





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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







def   __lowerCamelCase   (				__a  :Any       ,	__a  :int=False       )						->							List[str]:






		"""simple docstring"""

		A__ ,     A__     =  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   __lowerCamelCase   (				__a  :Tuple       )						->							Any:






		"""simple docstring"""

		A__     =  {}

		A__     =  R""".*sequential.(\d+).*"""
		A__     =  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:
								A__     =  key.replace(__a       ,	__a       )

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

						A__     =  key.replace(F'sequential.{sequential_layer}.'       ,	F'layers.{int(__a       )//3}.linear.'       )
				elif re.match(__a       ,	__a       ):
						A__     =  int(re.match(__a       ,	__a       ).group(1       )       )

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

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

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

						A__     =  query_layer
						A__     =  key_layer
						A__     =  value_layer
				else:
						A__     =  value

		return model_state_dict







def   __lowerCamelCase   (				__a  :Optional[Any]       ,	__a  :Tuple       ,	__a  :int       ,	__a  :Union[str, Any]=False       )						->							Tuple:






		"""simple docstring"""

		A__ ,     A__     =  init_clap(__a       ,	enable_fusion=__a       )

		clap_model.eval()
		A__     =  clap_model.state_dict()
		A__     =  rename_state_dict(__a       )

		A__     =  ClapConfig()
		A__     =  enable_fusion
		A__     =  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       :   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''')
					A       :   Optional[Any]					= parser.parse_args()

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










def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							Optional[Any]:






		"""simple docstring"""

		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )
		else:
				return a * actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )







def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							float:






		"""simple docstring"""

		if b < 0:
				return 1 / actual_power(__a       ,	__a       )
		return actual_power(__a       ,	__a       )


if __name__ == "__main__":
					print(power(-2, -3))



