






'''simple docstring'''






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A    	=   {
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

__A    	=   AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       Optional[int]=False  )    -> List[Any]:







   """simple docstring"""
   __snake_case							,__snake_case				:      Union[str, Any]     							= create_model(
       'HTSAT-tiny'  ,		'roberta'  ,		A  ,		precision='fp32'  ,		device='cuda:0' if torch.cuda.is_available() else 'cpu'  ,		enable_fusion=A  ,		fusion_type='aff_2d' if enable_fusion else None  ,		)
   return model, model_cfg




def        _SCREAMING_SNAKE_CASE  (				A  :       str  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      int     							= {}

   __snake_case				:      str     							= R'.*sequential.(\d+).*'
   __snake_case				:      Dict     							= R'.*_projection.(\d+).*'

   for key, value in state_dict.items():
      # check if any key needs to be modified
      for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
         if key_to_modify in key:
            __snake_case				:      List[Any]     							= key.replace(A  ,		A  )

      if re.match(A  ,		A  ):
         # replace sequential layers with list
         __snake_case				:      Dict     							= re.match(A  ,		A  ).group(1  )

         __snake_case				:      str     							= key.replace(F"""sequential.{sequential_layer}."""  ,		F"""layers.{int(A  )//3}.linear."""  )
      elif re.match(A  ,		A  ):
         __snake_case				:      str     							= int(re.match(A  ,		A  ).group(1  )  )

         # Because in CLAP they use `nn.Sequential`...
         __snake_case				:      Any     							= 1 if projecton_layer == 0 else 2

         __snake_case				:      Optional[Any]     							= key.replace(F"""_projection.{projecton_layer}."""  ,		F"""_projection.linear{transformers_projection_layer}."""  )

      if "audio" and "qkv" in key:
         # split qkv into query key and value
         __snake_case				:      str     							= value
         __snake_case				:      int     							= mixed_qkv.size(0  ) // 3

         __snake_case				:      Union[str, Any]     							= mixed_qkv[:qkv_dim]
         __snake_case				:      Dict     							= mixed_qkv[qkv_dim : qkv_dim * 2]
         __snake_case				:      str     							= mixed_qkv[qkv_dim * 2 :]

         __snake_case				:      Optional[int]     							= query_layer
         __snake_case				:      List[str]     							= key_layer
         __snake_case				:      Dict     							= value_layer
      else:
         __snake_case				:      Union[str, Any]     							= value

   return model_state_dict




def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  ,		A  :       Union[str, Any]  ,		A  :       Tuple  ,		A  :       Tuple=False  )    -> str:







   """simple docstring"""
   __snake_case							,__snake_case				:      Any     							= init_clap(A  ,		enable_fusion=A  )

   clap_model.eval()
   __snake_case				:      List[str]     							= clap_model.state_dict()
   __snake_case				:      Tuple     							= rename_state_dict(A  )

   __snake_case				:      int     							= ClapConfig()
   __snake_case				:      Tuple     							= enable_fusion
   __snake_case				:      List[str]     							= ClapModel(A  )

   # ignore the spectrogram embedding layer
   model.load_state_dict(A  ,		strict=A  )

   model.save_pretrained(A  )
   transformers_config.save_pretrained(A  )


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
      parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
      parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
      __A    	=   parser.parse_args()

      convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)







'''simple docstring'''






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> int:







   """simple docstring"""
   if not is_accelerate_available():
      return method
   __snake_case				:      Optional[Any]     							= version.parse(accelerate.__version__  ).base_version
   if version.parse(A  ) < version.parse('0.17.0'  ):
      return method

   def wrapper(self  :       Optional[Any]  ,		*A  :       Optional[Any]  ,		**A  :       Optional[int]  ):
      if hasattr(self  ,		'_hf_hook'  ) and hasattr(self._hf_hook  ,		'pre_forward'  ):
         self._hf_hook.pre_forward(self  )
      return method(self  ,		*A  ,		**A  )

   return wrapper