


'''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       _snake_case     (  A				,					A=False )		->					Union[str, Any]:
							lowerCAmelCase__     ,      lowerCAmelCase__      =			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       _snake_case     (  A )		->					List[str]:
							lowerCAmelCase__      =			{}

							lowerCAmelCase__      =			R'''.*sequential.(\d+).*'''
							lowerCAmelCase__      =			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__      =			key.replace(A				,					A )

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

																					lowerCAmelCase__      =			key.replace(F"""sequential.{sequential_layer}."""				,					F"""layers.{int(A )//3}.linear.""" )
														elif re.match(A				,					A ):
																					lowerCAmelCase__      =			int(re.match(A				,					A ).group(1 ) )

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

																					lowerCAmelCase__      =			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__      =			value
																					lowerCAmelCase__      =			mixed_qkv.size(0 ) // 3

																					lowerCAmelCase__      =			mixed_qkv[:qkv_dim]
																					lowerCAmelCase__      =			mixed_qkv[qkv_dim : qkv_dim * 2]
																					lowerCAmelCase__      =			mixed_qkv[qkv_dim * 2 :]

																					lowerCAmelCase__      =			query_layer
																					lowerCAmelCase__      =			key_layer
																					lowerCAmelCase__      =			value_layer
														else:
																					lowerCAmelCase__      =			value

							return model_state_dict


def       _snake_case     (  A				,					A				,					A				,					A=False )		->					int:
							lowerCAmelCase__     ,      lowerCAmelCase__      =			init_clap(A				,					enable_fusion=A )

							clap_model.eval()
							lowerCAmelCase__      =			clap_model.state_dict()
							lowerCAmelCase__      =			rename_state_dict(A )

							lowerCAmelCase__      =			ClapConfig()
							lowerCAmelCase__      =			enable_fusion
							lowerCAmelCase__      =			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__":
		__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)



'''simple docstring'''






from __future__ import annotations




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_ = 0     ) ->							List[Any]:
														lowerCAmelCase__      =			key







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							list[str]:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			key or self.__key or 1

														# make sure key is an appropriate size
														key %= 2_55

														return [chr(ord(lowerCamelCase_     ) ^ key     ) for ch in content]







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							list[str]:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			key or self.__key or 1

														# make sure key is an appropriate size
														key %= 2_55

														return [chr(ord(lowerCamelCase_     ) ^ key     ) for ch in content]







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = 0     ) ->							str:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			key or self.__key or 1

														# make sure key can be any size
														while key > 2_55:
																					key -= 2_55

														# This will be returned
														lowerCAmelCase__      =			''''''

														for ch in content:
																					ans += chr(ord(lowerCamelCase_     ) ^ key     )

														return ans







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = 0     ) ->							str:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			key or self.__key or 1

														# make sure key can be any size
														while key > 2_55:
																					key -= 2_55

														# This will be returned
														lowerCAmelCase__      =			''''''

														for ch in content:
																					ans += chr(ord(lowerCamelCase_     ) ^ key     )

														return ans







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = 0     ) ->							bool:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														try:
																					with open(lowerCamelCase_     ) as fin, open('''encrypt.out'''      ,					'''w+'''     ) as fout:
																												# actual encrypt-process
																												for line in fin:
																																			fout.write(self.encrypt_string(lowerCamelCase_      ,					lowerCamelCase_     )     )

														except OSError:
																					return False

														return True







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							bool:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														try:
																					with open(lowerCamelCase_     ) as fin, open('''decrypt.out'''      ,					'''w+'''     ) as fout:
																												# actual encrypt-process
																												for line in fin:
																																			fout.write(self.decrypt_string(lowerCamelCase_      ,					lowerCamelCase_     )     )

														except OSError:
																					return False

														return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")