




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()
SCREAMING_SNAKE_CASE__       :      Any  =   logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE__       :      Tuple  =   {
    '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',
}


SCREAMING_SNAKE_CASE__       :      List[Any]  =   {
    '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',
    },
}

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> Any:
       lowerCamelCase   :      List[str]			 =					model_type
       if use_small:
              key += "_small"
       return os.path.join(_SCREAMING_SNAKE_CASE ,REMOTE_MODEL_PATHS[key]["file_name"]    )

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

       # convert Bark model arguments to HF Bark model arguments
       lowerCamelCase   :      Union[str, Any]			 =					model_args.pop("n_head"    )
       lowerCamelCase   :      List[Any]			 =					model_args.pop("n_embd"    )
       lowerCamelCase   :      List[Any]			 =					model_args.pop("n_layer"    )

       lowerCamelCase   :      int			 =					ConfigClass(**checkpoint["model_args"]    )
       lowerCamelCase   :      Optional[Any]			 =					ModelClass(config=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					GenerationConfigClass()

       lowerCamelCase   :      Dict			 =					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(_SCREAMING_SNAKE_CASE    ):
                     # replace part of the key with corresponding layer name in HF implementation
                     lowerCamelCase   :      Union[str, Any]			 =					k[len(_SCREAMING_SNAKE_CASE    ) :]
                     for old_layer_name in new_layer_name_dict:
                            lowerCamelCase   :      List[Any]			 =					new_k.replace(_SCREAMING_SNAKE_CASE ,new_layer_name_dict[old_layer_name]    )

                     lowerCamelCase   :      int			 =					state_dict.pop(_SCREAMING_SNAKE_CASE    )

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

       lowerCamelCase   :      Optional[Any]			 =					"cpu"  # do conversion on cpu

       lowerCamelCase   :      Tuple			 =					_get_ckpt_path(_SCREAMING_SNAKE_CASE ,use_small=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[Any]			 =					_load_model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,model_type=_SCREAMING_SNAKE_CASE ,use_small=_SCREAMING_SNAKE_CASE    )

       # load bark initial model
       lowerCamelCase   :      Optional[int]			 =					_bark_load_model(_SCREAMING_SNAKE_CASE ,"cpu" ,model_type=_SCREAMING_SNAKE_CASE ,use_small=_SCREAMING_SNAKE_CASE    )

       if model_type == "text":
              lowerCamelCase   :      int			 =					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
       lowerCamelCase   :      Optional[int]			 =					5
       lowerCamelCase   :      int			 =					10

       if model_type in ["text", "coarse"]:
              lowerCamelCase   :      Union[str, Any]			 =					torch.randint(256 ,(batch_size, sequence_length) ,dtype=torch.int    )
              lowerCamelCase   :      Tuple			 =					bark_model(_SCREAMING_SNAKE_CASE    )[0]

              lowerCamelCase   :      Tuple			 =					model(_SCREAMING_SNAKE_CASE    )

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

       else:
              lowerCamelCase   :      str			 =					3
              lowerCamelCase   :      Union[str, Any]			 =					8
              lowerCamelCase   :      Optional[int]			 =					torch.randint(256 ,(batch_size, sequence_length, n_codes_total) ,dtype=torch.int    )

              lowerCamelCase   :      int			 =					model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      List[str]			 =					bark_model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

              lowerCamelCase   :      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(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,)	-> List[str]:
       lowerCamelCase   :      Dict			 =					os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      int			 =					BarkSemanticConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE ,"config.json"    )    )
       lowerCamelCase   :      Optional[int]			 =					BarkCoarseConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE ,"config.json"    )    )
       lowerCamelCase   :      Tuple			 =					BarkFineConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE ,"config.json"    )    )
       lowerCamelCase   :      Tuple			 =					EncodecConfig.from_pretrained("facebook/encodec_24khz"    )

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

       lowerCamelCase   :      Union[str, Any]			 =					BarkConfig.from_sub_model_configs(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config ,coarseAcoustic.generation_config ,fineAcoustic.generation_config    )

       lowerCamelCase   :      List[str]			 =					BarkModel(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					semantic
       lowerCamelCase   :      Optional[Any]			 =					coarseAcoustic
       lowerCamelCase   :      Union[str, Any]			 =					fineAcoustic
       lowerCamelCase   :      str			 =					codec

       lowerCamelCase   :      Tuple			 =					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__":
       SCREAMING_SNAKE_CASE__       :      Optional[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.')

