




"""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							      =     logging.get_logger(__name__)

set_seed(770)


a							      =     {
    '''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							      =     {
    '''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							      =     os.path.dirname(os.path.abspath(__file__))
a							      =     os.path.join(os.path.expanduser('''~'''), '''.cache''')
a							      =     os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')






def 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   str=False				)       ->   List[str]:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   int				)       ->   str:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   Dict							,    _snake_case				:   Dict=False							,    _snake_case				:   str="text"				)       ->   Optional[int]:






      '''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(_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(_snake_case							,    map_location=_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=_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(_snake_case				):
                  # replace part of the key with corresponding layer name in HF implementation
                  _A     =  k[len(_snake_case				) :]
                  for old_layer_name in new_layer_name_dict:
                        _A     =  new_k.replace(_snake_case							,    new_layer_name_dict[old_layer_name]				)

                  _A     =  state_dict.pop(_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(_snake_case				) != 0:
            raise ValueError(F'''extra keys found: {extra_keys}'''				)
      if len(_snake_case				) != 0:
            raise ValueError(F'''missing keys: {missing_keys}'''				)
      model.load_state_dict(_snake_case							,    strict=_snake_case				)
      _A     =  model.num_parameters(exclude_embeddings=_snake_case				)
      _A     =  checkpoint['best_val_loss'].item()
      logger.info(F'''model loaded: {round(n_params/1E6							,    1				)}M params, {round(_snake_case							,    3				)} loss'''				)
      model.eval()
      model.to(_snake_case				)
      del checkpoint, state_dict

      return model






def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   int=False							,    _snake_case				:   Optional[int]="text"				)       ->   List[Any]:






      '''simple docstring'''


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

      _A     =  'cpu'  # do conversion on cpu

      _A     =  _get_ckpt_path(_snake_case							,    use_small=_snake_case				)
      _A     =  _load_model(_snake_case							,    _snake_case							,    model_type=_snake_case							,    use_small=_snake_case				)

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

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

      if model.num_parameters(exclude_embeddings=_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(2_56							,    (batch_size, sequence_length)							,    dtype=torch.int				)
            _A     =  bark_model(_snake_case				)[0]

            _A     =  model(_snake_case				)

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

      else:
            _A     =  3
            _A     =  8
            _A     =  torch.randint(2_56							,    (batch_size, sequence_length, n_codes_total)							,    dtype=torch.int				)

            _A     =  model(_snake_case							,    _snake_case				)
            _A     =  bark_model(_snake_case							,    _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(_snake_case				).mkdir(exist_ok=_snake_case				)
      model.save_pretrained(_snake_case				)






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   Union[str, Any]							,    _snake_case				:   str							,    _snake_case				:   Optional[Any]							,    _snake_case				:   Dict							,    _snake_case				:   Optional[Any]							,    )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  os.path.join(_snake_case							,    _snake_case				)

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

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

      _A     =  BarkConfig.from_sub_model_configs(
          _snake_case							,    _snake_case							,    _snake_case							,    _snake_case				)

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

      _A     =  BarkModel(_snake_case				)

      _A     =  semantic
      _A     =  coarseAcoustic
      _A     =  fineAcoustic
      _A     =  codec

      _A     =  bark_generation_config

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


if __name__ == "__main__":
       a							      =     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							      =     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 tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


a__: Union[str, Any]							   =						'hf-internal-testing/tiny-random-bert'
a__: Optional[int]							   =						os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
a__: List[str]							   =						'9b8c223d42b2188cb49d29af482996f9d0f3e5a6'




class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		cached_file(__lowerCamelCase,__lowerCamelCase					)
        # Should have downloaded the file in here
        self.assertTrue(os.path.isdir(__lowerCamelCase					)					)
        # Cache should contain at least those three subfolders:
        for subfolder in ["blobs", "refs", "snapshots"]:
               self.assertTrue(os.path.isdir(os.path.join(__lowerCamelCase,__lowerCamelCase					)					)					)
        with open(os.path.join(__lowerCamelCase,'''refs''','''main'''					)					) as f:
               A__		   =		f.read()
        self.assertEqual(__lowerCamelCase,os.path.join(__lowerCamelCase,'''snapshots''',__lowerCamelCase,__lowerCamelCase					)					)
        self.assertTrue(os.path.isfile(__lowerCamelCase					)					)

