





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  :   Optional[int]            =AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")




def     a__ (					lowercase__  ,    lowercase__=False  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=create_model(
       "HTSAT-tiny"  ,    "roberta"  ,    _snake_case  ,    precision="fp32"  ,    device="cuda:0" if torch.cuda.is_available() else "cpu"  ,    enable_fusion=_snake_case  ,    fusion_type="aff_2d" if enable_fusion else None  ,    )
   return model, model_cfg




def     a__ (					lowercase__  ):






   '''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(_snake_case  ,    _snake_case  )

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

         UpperCAmelCase_											=key.replace(F'sequential.{sequential_layer}.'  ,    F'layers.{int(_snake_case  )//3}.linear.'  )
      elif re.match(_snake_case  ,    _snake_case  ):
         UpperCAmelCase_											=int(re.match(_snake_case  ,    _snake_case  ).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__  ,    lowercase__  ,    lowercase__  ,    lowercase__=False  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=init_clap(_snake_case  ,    enable_fusion=_snake_case  )

   clap_model.eval()
   UpperCAmelCase_											=clap_model.state_dict()
   UpperCAmelCase_											=rename_state_dict(_snake_case  )

   UpperCAmelCase_											=ClapConfig()
   UpperCAmelCase_											=enable_fusion
   UpperCAmelCase_											=ClapModel(_snake_case  )

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

   model.save_pretrained(_snake_case  )
   transformers_config.save_pretrained(_snake_case  )


if __name__ == "__main__":
     __lowercase  :   Optional[int]            =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  :   Dict            =parser.parse_args()

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




