





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_					:   int 							=						logging.get_logger(__name__)

set_seed(7_70)


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






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Optional[Any]=False			)	->			str:
     lowerCAmelCase_					:       Tuple    =		model_type
     if use_small:
          key += "_small"
     return os.path.join(_a					,   REMOTE_MODEL_PATHS[key]["""file_name"""]			)






def 							_lowerCAmelCase (  _a   :					Dict					,   _a   :					List[Any]			)	->			int:
     os.makedirs(_a					,   exist_ok=_a			)
     hf_hub_download(repo_id=_a					,   filename=_a					,   local_dir=_a			)






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

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

     lowerCAmelCase_					:       List[Any]    =		ConfigClass(**checkpoint["""model_args"""]			)
     lowerCAmelCase_					:       Dict    =		ModelClass(config=_a			)
     lowerCAmelCase_					:       Optional[Any]    =		GenerationConfigClass()

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

               lowerCAmelCase_					:       Tuple    =		state_dict.pop(_a			)

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

     lowerCAmelCase_					:       Dict    =		"""cpu"""  # do conversion on cpu

     lowerCAmelCase_					:       Optional[Any]    =		_get_ckpt_path(_a					,   use_small=_a			)
     lowerCAmelCase_					:       int    =		_load_model(_a					,   _a					,   model_type=_a					,   use_small=_a			)

     # load bark initial model
     lowerCAmelCase_					:       List[Any]    =		_bark_load_model(_a					,   """cpu"""					,   model_type=_a					,   use_small=_a			)

     if model_type == "text":
          lowerCAmelCase_					:       Union[str, 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
     lowerCAmelCase_					:       str    =		5
     lowerCAmelCase_					:       List[str]    =		10

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

          lowerCAmelCase_					:       Dict    =		model(_a			)

          # take last logits
          lowerCAmelCase_					:       Dict    =		output_new_model_total.logits[:, [-1], :]

     else:
          lowerCAmelCase_					:       Tuple    =		3
          lowerCAmelCase_					:       List[Any]    =		8
          lowerCAmelCase_					:       Any    =		torch.randint(2_56					,   (batch_size, sequence_length, n_codes_total)					,   dtype=torch.int			)

          lowerCAmelCase_					:       List[Any]    =		model(_a					,   _a			)
          lowerCAmelCase_					:       Any    =		bark_model(_a					,   _a			)

          lowerCAmelCase_					:       List[str]    =		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 							_lowerCAmelCase (  _a   :					Union[str, Any]					,   _a   :					Tuple					,   _a   :					Union[str, Any]					,   _a   :					Optional[Any]					,   _a   :					List[str]					,   _a   :					Dict					,   )	->			List[Any]:
     lowerCAmelCase_					:       Tuple    =		os.path.join(_a					,   _a			)

     lowerCAmelCase_					:       List[Any]    =		BarkSemanticConfig.from_pretrained(os.path.join(_a					,   """config.json"""			)			)
     lowerCAmelCase_					:       Dict    =		BarkCoarseConfig.from_pretrained(os.path.join(_a					,   """config.json"""			)			)
     lowerCAmelCase_					:       Optional[Any]    =		BarkFineConfig.from_pretrained(os.path.join(_a					,   """config.json"""			)			)
     lowerCAmelCase_					:       Optional[int]    =		EncodecConfig.from_pretrained("""facebook/encodec_24khz"""			)

     lowerCAmelCase_					:       Union[str, Any]    =		BarkSemanticModel.from_pretrained(_a			)
     lowerCAmelCase_					:       Tuple    =		BarkCoarseModel.from_pretrained(_a			)
     lowerCAmelCase_					:       Tuple    =		BarkFineModel.from_pretrained(_a			)
     lowerCAmelCase_					:       List[Any]    =		EncodecModel.from_pretrained("""facebook/encodec_24khz"""			)

     lowerCAmelCase_					:       Optional[int]    =		BarkConfig.from_sub_model_configs(
         _a					,   _a					,   _a					,   _a			)

     lowerCAmelCase_					:       Optional[int]    =		BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config					,   coarseAcoustic.generation_config					,   fineAcoustic.generation_config			)

     lowerCAmelCase_					:       Optional[Any]    =		BarkModel(_a			)

     lowerCAmelCase_					:       List[str]    =		semantic
     lowerCAmelCase_					:       List[Any]    =		coarseAcoustic
     lowerCAmelCase_					:       List[Any]    =		fineAcoustic
     lowerCAmelCase_					:       Tuple    =		codec

     lowerCAmelCase_					:       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_					:   int 							=						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_					:   int 							=						parser.parse_args()

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
