





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case           =			{
    """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""",
}

_snake_case           =			AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")


def _A       (   __magic_name__  , __magic_name__=False	):
   lowercase__       ,							lowercase__			       =  create_model(
       "HTSAT-tiny"  , "roberta"  , __magic_name__  , precision="fp32"  , device="cuda:0" if torch.cuda.is_available() else "cpu"  , enable_fusion=__magic_name__  , fusion_type="aff_2d" if enable_fusion else None  , )
   return model, model_cfg


def _A       (   __magic_name__	):
   lowercase__			       =  {}

   lowercase__			       =  R".*sequential.(\d+).*"
   lowercase__			       =  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:
            lowercase__			       =  key.replace(__magic_name__  , __magic_name__	)

      if re.match(__magic_name__  , __magic_name__	):
         # replace sequential layers with list
         lowercase__			       =  re.match(__magic_name__  , __magic_name__	).group(1	)

         lowercase__			       =  key.replace(f'''sequential.{sequential_layer}.'''  , f'''layers.{int(__magic_name__	)//3}.linear.'''	)
      elif re.match(__magic_name__  , __magic_name__	):
         lowercase__			       =  int(re.match(__magic_name__  , __magic_name__	).group(1	)	)

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

         lowercase__			       =  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
         lowercase__			       =  value
         lowercase__			       =  mixed_qkv.size(0	) // 3

         lowercase__			       =  mixed_qkv[:qkv_dim]
         lowercase__			       =  mixed_qkv[qkv_dim : qkv_dim * 2]
         lowercase__			       =  mixed_qkv[qkv_dim * 2 :]

         lowercase__			       =  query_layer
         lowercase__			       =  key_layer
         lowercase__			       =  value_layer
      else:
         lowercase__			       =  value

   return model_state_dict


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__=False	):
   lowercase__       ,							lowercase__			       =  init_clap(__magic_name__  , enable_fusion=__magic_name__	)

   clap_model.eval()
   lowercase__			       =  clap_model.state_dict()
   lowercase__			       =  rename_state_dict(__magic_name__	)

   lowercase__			       =  ClapConfig()
   lowercase__			       =  enable_fusion
   lowercase__			       =  ClapModel(__magic_name__	)

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

   model.save_pretrained(__magic_name__	)
   transformers_config.save_pretrained(__magic_name__	)


if __name__ == "__main__":
  _snake_case           =			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""")
  _snake_case           =			parser.parse_args()

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













def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   if height >= 1:
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)
      move_disk(__magic_name__  , __magic_name__	)
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)


def _A       (   __magic_name__  , __magic_name__	):
   print("moving disk from"  , __magic_name__  , "to"  , __magic_name__	)


def _A       (   ):
   lowercase__			       =  int(input("Height of hanoi: "	).strip()	)
   move_tower(__magic_name__  , "A"  , "B"  , "C"	)


if __name__ == "__main__":
  main()






