code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import unittest
  
  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      PreTrainedTokenizer,
      is_vision_available,
  )
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      nested_simplify,
      require_tf,
      require_torch,
      require_torch_or_tf,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_vision_available():
      from PIL import Image
  else:
      class 						__lowerCAmelCase   :
  
  
  
  
  
  
  
        '''simple docstring'''
  
  
        @staticmethod
        def 					UpperCamelCase__   (      *UpperCamelCase_:		List[Any]				,			**UpperCamelCase_:		Optional[int]					):
           pass
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class 						__lowerCAmelCase   (			unittest.TestCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    int     	=			MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    __lowerCamelCase       :    Optional[int]     	=			TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    def 					UpperCamelCase__   (      self:		Dict				,			UpperCamelCase_:		List[Any]				,			UpperCamelCase_:		int				,			UpperCamelCase_:		Optional[Any]					):
       UpperCamelCase_     =ImageClassificationPipeline(model=UpperCamelCase_				,			image_processor=UpperCamelCase_				,			top_k=2					)
       UpperCamelCase_     =[
           Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
           "http://images.cocodataset.org/val2017/000000039769.jpg",
       ]
       return image_classifier, examples
    def 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		List[Any]				,			UpperCamelCase_:		str					):
       UpperCamelCase_     =image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  
       self.assertEqual(
           UpperCamelCase_				,			[
               {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
               {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
           ]				,			)
  
       import datasets
  
       UpperCamelCase_     =datasets.load_dataset("hf-internal-testing/fixtures_image_utils"				,			"image"				,			split="test"					)
  
       # Accepts URL + PIL.Image + lists
       UpperCamelCase_     =image_classifier(
           [
               Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               # RGBA
               dataset[0]["file"],
               # LA
               dataset[1]["file"],
               # L
               dataset[2]["file"],
           ]					)
       self.assertEqual(
           UpperCamelCase_				,			[
               [
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
               ],
               [
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
               ],
               [
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
               ],
               [
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
               ],
               [
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
                   {"score": ANY(UpperCamelCase_					), "label": ANY(UpperCamelCase_					)},
               ],
           ]				,			)
    @require_torch
    def 					UpperCamelCase__   (      self:		str					):
       UpperCamelCase_     ="hf-internal-testing/tiny-random-vit"
       UpperCamelCase_     =pipeline("image-classification"				,			model=UpperCamelCase_					)
  
       UpperCamelCase_     =image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
       self.assertEqual(
           nested_simplify(UpperCamelCase_				,			decimals=4					)				,			[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]				,			)
  
       UpperCamelCase_     =image_classifier(
           [
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               "http://images.cocodataset.org/val2017/000000039769.jpg",
           ]				,			top_k=2				,			)
       self.assertEqual(
           nested_simplify(UpperCamelCase_				,			decimals=4					)				,			[
               [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
               [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
           ]				,			)
    @require_tf
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       UpperCamelCase_     ="hf-internal-testing/tiny-random-vit"
       UpperCamelCase_     =pipeline("image-classification"				,			model=UpperCamelCase_				,			framework="tf"					)
  
       UpperCamelCase_     =image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
       self.assertEqual(
           nested_simplify(UpperCamelCase_				,			decimals=4					)				,			[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]				,			)
  
       UpperCamelCase_     =image_classifier(
           [
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               "http://images.cocodataset.org/val2017/000000039769.jpg",
           ]				,			top_k=2				,			)
       self.assertEqual(
           nested_simplify(UpperCamelCase_				,			decimals=4					)				,			[
               [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
               [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
           ]				,			)
    def 					UpperCamelCase__   (      self:		Tuple					):
       UpperCamelCase_     =PreTrainedTokenizer()
  
       # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
       UpperCamelCase_     =pipeline(
           "image-classification"				,			model="hf-internal-testing/tiny-random-vit"				,			tokenizer=UpperCamelCase_					)
  
       self.assertIs(image_classifier.tokenizer				,			UpperCamelCase_					)
  
  
  
  
  
    @slow
    @require_torch
    def 					UpperCamelCase__   (      self:		Any					):
       # Perceiver is not tested by `run_pipeline_test` properly.
       # That is because the type of feature_extractor and model preprocessor need to be kept
       # in sync, which is not the case in the current design
       UpperCamelCase_     =pipeline("image-classification"				,			model="deepmind/vision-perceiver-conv"					)
       UpperCamelCase_     =image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
       self.assertEqual(
           nested_simplify(UpperCamelCase_				,			decimals=4					)				,			[
               {"score": 0.4385, "label": "tabby, tabby cat"},
               {"score": 0.321, "label": "tiger cat"},
               {"score": 0.0502, "label": "Egyptian cat"},
               {"score": 0.0137, "label": "crib, cot"},
               {"score": 0.007, "label": "radiator"},
           ]				,			)
  
       UpperCamelCase_     =pipeline("image-classification"				,			model="deepmind/vision-perceiver-fourier"					)
       UpperCamelCase_     =image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
       self.assertEqual(
           nested_simplify(UpperCamelCase_				,			decimals=4					)				,			[
               {"score": 0.5658, "label": "tabby, tabby cat"},
               {"score": 0.1309, "label": "tiger cat"},
               {"score": 0.0722, "label": "Egyptian cat"},
               {"score": 0.0707, "label": "remote control, remote"},
               {"score": 0.0082, "label": "computer keyboard, keypad"},
           ]				,			)
  
       UpperCamelCase_     =pipeline("image-classification"				,			model="deepmind/vision-perceiver-learned"					)
       UpperCamelCase_     =image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
       self.assertEqual(
           nested_simplify(UpperCamelCase_				,			decimals=4					)				,			[
               {"score": 0.3022, "label": "tabby, tabby cat"},
               {"score": 0.2362, "label": "Egyptian cat"},
               {"score": 0.1856, "label": "tiger cat"},
               {"score": 0.0324, "label": "remote control, remote"},
               {"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
           ]				,			)
  
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)
  