

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A_			:					Union[str, 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',
}

A_			:					Dict    			=     AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')

def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					str=False   )	-> Tuple:
	A__  ,  A__				:       Optional[Any]	  =						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 		UpperCamelCase   (lowercase_:					Optional[Any]   )	-> Optional[Any]:
	A__				:       Optional[Any]	  =						{}

	A__				:       Any	  =						r""".*sequential.(\d+).*"""
	A__				:       Optional[Any]	  =						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[str]	  =						key.replace(lowercase_       , lowercase_   )

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

			A__				:       Optional[int]	  =						key.replace(f"""sequential.{sequential_layer}."""       , f"""layers.{int(lowercase_   )//3}.linear."""   )
		elif re.match(lowercase_       , lowercase_   ):
			A__				:       Optional[Any]	  =						int(re.match(lowercase_       , lowercase_   ).group(1   )   )

			# Because in CLAP they use `nn.Sequential`...
			A__				:       Optional[int]	  =						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__				:       int	  =						value
			A__				:       Any	  =						mixed_qkv.size(0   ) // 3

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

			A__				:       str	  =						query_layer
			A__				:       Dict	  =						key_layer
			A__				:       int	  =						value_layer
		else:
			A__				:       Union[str, Any]	  =						value

	return model_state_dict

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					Union[str, Any]       , lowercase_:					List[str]       , lowercase_:					Optional[Any]=False   )	-> Optional[int]:
	A__  ,  A__				:       Any	  =						init_clap(lowercase_       , enable_fusion=lowercase_   )

	clap_model.eval()
	A__				:       Tuple	  =						clap_model.state_dict()
	A__				:       List[Any]	  =						rename_state_dict(lowercase_   )

	A__				:       Tuple	  =						ClapConfig()
	A__				:       Any	  =						enable_fusion
	A__				:       Optional[int]	  =						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__":
	A_			:					Union[str, 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')
	A_			:					Dict    			=     parser.parse_args()

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






def 		UpperCamelCase   (lowercase_:					list   )	-> list:
	A__				:       Union[str, Any]	  =						False
	while is_sorted is False:  # Until all the indices are traversed keep looping
		A__				:       Dict	  =						True
		for i in range(0       , len(lowercase_   ) - 1       , 2   ):  # iterating over all even indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False

		for i in range(1       , len(lowercase_   ) - 1       , 2   ):  # iterating over all odd indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False
	return input_list


if __name__ == "__main__":
	print('Enter list to be sorted')
	A_			:					Optional[int]    			=     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A_			:					Optional[int]    			=     odd_even_sort(input_list)
	print('The sorted list is')
	print(sorted_list)



