






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()
lowerCamelCase     :							Optional[Any]    =logging.get_logger(__name__)

set_seed(770)


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


lowerCamelCase     :							Tuple    ={
    '''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''',
    },
}

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=False      )			->     str:
     UpperCamelCase__      :       int	    =  model_type
     if use_small:
          key += "_small"
     return os.path.join(__lowerCAmelCase ,   REMOTE_MODEL_PATHS[key]["file_name"]      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Tuple:
     os.makedirs(__lowerCAmelCase ,   exist_ok=__lowerCAmelCase      )
     hf_hub_download(repo_id=__lowerCAmelCase ,   filename=__lowerCAmelCase ,   local_dir=__lowerCAmelCase      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=False ,   __lowerCAmelCase="text"      )			->     List[Any]:
     if model_type == "text":
          UpperCamelCase__      :       Tuple	    =  BarkSemanticModel
          UpperCamelCase__      :       Optional[Any]	    =  BarkSemanticConfig
          UpperCamelCase__      :       Any	    =  BarkSemanticGenerationConfig
     elif model_type == "coarse":
          UpperCamelCase__      :       Dict	    =  BarkCoarseModel
          UpperCamelCase__      :       Optional[int]	    =  BarkCoarseConfig
          UpperCamelCase__      :       List[Any]	    =  BarkCoarseGenerationConfig
     elif model_type == "fine":
          UpperCamelCase__      :       List[str]	    =  BarkFineModel
          UpperCamelCase__      :       int	    =  BarkFineConfig
          UpperCamelCase__      :       Optional[Any]	    =  BarkFineGenerationConfig
     else:
          raise NotImplementedError()
     UpperCamelCase__      :       List[str]	    =  f'{model_type}_small' if use_small else model_type
     UpperCamelCase__      :       Dict	    =  REMOTE_MODEL_PATHS[model_key]
     if not os.path.exists(__lowerCAmelCase      ):
          logger.info(f'{model_type} model not found, downloading into `{CACHE_DIR}`.'      )
          _download(model_info["repo_id"] ,   model_info["file_name"]      )
     UpperCamelCase__      :       str	    =  torch.load(__lowerCAmelCase ,   map_location=__lowerCAmelCase      )
     # this is a hack
     UpperCamelCase__      :       Optional[Any]	    =  checkpoint["model_args"]
     if "input_vocab_size" not in model_args:
          UpperCamelCase__      :       Optional[int]	    =  model_args["vocab_size"]
          UpperCamelCase__      :       Optional[Any]	    =  model_args["vocab_size"]
          del model_args["vocab_size"]

     # convert Bark model arguments to HF Bark model arguments
     UpperCamelCase__      :       List[str]	    =  model_args.pop("n_head"      )
     UpperCamelCase__      :       Optional[int]	    =  model_args.pop("n_embd"      )
     UpperCamelCase__      :       List[Any]	    =  model_args.pop("n_layer"      )

     UpperCamelCase__      :       int	    =  ConfigClass(**checkpoint["model_args"]      )
     UpperCamelCase__      :       Union[str, Any]	    =  ModelClass(config=__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[Any]	    =  GenerationConfigClass()

     UpperCamelCase__      :       Union[str, Any]	    =  model_generation_config
     UpperCamelCase__      :       Optional[Any]	    =  checkpoint["model"]
     # fixup checkpoint
     UpperCamelCase__      :       Tuple	    =  "_orig_mod."
     for k, v in list(state_dict.items()      ):
          if k.startswith(__lowerCAmelCase      ):
               # replace part of the key with corresponding layer name in HF implementation
               UpperCamelCase__      :       List[str]	    =  k[len(__lowerCAmelCase      ) :]
               for old_layer_name in new_layer_name_dict:
                    UpperCamelCase__      :       str	    =  new_k.replace(__lowerCAmelCase ,   new_layer_name_dict[old_layer_name]      )

               UpperCamelCase__      :       str	    =  state_dict.pop(__lowerCAmelCase      )

     UpperCamelCase__      :       Dict	    =  set(state_dict.keys()      ) - set(model.state_dict().keys()      )
     UpperCamelCase__      :       int	    =  {k for k in extra_keys if not k.endswith(".attn.bias"      )}
     UpperCamelCase__      :       Optional[Any]	    =  set(model.state_dict().keys()      ) - set(state_dict.keys()      )
     UpperCamelCase__      :       int	    =  {k for k in missing_keys if not k.endswith(".attn.bias"      )}
     if len(__lowerCAmelCase      ) != 0:
          raise ValueError(f'extra keys found: {extra_keys}'      )
     if len(__lowerCAmelCase      ) != 0:
          raise ValueError(f'missing keys: {missing_keys}'      )
     model.load_state_dict(__lowerCAmelCase ,   strict=__lowerCAmelCase      )
     UpperCamelCase__      :       List[Any]	    =  model.num_parameters(exclude_embeddings=__lowerCAmelCase      )
     UpperCamelCase__      :       str	    =  checkpoint["best_val_loss"].item()
     logger.info(f'model loaded: {round(n_params/1E6 ,   1      )}M params, {round(__lowerCAmelCase ,   3      )} loss'      )
     model.eval()
     model.to(__lowerCAmelCase      )
     del checkpoint, state_dict

     return model







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=False ,   __lowerCAmelCase="text"      )			->     str:
     if model_type not in ("text", "coarse", "fine"):
          raise NotImplementedError()

