






"""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		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[Any]=False      )		->str:
						A__						:  Union[str, Any]		  =  create_model(
						    """HTSAT-tiny""",						"""roberta""",						UpperCAmelCase__,						precision="""fp32""",						device="""cuda:0""" if torch.cuda.is_available() else """cpu""",						enable_fusion=UpperCAmelCase__,						fusion_type="""aff_2d""" if enable_fusion else None,						)
						return model, model_cfg




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any]      )		->List[Any]:
						A__						:  Optional[Any]		  =  {}

						A__						:  int		  =  R""".*sequential.(\d+).*"""
						A__						:  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:
																								A__						:  Optional[Any]		  =  key.replace(UpperCAmelCase__,						UpperCAmelCase__      )

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

																		A__						:  Dict		  =  key.replace(f'sequential.{sequential_layer}.',						f'layers.{int(UpperCAmelCase__      )//3}.linear.'      )
												elif re.match(UpperCAmelCase__,						UpperCAmelCase__      ):
																		A__						:  Dict		  =  int(re.match(UpperCAmelCase__,						UpperCAmelCase__      ).group(1      )      )

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

																		A__						:  Union[str, 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
																		A__						:  Tuple		  =  value
																		A__						:  Optional[int]		  =  mixed_qkv.size(0      ) // 3

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

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

						return model_state_dict




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					List[Any]=False      )		->Union[str, Any]:
						A__						:  List[Any]		  =  init_clap(UpperCAmelCase__,						enable_fusion=UpperCAmelCase__      )

						clap_model.eval()
						A__						:  Dict		  =  clap_model.state_dict()
						A__						:  str		  =  rename_state_dict(UpperCAmelCase__      )

						A__						:  Dict		  =  ClapConfig()
						A__						:  str		  =  enable_fusion
						A__						:  int		  =  ClapModel(UpperCAmelCase__      )

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

						model.save_pretrained(UpperCAmelCase__      )
						transformers_config.save_pretrained(UpperCAmelCase__      )


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)














"""simple docstring"""



def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->str:
						if a < 0 or b < 0:
												raise ValueError("""the value of both inputs must be positive"""      )

						A__						:  str		  =  str(bin(UpperCAmelCase__      )      )[2:]  # remove the leading "0b"
						A__						:  List[Any]		  =  str(bin(UpperCAmelCase__      )      )[2:]  # remove the leading "0b"

						A__						:  List[str]		  =  max(len(UpperCAmelCase__      ),						len(UpperCAmelCase__      )      )

						return "0b" + "".join(
						    str(int(char_a == """1""" and char_b == """1"""      )      )
						    for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__      ),						b_binary.zfill(UpperCAmelCase__      )      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()






