




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()
_lowerCAmelCase						:   List[Any]		=   logging.get_logger(__name__)

set_seed(7_7_0)


_lowerCAmelCase						:   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",
}


_lowerCAmelCase						:   Optional[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",
    },
}

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Any=False	):




 """simple docstring"""







 UpperCAmelCase__							 =						model_type
 if use_small:
  key += "_small"
 return os.path.join(_lowerCAmelCase						,							REMOTE_MODEL_PATHS[key]["file_name"]	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Union[str, Any]=False						,							_lowerCAmelCase							:   Union[str, Any]="text"	):




 """simple docstring"""







 if model_type == "text":
  UpperCAmelCase__							 =						BarkSemanticModel
  UpperCAmelCase__							 =						BarkSemanticConfig
  UpperCAmelCase__							 =						BarkSemanticGenerationConfig
 elif model_type == "coarse":
  UpperCAmelCase__							 =						BarkCoarseModel
  UpperCAmelCase__							 =						BarkCoarseConfig
  UpperCAmelCase__							 =						BarkCoarseGenerationConfig
 elif model_type == "fine":
  UpperCAmelCase__							 =						BarkFineModel
  UpperCAmelCase__							 =						BarkFineConfig
  UpperCAmelCase__							 =						BarkFineGenerationConfig
 else:
  raise NotImplementedError()
 UpperCAmelCase__							 =						F'''{model_type}_small''' if use_small else model_type
 UpperCAmelCase__							 =						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"]	)
 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase						,							map_location=_lowerCAmelCase	)
 # this is a hack
 UpperCAmelCase__							 =						checkpoint["model_args"]
 if "input_vocab_size" not in model_args:
  UpperCAmelCase__							 =						model_args["vocab_size"]
  UpperCAmelCase__							 =						model_args["vocab_size"]
  del model_args["vocab_size"]

 # convert Bark model arguments to HF Bark model arguments
 UpperCAmelCase__							 =						model_args.pop("n_head"	)
 UpperCAmelCase__							 =						model_args.pop("n_embd"	)
 UpperCAmelCase__							 =						model_args.pop("n_layer"	)

 UpperCAmelCase__							 =						ConfigClass(**checkpoint["model_args"]	)
 UpperCAmelCase__							 =						ModelClass(config=_lowerCAmelCase	)
 UpperCAmelCase__							 =						GenerationConfigClass()

 UpperCAmelCase__							 =						model_generation_config
 UpperCAmelCase__							 =						checkpoint["model"]
 # fixup checkpoint
 UpperCAmelCase__							 =						"_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
   UpperCAmelCase__							 =						k[len(_lowerCAmelCase	) :]
   for old_layer_name in new_layer_name_dict:
    UpperCAmelCase__							 =						new_k.replace(_lowerCAmelCase						,							new_layer_name_dict[old_layer_name]	)

   UpperCAmelCase__							 =						state_dict.pop(_lowerCAmelCase	)

 UpperCAmelCase__							 =						set(state_dict.keys()	) - set(model.state_dict().keys()	)
 UpperCAmelCase__							 =						{k for k in extra_keys if not k.endswith(".attn.bias"	)}
 UpperCAmelCase__							 =						set(model.state_dict().keys()	) - set(state_dict.keys()	)
 UpperCAmelCase__							 =						{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	)
 UpperCAmelCase__							 =						model.num_parameters(exclude_embeddings=_lowerCAmelCase	)
 UpperCAmelCase__							 =						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							:   str						,							_lowerCAmelCase							:   Tuple=False						,							_lowerCAmelCase							:   str="text"	):




 """simple docstring"""







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

 UpperCAmelCase__							 =						"cpu"  # do conversion on cpu

 UpperCAmelCase__							 =						_get_ckpt_path(_lowerCAmelCase						,							use_small=_lowerCAmelCase	)
 UpperCAmelCase__							 =						_load_model(_lowerCAmelCase						,							_lowerCAmelCase						,							model_type=_lowerCAmelCase						,							use_small=_lowerCAmelCase	)

