




"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__			=		{
    """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""",
}

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







def 	_snake_case     (							lowercase__				,       lowercase__=False						):
				_lowerCamelCase,					_lowerCamelCase	:     Union[str, 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 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     List[str]       =       {}

				_lowerCamelCase	:     Tuple       =       r'.*sequential.(\d+).*'
				_lowerCamelCase	:     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:
																_lowerCamelCase	:     Dict       =       key.replace(lowercase__				,       lowercase__						)

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

												_lowerCamelCase	:     Union[str, Any]       =       key.replace(f'''sequential.{sequential_layer}.'''				,       f'''layers.{int(lowercase__						)//3}.linear.'''						)
								elif re.match(lowercase__				,       lowercase__						):
												_lowerCamelCase	:     int       =       int(re.match(lowercase__				,       lowercase__						).group(1						)						)

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

												_lowerCamelCase	:     Any       =       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	:     Tuple       =       value
												_lowerCamelCase	:     str       =       mixed_qkv.size(0						) // 3

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

												_lowerCamelCase	:     Dict       =       query_layer
												_lowerCamelCase	:     Tuple       =       key_layer
												_lowerCamelCase	:     List[str]       =       value_layer
								else:
												_lowerCamelCase	:     Any       =       value

				return model_state_dict







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__=False						):
				_lowerCamelCase,					_lowerCamelCase	:     Dict       =       init_clap(lowercase__				,       enable_fusion=lowercase__						)

				clap_model.eval()
				_lowerCamelCase	:     Optional[int]       =       clap_model.state_dict()
				_lowerCamelCase	:     List[str]       =       rename_state_dict(lowercase__						)

				_lowerCamelCase	:     List[str]       =       ClapConfig()
				_lowerCamelCase	:     Tuple       =       enable_fusion
				_lowerCamelCase	:     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__":
			lowercase__			=		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""")
			lowercase__			=		parser.parse_args()

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





"""simple docstring"""






import re







def 	_snake_case     (							lowercase__						):

				if len(re.findall('[ATCG]'				,       lowercase__						)						) != len(lowercase__						):
								raise ValueError('Invalid Strand'						)

				return dna.translate(dna.maketrans('ATCG'				,       'TAGC'						)						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()