





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :   Any						   =  {
    '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',
}

_snake_case  :   int						   =  AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')

def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Any=False     ):
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     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[Any]     ):
   __lowerCAmelCase							     =     {}

   __lowerCAmelCase							     =     R'.*sequential.(\d+).*'
   __lowerCAmelCase							     =     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:
            __lowerCAmelCase							     =     key.replace(lowerCAmelCase_,     lowerCAmelCase_     )

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

         __lowerCAmelCase							     =     key.replace(F"""sequential.{sequential_layer}.""",     F"""layers.{int(lowerCAmelCase_     )//3}.linear."""     )
      elif re.match(lowerCAmelCase_,     lowerCAmelCase_     ):
         __lowerCAmelCase							     =     int(re.match(lowerCAmelCase_,     lowerCAmelCase_     ).group(1     )     )

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

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

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

         __lowerCAmelCase							     =     query_layer
         __lowerCAmelCase							     =     key_layer
         __lowerCAmelCase							     =     value_layer
      else:
         __lowerCAmelCase							     =     value

   return model_state_dict

def   a_   (			lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Union[str, Any]=False     ):
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     init_clap(lowerCAmelCase_,     enable_fusion=lowerCAmelCase_     )

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

   __lowerCAmelCase							     =     ClapConfig()
   __lowerCAmelCase							     =     enable_fusion
   __lowerCAmelCase							     =     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__":
     _snake_case  :   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')
     _snake_case  :   Optional[int]						   =  parser.parse_args()

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








_snake_case  :   List[str]						   =  {
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_snake_case  :   List[Any]						   =  {
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      str,     lowerCAmelCase_				:      str     ):
   __lowerCAmelCase							     =     from_type.lower().strip('s'     )
   __lowerCAmelCase							     =     to_type.lower().strip('s'     )

   __lowerCAmelCase							     =     UNIT_SYMBOL.get(lowerCAmelCase_,     lowerCAmelCase_     )
   __lowerCAmelCase							     =     UNIT_SYMBOL.get(lowerCAmelCase_,     lowerCAmelCase_     )

   if from_sanitized not in METRIC_CONVERSION:
      __lowerCAmelCase							     =     (
          F"""Invalid 'from_type' value: {from_type!r}.\n"""
          F"""Conversion abbreviations are: {", ".join(lowerCAmelCase_     )}"""
      )
      raise ValueError(lowerCAmelCase_     )
   if to_sanitized not in METRIC_CONVERSION:
      __lowerCAmelCase							     =     (
          F"""Invalid 'to_type' value: {to_type!r}.\n"""
          F"""Conversion abbreviations are: {", ".join(lowerCAmelCase_     )}"""
      )
      raise ValueError(lowerCAmelCase_     )
   __lowerCAmelCase							     =     METRIC_CONVERSION[from_sanitized]
   __lowerCAmelCase							     =     METRIC_CONVERSION[to_sanitized]
   __lowerCAmelCase							     =     1

   if from_exponent > to_exponent:
      __lowerCAmelCase							     =     from_exponent - to_exponent
   else:
      __lowerCAmelCase							     =     -(to_exponent - from_exponent)

   return value * pow(10,     lowerCAmelCase_     )


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

     testmod()

