



'''simple docstring'''


import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class 	UpperCAmelCase	:


 '''simple docstring'''




 @property
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      return self.get_dummy_input()




 @property
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      if self.block_type == "down":
           return (4, 3_2, 1_6, 1_6)
      elif self.block_type == "mid":
           return (4, 3_2, 3_2, 3_2)
      elif self.block_type == "up":
           return (4, 3_2, 6_4, 6_4)

      raise ValueError(f"""\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.""")




 def       lowercase_  (   self							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  ) ->      Optional[Any]:



      """simple docstring"""







      a_       		=4
      a_       		=3_2
      a_       		=(3_2, 3_2)

      a_       		=torch.manual_seed(0)
      a_       		=torch.device(_a)
      a_       		=(batch_size, num_channels) + sizes
      a_       		=randn_tensor(_a							,  generator=_a							,  device=_a)
      a_       		={"hidden_states": hidden_states}

      if include_temb:
           a_       		=1_2_8
           a_       		=randn_tensor((batch_size, temb_channels)							,  generator=_a							,  device=_a)

      if include_res_hidden_states_tuple:
           a_       		=torch.manual_seed(1)
           a_       		=(randn_tensor(_a							,  generator=_a							,  device=_a),)

      if include_encoder_hidden_states:
           a_       		=floats_tensor((batch_size, 3_2, 3_2)).to(_a)

      if include_skip_sample:
           a_       		=randn_tensor(((batch_size, 3) + sizes)							,  generator=_a							,  device=_a)

      return dummy_input




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		={
          "in_channels": 3_2,
          "out_channels": 3_2,
          "temb_channels": 1_2_8,
      }
      if self.block_type == "up":
           a_       		=3_2

      if self.block_type == "mid":
           init_dict.pop("out_channels")

      a_       		=self.dummy_input
      return init_dict, inputs_dict




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**_a)
      unet_block.to(_a)
      unet_block.eval()

      with torch.no_grad():
           a_       		=unet_block(**_a)

      if isinstance(_a							,  _a):
           a_       		=output[0]

      self.assertEqual(output.shape							,  self.output_shape)

      a_       		=output[0, -1, -3:, -3:]
      a_       		=torch.tensor(_a).to(_a)
      assert torch_all_close(output_slice.flatten()							,  _a							,  atol=5e-3)




 @unittest.skipIf(torch_device == "mps"							,  "Training is not supported in mps")
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**_a)
      model.to(_a)
      model.train()
      a_       		=model(**_a)

      if isinstance(_a							,  _a):
           a_       		=output[0]

      a_       		=torch.device(_a)
      a_       		=randn_tensor(output.shape							,  device=_a)
      a_       		=torch.nn.functional.mse_loss(_a							,  _a)
      loss.backward()







'''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()
lowercase							       =			logging.get_logger(__name__)

set_seed(770)


lowercase							       =			{
    '''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''',
}


lowercase							       =			{
    '''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''',
    },
}

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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__=False       ,	lowercase__="text"       ):


     '''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(lowercase__       ):
          logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""       )
          _download(model_info["repo_id"]       ,	model_info["file_name"]       )
     a_       		=torch.load(lowercase__       ,	map_location=lowercase__       )
     # 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=lowercase__       )
     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(lowercase__       ):
               # replace part of the key with corresponding layer name in HF implementation
               a_       		=k[len(lowercase__       ) :]
               for old_layer_name in new_layer_name_dict:
                    a_       		=new_k.replace(lowercase__       ,	new_layer_name_dict[old_layer_name]       )

               a_       		=state_dict.pop(lowercase__       )

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

     return model






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ,	lowercase__="text"       ):


     '''simple docstring'''







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

     a_       		="cpu"  # do conversion on cpu

     a_       		=_get_ckpt_path(lowercase__       ,	use_small=lowercase__       )
     a_       		=_load_model(lowercase__       ,	lowercase__       ,	model_type=lowercase__       ,	use_small=lowercase__       )

     # load bark initial model
     a_       		=_bark_load_model(lowercase__       ,	"cpu"       ,	model_type=lowercase__       ,	use_small=lowercase__       )

     if model_type == "text":
          a_       		=bark_model["model"]

     if model.num_parameters(exclude_embeddings=lowercase__       ) != 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_       		=1_0

     if model_type in ["text", "coarse"]:
          a_       		=torch.randint(2_5_6       ,	(batch_size, sequence_length)       ,	dtype=torch.int       )
          a_       		=bark_model(lowercase__       )[0]

          a_       		=model(lowercase__       )

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

     else:
          a_       		=3
          a_       		=8
          a_       		=torch.randint(2_5_6       ,	(batch_size, sequence_length, n_codes_total)       ,	dtype=torch.int       )

          a_       		=model(lowercase__       ,	lowercase__       )
          a_       		=bark_model(lowercase__       ,	lowercase__       )

          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(lowercase__       ).mkdir(exist_ok=lowercase__       )
     model.save_pretrained(lowercase__       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	):


     '''simple docstring'''







     a_       		=os.path.join(lowercase__       ,	lowercase__       )

     a_       		=BarkSemanticConfig.from_pretrained(os.path.join(lowercase__       ,	"config.json"       )       )
     a_       		=BarkCoarseConfig.from_pretrained(os.path.join(lowercase__       ,	"config.json"       )       )
     a_       		=BarkFineConfig.from_pretrained(os.path.join(lowercase__       ,	"config.json"       )       )
     a_       		=EncodecConfig.from_pretrained("facebook/encodec_24khz"       )

     a_       		=BarkSemanticModel.from_pretrained(lowercase__       )
     a_       		=BarkCoarseModel.from_pretrained(lowercase__       )
     a_       		=BarkFineModel.from_pretrained(lowercase__       )
     a_       		=EncodecModel.from_pretrained("facebook/encodec_24khz"       )

     a_       		=BarkConfig.from_sub_model_configs(
         lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       )

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

     a_       		=BarkModel(lowercase__       )

     a_       		=semantic
     a_       		=coarseAcoustic
     a_       		=fineAcoustic
     a_       		=codec

     a_       		=bark_generation_config

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


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

  lowercase							       =			parser.parse_args()

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


