





'''simple docstring'''


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase_   :   Optional[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",
}

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







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]=False     )   ->    List[Any]:
			a__   ,				a__     = create_model(
			    "HTSAT-tiny"					,  "roberta"					,  __lowerCamelCase					,  precision="fp32"					,  device="cuda:0" if torch.cuda.is_available() else "cpu"					,  enable_fusion=__lowerCamelCase					,  fusion_type="aff_2d" if enable_fusion else None					,  )
			return model, model_cfg







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

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

									a__     = key.replace(f'''sequential.{sequential_layer}.'''					,  f'''layers.{int(__lowerCamelCase     )//3}.linear.'''     )
						elif re.match(__lowerCamelCase					,  __lowerCamelCase     ):
									a__     = int(re.match(__lowerCamelCase					,  __lowerCamelCase     ).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  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     str=False     )   ->    List[str]:
			a__   ,				a__     = init_clap(__lowerCamelCase					,  enable_fusion=__lowerCamelCase     )

			clap_model.eval()
			a__     = clap_model.state_dict()
			a__     = rename_state_dict(__lowerCamelCase     )

			a__     = ClapConfig()
			a__     = enable_fusion
			a__     = ClapModel(__lowerCamelCase     )

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

			model.save_pretrained(__lowerCamelCase     )
			transformers_config.save_pretrained(__lowerCamelCase     )


if __name__ == "__main__":
		lowerCAmelCase_   :   Union[str, Any]       =					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")
		lowerCAmelCase_   :   str       =					parser.parse_args()

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










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    bool:
			a__     = 0
			for ch in input_str:
						a__     = ord(__lowerCamelCase     )
						a__     = pow(2					,  __lowerCamelCase     )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
		import doctest

		doctest.testmod()



