

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a__				=		{
    '''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__				=		AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def    __UpperCAmelCase (	__a      :			Optional[int]		,__a      :			Optional[int]=False	)			->     Optional[Any]:

					"""simple docstring"""

					_a      ,					_a	:    Dict         =		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      :			Tuple	)			->     Tuple:

					"""simple docstring"""

					_a	:    Any         =		{}

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

										if re.match(__a		,__a	):
															# replace sequential layers with list
															_a	:    Optional[Any]         =		re.match(__a		,__a	).group(1	)

															_a	:    Optional[Any]         =		key.replace(F"""sequential.{sequential_layer}."""		,F"""layers.{int(__a	)//3}.linear."""	)
										elif re.match(__a		,__a	):
															_a	:    Optional[int]         =		int(re.match(__a		,__a	).group(1	)	)

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

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

															_a	:    List[str]         =		mixed_qkv[:qkv_dim]
															_a	:    int         =		mixed_qkv[qkv_dim : qkv_dim * 2]
															_a	:    Tuple         =		mixed_qkv[qkv_dim * 2 :]

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

					return model_state_dict



def    __UpperCAmelCase (	__a      :			Tuple		,__a      :			Tuple		,__a      :			Dict		,__a      :			Any=False	)			->     Dict:

					"""simple docstring"""

					_a      ,					_a	:    Optional[Any]         =		init_clap(__a		,enable_fusion=__a	)

					clap_model.eval()
					_a	:    Tuple         =		clap_model.state_dict()
					_a	:    Tuple         =		rename_state_dict(__a	)

					_a	:    Any         =		ClapConfig()
					_a	:    Tuple         =		enable_fusion
					_a	:    Any         =		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__				=		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__				=		parser.parse_args()

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






from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



