




'''simple docstring'''



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase_			=						{
    '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',
}

lowerCamelCase_			=						AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			Tuple=False  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				create_model(
       "HTSAT-tiny"	,					"roberta"	,					__A	,					precision="fp32"	,					device="cuda:0" if torch.cuda.is_available() else "cpu"	,					enable_fusion=__A	,					fusion_type="aff_2d" if enable_fusion else None	,					)
   return model, model_cfg
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				{}

   _SCREAMING_SNAKE_CASE	=				R".*sequential.(\d+).*"
   _SCREAMING_SNAKE_CASE	=				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:
            _SCREAMING_SNAKE_CASE	=				key.replace(__A	,					__A  )

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

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

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

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

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

         _SCREAMING_SNAKE_CASE	=				query_layer
         _SCREAMING_SNAKE_CASE	=				key_layer
         _SCREAMING_SNAKE_CASE	=				value_layer
      else:
         _SCREAMING_SNAKE_CASE	=				value

   return model_state_dict
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict	,					__A    :			Optional[Any]	,					__A    :			Union[str, Any]	,					__A    :			Optional[Any]=False  )			->			int:
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				init_clap(__A	,					enable_fusion=__A  )

   clap_model.eval()
   _SCREAMING_SNAKE_CASE	=				clap_model.state_dict()
   _SCREAMING_SNAKE_CASE	=				rename_state_dict(__A  )

   _SCREAMING_SNAKE_CASE	=				ClapConfig()
   _SCREAMING_SNAKE_CASE	=				enable_fusion
   _SCREAMING_SNAKE_CASE	=				ClapModel(__A  )

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

   model.save_pretrained(__A  )
   transformers_config.save_pretrained(__A  )


if __name__ == "__main__":
 lowerCamelCase_			=						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')
 lowerCamelCase_			=						parser.parse_args()

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







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

