





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase__						:			Optional[Any]									={
    '''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__						:			List[str]									=AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=False				)						->  List[Any]:
    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      _lowercase					(						_UpperCAmelCase				)						->  Optional[int]:
    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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False				)						->  str:
    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__":
       UpperCAmelCase__						:			List[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''')
       UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()

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












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       __A     (						a    ):


				@staticmethod
				@abstractmethod
				def _snake_case       (		UpperCAmelCase_							):
								raise NotImplementedError()


				@abstractmethod
				def _snake_case       (		self							):
								raise NotImplementedError()





