



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase__   =       {
    "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",
}

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





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







			'''simple docstring'''
			_UpperCAmelCase		       =   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	(     _UpperCAmelCase				:   Optional[Any]    )				-> Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   {}

			_UpperCAmelCase		       =   r'''.*sequential.(\d+).*'''
			_UpperCAmelCase		       =   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:
												_UpperCAmelCase		       =   key.replace(__UpperCAmelCase							,   __UpperCAmelCase    )

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

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

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

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

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

									_UpperCAmelCase		       =   query_layer
									_UpperCAmelCase		       =   key_layer
									_UpperCAmelCase		       =   value_layer
						else:
									_UpperCAmelCase		       =   value

			return model_state_dict





def 							A	(     _UpperCAmelCase				:   Optional[Any]							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   List[Any]=False    )				-> Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   init_clap(__UpperCAmelCase							,   enable_fusion=__UpperCAmelCase    )

			clap_model.eval()
			_UpperCAmelCase		       =   clap_model.state_dict()
			_UpperCAmelCase		       =   rename_state_dict(__UpperCAmelCase    )

			_UpperCAmelCase		       =   ClapConfig()
			_UpperCAmelCase		       =   enable_fusion
			_UpperCAmelCase		       =   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__":
							UpperCAmelCase__   =       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")
							UpperCAmelCase__   =       parser.parse_args()

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







UpperCAmelCase_     							= 'Input must be a string of 8 numbers plus letter'
UpperCAmelCase_     							= 'TRWAGMYFPDXBNJZSQVHLCKE'




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str      )      ->	bool:

    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				Any          =      f"Expected string as input, found {type(__UpperCAmelCase      ).__name__}"
        raise TypeError(__UpperCAmelCase      )

    UpperCamelCase__							:				int          =      spanish_id.replace('''-'''		,   ''''''      ).upper()
    if len(__UpperCAmelCase      ) != 9:
        raise ValueError(__UpperCAmelCase      )

    try:
        UpperCamelCase__							:				List[str]          =      int(spanish_id_clean[0:8]      )
        UpperCamelCase__							:				Optional[int]          =      spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(__UpperCAmelCase      ) from ex

    if letter.isdigit():
        raise ValueError(__UpperCAmelCase      )

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

