





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__      ={
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =['PerceiverFeatureExtractor']
       UpperCamelCase__      =['PerceiverImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'PerceiverForImageClassificationConvProcessing',
           'PerceiverForImageClassificationFourier',
           'PerceiverForImageClassificationLearned',
           'PerceiverForMaskedLM',
           'PerceiverForMultimodalAutoencoding',
           'PerceiverForOpticalFlow',
           'PerceiverForSequenceClassification',
           'PerceiverLayer',
           'PerceiverModel',
           'PerceiverPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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)