


'''simple docstring'''
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__			(   lowerCAmelCase__    ,   lowerCAmelCase__=False		)       ->      List[str]:
    UpperCAmelCase__		,		UpperCAmelCase__     :						List[str]						=							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    a__			(   lowerCAmelCase__		)       ->      Optional[Any]:
    UpperCAmelCase__     :						int						=							{}

    UpperCAmelCase__     :						Any						=							R'''.*sequential.(\d+).*'''
    UpperCAmelCase__     :						Optional[int]						=							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__     :						List[str]						=							key.replace(lowerCAmelCase__    ,   lowerCAmelCase__		)

        if re.match(lowerCAmelCase__    ,   lowerCAmelCase__		):
            # replace sequential layers with list
            UpperCAmelCase__     :						Any						=							re.match(lowerCAmelCase__    ,   lowerCAmelCase__		).group(1		)

            UpperCAmelCase__     :						Any						=							key.replace(F"""sequential.{sequential_layer}."""    ,   F"""layers.{int(lowerCAmelCase__		)//3}.linear."""		)
        elif re.match(lowerCAmelCase__    ,   lowerCAmelCase__		):
            UpperCAmelCase__     :						Union[str, Any]						=							int(re.match(lowerCAmelCase__    ,   lowerCAmelCase__		).group(1		)		)

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

            UpperCAmelCase__     :						Union[str, Any]						=							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__     :						Dict						=							value
            UpperCAmelCase__     :						List[Any]						=							mixed_qkv.size(0		) // 3

            UpperCAmelCase__     :						str						=							mixed_qkv[:qkv_dim]
            UpperCAmelCase__     :						Dict						=							mixed_qkv[qkv_dim : qkv_dim * 2]
            UpperCAmelCase__     :						Union[str, Any]						=							mixed_qkv[qkv_dim * 2 :]

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

    return model_state_dict





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__=False		)       ->      Optional[int]:
    UpperCAmelCase__		,		UpperCAmelCase__     :						Dict						=							init_clap(lowerCAmelCase__    ,   enable_fusion=lowerCAmelCase__		)

    clap_model.eval()
    UpperCAmelCase__     :						Union[str, Any]						=							clap_model.state_dict()
    UpperCAmelCase__     :						Optional[int]						=							rename_state_dict(lowerCAmelCase__		)

    UpperCAmelCase__     :						Tuple						=							ClapConfig()
    UpperCAmelCase__     :						Tuple						=							enable_fusion
    UpperCAmelCase__     :						Union[str, Any]						=							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__":
  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)



import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			List[Any]		    =			ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
	    )       )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""			,type=a__			,default=1			,help="""Number of TPU cores to use (1 or 8)."""       )

	# positional
	parser.add_argument(
	    """training_script"""			,type=a__			,help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )			,)

	# rest from the training program
	parser.add_argument("""training_script_args"""			,nargs=a__       )

	return parser.parse_args()
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	__A :			Union[str, Any]		    =			parse_args()

	# Import training_script as a module.
	__A :			List[Any]		    =			Path(args.training_script       )
	sys.path.append(str(script_fpath.parent.resolve()       )       )
	__A :			str		    =			script_fpath.stem
	__A :			int		    =			importlib.import_module(a__       )

	# Patch sys.argv
	__A :			List[str]		    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores       )]

	xmp.spawn(mod._mp_fn			,args=()			,nprocs=args.num_cores       )


if __name__ == "__main__":
			main()