        # File is cached at the same place the second time.
        A__		   =		cached_file(__lowerCamelCase,__lowerCamelCase					)
        self.assertEqual(__lowerCamelCase,__lowerCamelCase					)

        # Using a specific revision to test the full commit hash.
        A__		   =		cached_file(__lowerCamelCase,__lowerCamelCase,revision='''9b8c223'''					)
        self.assertEqual(__lowerCamelCase,os.path.join(__lowerCamelCase,'''snapshots''',__lowerCamelCase,__lowerCamelCase					)					)



 def 							UpperCamelCase     (				self					):
        with self.assertRaisesRegex(__lowerCamelCase,'''is not a valid model identifier'''					):
               A__		   =		cached_file('''tiny-random-bert''',__lowerCamelCase					)

        with self.assertRaisesRegex(__lowerCamelCase,'''is not a valid git identifier'''					):
               A__		   =		cached_file(__lowerCamelCase,__lowerCamelCase,revision='''aaaa'''					)

        with self.assertRaisesRegex(__lowerCamelCase,'''does not appear to have a file named'''					):
               A__		   =		cached_file(__lowerCamelCase,'''conf'''					)



 def 							UpperCamelCase     (				self					):
        with self.assertRaisesRegex(__lowerCamelCase,'''does not appear to have a file named'''					):
               A__		   =		cached_file(__lowerCamelCase,'''conf'''					)

        with open(os.path.join(__lowerCamelCase,'''refs''','''main'''					)					) as f:
               A__		   =		f.read()
        self.assertTrue(os.path.isfile(os.path.join(__lowerCamelCase,'''.no_exist''',__lowerCamelCase,'''conf'''					)					)					)

        A__		   =		cached_file(__lowerCamelCase,'''conf''',_raise_exceptions_for_missing_entries=__lowerCamelCase					)
        self.assertIsNone(__lowerCamelCase					)

        A__		   =		cached_file(__lowerCamelCase,'''conf''',local_files_only=__lowerCamelCase,_raise_exceptions_for_missing_entries=__lowerCamelCase					)
        self.assertIsNone(__lowerCamelCase					)

        A__		   =		mock.Mock()
        A__		   =		500
        A__		   =		{}
        A__		   =		HTTPError
        A__		   =		{}

        # Under the mock environment we get a 500 error when trying to reach the tokenizer.
        with mock.patch('''requests.Session.request''',return_value=__lowerCamelCase					) as mock_head:
               A__		   =		cached_file(__lowerCamelCase,'''conf''',_raise_exceptions_for_connection_errors=__lowerCamelCase					)
               self.assertIsNone(__lowerCamelCase					)
               # This check we did call the fake head request
               mock_head.assert_called()



 def 							UpperCamelCase     (				self					):
        self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''',__lowerCamelCase					)					)
        self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''',__lowerCamelCase					)					)
        self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''',__lowerCamelCase					)					)



 def 							UpperCamelCase     (				self					):
        # `get_file_from_repo` returns None if the file does not exist
        self.assertIsNone(get_file_from_repo('''bert-base-cased''','''ahah.txt'''					)					)

        # The function raises if the repository does not exist.
        with self.assertRaisesRegex(__lowerCamelCase,'''is not a valid model identifier'''					):
               get_file_from_repo('''bert-base-case''',__lowerCamelCase					)

        # The function raises if the revision does not exist.
        with self.assertRaisesRegex(__lowerCamelCase,'''is not a valid git identifier'''					):
               get_file_from_repo('''bert-base-cased''',__lowerCamelCase,revision='''ahaha'''					)

        A__		   =		get_file_from_repo('''bert-base-cased''',__lowerCamelCase					)
        # The name is the cached name which is not very easy to test, so instead we load the content.
        A__		   =		json.loads(open(__lowerCamelCase,'''r'''					).read()					)
        self.assertEqual(config['''hidden_size'''],768					)




 def 							UpperCamelCase     (				self					):
        with tempfile.TemporaryDirectory() as tmp_dir:
               A__		   =		Path(__lowerCamelCase					) / '''a.txt'''
               filename.touch()
               self.assertEqual(get_file_from_repo(__lowerCamelCase,'''a.txt'''					),str(__lowerCamelCase					)					)

               self.assertIsNone(get_file_from_repo(__lowerCamelCase,'''b.txt'''					)					)





