





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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







def    a__							(							lowercase   :	Optional[int],   lowercase   :	Optional[int]=False )						->						Tuple:





     """simple docstring"""

     _UpperCamelCase       ,							_UpperCamelCase							       =			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    a__							(							lowercase   :	List[Any] )						->						Any:





     """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(lowercase,   lowercase )

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

               _UpperCamelCase							       =			key.replace(F"""sequential.{sequential_layer}.""",   F"""layers.{int(lowercase )//3}.linear.""" )
          elif re.match(lowercase,   lowercase ):
               _UpperCamelCase							       =			int(re.match(lowercase,   lowercase ).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__							(							lowercase   :	Optional[int],   lowercase   :	Dict,   lowercase   :	List[str],   lowercase   :	Dict=False )						->						Any:





     """simple docstring"""

     _UpperCamelCase       ,							_UpperCamelCase							       =			init_clap(lowercase,   enable_fusion=lowercase )

     clap_model.eval()
     _UpperCamelCase							       =			clap_model.state_dict()
     _UpperCamelCase							       =			rename_state_dict(lowercase )

     _UpperCamelCase							       =			ClapConfig()
     _UpperCamelCase							       =			enable_fusion
     _UpperCamelCase							       =			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__		:     Optional[Any]			=							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__		:     Tuple			=							parser.parse_args()

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










'''simple docstring'''

def    a__							(							lowercase   :	str )						->						int:





     """simple docstring"""

     assert column_title.isupper()
     _UpperCamelCase							       =			0
     _UpperCamelCase							       =			len(lowercase ) - 1
     _UpperCamelCase							       =			0

     while index >= 0:
          _UpperCamelCase							       =			(ord(column_title[index] ) - 64) * pow(26,   lowercase )
          answer += value
          power += 1
          index -= 1

     return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()



