code: 
  
  
  
  
  import argparse
  import json
  from collections import OrderedDict
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  __A :		List[Any]      							=    logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  __A :		Any      							=    []
  for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
    )
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
    )
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
    )
    rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
    rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
    rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
    )
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
    )
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
            F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
        )
    )
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
            F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
        )
    )
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
    )
    rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
    )
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
    )
    rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
    rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
  
  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  rename_keys.extend(
      [
          ('''input_proj.weight''', '''input_projection.weight'''),
          ('''input_proj.bias''', '''input_projection.bias'''),
          ('''query_embed.weight''', '''query_position_embeddings.weight'''),
          ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
          ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
          ('''class_embed.weight''', '''class_labels_classifier.weight'''),
          ('''class_embed.bias''', '''class_labels_classifier.bias'''),
          ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
          ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
          ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
          ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
          ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
          ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
      ]
  )
  def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       List[Any]						,snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   str          =							state_dict.pop(lowercase_    )
      UpperCamelCase				:   Dict          =							val
  def 					A_			(		snake_case_     :       Union[str, Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							OrderedDict()
      for key, value in state_dict.items():
          if "backbone.0.body" in key:
              UpperCamelCase				:   str          =							key.replace("""backbone.0.body"""						,"""backbone.conv_encoder.model"""    )
              UpperCamelCase				:   Tuple          =							value
          else:
              UpperCamelCase				:   List[Any]          =							value
  
      return new_state_dict
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[Any]=False    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Tuple          =							""""""
      if is_panoptic:
          UpperCamelCase				:   List[str]          =							"""detr."""
  
      # first: transformer encoder
      for i in range(6    ):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          UpperCamelCase				:   List[Any]          =							state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'    )
          UpperCamelCase				:   Tuple          =							state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'    )
          # next, add query, keys and values (in that order) to the state dict
          UpperCamelCase				:   List[Any]          =							in_proj_weight[:2_5_6, :]
          UpperCamelCase				:   Union[str, Any]          =							in_proj_bias[:2_5_6]
          UpperCamelCase				:   List[str]          =							in_proj_weight[2_5_6:5_1_2, :]
          UpperCamelCase				:   Tuple          =							in_proj_bias[2_5_6:5_1_2]
          UpperCamelCase				:   str          =							in_proj_weight[-2_5_6:, :]
          UpperCamelCase				:   str          =							in_proj_bias[-2_5_6:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6    ):
          # read in weights + bias of input projection layer of self-attention
          UpperCamelCase				:   List[Any]          =							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'    )
          UpperCamelCase				:   Dict          =							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'    )
          # next, add query, keys and values (in that order) to the state dict
          UpperCamelCase				:   List[str]          =							in_proj_weight[:2_5_6, :]
          UpperCamelCase				:   Tuple          =							in_proj_bias[:2_5_6]
          UpperCamelCase				:   Optional[int]          =							in_proj_weight[2_5_6:5_1_2, :]
          UpperCamelCase				:   str          =							in_proj_bias[2_5_6:5_1_2]
          UpperCamelCase				:   Tuple          =							in_proj_weight[-2_5_6:, :]
          UpperCamelCase				:   List[Any]          =							in_proj_bias[-2_5_6:]
          # read in weights + bias of input projection layer of cross-attention
          UpperCamelCase				:   List[Any]          =							state_dict.pop(
              f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'    )
          UpperCamelCase				:   List[Any]          =							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'    )
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          UpperCamelCase				:   List[Any]          =							in_proj_weight_cross_attn[:2_5_6, :]
          UpperCamelCase				:   Optional[Any]          =							in_proj_bias_cross_attn[:2_5_6]
          UpperCamelCase				:   List[Any]          =							in_proj_weight_cross_attn[2_5_6:5_1_2, :]
          UpperCamelCase				:   Union[str, Any]          =							in_proj_bias_cross_attn[2_5_6:5_1_2]
          UpperCamelCase				:   Dict          =							in_proj_weight_cross_attn[-2_5_6:, :]
          UpperCamelCase				:   int          =							in_proj_bias_cross_attn[-2_5_6:]
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase				:   int          =							Image.open(requests.get(lowercase_						,stream=lowercase_    ).raw    )
  
