






'''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()
__UpperCamelCase   :							Optional[int]         =   logging.get_logger(__name__)

set_seed(770)


__UpperCamelCase   :							Optional[int]         =   {
    """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   :							List[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""",
    },
}

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						Any=False     )	->      List[str]:

   """simple docstring"""
   __a					  =     model_type
   if use_small:
      key += "_small"
   return os.path.join(SCREAMING_SNAKE_CASE__,       REMOTE_MODEL_PATHS[key]['file_name']     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[int]:

   """simple docstring"""
   os.makedirs(SCREAMING_SNAKE_CASE__,       exist_ok=SCREAMING_SNAKE_CASE__     )
   hf_hub_download(repo_id=SCREAMING_SNAKE_CASE__,       filename=SCREAMING_SNAKE_CASE__,       local_dir=SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						Dict,       SCREAMING_SNAKE_CASE__:						Union[str, Any]=False,       SCREAMING_SNAKE_CASE__:						Union[str, Any]="text"     )	->      List[str]:

   """simple docstring"""
   if model_type == "text":
      __a					  =     BarkSemanticModel
      __a					  =     BarkSemanticConfig
      __a					  =     BarkSemanticGenerationConfig
   elif model_type == "coarse":
      __a					  =     BarkCoarseModel
      __a					  =     BarkCoarseConfig
      __a					  =     BarkCoarseGenerationConfig
   elif model_type == "fine":
      __a					  =     BarkFineModel
      __a					  =     BarkFineConfig
      __a					  =     BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   __a					  =     f"""{model_type}_small""" if use_small else model_type
   __a					  =     REMOTE_MODEL_PATHS[model_key]
   if not os.path.exists(SCREAMING_SNAKE_CASE__     ):
      logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""     )
      _download(model_info['repo_id'],       model_info['file_name']     )
   __a					  =     torch.load(SCREAMING_SNAKE_CASE__,       map_location=SCREAMING_SNAKE_CASE__     )
   # this is a hack
   __a					  =     checkpoint['model_args']
   if "input_vocab_size" not in model_args:
      __a					  =     model_args['vocab_size']
      __a					  =     model_args['vocab_size']
      del model_args["vocab_size"]

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

   __a					  =     ConfigClass(**checkpoint['model_args']     )
   __a					  =     ModelClass(config=SCREAMING_SNAKE_CASE__     )
   __a					  =     GenerationConfigClass()

   __a					  =     model_generation_config
   __a					  =     checkpoint['model']
   # fixup checkpoint
   __a					  =     '_orig_mod.'
   for k, v in list(state_dict.items()     ):
      if k.startswith(SCREAMING_SNAKE_CASE__     ):
         # replace part of the key with corresponding layer name in HF implementation
         __a					  =     k[len(SCREAMING_SNAKE_CASE__     ) :]
         for old_layer_name in new_layer_name_dict:
            __a					  =     new_k.replace(SCREAMING_SNAKE_CASE__,       new_layer_name_dict[old_layer_name]     )

         __a					  =     state_dict.pop(SCREAMING_SNAKE_CASE__     )

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

   return model



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						List[Any]=False,       SCREAMING_SNAKE_CASE__:						List[Any]="text"     )	->      Dict:

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

   __a					  =     'cpu'  # do conversion on cpu

   __a					  =     _get_ckpt_path(SCREAMING_SNAKE_CASE__,       use_small=SCREAMING_SNAKE_CASE__     )
   __a					  =     _load_model(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       model_type=SCREAMING_SNAKE_CASE__,       use_small=SCREAMING_SNAKE_CASE__     )

   # load bark initial model
   __a					  =     _bark_load_model(SCREAMING_SNAKE_CASE__,       'cpu',       model_type=SCREAMING_SNAKE_CASE__,       use_small=SCREAMING_SNAKE_CASE__     )

   if model_type == "text":
      __a					  =     bark_model['model']

   if model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE__     ) != 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
   __a					  =     5
   __a					  =     10

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

      __a					  =     model(SCREAMING_SNAKE_CASE__     )

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

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

      __a					  =     model(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
      __a					  =     bark_model(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

      __a					  =     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(SCREAMING_SNAKE_CASE__     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )
   model.save_pretrained(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						List[Any],       SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						Tuple,       )	->      List[Any]:

   """simple docstring"""
   __a					  =     os.path.join(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   __a					  =     BarkSemanticConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__,       'config.json'     )     )
   __a					  =     BarkCoarseConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__,       'config.json'     )     )
   __a					  =     BarkFineConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__,       'config.json'     )     )
   __a					  =     EncodecConfig.from_pretrained('facebook/encodec_24khz'     )

   __a					  =     BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
   __a					  =     BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
   __a					  =     BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
   __a					  =     EncodecModel.from_pretrained('facebook/encodec_24khz'     )

   __a					  =     BarkConfig.from_sub_model_configs(
       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

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

   __a					  =     BarkModel(SCREAMING_SNAKE_CASE__     )

   __a					  =     semantic
   __a					  =     coarseAcoustic
   __a					  =     fineAcoustic
   __a					  =     codec

   __a					  =     bark_generation_config

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


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

 __UpperCamelCase   :							List[Any]         =   parser.parse_args()

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







'''simple docstring'''



import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase   :							int         =   logging.get_logger(__name__)

__UpperCamelCase   :							List[str]         =   {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

__UpperCamelCase   :							List[str]         =   {
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

__UpperCamelCase   :							Union[str, Any]         =   {
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      List[str]:

