






"""simple docstring"""
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase_							      = {
    '''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''',
}

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

def 							snake_case (  A__      ,A__=False		):
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   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__		):
			UpperCAmelCase_	:					Optional[int]     	=   {}

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

						if re.match(A__      ,A__		):
									# replace sequential layers with list
									UpperCAmelCase_	:					int     	=   re.match(A__      ,A__		).group(1		)

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

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

									UpperCAmelCase_	:					List[str]     	=   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
									UpperCAmelCase_	:					Union[str, Any]     	=   value
									UpperCAmelCase_	:					Tuple     	=   mixed_qkv.size(0		) // 3

									UpperCAmelCase_	:					Union[str, Any]     	=   mixed_qkv[:qkv_dim]
									UpperCAmelCase_	:					Any     	=   mixed_qkv[qkv_dim : qkv_dim * 2]
									UpperCAmelCase_	:					str     	=   mixed_qkv[qkv_dim * 2 :]

									UpperCAmelCase_	:					Any     	=   query_layer
									UpperCAmelCase_	:					Union[str, Any]     	=   key_layer
									UpperCAmelCase_	:					List[Any]     	=   value_layer
						else:
									UpperCAmelCase_	:					List[Any]     	=   value

			return model_state_dict

def 							snake_case (  A__      ,A__      ,A__      ,A__=False		):
			UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   init_clap(A__      ,enable_fusion=A__		)

			clap_model.eval()
			UpperCAmelCase_	:					Tuple     	=   clap_model.state_dict()
			UpperCAmelCase_	:					Optional[int]     	=   rename_state_dict(A__		)

			UpperCAmelCase_	:					Optional[Any]     	=   ClapConfig()
			UpperCAmelCase_	:					str     	=   enable_fusion
			UpperCAmelCase_	:					Any     	=   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__":
			lowerCamelCase_							      = 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''')
			lowerCamelCase_							      = parser.parse_args()

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





def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      set()
				# To detect a back edge, keep track of vertices currently in the recursion stack
				lowercase         =      set()
				return any(
				    node not in visited and depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						)
				    for node in graph						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				visited.add(__magic_name__						)
				rec_stk.add(__magic_name__						)

				for node in graph[vertex]:
								if node not in visited:
												if depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):
																return True
								elif node in rec_stk:
												return True

    # The node needs to be removed from recursion stack before function ends
				rec_stk.remove(__magic_name__						)
				return False


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

						testmod()
