


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


SCREAMING_SNAKE_CASE			: 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",
}

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



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=False					)       ->		Any:
    _lowercase						,							_lowercase			:				Dict 						=						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 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    _lowercase			:				Tuple 						=						{}

    _lowercase			:				Optional[Any] 						=						R'.*sequential.(\d+).*'
    _lowercase			:				Union[str, 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:
                _lowercase			:				str 						=						key.replace(lowerCamelCase_				,					lowerCamelCase_					)

        if re.match(lowerCamelCase_				,					lowerCamelCase_					):
            # replace sequential layers with list
            _lowercase			:				Dict 						=						re.match(lowerCamelCase_				,					lowerCamelCase_					).group(1					)

            _lowercase			:				List[Any] 						=						key.replace(F'''sequential.{sequential_layer}.'''				,					F'''layers.{int(lowerCamelCase_					)//3}.linear.'''					)
        elif re.match(lowerCamelCase_				,					lowerCamelCase_					):
            _lowercase			:				List[Any] 						=						int(re.match(lowerCamelCase_				,					lowerCamelCase_					).group(1					)					)

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

            _lowercase			:				str 						=						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
            _lowercase			:				Union[str, Any] 						=						value
            _lowercase			:				Union[str, Any] 						=						mixed_qkv.size(0					) // 3

            _lowercase			:				Any 						=						mixed_qkv[:qkv_dim]
            _lowercase			:				Tuple 						=						mixed_qkv[qkv_dim : qkv_dim * 2]
            _lowercase			:				str 						=						mixed_qkv[qkv_dim * 2 :]

            _lowercase			:				Optional[Any] 						=						query_layer
            _lowercase			:				Optional[Any] 						=						key_layer
            _lowercase			:				int 						=						value_layer
        else:
            _lowercase			:				Optional[Any] 						=						value

    return model_state_dict



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		Optional[int]:
    _lowercase						,							_lowercase			:				Tuple 						=						init_clap(lowerCamelCase_				,					enable_fusion=lowerCamelCase_					)

    clap_model.eval()
    _lowercase			:				Optional[int] 						=						clap_model.state_dict()
    _lowercase			:				Dict 						=						rename_state_dict(lowerCamelCase_					)

    _lowercase			:				Optional[int] 						=						ClapConfig()
    _lowercase			:				Tuple 						=						enable_fusion
    _lowercase			:				int 						=						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__":
 SCREAMING_SNAKE_CASE			: 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")
 SCREAMING_SNAKE_CASE			: 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'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


if __name__ == "__main__":
							print(f"""{solution() = }""")




