



"""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()
A:     Tuple										=	logging.get_logger(__name__)

set_seed(7_7_0)


A:     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",
}


A:     str										=	{
    "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",
    },
}

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

def    _snake_case		(		UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	Dict=False		):
 UpperCAmelCase			:      Optional[Any]			=			model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCamelCase    ,	REMOTE_MODEL_PATHS[key]["""file_name"""]		)

def    _snake_case		(		UpperCamelCase		:	Dict    ,	UpperCamelCase		:	Optional[int]		):
 os.makedirs(UpperCamelCase    ,	exist_ok=UpperCamelCase		)
 hf_hub_download(repo_id=UpperCamelCase    ,	filename=UpperCamelCase    ,	local_dir=UpperCamelCase		)

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

 # convert Bark model arguments to HF Bark model arguments
 UpperCAmelCase			:      Dict			=			model_args.pop("""n_head"""		)
 UpperCAmelCase			:      Tuple			=			model_args.pop("""n_embd"""		)
 UpperCAmelCase			:      int			=			model_args.pop("""n_layer"""		)

 UpperCAmelCase			:      Optional[int]			=			ConfigClass(**checkpoint["""model_args"""]		)
 UpperCAmelCase			:      Any			=			ModelClass(config=UpperCamelCase		)
 UpperCAmelCase			:      Any			=			GenerationConfigClass()

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

   UpperCAmelCase			:      Union[str, Any]			=			state_dict.pop(UpperCamelCase		)

 UpperCAmelCase			:      Any			=			set(state_dict.keys()		) - set(model.state_dict().keys()		)
 UpperCAmelCase			:      int			=			{k for k in extra_keys if not k.endswith(""".attn.bias"""		)}
 UpperCAmelCase			:      List[Any]			=			set(model.state_dict().keys()		) - set(state_dict.keys()		)
 UpperCAmelCase			:      List[str]			=			{k for k in missing_keys if not k.endswith(""".attn.bias"""		)}
 if len(UpperCamelCase		) != 0:
  raise ValueError(F"extra keys found: {extra_keys}"		)
 if len(UpperCamelCase		) != 0:
  raise ValueError(F"missing keys: {missing_keys}"		)
 model.load_state_dict(UpperCamelCase    ,	strict=UpperCamelCase		)
 UpperCAmelCase			:      Optional[int]			=			model.num_parameters(exclude_embeddings=UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			checkpoint["""best_val_loss"""].item()
 logger.info(F"model loaded: {round(n_params/1e6    ,	1		)}M params, {round(UpperCamelCase    ,	3		)} loss"		)
 model.eval()
 model.to(UpperCamelCase		)
 del checkpoint, state_dict

 return model

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Any=False    ,	UpperCamelCase		:	Union[str, Any]="text"		):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 UpperCAmelCase			:      int			=			"""cpu"""  # do conversion on cpu

 UpperCAmelCase			:      str			=			_get_ckpt_path(UpperCamelCase    ,	use_small=UpperCamelCase		)
 UpperCAmelCase			:      int			=			_load_model(UpperCamelCase    ,	UpperCamelCase    ,	model_type=UpperCamelCase    ,	use_small=UpperCamelCase		)

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

 if model_type == "text":
  UpperCAmelCase			:      List[Any]			=			bark_model["""model"""]

 if model.num_parameters(exclude_embeddings=UpperCamelCase		) != 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
 UpperCAmelCase			:      Dict			=			5
 UpperCAmelCase			:      Any			=			10

 if model_type in ["text", "coarse"]:
  UpperCAmelCase			:      str			=			torch.randint(256    ,	(batch_size, sequence_length)    ,	dtype=torch.int		)
  UpperCAmelCase			:      str			=			bark_model(UpperCamelCase		)[0]

  UpperCAmelCase			:      Optional[Any]			=			model(UpperCamelCase		)

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

 else:
  UpperCAmelCase			:      Any			=			3
  UpperCAmelCase			:      Dict			=			8
  UpperCAmelCase			:      str			=			torch.randint(256    ,	(batch_size, sequence_length, n_codes_total)    ,	dtype=torch.int		)

  UpperCAmelCase			:      int			=			model(UpperCamelCase    ,	UpperCamelCase		)
  UpperCAmelCase			:      Optional[Any]			=			bark_model(UpperCamelCase    ,	UpperCamelCase		)

  UpperCAmelCase			:      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(UpperCamelCase		).mkdir(exist_ok=UpperCamelCase		)
 model.save_pretrained(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	Dict    ,	UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	str    ,	):
 UpperCAmelCase			:      Union[str, Any]			=			os.path.join(UpperCamelCase    ,	UpperCamelCase		)

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

 UpperCAmelCase			:      Union[str, Any]			=			BarkSemanticModel.from_pretrained(UpperCamelCase		)
 UpperCAmelCase			:      str			=			BarkCoarseModel.from_pretrained(UpperCamelCase		)
 UpperCAmelCase			:      int			=			BarkFineModel.from_pretrained(UpperCamelCase		)
 UpperCAmelCase			:      Any			=			EncodecModel.from_pretrained("""facebook/encodec_24khz"""		)

 UpperCAmelCase			:      Union[str, Any]			=			BarkConfig.from_sub_model_configs(
     UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

 UpperCAmelCase			:      Tuple			=			BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config    ,	coarseAcoustic.generation_config    ,	fineAcoustic.generation_config		)

 UpperCAmelCase			:      Any			=			BarkModel(UpperCamelCase		)

 UpperCAmelCase			:      List[Any]			=			semantic
 UpperCAmelCase			:      List[str]			=			coarseAcoustic
 UpperCAmelCase			:      Union[str, Any]			=			fineAcoustic
 UpperCAmelCase			:      Union[str, Any]			=			codec

 UpperCAmelCase			:      Dict			=			bark_generation_config

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


if __name__ == "__main__":
      A:     List[str]										=	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.")

      A:     Dict										=	parser.parse_args()

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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A:     Union[str, Any]										=	{
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     List[str]										=	["SpeechT5Tokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Dict										=	[
          "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SpeechT5ForSpeechToText",
          "SpeechT5ForSpeechToSpeech",
          "SpeechT5ForTextToSpeech",
          "SpeechT5Model",
          "SpeechT5PreTrainedModel",
          "SpeechT5HifiGan",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A:     Dict										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