   """simple docstring"""
   __a					  =     set()
   __a					  =     word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)     )
      __a					  =     char

   __a					  =     set(SCREAMING_SNAKE_CASE__     )
   return pairs



class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =VOCAB_FILES_NAMES
      __a	       =PRETRAINED_VOCAB_FILES_MAP
      __a	       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase="<s>"			, lowerCamelCase="</s>"			, lowerCamelCase="</s>"			, lowerCamelCase="<s>"			, lowerCamelCase="<unk>"			, lowerCamelCase="<pad>"			, lowerCamelCase="<mask>"			, **lowerCamelCase			, )				->str:



         '''simple docstring'''
         super().__init__(
             bos_token=lowerCamelCase			, eos_token=lowerCamelCase			, unk_token=lowerCamelCase			, sep_token=lowerCamelCase			, cls_token=lowerCamelCase			, pad_token=lowerCamelCase			, mask_token=lowerCamelCase			, **lowerCamelCase			, )

         __a					  =     vocab_file
         __a					  =     merges_file

         __a					  =     {}
         __a					  =     0
         __a					  =     1
         __a					  =     2
         __a					  =     3

         self.add_from_file(lowerCamelCase   )

         __a					  =     {v: k for k, v in self.encoder.items()}

         with open(lowerCamelCase			, encoding='utf-8'   ) as merges_handle:
            __a					  =     merges_handle.read().split('\n'   )[:-1]
         __a					  =     [tuple(merge.split()[:-1]   ) for merge in merges]
         __a					  =     dict(zip(lowerCamelCase			, range(len(lowerCamelCase   )   )   )   )
         __a					  =     {}




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''

         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __a					  =     [self.cls_token_id]
         __a					  =     [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = False   )				->List[int]:



         '''simple docstring'''

         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCamelCase			, token_ids_a=lowerCamelCase			, already_has_special_tokens=lowerCamelCase   )

         if token_ids_a is None:
            return [1] + ([0] * len(lowerCamelCase   )) + [1]
         return [1] + ([0] * len(lowerCamelCase   )) + [1, 1] + ([0] * len(lowerCamelCase   )) + [1]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     [self.sep_token_id]
         __a					  =     [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]




      @property
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         return len(self.encoder   )




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         return dict(self.encoder			, **self.added_tokens_encoder   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         if token in self.cache:
            return self.cache[token]
         __a					  =     tuple(lowerCamelCase   )
         __a					  =     tuple(list(word[:-1]   ) + [word[-1] + '</w>']   )
         __a					  =     get_pairs(lowerCamelCase   )

         if not pairs:
            return token

         while True:
            __a					  =     min(lowerCamelCase			, key=lambda lowerCamelCase   : self.bpe_ranks.get(lowerCamelCase			, float('inf'   )   )   )
            if bigram not in self.bpe_ranks:
               break
            __a  ,      __a					  =     bigram
            __a					  =     []
            __a					  =     0
            while i < len(lowerCamelCase   ):
               try:
                  __a					  =     word.index(lowerCamelCase			, lowerCamelCase   )
               except ValueError:
                  new_word.extend(word[i:]   )
                  break
               else:
                  new_word.extend(word[i:j]   )
                  __a					  =     j

               if word[i] == first and i < len(lowerCamelCase   ) - 1 and word[i + 1] == second:
                  new_word.append(first + second   )
                  i += 2
               else:
                  new_word.append(word[i]   )
                  i += 1
            __a					  =     tuple(lowerCamelCase   )
            __a					  =     new_word
            if len(lowerCamelCase   ) == 1:
               break
            else:
               __a					  =     get_pairs(lowerCamelCase   )
         __a					  =     '@@ '.join(lowerCamelCase   )
         __a					  =     word[:-4]
         __a					  =     word
         return word




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     []

         __a					  =     re.findall(r'\S+\n?'			, lowerCamelCase   )

         for token in words:
            split_tokens.extend(list(self.bpe(lowerCamelCase   ).split(' '   )   )   )
         return split_tokens




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return self.encoder.get(lowerCamelCase			, self.encoder.get(self.unk_token   )   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return self.decoder.get(lowerCamelCase			, self.unk_token   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     ' '.join(lowerCamelCase   ).replace('@@ '			, ''   ).strip()
         return out_string




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->Tuple[str]:



         '''simple docstring'''
         if not os.path.isdir(lowerCamelCase   ):
            logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
            return
         __a					  =     os.path.join(
             lowerCamelCase			, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
         __a					  =     os.path.join(
             lowerCamelCase			, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase   ):
            copyfile(self.vocab_file			, lowerCamelCase   )

         if os.path.abspath(self.merges_file   ) != os.path.abspath(lowerCamelCase   ):
            copyfile(self.merges_file			, lowerCamelCase   )

         return out_vocab_file, out_merge_file






      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->str:



         '''simple docstring'''
         if isinstance(lowerCamelCase			, lowerCamelCase   ):
            try:
               with open(lowerCamelCase			, 'r'			, encoding='utf-8'   ) as fd:
                  self.add_from_file(lowerCamelCase   )
            except FileNotFoundError as fnfe:
               raise fnfe
            except UnicodeError:
               raise Exception(F"""Incorrect encoding detected in {f}, please rebuild the dataset"""   )
            return

         __a					  =     f.readlines()
         for lineTmp in lines:
            __a					  =     lineTmp.strip()
            __a					  =     line.rfind(' '   )
            if idx == -1:
               raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''   )
            __a					  =     line[:idx]
            __a					  =     len(self.encoder   )