





'''simple docstring'''




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__   :				Tuple      =   {
    '''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__   :				List[str]      =   AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')






def 	_lowerCAmelCase      (  __snake_case  :			Optional[int]			,							__snake_case  :			Dict=False      )					->					int:
     __A			,__A						:			List[Any]								=	create_model(
         'HTSAT-tiny'			,							'roberta'			,							__snake_case			,							precision='fp32'			,							device='cuda:0' if torch.cuda.is_available() else 'cpu'			,							enable_fusion=__snake_case			,							fusion_type='aff_2d' if enable_fusion else None			,							)
     return model, model_cfg






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					Tuple:
     __A						:			List[Any]								=	{}

     __A						:			Union[str, Any]								=	r'.*sequential.(\d+).*'
     __A						:			Optional[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						:			int								=	key.replace(__snake_case			,							__snake_case      )

          if re.match(__snake_case			,							__snake_case      ):
               # replace sequential layers with list
               __A						:			Any								=	re.match(__snake_case			,							__snake_case      ).group(1      )

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

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

               __A						:			Optional[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						:			Optional[int]								=	value
               __A						:			Any								=	mixed_qkv.size(0      ) // 3

               __A						:			str								=	mixed_qkv[:qkv_dim]
               __A						:			Any								=	mixed_qkv[qkv_dim : qkv_dim * 2]
               __A						:			List[str]								=	mixed_qkv[qkv_dim * 2 :]

               __A						:			List[str]								=	query_layer
               __A						:			Optional[int]								=	key_layer
               __A						:			List[str]								=	value_layer
          else:
               __A						:			Any								=	value

     return model_state_dict






def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			Any			,							__snake_case  :			Tuple			,							__snake_case  :			Dict=False      )					->					List[str]:
     __A			,__A						:			List[str]								=	init_clap(__snake_case			,							enable_fusion=__snake_case      )

     clap_model.eval()
     __A						:			Optional[Any]								=	clap_model.state_dict()
     __A						:			Any								=	rename_state_dict(__snake_case      )

     __A						:			Optional[int]								=	ClapConfig()
     __A						:			List[str]								=	enable_fusion
     __A						:			Tuple								=	ClapModel(__snake_case      )

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

     model.save_pretrained(__snake_case      )
     transformers_config.save_pretrained(__snake_case      )


if __name__ == "__main__":
  lowercase__   :				str      =   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__   :				List[str]      =   parser.parse_args()

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






'''simple docstring'''




lowercase__   :				Any      =   {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowercase__   :				List[Any]      =   ['''a''', '''b''', '''c''', '''d''', '''e''']






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			Tuple			,							__snake_case  :			int      )					->					Tuple:
     __A						:			List[str]								=	start
     # add current to visited
     visited.append(__snake_case      )
     __A						:			Optional[int]								=	edges[current]
     for neighbor in neighbors:
          # if neighbor not in visited, visit
          if neighbor not in visited:
               __A						:			int								=	topological_sort(__snake_case			,							__snake_case			,							__snake_case      )
    # if all neighbors visited add current to sort
     sort.append(__snake_case      )
     # if all vertices haven't been visited select a new one to visit
     if len(__snake_case      ) != len(__snake_case      ):
          for vertice in vertices:
               if vertice not in visited:
                    __A						:			Dict								=	topological_sort(__snake_case			,							__snake_case			,							__snake_case      )
    # return sort
     return sort


if __name__ == "__main__":
  lowercase__   :				Tuple      =   topological_sort('''a''', [], [])
  print(sort)