




"""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)







"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      ['''image_processor''', '''tokenizer''']
 UpperCAmelCase       :	Optional[int]     =      '''ViTImageProcessor'''
 UpperCAmelCase       :	int     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			Dict						):
       _A     =  None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'       ,				_UpperCAmelCase       ,				)
             _A     =  kwargs.pop('feature_extractor'						)

       _A     =  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__(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
             _A     =  self.tokenizer(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if images is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None and images is not None:
             _A     =  {
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             _A     =  image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A     =  {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase						)       ,				tensor_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor_class



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor

