


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase__   	=							{
    "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",
}

UpperCAmelCase__   	=							AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=False						)     ->			str:







 """simple docstring"""


 _lowercase      ,    _lowercase							      =create_model(
     '''HTSAT-tiny'''     ,						'''roberta'''     ,						_UpperCamelCase     ,						precision='''fp32'''     ,						device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''     ,						enable_fusion=_UpperCamelCase     ,						fusion_type='''aff_2d''' if enable_fusion else None     ,						)
 return model, model_cfg
def 				UpperCAmelCase_					(							__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      ={}

 _lowercase							      =r'''.*sequential.(\d+).*'''
 _lowercase							      =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:
    _lowercase							      =key.replace(_UpperCamelCase     ,						_UpperCamelCase						)

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

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

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

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

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

   _lowercase							      =query_layer
   _lowercase							      =key_layer
   _lowercase							      =value_layer
  else:
   _lowercase							      =value

 return model_state_dict
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=False						)     ->			List[str]:







 """simple docstring"""


 _lowercase      ,    _lowercase							      =init_clap(_UpperCamelCase     ,						enable_fusion=_UpperCamelCase						)

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

 _lowercase							      =ClapConfig()
 _lowercase							      =enable_fusion
 _lowercase							      =ClapModel(_UpperCamelCase						)

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

 model.save_pretrained(_UpperCamelCase						)
 transformers_config.save_pretrained(_UpperCamelCase						)


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

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




'''simple docstring'''
from __future__ import annotations

import math


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """simple docstring"""




     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5							, int(math.sqrt(_UpperCamelCase				) + 1				)							, 6				):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =str(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =[n]
     for i in range(1							, len(_UpperCamelCase				)				):
          list_nums.append(int(str_num[i:]				)				)
          list_nums.append(int(str_num[:-i]				)				)
     return list_nums


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """simple docstring"""




     if len(str(_UpperCamelCase				)				) > 3:
          if not is_prime(int(str(_UpperCamelCase				)[-3:]				)				) or not is_prime(int(str(_UpperCamelCase				)[:3]				)				):
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int = 11				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =13
     while len(_UpperCamelCase				) != count:
          if validate(_UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =list_truncated_nums(_UpperCamelCase				)
               if all(is_prime(_UpperCamelCase				) for i in list_nums				):
                    list_truncated_primes.append(_UpperCamelCase				)
          num += 2
     return list_truncated_primes


def        _lowerCAmelCase					(						)      ->			int:
     """simple docstring"""




     return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(f'''{sum(compute_truncated_primes(1_1)) = }''')