       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()

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












"""simple docstring"""


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["image_processor", "tokenizer"]
 lowercase__ 					=					"AutoImageProcessor"
 lowercase__ 					=					"AutoTokenizer"


 def __init__(				self:		List[str],      a_:		List[str]=None,      a_:		Tuple=None,      **a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		str								=  None
   if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead.""",      a_,      )
     _snake_case							:		str								=  kwargs.pop("""feature_extractor"""		)

   _snake_case							:		Union[str, Any]								=  image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""		)
   if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""		)

   super().__init__(a_,      a_		)
   _snake_case							:		Dict								=  self.image_processor
   _snake_case							:		Any								=  False


 def __call__(				self:		Any,      *a_:		Any,      **a_:		Tuple		):




   '''simple docstring'''



   if self._in_target_context_manager:
     return self.current_processor(*a_,      **a_		)

   _snake_case							:		Dict								=  kwargs.pop("""images""",      a_		)
   _snake_case							:		Optional[Any]								=  kwargs.pop("""text""",      a_		)
   if len(a_		) > 0:
     _snake_case							:		Optional[int]								=  args[0]
     _snake_case							:		Tuple								=  args[1:]

   if images is None and text is None:
     raise ValueError("""You need to specify either an `images` or `text` input to process."""		)

   if images is not None:
     _snake_case							:		Tuple								=  self.image_processor(a_,      *a_,      **a_		)
   if text is not None:
     _snake_case							:		Tuple								=  self.tokenizer(a_,      **a_		)

   if text is None:
     return inputs
   elif images is None:
     return encodings
   else:
     _snake_case							:		List[str]								=  encodings["""input_ids"""]
     return inputs


 def  UpperCamelCase_   (				self:		Optional[int],      *a_:		Tuple,      **a_:		List[str]		):




   '''simple docstring'''



   return self.tokenizer.batch_decode(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		int,      *a_:		List[str],      **a_:		int		):




   '''simple docstring'''



   return self.tokenizer.decode(*a_,      **a_		)


 @contextmanager
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   warnings.warn(
       """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
       """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
       """your images inputs, or in a separate call."""		)
   _snake_case							:		Any								=  True
   _snake_case							:		Optional[int]								=  self.tokenizer
   yield
   _snake_case							:		int								=  self.image_processor
   _snake_case							:		Optional[int]								=  False


 def  UpperCamelCase_   (				self:		Dict,      a_:		Optional[Any],      a_:		str=False,      a_:		Optional[Any]=None		):




   '''simple docstring'''



   if added_vocab is None:
     _snake_case							:		Dict								=  self.tokenizer.get_added_vocab()

   _snake_case							:		str								=  {}

   while tokens:
     _snake_case							:		Union[str, Any]								=  re.search(r"""<s_(.*?)>""",      a_,      re.IGNORECASE		)
     if start_token is None:
       break
     _snake_case							:		List[Any]								=  start_token.group(1		)
     _snake_case							:		str								=  re.search(rf"</s_{key}>",      a_,      re.IGNORECASE		)
     _snake_case							:		Dict								=  start_token.group()
     if end_token is None:
       _snake_case							:		List[Any]								=  tokens.replace(a_,      """"""		)
     else:
       _snake_case							:		List[str]								=  end_token.group()
       _snake_case							:		str								=  re.escape(a_		)
       _snake_case							:		str								=  re.escape(a_		)
       _snake_case							:		Union[str, Any]								=  re.search(f"{start_token_escaped}(.*?){end_token_escaped}",      a_,      re.IGNORECASE		)
       if content is not None:
         _snake_case							:		int								=  content.group(1		).strip()
         if r"<s_" in content and r"</s_" in content:  # non-leaf node
           _snake_case							:		List[Any]								=  self.tokenajson(a_,      is_inner_value=a_,      added_vocab=a_		)
           if value:
             if len(a_		) == 1:
               _snake_case							:		List[str]								=  value[0]
             _snake_case							:		List[str]								=  value
         else:  # leaf nodes
           _snake_case							:		Tuple								=  []
           for leaf in content.split(r"""<sep/>"""		):
             _snake_case							:		Tuple								=  leaf.strip()
             if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
               _snake_case							:		int								=  leaf[1:-2]  # for categorical special tokens
             output[key].append(a_		)
           if len(output[key]		) == 1:
             _snake_case							:		int								=  output[key][0]

       _snake_case							:		Any								=  tokens[tokens.find(a_		) + len(a_		) :].strip()
       if tokens[:6] == r"<sep/>":  # non-leaf nodes
         return [output] + self.tokenajson(tokens[6:],      is_inner_value=a_,      added_vocab=a_		)

   if len(a_		):
     return [output] if is_inner_value else output
   else:
     return [] if is_inner_value else {"text_sequence": tokens}


 @property
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   warnings.warn(
       """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""",      a_,      )
   return self.image_processor_class


 @property
 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   warnings.warn(
       """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""",      a_,      )
   return self.image_processor