     UpperCamelCase__      :       List[Any]	    =  "cpu"  # do conversion on cpu

     UpperCamelCase__      :       int	    =  _get_ckpt_path(__lowerCAmelCase ,   use_small=__lowerCAmelCase      )
     UpperCamelCase__      :       Dict	    =  _load_model(__lowerCAmelCase ,   __lowerCAmelCase ,   model_type=__lowerCAmelCase ,   use_small=__lowerCAmelCase      )

     # load bark initial model
     UpperCamelCase__      :       List[str]	    =  _bark_load_model(__lowerCAmelCase ,   "cpu" ,   model_type=__lowerCAmelCase ,   use_small=__lowerCAmelCase      )

     if model_type == "text":
          UpperCamelCase__      :       Dict	    =  bark_model["model"]

     if model.num_parameters(exclude_embeddings=__lowerCAmelCase      ) != 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
     UpperCamelCase__      :       int	    =  5
     UpperCamelCase__      :       Optional[int]	    =  10

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

          UpperCamelCase__      :       Any	    =  model(__lowerCAmelCase      )

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

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

          UpperCamelCase__      :       Union[str, Any]	    =  model(__lowerCAmelCase ,   __lowerCAmelCase      )
          UpperCamelCase__      :       Optional[Any]	    =  bark_model(__lowerCAmelCase ,   __lowerCAmelCase      )

          UpperCamelCase__      :       Dict	    =  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(__lowerCAmelCase      ).mkdir(exist_ok=__lowerCAmelCase      )
     model.save_pretrained(__lowerCAmelCase      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   )			->     Dict:
     UpperCamelCase__      :       Dict	    =  os.path.join(__lowerCAmelCase ,   __lowerCAmelCase      )

     UpperCamelCase__      :       Optional[Any]	    =  BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase ,   "config.json"      )      )
     UpperCamelCase__      :       Optional[Any]	    =  BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase ,   "config.json"      )      )
     UpperCamelCase__      :       Dict	    =  BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase ,   "config.json"      )      )
     UpperCamelCase__      :       Optional[int]	    =  EncodecConfig.from_pretrained("facebook/encodec_24khz"      )

     UpperCamelCase__      :       Dict	    =  BarkSemanticModel.from_pretrained(__lowerCAmelCase      )
     UpperCamelCase__      :       Union[str, Any]	    =  BarkCoarseModel.from_pretrained(__lowerCAmelCase      )
     UpperCamelCase__      :       Dict	    =  BarkFineModel.from_pretrained(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[Any]	    =  EncodecModel.from_pretrained("facebook/encodec_24khz"      )

     UpperCamelCase__      :       str	    =  BarkConfig.from_sub_model_configs(
         __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )

     UpperCamelCase__      :       List[str]	    =  BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config ,   coarseAcoustic.generation_config ,   fineAcoustic.generation_config      )

     UpperCamelCase__      :       List[Any]	    =  BarkModel(__lowerCAmelCase      )

     UpperCamelCase__      :       int	    =  semantic
     UpperCamelCase__      :       Any	    =  coarseAcoustic
     UpperCamelCase__      :       Tuple	    =  fineAcoustic
     UpperCamelCase__      :       Dict	    =  codec

     UpperCamelCase__      :       Optional[Any]	    =  bark_generation_config

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


if __name__ == "__main__":
 lowerCamelCase     :							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.''')

 lowerCamelCase     :							List[str]    =parser.parse_args()

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







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor





class       __a  (   unittest.TestCase	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[Any]=7							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Dict=18							,       SCREAMING_SNAKE_CASE :       str=30							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4_00							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       int=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  parent
      UpperCamelCase__      :       Dict	    =  batch_size
      UpperCamelCase__      :       Optional[Any]	    =  num_channels
      UpperCamelCase__      :       int	    =  image_size
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       List[str]	    =  max_resolution
      UpperCamelCase__      :       Union[str, Any]	    =  do_resize
      UpperCamelCase__      :       Optional[Any]	    =  size_divisor
      UpperCamelCase__      :       int	    =  do_rescale







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }







@require_torch
@require_vision
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	List[Any]				=			GLPNImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  GLPNImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "resample"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_rescale"						)						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       Image.Image						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       List[Any]	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Optional[Any]	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       np.ndarray						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       str	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       torch.Tensor						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       Dict	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)