      return im
  @torch.no_grad()
  def 					A_			(		snake_case_     :       str						,snake_case_     :       Any    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
          UpperCamelCase				:   Union[str, Any]          =							"""resnet101"""
      if "dc5" in model_name:
          UpperCamelCase				:   Union[str, Any]          =							True
      UpperCamelCase				:   Union[str, Any]          =							"""panoptic""" in model_name
      if is_panoptic:
          UpperCamelCase				:   str          =							2_5_0
      else:
          UpperCamelCase				:   Dict          =							9_1
          UpperCamelCase				:   Optional[Any]          =							"""huggingface/label-files"""
          UpperCamelCase				:   Optional[Any]          =							"""coco-detection-id2label.json"""
          UpperCamelCase				:   Any          =							json.load(open(hf_hub_download(lowercase_						,lowercase_						,repo_type="""dataset"""    )						,"""r"""    )    )
          UpperCamelCase				:   Any          =							{int(lowercase_    ): v for k, v in idalabel.items()}
          UpperCamelCase				:   List[str]          =							idalabel
          UpperCamelCase				:   Tuple          =							{v: k for k, v in idalabel.items()}
  
      # load image processor
      UpperCamelCase				:   List[Any]          =							"""coco_panoptic""" if is_panoptic else """coco_detection"""
      UpperCamelCase				:   List[str]          =							DetrImageProcessor(format=lowercase_    )
  
      # prepare image
      UpperCamelCase				:   str          =							prepare_img()
      UpperCamelCase				:   Tuple          =							image_processor(images=lowercase_						,return_tensors="""pt"""    )
      UpperCamelCase				:   str          =							encoding["""pixel_values"""]
  
      logger.info(f'Converting model {model_name}...'    )
  
      # load original model from torch hub
      UpperCamelCase				:   List[str]          =							torch.hub.load("""facebookresearch/detr"""						,lowercase_						,pretrained=lowercase_    ).eval()
      UpperCamelCase				:   Any          =							detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
          if is_panoptic:
              UpperCamelCase				:   Dict          =							"""detr.""" + src
          rename_key(lowercase_						,lowercase_						,lowercase_    )
      UpperCamelCase				:   Dict          =							rename_backbone_keys(lowercase_    )
      # query, key and value matrices need special treatment
      read_in_q_k_v(lowercase_						,is_panoptic=lowercase_    )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      UpperCamelCase				:   Any          =							"""detr.model.""" if is_panoptic else """model."""
      for key in state_dict.copy().keys():
          if is_panoptic:
              if (
                  key.startswith("""detr"""    )
                  and not key.startswith("""class_labels_classifier"""    )
                  and not key.startswith("""bbox_predictor"""    )
              ):
                  UpperCamelCase				:   int          =							state_dict.pop(lowercase_    )
                  UpperCamelCase				:   Optional[Any]          =							val
              elif "class_labels_classifier" in key or "bbox_predictor" in key:
                  UpperCamelCase				:   Union[str, Any]          =							state_dict.pop(lowercase_    )
                  UpperCamelCase				:   List[Any]          =							val
              elif key.startswith("""bbox_attention"""    ) or key.startswith("""mask_head"""    ):
                  continue
              else:
                  UpperCamelCase				:   List[Any]          =							state_dict.pop(lowercase_    )
                  UpperCamelCase				:   List[str]          =							val
          else:
              if not key.startswith("""class_labels_classifier"""    ) and not key.startswith("""bbox_predictor"""    ):
                  UpperCamelCase				:   Union[str, Any]          =							state_dict.pop(lowercase_    )
                  UpperCamelCase				:   List[str]          =							val
      # finally, create HuggingFace model and load state dict
      UpperCamelCase				:   Optional[Any]          =							DetrForSegmentation(lowercase_    ) if is_panoptic else DetrForObjectDetection(lowercase_    )
      model.load_state_dict(lowercase_    )
      model.eval()
      # verify our conversion
      UpperCamelCase				:   List[str]          =							detr(lowercase_    )
      UpperCamelCase				:   str          =							model(lowercase_    )
      assert torch.allclose(outputs.logits						,original_outputs["""pred_logits"""]						,atol=1e-4    )
      assert torch.allclose(outputs.pred_boxes						,original_outputs["""pred_boxes"""]						,atol=1e-4    )
      if is_panoptic:
          assert torch.allclose(outputs.pred_masks						,original_outputs["""pred_masks"""]						,atol=1e-4    )
  