 # load bark initial model
 UpperCAmelCase__							 =						_bark_load_model(_lowerCAmelCase						,							"cpu"						,							model_type=_lowerCAmelCase						,							use_small=_lowerCAmelCase	)

 if model_type == "text":
  UpperCAmelCase__							 =						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
 UpperCAmelCase__							 =						5
 UpperCAmelCase__							 =						10

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

  UpperCAmelCase__							 =						model(_lowerCAmelCase	)

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

 else:
  UpperCAmelCase__							 =						3
  UpperCAmelCase__							 =						8
  UpperCAmelCase__							 =						torch.randint(256						,							(batch_size, sequence_length, n_codes_total)						,							dtype=torch.int	)

  UpperCAmelCase__							 =						model(_lowerCAmelCase						,							_lowerCAmelCase	)
  UpperCAmelCase__							 =						bark_model(_lowerCAmelCase						,							_lowerCAmelCase	)

  UpperCAmelCase__							 =						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							:   Union[str, Any]						,							_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   int						,							):




 """simple docstring"""







 UpperCAmelCase__							 =						os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)

 UpperCAmelCase__							 =						BarkSemanticConfig.from_pretrained(os.path.join(_lowerCAmelCase						,							"config.json"	)	)
 UpperCAmelCase__							 =						BarkCoarseConfig.from_pretrained(os.path.join(_lowerCAmelCase						,							"config.json"	)	)
 UpperCAmelCase__							 =						BarkFineConfig.from_pretrained(os.path.join(_lowerCAmelCase						,							"config.json"	)	)
 UpperCAmelCase__							 =						EncodecConfig.from_pretrained("facebook/encodec_24khz"	)

 UpperCAmelCase__							 =						BarkSemanticModel.from_pretrained(_lowerCAmelCase	)
 UpperCAmelCase__							 =						BarkCoarseModel.from_pretrained(_lowerCAmelCase	)
 UpperCAmelCase__							 =						BarkFineModel.from_pretrained(_lowerCAmelCase	)
 UpperCAmelCase__							 =						EncodecModel.from_pretrained("facebook/encodec_24khz"	)

 UpperCAmelCase__							 =						BarkConfig.from_sub_model_configs(
     _lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)

 UpperCAmelCase__							 =						BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config						,							coarseAcoustic.generation_config						,							fineAcoustic.generation_config	)

 UpperCAmelCase__							 =						BarkModel(_lowerCAmelCase	)

 UpperCAmelCase__							 =						semantic
 UpperCAmelCase__							 =						coarseAcoustic
 UpperCAmelCase__							 =						fineAcoustic
 UpperCAmelCase__							 =						codec

 UpperCAmelCase__							 =						bark_generation_config

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


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

  _lowerCAmelCase						:   Tuple		=   parser.parse_args()

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











import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase						:   Tuple		=   logging.get_logger(__name__)


_lowerCAmelCase						:   Union[str, Any]		=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase						:   str		=   {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase						:   List[Any]		=   {
    "allenai/led-base-16384": 1_6_3_8_4,
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_        =		LEDTokenizer
      UpperCAmelCase_        =		["""input_ids""", """attention_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :int="replace" ,      lowerCamelCase   :List[Any]="<s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :int="<s>" ,      lowerCamelCase   :Optional[Any]="<unk>" ,      lowerCamelCase   :Dict="<pad>" ,      lowerCamelCase   :Tuple="<mask>" ,      lowerCamelCase   :str=False ,      lowerCamelCase   :Union[str, Any]=True ,      **lowerCamelCase   :List[str] ,      )     ->			Tuple:
       super().__init__(
           lowerCamelCase ,      lowerCamelCase ,      tokenizer_file=lowerCamelCase ,      errors=lowerCamelCase ,      bos_token=lowerCamelCase ,      eos_token=lowerCamelCase ,      sep_token=lowerCamelCase ,      cls_token=lowerCamelCase ,      unk_token=lowerCamelCase ,      pad_token=lowerCamelCase ,      mask_token=lowerCamelCase ,      add_prefix_space=lowerCamelCase ,      trim_offsets=lowerCamelCase ,      **lowerCamelCase ,      )

