




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a_				:    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''',
}

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





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    str=False     )  ->     Tuple:
			__lowerCamelCase					:	Union[str, Any]	       =				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 						UpperCAmelCase				(			A__:    List[str]     )  ->     List[str]:
			__lowerCamelCase					:	Union[str, Any]	       =				{}

			__lowerCamelCase					:	int	       =				r'.*sequential.(\d+).*'
			__lowerCamelCase					:	List[Any]	       =				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					:	str	       =				key.replace(A__							,				A__     )

						if re.match(A__							,				A__     ):
									# replace sequential layers with list
									__lowerCamelCase					:	Optional[int]	       =				re.match(A__							,				A__     ).group(1     )

									__lowerCamelCase					:	Dict	       =				key.replace(f'''sequential.{sequential_layer}.'''							,				f'''layers.{int(A__     )//3}.linear.'''     )
						elif re.match(A__							,				A__     ):
									__lowerCamelCase					:	List[str]	       =				int(re.match(A__							,				A__     ).group(1     )     )

									# Because in CLAP they use `nn.Sequential`...
									__lowerCamelCase					:	List[Any]	       =				1 if projecton_layer == 0 else 2

									__lowerCamelCase					:	Tuple	       =				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					:	Optional[int]	       =				value
									__lowerCamelCase					:	Optional[int]	       =				mixed_qkv.size(0     ) // 3

									__lowerCamelCase					:	Tuple	       =				mixed_qkv[:qkv_dim]
									__lowerCamelCase					:	List[Any]	       =				mixed_qkv[qkv_dim : qkv_dim * 2]
									__lowerCamelCase					:	Any	       =				mixed_qkv[qkv_dim * 2 :]

									__lowerCamelCase					:	str	       =				query_layer
									__lowerCamelCase					:	Tuple	       =				key_layer
									__lowerCamelCase					:	List[Any]	       =				value_layer
						else:
									__lowerCamelCase					:	int	       =				value

			return model_state_dict





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    int							,				A__:    List[Any]							,				A__:    List[str]=False     )  ->     str:
			__lowerCamelCase					:	Union[str, Any]	       =				init_clap(A__							,				enable_fusion=A__     )

			clap_model.eval()
			__lowerCamelCase					:	Any	       =				clap_model.state_dict()
			__lowerCamelCase					:	Tuple	       =				rename_state_dict(A__     )

			__lowerCamelCase					:	Optional[int]	       =				ClapConfig()
			__lowerCamelCase					:	Dict	       =				enable_fusion
			__lowerCamelCase					:	int	       =				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_				:    Optional[int]										=     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_				:    Union[str, Any]										=     parser.parse_args()

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









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_				:    List[Any]										=     {
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    int										=     [
					    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''EncodecModel''',
					    '''EncodecPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_encodec import (
					    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EncodecConfig,
					)
					from .feature_extraction_encodec import EncodecFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encodec import (
										    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EncodecModel,
										    EncodecPreTrainedModel,
										)

else:
					import sys

					a_				:    Tuple										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



