






'''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(7_7_0)


__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        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Dict=False  )    -> Any:







   """simple docstring"""
   __snake_case				:      Any     							= model_type
   if use_small:
      key += "_small"
   return os.path.join(A  ,		REMOTE_MODEL_PATHS[key]['file_name']  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Any  )    -> List[Any]:







   """simple docstring"""
   os.makedirs(A  ,		exist_ok=A  )
   hf_hub_download(repo_id=A  ,		filename=A  ,		local_dir=A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       int  ,		A  :       Dict=False  ,		A  :       Dict="text"  )    -> str:







   """simple docstring"""
   if model_type == "text":
      __snake_case				:      List[Any]     							= BarkSemanticModel
      __snake_case				:      int     							= BarkSemanticConfig
      __snake_case				:      Any     							= BarkSemanticGenerationConfig
   elif model_type == "coarse":
      __snake_case				:      Union[str, Any]     							= BarkCoarseModel
      __snake_case				:      Optional[int]     							= BarkCoarseConfig
      __snake_case				:      str     							= BarkCoarseGenerationConfig
   elif model_type == "fine":
      __snake_case				:      Dict     							= BarkFineModel
      __snake_case				:      Optional[Any]     							= BarkFineConfig
      __snake_case				:      str     							= BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   __snake_case				:      Any     							= F"""{model_type}_small""" if use_small else model_type
   __snake_case				:      List[Any]     							= 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']  )
   __snake_case				:      List[Any]     							= torch.load(A  ,		map_location=A  )
   # this is a hack
   __snake_case				:      Optional[Any]     							= checkpoint['model_args']
   if "input_vocab_size" not in model_args:
      __snake_case				:      Optional[Any]     							= model_args['vocab_size']
      __snake_case				:      Optional[Any]     							= model_args['vocab_size']
      del model_args["vocab_size"]

   # convert Bark model arguments to HF Bark model arguments
   __snake_case				:      Any     							= model_args.pop('n_head'  )
   __snake_case				:      Tuple     							= model_args.pop('n_embd'  )
   __snake_case				:      int     							= model_args.pop('n_layer'  )

   __snake_case				:      str     							= ConfigClass(**checkpoint['model_args']  )
   __snake_case				:      List[str]     							= ModelClass(config=A  )
   __snake_case				:      List[Any]     							= GenerationConfigClass()

   __snake_case				:      Optional[Any]     							= model_generation_config
   __snake_case				:      int     							= checkpoint['model']
   # fixup checkpoint
   __snake_case				:      Optional[Any]     							= '_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
         __snake_case				:      Union[str, Any]     							= k[len(A  ) :]
         for old_layer_name in new_layer_name_dict:
            __snake_case				:      List[Any]     							= new_k.replace(A  ,		new_layer_name_dict[old_layer_name]  )

         __snake_case				:      Optional[int]     							= state_dict.pop(A  )

   __snake_case				:      Union[str, Any]     							= set(state_dict.keys()  ) - set(model.state_dict().keys()  )
   __snake_case				:      List[str]     							= {k for k in extra_keys if not k.endswith('.attn.bias'  )}
   __snake_case				:      Union[str, Any]     							= set(model.state_dict().keys()  ) - set(state_dict.keys()  )
   __snake_case				:      Optional[Any]     							= {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  )
   __snake_case				:      int     							= model.num_parameters(exclude_embeddings=A  )
   __snake_case				:      List[Any]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       Optional[int]=False  ,		A  :       Union[str, Any]="text"  )    -> Dict:







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

   __snake_case				:      List[str]     							= 'cpu'  # do conversion on cpu

   __snake_case				:      str     							= _get_ckpt_path(A  ,		use_small=A  )
   __snake_case				:      List[Any]     							= _load_model(A  ,		A  ,		model_type=A  ,		use_small=A  )

   # load bark initial model
   __snake_case				:      Optional[int]     							= _bark_load_model(A  ,		'cpu'  ,		model_type=A  ,		use_small=A  )

   if model_type == "text":
      __snake_case				:      str     							= 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
   __snake_case				:      Optional[Any]     							= 5
   __snake_case				:      Optional[Any]     							= 10

   if model_type in ["text", "coarse"]:
      __snake_case				:      Any     							= torch.randint(2_56  ,		(batch_size, sequence_length)  ,		dtype=torch.int  )
      __snake_case				:      Union[str, Any]     							= bark_model(A  )[0]

      __snake_case				:      Optional[int]     							= model(A  )

      # take last logits
      __snake_case				:      List[Any]     							= output_new_model_total.logits[:, [-1], :]

   else:
      __snake_case				:      Tuple     							= 3
      __snake_case				:      List[str]     							= 8
      __snake_case				:      str     							= torch.randint(2_56  ,		(batch_size, sequence_length, n_codes_total)  ,		dtype=torch.int  )

