





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :    int						    =  {
    '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  :    int						    =  AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')

def   a_    ( lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Dict=False	):
   __lowerCAmelCase	,	__lowerCAmelCase								=	create_model(
       'HTSAT-tiny',	'roberta',	UpperCamelCase_,	precision='fp32',	device='cuda:0' if torch.cuda.is_available() else 'cpu',	enable_fusion=UpperCamelCase_,	fusion_type='aff_2d' if enable_fusion else None,	)
   return model, model_cfg

def   a_    ( lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	{}

   __lowerCAmelCase								=	R'.*sequential.(\d+).*'
   __lowerCAmelCase								=	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:
            __lowerCAmelCase								=	key.replace(UpperCamelCase_,	UpperCamelCase_	)

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

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

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

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

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

         __lowerCAmelCase								=	query_layer
         __lowerCAmelCase								=	key_layer
         __lowerCAmelCase								=	value_layer
      else:
         __lowerCAmelCase								=	value

   return model_state_dict

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			List[str]=False	):
   __lowerCAmelCase	,	__lowerCAmelCase								=	init_clap(UpperCamelCase_,	enable_fusion=UpperCamelCase_	)

   clap_model.eval()
   __lowerCAmelCase								=	clap_model.state_dict()
   __lowerCAmelCase								=	rename_state_dict(UpperCamelCase_	)

   __lowerCAmelCase								=	ClapConfig()
   __lowerCAmelCase								=	enable_fusion
   __lowerCAmelCase								=	ClapModel(UpperCamelCase_	)

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

   model.save_pretrained(UpperCamelCase_	)
   transformers_config.save_pretrained(UpperCamelCase_	)


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

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



"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_				):

		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'float' object cannot be interpreted as an integer"""				)
		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'str' object cannot be interpreted as an integer"""				)

		if num == 0:
				return "0b0"

		__SCREAMING_SNAKE_CASE            = False

		if num < 0:
				__SCREAMING_SNAKE_CASE            = True
				__SCREAMING_SNAKE_CASE            = -num

		__SCREAMING_SNAKE_CASE            = []
		while num > 0:
				binary.insert(0  ,				num % 2				)
				num >>= 1

		if negative:
				return "-0b" + "".join(str(UpperCamelCase_				) for e in binary				)

		return "0b" + "".join(str(UpperCamelCase_				) for e in binary				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




