code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  A_			  =    logging.get_logger(__name__)
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A			):
     UpperCamelCase_     =WavLMForSequenceClassification.from_pretrained(A    ,       config=A			)
     UpperCamelCase_     =downstream_dict["projector.weight"]
     UpperCamelCase_     =downstream_dict["projector.bias"]
     UpperCamelCase_     =downstream_dict["model.post_net.linear.weight"]
     UpperCamelCase_     =downstream_dict["model.post_net.linear.bias"]
     return model
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A			):
     UpperCamelCase_     =WavLMForAudioFrameClassification.from_pretrained(A    ,       config=A			)
     UpperCamelCase_     =downstream_dict["model.linear.weight"]
     UpperCamelCase_     =downstream_dict["model.linear.bias"]
     return model
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A			):
     UpperCamelCase_     =WavLMForXVector.from_pretrained(A    ,       config=A			)
     UpperCamelCase_     =downstream_dict["connector.weight"]
     UpperCamelCase_     =downstream_dict["connector.bias"]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
        UpperCamelCase_     =downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        UpperCamelCase_     =downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
     UpperCamelCase_     =downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
     UpperCamelCase_     =downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
     UpperCamelCase_     =downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
     UpperCamelCase_     =downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
     UpperCamelCase_     =downstream_dict["objective.W"]
     return model
  
  
  
  
  @torch.no_grad()
  def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A			):
     UpperCamelCase_     =torch.load(A    ,       map_location="cpu"			)
  
     UpperCamelCase_     =checkpoint["Downstream"]
  
     UpperCamelCase_     =WavLMConfig.from_pretrained(A			)
     UpperCamelCase_     =WavaVecaFeatureExtractor.from_pretrained(
         A    ,       return_attention_mask=A    ,       do_normalize=A			)
  
     UpperCamelCase_     =hf_config.architectures[0]
     if arch.endswith("ForSequenceClassification"			):
        UpperCamelCase_     =convert_classification(A    ,       A    ,       A			)
     elif arch.endswith("ForAudioFrameClassification"			):
        UpperCamelCase_     =convert_diarization(A    ,       A    ,       A			)
     elif arch.endswith("ForXVector"			):
        UpperCamelCase_     =convert_xvector(A    ,       A    ,       A			)
     else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""			)
  
     if hf_config.use_weighted_layer_sum:
        UpperCamelCase_     =checkpoint["Featurizer"]["weights"]
  
     hf_feature_extractor.save_pretrained(A			)
     hf_model.save_pretrained(A			)
  
  
  if __name__ == "__main__":
      A_			  =    argparse.ArgumentParser()
      parser.add_argument(
          "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
      )
      parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
      parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
      A_			  =    parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  A_			  =    {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     UpperCamelCase_     =f"""pretrained_models/{model_name}"""
     if not os.path.isfile(A			):
        os.makedirs("pretrained_models"    ,       exist_ok=A			)
        UpperCamelCase_     =f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(A    ,       "pretrained_models"			)
     UpperCamelCase_     =torch.load(A    ,       map_location=lambda A    ,       A			: storage			)
     return model
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     UpperCamelCase_     =download_model(pretrained_models[args.image_size]			)
  
     UpperCamelCase_     =state_dict["x_embedder.proj.weight"]
     UpperCamelCase_     =state_dict["x_embedder.proj.bias"]
     state_dict.pop("x_embedder.proj.weight"			)
     state_dict.pop("x_embedder.proj.bias"			)
  
     for depth in range(28			):
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.0.weight"
        ]
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.0.bias"
        ]
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.2.weight"
        ]
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.2.bias"
        ]
        UpperCamelCase_     =state_dict[
            "y_embedder.embedding_table.weight"
        ]
  
        UpperCamelCase_     =state_dict[
            f"""blocks.{depth}.adaLN_modulation.1.weight"""
        ]
        UpperCamelCase_     =state_dict[
            f"""blocks.{depth}.adaLN_modulation.1.bias"""
        ]
  
        UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]    ,       3    ,       dim=0			)
        UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]    ,       3    ,       dim=0			)
  
        UpperCamelCase_     =q
        UpperCamelCase_     =q_bias
        UpperCamelCase_     =k
        UpperCamelCase_     =k_bias
        UpperCamelCase_     =v
        UpperCamelCase_     =v_bias
  
        UpperCamelCase_     =state_dict[
            f"""blocks.{depth}.attn.proj.weight"""
        ]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.attn.proj.bias"""]
  
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.bias"""]
  
        state_dict.pop(f"""blocks.{depth}.attn.qkv.weight"""			)
        state_dict.pop(f"""blocks.{depth}.attn.qkv.bias"""			)
        state_dict.pop(f"""blocks.{depth}.attn.proj.weight"""			)
        state_dict.pop(f"""blocks.{depth}.attn.proj.bias"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc1.weight"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc1.bias"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc2.weight"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc2.bias"""			)
        state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.weight"""			)
        state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.bias"""			)
  
     state_dict.pop("t_embedder.mlp.0.weight"			)
     state_dict.pop("t_embedder.mlp.0.bias"			)
     state_dict.pop("t_embedder.mlp.2.weight"			)
     state_dict.pop("t_embedder.mlp.2.bias"			)
     state_dict.pop("y_embedder.embedding_table.weight"			)
  
     UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.weight"]
     UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.bias"]
     UpperCamelCase_     =state_dict["final_layer.linear.weight"]
     UpperCamelCase_     =state_dict["final_layer.linear.bias"]
  
     state_dict.pop("final_layer.linear.weight"			)
     state_dict.pop("final_layer.linear.bias"			)
     state_dict.pop("final_layer.adaLN_modulation.1.weight"			)
     state_dict.pop("final_layer.adaLN_modulation.1.bias"			)
  
     # DiT XL/2
     UpperCamelCase_     =TransformeraDModel(
         sample_size=args.image_size // 8    ,       num_layers=28    ,       attention_head_dim=72    ,       in_channels=4    ,       out_channels=8    ,       patch_size=2    ,       attention_bias=A    ,       num_attention_heads=16    ,       activation_fn="gelu-approximate"    ,       num_embeds_ada_norm=1_000    ,       norm_type="ada_norm_zero"    ,       norm_elementwise_affine=A    ,       )
     transformer.load_state_dict(A    ,       strict=A			)
  
     UpperCamelCase_     =DDIMScheduler(
         num_train_timesteps=1_000    ,       beta_schedule="linear"    ,       prediction_type="epsilon"    ,       clip_sample=A    ,       )
  
     UpperCamelCase_     =AutoencoderKL.from_pretrained(args.vae_model			)
  
     UpperCamelCase_     =DiTPipeline(transformer=A    ,       vae=A    ,       scheduler=A			)
  
     if args.save:
        pipeline.save_pretrained(args.checkpoint_path			)
  
  
  if __name__ == "__main__":
      A_			  =    argparse.ArgumentParser()
  
      parser.add_argument(
          "--image_size",
          default=256,
          type=int,
          required=False,
          help="Image size of pretrained model, either 256 or 512.",
      )
      parser.add_argument(
          "--vae_model",
          default="stabilityai/sd-vae-ft-ema",
          type=str,
          required=False,
          help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",
      )
      parser.add_argument(
          "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
      )
      parser.add_argument(
          "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."
      )
  
      A_			  =    parser.parse_args()
      main(args)
  