      # Save model and image processor
      logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'    )
      Path(lowercase_    ).mkdir(exist_ok=lowercase_    )
      model.save_pretrained(lowercase_    )
      image_processor.save_pretrained(lowercase_    )
  
  
  if __name__ == "__main__":
    __A :		int      							=    argparse.ArgumentParser()
  
    parser.add_argument(
        '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the DETR model you\'d like to convert.'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
    )
    __A :		Union[str, Any]      							=    parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_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 :		int      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							f'pretrained_models/{model_name}'
      if not os.path.isfile(snake_case_    ):
          os.makedirs("""pretrained_models"""						,exist_ok=snake_case_    )
          UpperCamelCase				:   Dict          =							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
          download_url(snake_case_						,"""pretrained_models"""    )
      UpperCamelCase				:   List[Any]          =							torch.load(snake_case_						,map_location=lambda snake_case_						,snake_case_    : storage    )
      return model
  def 					A_			(		snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )
  
      UpperCamelCase				:   str          =							state_dict["""x_embedder.proj.weight"""]
      UpperCamelCase				:   Optional[Any]          =							state_dict["""x_embedder.proj.bias"""]
      state_dict.pop("""x_embedder.proj.weight"""    )
      state_dict.pop("""x_embedder.proj.bias"""    )
  
      for depth in range(2_8    ):
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.0.weight"""
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[
              """t_embedder.mlp.0.bias"""
          ]
          UpperCamelCase				:   str          =							state_dict[
              """t_embedder.mlp.2.weight"""
          ]
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.2.bias"""
          ]
          UpperCamelCase				:   int          =							state_dict[
              """y_embedder.embedding_table.weight"""
          ]
  
          UpperCamelCase				:   List[Any]          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.weight'
          ]
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.bias'
          ]
  
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']						,3						,dim=0    )
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']						,3						,dim=0    )
  
          UpperCamelCase				:   int          =							q
          UpperCamelCase				:   Tuple          =							q_bias
          UpperCamelCase				:   List[Any]          =							k
          UpperCamelCase				:   int          =							k_bias
          UpperCamelCase				:   List[str]          =							v
          UpperCamelCase				:   str          =							v_bias
  
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.attn.proj.weight'
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[f'blocks.{depth}.attn.proj.bias']
  
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc1.weight']
          UpperCamelCase				:   Tuple          =							state_dict[f'blocks.{depth}.mlp.fc1.bias']
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc2.weight']
          UpperCamelCase				:   Tuple          =							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				:   List[str]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
      UpperCamelCase				:   str          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
      UpperCamelCase				:   Tuple          =							state_dict["""final_layer.linear.weight"""]
      UpperCamelCase				:   List[Any]          =							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				:   int          =							TransformeraDModel(
          sample_size=args.image_size // 8						,num_layers=2_8						,attention_head_dim=7_2						,in_channels=4						,out_channels=8						,patch_size=2						,attention_bias=snake_case_						,num_attention_heads=1_6						,activation_fn="""gelu-approximate"""						,num_embeds_ada_norm=1_0_0_0						,norm_type="""ada_norm_zero"""						,norm_elementwise_affine=snake_case_						,)
      transformer.load_state_dict(snake_case_						,strict=snake_case_    )
  
      UpperCamelCase				:   str          =							DDIMScheduler(
          num_train_timesteps=1_0_0_0						,beta_schedule="""linear"""						,prediction_type="""epsilon"""						,clip_sample=snake_case_						,)
  
      UpperCamelCase				:   Optional[Any]          =							AutoencoderKL.from_pretrained(args.vae_model    )
  
      UpperCamelCase				:   List[Any]          =							DiTPipeline(transformer=snake_case_						,vae=snake_case_						,scheduler=snake_case_    )
  
      if args.save:
          pipeline.save_pretrained(args.checkpoint_path    )
  
  
  if __name__ == "__main__":
    __A :		Any      							=    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 :		Dict      							=    parser.parse_args()
    main(args)
  
  
  
  
  