





"""simple docstring"""






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      __A   (      a_    :Any ,							a_    :Union[str, Any]=False)		->		Any:
				__a							,    __a			:    str      							=	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      __A   (      a_    :Union[str, Any])		->		List[str]:
				__a			:    str      							=	{}

				__a			:    str      							=	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			:    List[Any]      							=	key.replace(a_ ,							a_)

								if re.match(a_ ,							a_):
												# replace sequential layers with list
												__a			:    str      							=	re.match(a_ ,							a_).group(1)

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

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

												__a			:    Optional[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
												__a			:    Optional[Any]      							=	value
												__a			:    Tuple      							=	mixed_qkv.size(0) // 3

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

												__a			:    Union[str, Any]      							=	query_layer
												__a			:    int      							=	key_layer
												__a			:    Tuple      							=	value_layer
								else:
												__a			:    str      							=	value

				return model_state_dict

def      __A   (      a_    :str ,							a_    :Optional[int] ,							a_    :Optional[int] ,							a_    :Optional[Any]=False)		->		List[Any]:
				__a							,    __a			:    List[str]      							=	init_clap(a_ ,							enable_fusion=a_)

				clap_model.eval()
				__a			:    Optional[Any]      							=	clap_model.state_dict()
				__a			:    List[str]      							=	rename_state_dict(a_)

				__a			:    Optional[int]      							=	ClapConfig()
				__a			:    int      							=	enable_fusion
				__a			:    Optional[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			      =	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)



'''simple docstring'''






from typing import Any

import numpy as np





def lowerCamelCase__     (		__lowerCamelCase			:				np.ndarray				):







   '''simple docstring'''




   return np.array_equal(__lowerCamelCase			,       matrix.conjugate().T				)





def lowerCamelCase__     (		__lowerCamelCase			:				np.ndarray			,       __lowerCamelCase			:				np.ndarray				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =v.conjugate().T
   _UpperCAmelCase  :  Optional[int]				       =v_star.dot(__lowerCamelCase				)
   assert isinstance(__lowerCamelCase			,       np.ndarray				)
   return (v_star_dot.dot(__lowerCamelCase				)) / (v_star.dot(__lowerCamelCase				))





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]				)
   _UpperCAmelCase  :  List[str]				       =np.array([[1], [2], [3]]				)
   assert is_hermitian(__lowerCamelCase				), f"{a} is not hermitian."
   print(rayleigh_quotient(__lowerCamelCase			,       __lowerCamelCase				)				)

   _UpperCAmelCase  :  List[str]				       =np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]				)
   assert is_hermitian(__lowerCamelCase				), f"{a} is not hermitian."
   assert rayleigh_quotient(__lowerCamelCase			,       __lowerCamelCase				) == float(3				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()

