
                    _SCREAMING_SNAKE_CASE       :	List[Any]						    =							val[:dim, :]
                    _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							val[
                        dim : dim * 2, :
                    ]
                    _SCREAMING_SNAKE_CASE       :	Tuple						    =							val[-dim:, :]
               else:
                    _SCREAMING_SNAKE_CASE       :	Dict						    =							val[:dim]
                    _SCREAMING_SNAKE_CASE       :	Any						    =							val[dim : dim * 2]
                    _SCREAMING_SNAKE_CASE       :	Any						    =							val[-dim:]
          else:
               _SCREAMING_SNAKE_CASE       :	str						    =							rename_key(a__	)
               # squeeze if necessary
               if (
                   "text_projection.0" in new_name
                   or "text_projection.3" in new_name
                   or "visual_projection.0" in new_name
                   or "visual_projection.3" in new_name
               ):
                    _SCREAMING_SNAKE_CASE       :	Tuple						    =							val.squeeze_()
               else:
                    _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							val

     return orig_state_dict






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							Image.open(requests.get(a__,       stream=a__	).raw	)
     return im






@torch.no_grad()
def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase="groupvit-gcc-yfcc",       __lowerCamelCase=False	):
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							GroupViTConfig()
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							GroupViTModel(a__	).eval()

     _SCREAMING_SNAKE_CASE       :	str						    =							torch.load(a__,       map_location="cpu"	)["model"]
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							convert_state_dict(a__,       a__	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Any						    =							model.load_state_dict(a__,       strict=a__	)
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a__	) == 0)

     # verify result
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"	)
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							prepare_img()
     _SCREAMING_SNAKE_CASE       :	Dict						    =							processor(text=["a photo of a cat", "a photo of a dog"],       images=a__,       padding=a__,       return_tensors="pt"	)

     with torch.no_grad():
          _SCREAMING_SNAKE_CASE       :	Dict						    =							model(**a__	)

     if model_name == "groupvit-gcc-yfcc":
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.tensor([[13.3523, 6.3629]]	)
     elif model_name == "groupvit-gcc-redcaps":
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.tensor([[16.1873, 8.6230]]	)
     else:
          raise ValueError(f"""Model name {model_name} not supported."""	)
     assert torch.allclose(outputs.logits_per_image,       a__,       atol=1e-3	)

     processor.save_pretrained(a__	)
     model.save_pretrained(a__	)
     print("Successfully saved processor and model to",       a__	)

     if push_to_hub:
          print("Pushing to the hub..."	)
          processor.push_to_hub(a__,       organization="nielsr"	)
          model.push_to_hub(a__,       organization="nielsr"	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
       )
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
       parser.add_argument(
           '--model_name',
           default='groupvit-gccy-fcc',
           type=str,
           help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
       )
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
       )
       UpperCamelCase__      =parser.parse_args()

       convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)






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(770)


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     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=False	):
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model_type
     if use_small:
          key += "_small"
     return os.path.join(__lowerCamelCase,       REMOTE_MODEL_PATHS[key]["file_name"]	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     os.makedirs(__lowerCamelCase,       exist_ok=__lowerCamelCase	)
     hf_hub_download(repo_id=__lowerCamelCase,       filename=__lowerCamelCase,       local_dir=__lowerCamelCase	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase=False,       __lowerCamelCase="text"	):
     if model_type == "text":
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkSemanticModel
          _SCREAMING_SNAKE_CASE       :	Any						    =							BarkSemanticConfig
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							BarkSemanticGenerationConfig
     elif model_type == "coarse":
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							BarkCoarseModel
          _SCREAMING_SNAKE_CASE       :	Any						    =							BarkCoarseConfig
          _SCREAMING_SNAKE_CASE       :	str						    =							BarkCoarseGenerationConfig
     elif model_type == "fine":
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							BarkFineModel
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							BarkFineConfig
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							BarkFineGenerationConfig
     else:
          raise NotImplementedError()
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							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(__lowerCamelCase	):
          logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""	)
          _download(model_info["repo_id"],       model_info["file_name"]	)
     _SCREAMING_SNAKE_CASE       :	str						    =							torch.load(__lowerCamelCase,       map_location=__lowerCamelCase	)
     # this is a hack
     _SCREAMING_SNAKE_CASE       :	Any						    =							checkpoint["model_args"]
     if "input_vocab_size" not in model_args:
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model_args["vocab_size"]
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model_args["vocab_size"]
          del model_args["vocab_size"]

     # convert Bark model arguments to HF Bark model arguments
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model_args.pop("n_head"	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							model_args.pop("n_embd"	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							model_args.pop("n_layer"	)

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							ConfigClass(**checkpoint["model_args"]	)
     _SCREAMING_SNAKE_CASE       :	int						    =							ModelClass(config=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							GenerationConfigClass()

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

               _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							state_dict.pop(__lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							set(state_dict.keys()	) - set(model.state_dict().keys()	)
     _SCREAMING_SNAKE_CASE       :	int						    =							{k for k in extra_keys if not k.endswith(".attn.bias"	)}
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							set(model.state_dict().keys()	) - set(state_dict.keys()	)
     _SCREAMING_SNAKE_CASE       :	List[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	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							model.num_parameters(exclude_embeddings=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	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     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=False,       __lowerCamelCase="text"	):
     if model_type not in ("text", "coarse", "fine"):
          raise NotImplementedError()

     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"cpu"  # do conversion on cpu

     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							_get_ckpt_path(__lowerCamelCase,       use_small=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							_load_model(__lowerCamelCase,       __lowerCamelCase,       model_type=__lowerCamelCase,       use_small=__lowerCamelCase	)

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

     if model_type == "text":
          _SCREAMING_SNAKE_CASE       :	str						    =							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
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							5
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							10

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

          _SCREAMING_SNAKE_CASE       :	Any						    =							model(__lowerCamelCase	)

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

     else:
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							3
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							8
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.randint(256,       (batch_size, sequence_length, n_codes_total),       dtype=torch.int	)

          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model(__lowerCamelCase,       __lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	Dict						    =							bark_model(__lowerCamelCase,       __lowerCamelCase	)

          _SCREAMING_SNAKE_CASE       :	List[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(__lowerCamelCase	).mkdir(exist_ok=__lowerCamelCase	)
     model.save_pretrained(__lowerCamelCase	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       ):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							os.path.join(__lowerCamelCase,       __lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	int						    =							BarkSemanticConfig.from_pretrained(os.path.join(__lowerCamelCase,       "config.json"	)	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkCoarseConfig.from_pretrained(os.path.join(__lowerCamelCase,       "config.json"	)	)
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							BarkFineConfig.from_pretrained(os.path.join(__lowerCamelCase,       "config.json"	)	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							EncodecConfig.from_pretrained("facebook/encodec_24khz"	)

     _SCREAMING_SNAKE_CASE       :	int						    =							BarkSemanticModel.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							BarkCoarseModel.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							BarkFineModel.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							EncodecModel.from_pretrained("facebook/encodec_24khz"	)

     _SCREAMING_SNAKE_CASE       :	Any						    =							BarkConfig.from_sub_model_configs(
         __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)

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

     _SCREAMING_SNAKE_CASE       :	str						    =							BarkModel(__lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							semantic
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							coarseAcoustic
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							fineAcoustic
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							codec

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							bark_generation_config

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


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)