




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__				:      Union[str, 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",
}

lowercase__				:      Union[str, Any]	   = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=False)      ->					Dict:
     a  ,				a     =				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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Dict:
     a     =				{}

     a     =				r".*sequential.(\d+).*"
     a     =				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     =				key.replace(__UpperCamelCase		, __UpperCamelCase)

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

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

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

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

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

               a     =				query_layer
               a     =				key_layer
               a     =				value_layer
          else:
               a     =				value

     return model_state_dict

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=False)      ->					Tuple:
     a  ,				a     =				init_clap(__UpperCamelCase		, enable_fusion=__UpperCamelCase)

     clap_model.eval()
     a     =				clap_model.state_dict()
     a     =				rename_state_dict(__UpperCamelCase)

     a     =				ClapConfig()
     a     =				enable_fusion
     a     =				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__":
   lowercase__				:      List[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__				:      int	   = parser.parse_args()

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







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

