






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__				   =		logging.get_logger(__name__)

set_seed(770)


UpperCAmelCase__				   =		{
    "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__				   =		{
    "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__				   =		os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase__				   =		os.path.join(os.path.expanduser("~"), ".cache")
UpperCAmelCase__				   =		os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def 						_a    (      a :List[str]	,							a :Union[str, Any]=False )							->			List[Any]:
 a		  =      model_type
 if use_small:
  key += "_small"
 return os.path.join(a	,							REMOTE_MODEL_PATHS[key]['''file_name'''] )
def 						_a    (      a :Any	,							a :Optional[Any] )							->			Tuple:
 os.makedirs(a	,							exist_ok=a )
 hf_hub_download(repo_id=a	,							filename=a	,							local_dir=a )
def 						_a    (      a :Union[str, Any]	,							a :Optional[Any]	,							a :Optional[Any]=False	,							a :Optional[Any]="text" )							->			List[str]:
 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(a ):
  logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
  _download(model_info['''repo_id''']	,							model_info['''file_name'''] )
 a		  =      torch.load(a	,							map_location=a )
 # 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=a )
 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(a ):
   # replace part of the key with corresponding layer name in HF implementation
   a		  =      k[len(a ) :]
   for old_layer_name in new_layer_name_dict:
    a		  =      new_k.replace(a	,							new_layer_name_dict[old_layer_name] )

   a		  =      state_dict.pop(a )

 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(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 )
 a		  =      model.num_parameters(exclude_embeddings=a )
 a		  =      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 						_a    (      a :Any	,							a :List[str]=False	,							a :Dict="text" )							->			Any:
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 a		  =      '''cpu'''  # do conversion on cpu

 a		  =      _get_ckpt_path(a	,							use_small=a )
 a		  =      _load_model(a	,							a	,							model_type=a	,							use_small=a )

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

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

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

  a		  =      model(a )

  # 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(a	,							a )
  a		  =      bark_model(a	,							a )

  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(a ).mkdir(exist_ok=a )
 model.save_pretrained(a )
def 						_a    (      a :int	,							a :Optional[Any]	,							a :Dict	,							a :List[str]	,							a :Tuple	,							a :Optional[int]	,							)							->			Union[str, Any]:
 a		  =      os.path.join(a	,							a )

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

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

 a		  =      BarkConfig.from_sub_model_configs(
     a	,							a	,							a	,							a )

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

 a		  =      BarkModel(a )

 a		  =      semantic
 a		  =      coarseAcoustic
 a		  =      fineAcoustic
 a		  =      codec

 a		  =      bark_generation_config

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


if __name__ == "__main__":
     UpperCAmelCase__				   =		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__				   =		parser.parse_args()

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










import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      tempfile.mkdtemp()

       # fmt: off
       a		  =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
       # fmt: on
       a		  =      os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''vocab_file''']  )
       with open(self.vocab_file ,					'''w''' ,					encoding='''utf-8'''  ) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )

       a		  =      {
           '''do_resize''': True,
           '''size''': {'''height''': 18, '''width''': 18},
           '''do_normalize''': True,
           '''image_mean''': [0.5, 0.5, 0.5],
           '''image_std''': [0.5, 0.5, 0.5],
       }
       a		  =      os.path.join(self.tmpdirname ,					__UpperCAmelCase  )
       with open(self.image_processor_file ,					'''w''' ,					encoding='''utf-8'''  ) as fp:
        json.dump(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					**__UpperCAmelCase      :						List[Any]  )    ->int:




       """simple docstring"""


       return BertTokenizer.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					**__UpperCAmelCase      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


       return ViTImageProcessor.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       shutil.rmtree(self.tmpdirname  )







      def       __lowerCAmelCase					(      self      :						str  )    ->Dict:




       """simple docstring"""


       a		  =      [np.random.randint(255 ,					size=(3, 30, 400) ,					dtype=np.uinta  )]

       a		  =      [Image.fromarray(np.moveaxis(__UpperCAmelCase ,					0 ,					-1  )  ) for x in image_inputs]

       return image_inputs







      def       __lowerCAmelCase					(      self      :						Any  )    ->Tuple:




       """simple docstring"""


       a		  =      self.get_tokenizer()
       a		  =      self.get_image_processor()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       processor.save_pretrained(self.tmpdirname  )
       a		  =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname  )

       self.assertEqual(processor.tokenizer.get_vocab() ,					tokenizer.get_vocab()  )
       self.assertIsInstance(processor.tokenizer ,					(BertTokenizer, BertTokenizerFast)  )

       self.assertEqual(processor.image_processor.to_json_string() ,					image_processor.to_json_string()  )
       self.assertIsInstance(processor.image_processor ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Dict:




       """simple docstring"""


       a		  =      VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()  )
       processor.save_pretrained(self.tmpdirname  )

       a		  =      self.get_tokenizer(bos_token='''(BOS)''' ,					eos_token='''(EOS)'''  )
       a		  =      self.get_image_processor(do_normalize=__UpperCAmelCase ,					padding_value=1.0  )

       a		  =      VisionTextDualEncoderProcessor.from_pretrained(
           self.tmpdirname ,					bos_token='''(BOS)''' ,					eos_token='''(EOS)''' ,					do_normalize=__UpperCAmelCase ,					padding_value=1.0  )

       self.assertEqual(processor.tokenizer.get_vocab() ,					tokenizer_add_kwargs.get_vocab()  )
       self.assertIsInstance(processor.tokenizer ,					(BertTokenizer, BertTokenizerFast)  )

       self.assertEqual(processor.image_processor.to_json_string() ,					image_processor_add_kwargs.to_json_string()  )
       self.assertIsInstance(processor.image_processor ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Dict:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      self.prepare_image_inputs()

       a		  =      image_processor(__UpperCAmelCase ,					return_tensors='''np'''  )
       a		  =      processor(images=__UpperCAmelCase ,					return_tensors='''np'''  )

       for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum() ,					input_processor[key].sum() ,					delta=1e-2  )







      def       __lowerCAmelCase					(      self      :						List[str]  )    ->str:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''

       a		  =      processor(text=__UpperCAmelCase  )

       a		  =      tokenizer(__UpperCAmelCase  )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key] ,					encoded_processor[key]  )







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''
       a		  =      self.prepare_image_inputs()

       a		  =      processor(text=__UpperCAmelCase ,					images=__UpperCAmelCase  )

       self.assertListEqual(list(inputs.keys()  ) ,					['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']  )

       # test if it raises when no input is passed
       with self.assertRaises(__UpperCAmelCase  ):
        processor()







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       a		  =      processor.batch_decode(__UpperCAmelCase  )
       a		  =      tokenizer.batch_decode(__UpperCAmelCase  )

       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Dict:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''
       a		  =      self.prepare_image_inputs()

       a		  =      processor(text=__UpperCAmelCase ,					images=__UpperCAmelCase  )

       self.assertListEqual(list(inputs.keys()  ) ,					processor.model_input_names  )