       UpperCAmelCase__							 =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
       if pre_tok_state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
        UpperCAmelCase__							 =						getattr(lowerCamelCase ,      pre_tok_state.pop("type"      )      )
        UpperCAmelCase__							 =						add_prefix_space
        UpperCAmelCase__							 =						pre_tok_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCAmelCase__							 =						"post_processor"
       UpperCAmelCase__							 =						getattr(self.backend_tokenizer ,      lowerCamelCase ,      lowerCamelCase      )
       if tokenizer_component_instance:
        UpperCAmelCase__							 =						json.loads(tokenizer_component_instance.__getstate__()      )

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
         UpperCAmelCase__							 =						tuple(state["sep"]      )
        if "cls" in state:
         UpperCAmelCase__							 =						tuple(state["cls"]      )

        UpperCAmelCase__							 =						False

        if state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
         UpperCAmelCase__							 =						add_prefix_space
         UpperCAmelCase__							 =						True

        if state.get("trim_offsets" ,      lowerCamelCase      ) != trim_offsets:
         UpperCAmelCase__							 =						trim_offsets
         UpperCAmelCase__							 =						True

        if changes_to_apply:
         UpperCAmelCase__							 =						getattr(lowerCamelCase ,      state.pop("type"      )      )
         UpperCAmelCase__							 =						component_class(**lowerCamelCase      )
         setattr(self.backend_tokenizer ,      lowerCamelCase ,      lowerCamelCase      )
      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			str:
       if self._mask_token is None:
        if self.verbose:
         logger.error("Using mask_token, but it is not set yet."      )
        return None
       return str(self._mask_token      )
      @mask_token.setter
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :List[str]      )     ->			Dict:
       UpperCAmelCase__							 =						AddedToken(lowerCamelCase ,      lstrip=lowerCamelCase ,      rstrip=lowerCamelCase      ) if isinstance(lowerCamelCase ,      lowerCamelCase      ) else value
       UpperCAmelCase__							 =						value
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :int      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						kwargs.get("is_split_into_words" ,      lowerCamelCase      )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."      )

       return super()._batch_encode_plus(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :int ,      *lowerCamelCase   :Optional[Any] ,      **lowerCamelCase   :Optional[int]      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						kwargs.get("is_split_into_words" ,      lowerCamelCase      )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."      )

       return super()._encode_plus(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:
       UpperCAmelCase__							 =						self._tokenizer.model.save(lowerCamelCase ,      name=lowerCamelCase      )
       return tuple(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[Any]=None      )     ->			Dict:
       UpperCAmelCase__							 =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :List[int] ,      lowerCamelCase   :Optional[List[int]] = None      )     ->			List[int]:
       UpperCAmelCase__							 =						[self.sep_token_id]
       UpperCAmelCase__							 =						[self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep      ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]







      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Union[Dict[str, EncodedInput], BatchEncoding] ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Optional[bool] = None ,      )     ->			dict:
       UpperCAmelCase__							 =						super()._pad(
           encoded_inputs=lowerCamelCase ,      max_length=lowerCamelCase ,      padding_strategy=lowerCamelCase ,      pad_to_multiple_of=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      )

       # Load from model defaults
       if return_attention_mask is None:
        UpperCAmelCase__							 =						"attention_mask" in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
        UpperCAmelCase__							 =						encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        UpperCAmelCase__							 =						len(encoded_inputs["global_attention_mask"]      ) != len(lowerCamelCase      )

        if needs_to_be_padded:
         UpperCAmelCase__							 =						len(lowerCamelCase      ) - len(encoded_inputs["global_attention_mask"]      )

         if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          UpperCAmelCase__							 =						(
              encoded_inputs["global_attention_mask"] + [-1] * difference
          )
         elif self.padding_side == "left":
          UpperCAmelCase__							 =						[-1] * difference + encoded_inputs[
              "global_attention_mask"
          ]
         else:
          raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

       return encoded_inputs





