






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCamelCase			:	Optional[Any]								={
    '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			:	Dict								=AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')



def  a__ (__lowercase							:int					,  __lowercase							:Dict=False   )		-> Optional[int]:
				_A  ,				_A	:				Optional[int]   =      create_model(
				    '''HTSAT-tiny'''					,  '''roberta'''					,  __lowercase					,  precision='''fp32'''					,  device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''					,  enable_fusion=__lowercase					,  fusion_type='''aff_2d''' if enable_fusion else None					,  )
				return model, model_cfg



def  a__ (__lowercase							:List[Any]   )		-> List[Any]:
				_A	:				Union[str, Any]   =      {}

				_A	:				List[Any]   =      R'''.*sequential.(\d+).*'''
				_A	:				Tuple   =      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(__lowercase					,  __lowercase   )

								if re.match(__lowercase					,  __lowercase   ):
												# replace sequential layers with list
												_A	:				Optional[int]   =      re.match(__lowercase					,  __lowercase   ).group(1   )

												_A	:				str   =      key.replace(f"""sequential.{sequential_layer}."""					,  f"""layers.{int(__lowercase   )//3}.linear."""   )
								elif re.match(__lowercase					,  __lowercase   ):
												_A	:				str   =      int(re.match(__lowercase					,  __lowercase   ).group(1   )   )

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

												_A	:				Optional[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	:				List[Any]   =      mixed_qkv.size(0   ) // 3

												_A	:				List[Any]   =      mixed_qkv[:qkv_dim]
												_A	:				Optional[int]   =      mixed_qkv[qkv_dim : qkv_dim * 2]
												_A	:				Dict   =      mixed_qkv[qkv_dim * 2 :]

												_A	:				Optional[int]   =      query_layer
												_A	:				Any   =      key_layer
												_A	:				Optional[int]   =      value_layer
								else:
												_A	:				Any   =      value

				return model_state_dict



def  a__ (__lowercase							:List[Any]					,  __lowercase							:Union[str, Any]					,  __lowercase							:List[str]					,  __lowercase							:Dict=False   )		-> Optional[Any]:
				_A  ,				_A	:				Any   =      init_clap(__lowercase					,  enable_fusion=__lowercase   )

				clap_model.eval()
				_A	:				Optional[Any]   =      clap_model.state_dict()
				_A	:				str   =      rename_state_dict(__lowercase   )

				_A	:				Optional[Any]   =      ClapConfig()
				_A	:				str   =      enable_fusion
				_A	:				List[Any]   =      ClapModel(__lowercase   )

				# ignore the spectrogram embedding layer
				model.load_state_dict(__lowercase					,  strict=__lowercase   )

				model.save_pretrained(__lowercase   )
				transformers_config.save_pretrained(__lowercase   )


if __name__ == "__main__":
	_UpperCamelCase			:	Any								=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			:	str								=parser.parse_args()

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











from collections import defaultdict



def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> bool:
				_A	:				Union[str, Any]   =      first_str.lower().strip()
				_A	:				int   =      second_str.lower().strip()

				# Remove whitespace
				_A	:				int   =      first_str.replace(''' '''					,  ''''''   )
				_A	:				Optional[Any]   =      second_str.replace(''' '''					,  ''''''   )

				# Strings of different lengths are not anagrams
				if len(__lowercase   ) != len(__lowercase   ):
								return False

				# Default values for count should be 0
				_A	:				defaultdict[str, int]   =      defaultdict(__lowercase   )

				# For each character in input strings,
				# increment count in the corresponding
				for i in range(len(__lowercase   )   ):
								count[first_str[i]] += 1
								count[second_str[i]] -= 1

				return all(_count == 0 for _count in count.values()   )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	_UpperCamelCase			:	Dict								=input('Enter the first string ').strip()
	_UpperCamelCase			:	Union[str, Any]								=input('Enter the second string ').strip()

	_UpperCamelCase			:	Dict								=check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')



