"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCAmelCase    :       List[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""",
}

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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :List[str]=False		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase							,							lowercase														=	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 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[Any]		) ->    Dict:
      '''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(lowerCAmelCase__					,		lowerCAmelCase__		)

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

                  lowercase														=	key.replace(f'sequential.{sequential_layer}.'					,		f'layers.{int(lowerCAmelCase__		)//3}.linear.'		)
            elif re.match(lowerCAmelCase__					,		lowerCAmelCase__		):
                  lowercase														=	int(re.match(lowerCAmelCase__					,		lowerCAmelCase__		).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__       (				lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :Optional[int]=False		) ->    int:
      '''simple docstring'''


      lowercase							,							lowercase														=	init_clap(lowerCAmelCase__					,		enable_fusion=lowerCAmelCase__		)

      clap_model.eval()
      lowercase														=	clap_model.state_dict()
      lowercase														=	rename_state_dict(lowerCAmelCase__		)

      lowercase														=	ClapConfig()
      lowercase														=	enable_fusion
      lowercase														=	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__":
       __lowerCAmelCase    :       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""")
       __lowerCAmelCase    :       str					     =parser.parse_args()

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




"""simple docstring"""






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      output_path.parent.mkdir(parents=lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		use_external_data_format=lowerCAmelCase__					,		enable_onnx_checker=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)
      else:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).to(device=lowerCAmelCase__					,		dtype=lowerCAmelCase__		),
              False,
          )					,		output_path=output_path / """vae_decoder""" / """model.onnx"""					,		ordered_input_names=["""latent_sample""", """return_dict"""]					,		output_names=["""sample"""]					,		dynamic_axes={
              """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
          }					,		opset=lowerCAmelCase__					,		)
      del vae_decoder


if __name__ == "__main__":
       __lowerCAmelCase    :       Tuple					     =argparse.ArgumentParser()

       parser.add_argument(
           """--model_path""",
           type=str,
           required=True,
           help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
       )

       parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
       parser.add_argument(
           """--opset""",
           default=1_4,
           type=int,
           help="""The version of the ONNX operator set to use.""",
       )
       parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

       __lowerCAmelCase    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



