



"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__snake_case							:   Optional[int]      =    {
    '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',
}

__snake_case							:   List[Any]      =    AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')


def 			_lowercase				(  __snake_case					,__snake_case=False	)						->   Any:
			__lowerCAmelCase      :  Union[str, Any]												=       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 			_lowercase				(  __snake_case	)						->   int:
			__lowerCAmelCase      :  Optional[int]												=       {}

			__lowerCAmelCase      :  Tuple												=       r'.*sequential.(\d+).*'
			__lowerCAmelCase      :  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:
												__lowerCAmelCase      :  Union[str, Any]												=       key.replace(lowerCAmelCase_					,lowerCAmelCase_	)

						if re.match(lowerCAmelCase_					,lowerCAmelCase_	):
									# replace sequential layers with list
									__lowerCAmelCase      :  List[str]												=       re.match(lowerCAmelCase_					,lowerCAmelCase_	).group(1	)

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

									# Because in CLAP they use `nn.Sequential`...
									__lowerCAmelCase      :  Optional[Any]												=       1 if projecton_layer == 0 else 2

									__lowerCAmelCase      :  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
									__lowerCAmelCase      :  str												=       value
									__lowerCAmelCase      :  List[str]												=       mixed_qkv.size(0	) // 3

									__lowerCAmelCase      :  str												=       mixed_qkv[:qkv_dim]
									__lowerCAmelCase      :  int												=       mixed_qkv[qkv_dim : qkv_dim * 2]
									__lowerCAmelCase      :  Any												=       mixed_qkv[qkv_dim * 2 :]

									__lowerCAmelCase      :  List[Any]												=       query_layer
									__lowerCAmelCase      :  Union[str, Any]												=       key_layer
									__lowerCAmelCase      :  Tuple												=       value_layer
						else:
									__lowerCAmelCase      :  Dict												=       value

			return model_state_dict


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case=False	)						->   int:
			__lowerCAmelCase      :  Optional[Any]												=       init_clap(lowerCAmelCase_					,enable_fusion=lowerCAmelCase_	)

			clap_model.eval()
			__lowerCAmelCase      :  Tuple												=       clap_model.state_dict()
			__lowerCAmelCase      :  Optional[int]												=       rename_state_dict(lowerCAmelCase_	)

			__lowerCAmelCase      :  List[str]												=       ClapConfig()
			__lowerCAmelCase      :  Tuple												=       enable_fusion
			__lowerCAmelCase      :  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__":
	__snake_case							:   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')
	__snake_case							:   Any      =    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

from collections.abc import Sequence
from typing import Literal



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str | Literal[False]:
		_a     :      Optional[int]        =							list(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							list(lowerCAmelCase_  )
		_a     :      Union[str, Any]        =							0
		for i in range(len(lowerCAmelCase_  )  ):
				if lista[i] != lista[i]:
						count += 1
						_a     :      Optional[int]        =							'_'
		if count > 1:
				return False
		else:
				return "".join(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      list[str]:
		_a     :      Optional[int]        =							[]
		while True:
				_a     :      Any        =							['$'] * len(lowerCAmelCase_  )
				_a     :      List[str]        =							[]
				for i in range(len(lowerCAmelCase_  )  ):
						for j in range(i + 1						,			len(lowerCAmelCase_  )  ):
								_a     :      Optional[int]        =							compare_string(binary[i]						,			binary[j]  )
								if k is False:
										_a     :      Optional[Any]        =							'*'
										_a     :      Optional[Any]        =							'*'
										temp.append('X'  )
				for i in range(len(lowerCAmelCase_  )  ):
						if checka[i] == "$":
								pi.append(binary[i]  )
				if len(lowerCAmelCase_  ) == 0:
						return pi
				_a     :      Any        =							list(set(lowerCAmelCase_  )  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      int        =							[]
		for minterm in minterms:
				_a     :      Optional[int]        =							''
				for _ in range(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							str(minterm % 2  ) + string
						minterm //= 2
				temp.append(lowerCAmelCase_  )
		return temp



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      bool:
		_a     :      int        =							list(lowerCAmelCase_  )
		_a     :      Union[str, Any]        =							list(lowerCAmelCase_  )
		_a     :      str        =							0
		for i in range(len(lowerCAmelCase_  )  ):
				if lista[i] != lista[i]:
						count_n += 1
		return count_n == count



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							[]
		_a     :      Optional[Any]        =							[0] * len(lowerCAmelCase_  )
		for i in range(len(chart[0]  )  ):
				_a     :      Union[str, Any]        =							0
				_a     :      int        =							-1
				for j in range(len(lowerCAmelCase_  )  ):
						if chart[j][i] == 1:
								count += 1
								_a     :      int        =							j
				if count == 1:
						_a     :      List[Any]        =							1
		for i in range(len(lowerCAmelCase_  )  ):
				if select[i] == 1:
						for j in range(len(chart[0]  )  ):
								if chart[i][j] == 1:
										for k in range(len(lowerCAmelCase_  )  ):
												_a     :      Any        =							0
						temp.append(prime_implicants[i]  )
		while True:
				_a     :      Union[str, Any]        =							0
				_a     :      List[Any]        =							-1
				_a     :      str        =							0
				for i in range(len(lowerCAmelCase_  )  ):
						_a     :      Union[str, Any]        =							chart[i].count(1  )
						if count_n > max_n:
								_a     :      Any        =							count_n
								_a     :      int        =							i

				if max_n == 0:
						return temp

				temp.append(prime_implicants[rem]  )

				for i in range(len(chart[0]  )  ):
						if chart[rem][i] == 1:
								for j in range(len(lowerCAmelCase_  )  ):
										_a     :      List[str]        =							0



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[list[int]]:
		_a     :      int        =							[[0 for x in range(len(lowerCAmelCase_  )  )] for x in range(len(lowerCAmelCase_  )  )]
		for i in range(len(lowerCAmelCase_  )  ):
				_a     :      str        =							prime_implicants[i].count('_'  )
				for j in range(len(lowerCAmelCase_  )  ):
						if is_for_table(prime_implicants[i]						,			binary[j]						,			lowerCAmelCase_  ):
								_a     :      Optional[Any]        =							1

		return chart



def      __lowerCamelCase     (      )			->      None:
		_a     :      Optional[int]        =							int(input('Enter the no. of variables\n'  )  )
		_a     :      List[Any]        =							[
		    float(lowerCAmelCase_  )
		    for x in input(
		        'Enter the decimal representation of Minterms \'Spaces Separated\'\n'  ).split()
		]
		_a     :      List[str]        =							decimal_to_binary(lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      Dict        =							check(lowerCAmelCase_  )
		print('Prime Implicants are:'  )
		print(lowerCAmelCase_  )
		_a     :      List[Any]        =							prime_implicant_chart(lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      int        =							selection(lowerCAmelCase_						,			lowerCAmelCase_  )
		print('Essential Prime Implicants are:'  )
		print(lowerCAmelCase_  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




