






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCamelCase   :   Dict      =    {
    """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   :   Optional[int]      =    AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase=False       ):




	'''simple docstring'''







	__lowercase							,    __lowercase   							=	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 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	{}

	__lowercase   							=	r""".*sequential.(\d+).*"""
	__lowercase   							=	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   							=	key.replace(lowerCamelCase					,	lowerCamelCase       )

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

			__lowercase   							=	key.replace(F'sequential.{sequential_layer}.'					,	F'layers.{int(lowerCamelCase       )//3}.linear.'       )
		elif re.match(lowerCamelCase					,	lowerCamelCase       ):
			__lowercase   							=	int(re.match(lowerCamelCase					,	lowerCamelCase       ).group(1       )       )

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

			__lowercase   							=	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   							=	value
			__lowercase   							=	mixed_qkv.size(0       ) // 3

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

			__lowercase   							=	query_layer
			__lowercase   							=	key_layer
			__lowercase   							=	value_layer
		else:
			__lowercase   							=	value

	return model_state_dict





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase=False       ):




	'''simple docstring'''







	__lowercase							,    __lowercase   							=	init_clap(lowerCamelCase					,	enable_fusion=lowerCamelCase       )

	clap_model.eval()
	__lowercase   							=	clap_model.state_dict()
	__lowercase   							=	rename_state_dict(lowerCamelCase       )

	__lowercase   							=	ClapConfig()
	__lowercase   							=	enable_fusion
	__lowercase   							=	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   :   List[str]      =    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   :   Optional[Any]      =    parser.parse_args()

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







import argparse
import hashlib  # hashlib is only used inside the Test class
import struct


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						Tuple	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      data
						__UpperCamelCase					:	List[str]             =      [0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]






	@staticmethod
	def 					lowerCamelCase__     (					lowerCAmelCase		:						str					,     lowerCAmelCase		:						Union[str, Any]	)	->       str:







						"""simple docstring"""







						return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      b"""\x80""" + b"""\x00""" * (63 - (len(self.data	) + 8) % 64)
						__UpperCamelCase					:	int             =      self.data + padding + struct.pack(""">Q"""					,     8 * len(self.data	)	)
						return padded_data






	def 					lowerCamelCase__     (					self		:						str	)	->       str:







						"""simple docstring"""







						return [
						    self.padded_data[i : i + 64] for i in range(0					,     len(self.padded_data	)					,     64	)
						]






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Tuple	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      list(struct.unpack(""">16L"""					,     lowerCAmelCase	)	) + [0] * 64
						for i in range(16					,     80	):
											__UpperCamelCase					:	Dict             =      self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])					,     1	)
						return w






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.padding()
						__UpperCamelCase					:	int             =      self.split_blocks()
						for block in self.blocks:
											__UpperCamelCase					:	Union[str, Any]             =      self.expand_block(lowerCAmelCase	)
											__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	str             =      self.h
											for i in range(0					,     80	):
																if 0 <= i < 20:
																					__UpperCamelCase					:	List[Any]             =      (b & c) | ((~b) & d)
																					__UpperCamelCase					:	str             =      0x5_a_8_2_7_9_9_9
																elif 20 <= i < 40:
																					__UpperCamelCase					:	Tuple             =      b ^ c ^ d
																					__UpperCamelCase					:	List[Any]             =      0x6_e_d_9_e_b_a_1
																elif 40 <= i < 60:
																					__UpperCamelCase					:	Tuple             =      (b & c) | (b & d) | (c & d)
																					__UpperCamelCase					:	List[str]             =      0x8_f_1_b_b_c_d_c
																elif 60 <= i < 80:
																					__UpperCamelCase					:	Optional[int]             =      b ^ c ^ d
																					__UpperCamelCase					:	Tuple             =      0xc_a_6_2_c_1_d_6
																__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      (
																    self.rotate(lowerCAmelCase					,     5	) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
																    a,
																    self.rotate(lowerCAmelCase					,     30	),
																    c,
																    d,
																)
											__UpperCamelCase					:	List[Any]             =      (
											    self.h[0] + a & 0xf_f_f_f_f_f_f_f,
											    self.h[1] + b & 0xf_f_f_f_f_f_f_f,
											    self.h[2] + c & 0xf_f_f_f_f_f_f_f,
											    self.h[3] + d & 0xf_f_f_f_f_f_f_f,
											    self.h[4] + e & 0xf_f_f_f_f_f_f_f,
											)
						return ("{:08x}" * 5).format(*self.h	)






def 				A__ ()     ->						int:
					__UpperCamelCase					:	Optional[int]             =      b"""Test String"""
					assert SHAaHash(snake_case				).final_hash() == hashlib.shaa(snake_case				).hexdigest()  # noqa: S324





def 				A__ ()     ->						Tuple:
					__UpperCamelCase					:	Optional[Any]             =      argparse.ArgumentParser(description="""Process some strings or files"""				)
					parser.add_argument(
					    """--string"""		,	dest="""input_string"""		,	default="""Hello World!! Welcome to Cryptography"""		,	help="""Hash the string"""		,	)
					parser.add_argument("""--file"""		,	dest="""input_file"""		,	help="""Hash contents of a file"""				)
					__UpperCamelCase					:	List[Any]             =      parser.parse_args()
					__UpperCamelCase					:	List[str]             =      args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file		,	"""rb"""				) as f:
															__UpperCamelCase					:	Tuple             =      f.read()
					else:
										__UpperCamelCase					:	Any             =      bytes(snake_case		,	"""utf-8"""				)
					print(SHAaHash(snake_case				).final_hash()				)


if __name__ == "__main__":
	main()
	import doctest

	doctest.testmod()






