






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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase=False	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase					, _UpperCAmelCase									=			create_model(
						    'HTSAT-tiny' ,		'roberta' ,		__lowerCAmelCase ,		precision='fp32' ,		device='cuda:0' if torch.cuda.is_available() else 'cpu' ,		enable_fusion=__lowerCAmelCase ,		fusion_type='aff_2d' if enable_fusion else None ,		)
						return model, model_cfg


def __A    (      __lowerCAmelCase	)->  List[str]:





						"""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(__lowerCAmelCase ,		__lowerCAmelCase	)

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

																		_UpperCAmelCase									=			key.replace(F"""sequential.{sequential_layer}.""" ,		F"""layers.{int(__lowerCAmelCase	)//3}.linear."""	)
												elif re.match(__lowerCAmelCase ,		__lowerCAmelCase	):
																		_UpperCAmelCase									=			int(re.match(__lowerCAmelCase ,		__lowerCAmelCase	).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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=False	)->  int:





						"""simple docstring"""



						_UpperCAmelCase					, _UpperCAmelCase									=			init_clap(__lowerCAmelCase ,		enable_fusion=__lowerCAmelCase	)

						clap_model.eval()
						_UpperCAmelCase									=			clap_model.state_dict()
						_UpperCAmelCase									=			rename_state_dict(__lowerCAmelCase	)

						_UpperCAmelCase									=			ClapConfig()
						_UpperCAmelCase									=			enable_fusion
						_UpperCAmelCase									=			ClapModel(__lowerCAmelCase	)

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

						model.save_pretrained(__lowerCAmelCase	)
						transformers_config.save_pretrained(__lowerCAmelCase	)


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)



from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						str					=					[]
      __lowerCAmelCase					:						List[Any]					=					[]
      __lowerCAmelCase					:						str					=					[]

      for rt in rc.restypes:
            __lowerCAmelCase					:						List[Any]					=					rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
            restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]						)
            __lowerCAmelCase					:						List[str]					=					{name: i for i, name in enumerate(lowercase__						)}
            restype_atomaa_to_atomaa_list.append(
                [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]						)

            restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]						)

      # Add dummy mapping for restype 'UNK'
      restype_atomaa_to_atomaa_list.append([0] * 1_4						)
      restype_atomaa_to_atomaa_list.append([0] * 3_7						)
      restype_atomaa_mask_list.append([0.0] * 1_4						)

      __lowerCAmelCase					:						List[Any]					=					torch.tensor(
          lowercase__   ,	dtype=torch.intaa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						Optional[Any]					=					torch.tensor(
          lowercase__   ,	dtype=torch.intaa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						Tuple					=					torch.tensor(
          lowercase__   ,	dtype=torch.floataa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						List[Any]					=					protein['''aatype'''].to(torch.long						)

      # create the mapping for (residx, atom14) --> atom37, i.e. an array
      # with shape (num_res, 14) containing the atom37 indices for this protein
      __lowerCAmelCase					:						Any					=					restype_atomaa_to_atomaa[protein_aatype]
      __lowerCAmelCase					:						Union[str, Any]					=					restype_atomaa_mask[protein_aatype]

      __lowerCAmelCase					:						int					=					residx_atomaa_mask
      __lowerCAmelCase					:						List[str]					=					residx_atomaa_to_atomaa.long()

      # create the gather indices for mapping back
      __lowerCAmelCase					:						int					=					restype_atomaa_to_atomaa[protein_aatype]
      __lowerCAmelCase					:						Union[str, Any]					=					residx_atomaa_to_atomaa.long()

      # create the corresponding mask
      __lowerCAmelCase					:						str					=					torch.zeros([2_1, 3_7]   ,	dtype=torch.floataa   ,	device=protein['''aatype'''].device						)
      for restype, restype_letter in enumerate(rc.restypes						):
            __lowerCAmelCase					:						Optional[int]					=					rc.restype_atoa[restype_letter]
            __lowerCAmelCase					:						Optional[Any]					=					rc.residue_atoms[restype_name]
            for atom_name in atom_names:
                  __lowerCAmelCase					:						str					=					rc.atom_order[atom_name]
                  __lowerCAmelCase					:						List[Any]					=					1

      __lowerCAmelCase					:						Union[str, Any]					=					restype_atomaa_mask[protein_aatype]
      __lowerCAmelCase					:						Any					=					residx_atomaa_mask

      return protein







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Dict					=					tree_map(lambda lowercase__						: torch.tensor(lowercase__   ,	device=batch['''aatype'''].device						)   ,	lowercase__   ,	np.ndarray						)
      __lowerCAmelCase					:						Tuple					=					tensor_tree_map(lambda lowercase__						: np.array(lowercase__						)   ,	make_atomaa_masks(lowercase__						)						)
      return out


