
"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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



def  lowerCamelCase__						(     __snake_case,	__snake_case=False      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    {}

					_UpperCamelCase									=    R'''.*sequential.(\d+).*'''
					_UpperCamelCase									=    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									=    key.replace(__lowercase,	__lowercase      )

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

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

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

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

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

															_UpperCamelCase									=    query_layer
															_UpperCamelCase									=    key_layer
															_UpperCamelCase									=    value_layer
										else:
															_UpperCamelCase									=    value

					return model_state_dict



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case=False      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    init_clap(__lowercase,	enable_fusion=__lowercase      )

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

					_UpperCamelCase									=    ClapConfig()
					_UpperCamelCase									=    enable_fusion
					_UpperCamelCase									=    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								=						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								=						parser.parse_args()

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














def  a__ (__lowercase							:int					,  __lowercase							:int   )		-> int:
				return abs(__lowercase   ) if a == 0 else greatest_common_divisor(b % a					,  __lowercase   )



def  a__ (__lowercase							:int					,  __lowercase							:int   )		-> int:
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								_A  ,				_A	:				Union[str, Any]   =      y, x % y
				return abs(__lowercase   )



def  a__ ()		-> Optional[Any]:
				try:
								_A	:				Optional[Any]   =      input('''Enter two integers separated by comma (,): '''   ).split(''','''   )
								_A	:				List[Any]   =      int(nums[0]   )
								_A	:				Optional[int]   =      int(nums[1]   )
								print(
								    f"""greatest_common_divisor({num_a}, {num_a}) = """
								    f"""{greatest_common_divisor(__lowercase					,  __lowercase   )}"""   )
								print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowercase					,  __lowercase   )}"""   )
				except (IndexError, UnboundLocalError, ValueError):
								print('''Wrong input'''   )


if __name__ == "__main__":
	main()



