

"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a					: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",
}

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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase=False							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	create_model(
          """HTSAT-tiny"""  ,					"""roberta"""  ,					_SCREAMING_SNAKE_CASE  ,					precision="""fp32"""  ,					device="""cuda:0""" if torch.cuda.is_available() else """cpu"""  ,					enable_fusion=_SCREAMING_SNAKE_CASE  ,					fusion_type="""aff_2d""" if enable_fusion else None  ,					)
      return model, model_cfg




def  _lowercase					(    __lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{}

      SCREAMING_SNAKE_CASE__   :      Any      		=	r""".*sequential.(\d+).*"""
      SCREAMING_SNAKE_CASE__   :      Dict      		=	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:
                        SCREAMING_SNAKE_CASE__   :      List[Any]      		=	key.replace(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE							)

            if re.match(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE							):
                  # replace sequential layers with list
                  SCREAMING_SNAKE_CASE__   :      Any      		=	re.match(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE							).group(1							)

                  SCREAMING_SNAKE_CASE__   :      int      		=	key.replace(F'''sequential.{sequential_layer}.'''  ,					F'''layers.{int(_SCREAMING_SNAKE_CASE							)//3}.linear.'''							)
            elif re.match(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE							):
                  SCREAMING_SNAKE_CASE__   :      str      		=	int(re.match(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE							).group(1							)							)

                  # Because in CLAP they use `nn.Sequential`...
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	1 if projecton_layer == 0 else 2

                  SCREAMING_SNAKE_CASE__   :      Dict      		=	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
                  SCREAMING_SNAKE_CASE__   :      int      		=	value
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	mixed_qkv.size(0							) // 3

                  SCREAMING_SNAKE_CASE__   :      int      		=	mixed_qkv[:qkv_dim]
                  SCREAMING_SNAKE_CASE__   :      str      		=	mixed_qkv[qkv_dim : qkv_dim * 2]
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	mixed_qkv[qkv_dim * 2 :]

                  SCREAMING_SNAKE_CASE__   :      Any      		=	query_layer
                  SCREAMING_SNAKE_CASE__   :      Any      		=	key_layer
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	value_layer
            else:
                  SCREAMING_SNAKE_CASE__   :      Any      		=	value

      return model_state_dict




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=False							)       ->				Optional[Any]:
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	init_clap(_SCREAMING_SNAKE_CASE  ,					enable_fusion=_SCREAMING_SNAKE_CASE							)

      clap_model.eval()
      SCREAMING_SNAKE_CASE__   :      Dict      		=	clap_model.state_dict()
      SCREAMING_SNAKE_CASE__   :      Any      		=	rename_state_dict(_SCREAMING_SNAKE_CASE							)

      SCREAMING_SNAKE_CASE__   :      int      		=	ClapConfig()
      SCREAMING_SNAKE_CASE__   :      int      		=	enable_fusion
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	ClapModel(_SCREAMING_SNAKE_CASE							)

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

      model.save_pretrained(_SCREAMING_SNAKE_CASE							)
      transformers_config.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
  a					: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")
  a					:int    = parser.parse_args()

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







"""simple docstring"""


a					:List[str]    = [
    (1_000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	0
      while place < len(__lowerCAmelCase							):
            if (place + 1 < len(__lowerCAmelCase							)) and (vals[roman[place]] < vals[roman[place + 1]]):
                  total += vals[roman[place + 1]] - vals[roman[place]]
                  place += 2
            else:
                  total += vals[roman[place]]
                  place += 1
      return total




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for arabic, roman in ROMAN:
            ((SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__))   :      List[str]      		=	divmod(__lowerCAmelCase  ,					__lowerCAmelCase							)
            result.append(roman * factor							)
            if number == 0:
                  break
      return "".join(__lowerCAmelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