      __snake_case				:      Optional[Any]     							= model(A  ,		A  )
      __snake_case				:      Optional[Any]     							= bark_model(A  ,		A  )

      __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  ,		A  :       Dict  ,		A  :       Optional[int]  ,		A  :       int  ,		A  :       List[str]  ,		A  :       str  ,		)    -> int:







   """simple docstring"""
   __snake_case				:      Tuple     							= os.path.join(A  ,		A  )

   __snake_case				:      List[Any]     							= BarkSemanticConfig.from_pretrained(os.path.join(A  ,		'config.json'  )  )
   __snake_case				:      List[Any]     							= BarkCoarseConfig.from_pretrained(os.path.join(A  ,		'config.json'  )  )
   __snake_case				:      Any     							= BarkFineConfig.from_pretrained(os.path.join(A  ,		'config.json'  )  )
   __snake_case				:      Union[str, Any]     							= EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

   __snake_case				:      List[str]     							= BarkSemanticModel.from_pretrained(A  )
   __snake_case				:      Tuple     							= BarkCoarseModel.from_pretrained(A  )
   __snake_case				:      Optional[Any]     							= BarkFineModel.from_pretrained(A  )
   __snake_case				:      Optional[int]     							= EncodecModel.from_pretrained('facebook/encodec_24khz'  )

   __snake_case				:      List[Any]     							= BarkConfig.from_sub_model_configs(
       A  ,		A  ,		A  ,		A  )

   __snake_case				:      Optional[int]     							= BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config  ,		coarseAcoustic.generation_config  ,		fineAcoustic.generation_config  )

   __snake_case				:      Union[str, Any]     							= BarkModel(A  )

   __snake_case				:      List[Any]     							= semantic
   __snake_case				:      int     							= coarseAcoustic
   __snake_case				:      Union[str, Any]     							= fineAcoustic
   __snake_case				:      Any     							= codec

   __snake_case				:      Dict     							= bark_generation_config

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


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'''






from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any




@dataclass
class a_    :
   _snake_case        =							42
   _snake_case        =							None
   _snake_case        =							None




def        _SCREAMING_SNAKE_CASE  (				)    -> Node | None:







   """simple docstring"""
   __snake_case				:      str     							= Node(1  )
   __snake_case				:      Tuple     							= Node(2  )
   __snake_case				:      Optional[int]     							= Node(3  )
   __snake_case				:      List[str]     							= Node(4  )
   __snake_case				:      List[str]     							= Node(5  )
   return tree




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [root.data, *preorder(root.left  ), *preorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return postorder(root.left  ) + postorder(root.right  ) + [root.data] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [*inorder(root.left  ), root.data, *inorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> int:







   """simple docstring"""
   return (max(height(root.left  )  ,		height(root.right  )  ) + 1) if root else 0




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   if root is None:
      return output

   __snake_case				:      Optional[int]     							= deque([root]  )

   while process_queue:
      __snake_case				:      List[str]     							= process_queue.popleft()
      output.append(node.data  )

      if node.left:
         process_queue.append(node.left  )
      if node.right:
         process_queue.append(node.right  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if not root:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.left  ,		level - 1  )
         populate_output(root.right  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if root is None:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.right  ,		level - 1  )
         populate_output(root.left  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None] | list[Any]:







   """simple docstring"""
   if root is None:
      return []

   __snake_case				:      list[Sequence[Node | None]]     							= []

   __snake_case				:      List[Any]     							= 0
   __snake_case				:      int     							= height(A  )

   for h in range(1  ,		height_tree + 1  ):
      if not flag:
         output.append(get_nodes_from_left_to_right(A  ,		A  )  )
         __snake_case				:      int     							= 1
      else:
         output.append(get_nodes_from_right_to_left(A  ,		A  )  )
         __snake_case				:      Tuple     							= 0

   return output




def        _SCREAMING_SNAKE_CASE  (				)    -> None:  # Main function for testing.







   """simple docstring"""
   __snake_case				:      Optional[int]     							= make_tree()
   print(F"""In-order Traversal: {inorder(A  )}"""  )
   print(F"""Pre-order Traversal: {preorder(A  )}"""  )
   print(F"""Post-order Traversal: {postorder(A  )}"""  ,		'\n'  )

   print(F"""Height of Tree: {height(A  )}"""  ,		'\n'  )

   print('Complete Level Order Traversal: '  )
   print(level_order(A  )  ,		'\n'  )

   print('Level-wise order Traversal: '  )

   for level in range(1  ,		height(A  ) + 1  ):
      print(F"""Level {level}:"""  ,		get_nodes_from_left_to_right(A  ,		level=A  )  )

   print('\nZigZag order Traversal: '  )
   print(zigzag(A  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()