code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  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()
  lowerCamelCase_     :     Optional[int]    		=						logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  lowerCamelCase_     :     Dict    		=						[]
  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		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=state_dict.pop(lowercase	)
      a					=val
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=OrderedDict()
      for key, value in state_dict.items():
          if "backbone.0.body" in key:
              a					=key.replace('''backbone.0.body''' ,					'''backbone.conv_encoder.model'''	)
              a					=value
          else:
              a					=value
  
      return new_state_dict
  def _A		(		lowercase ,					lowercase=False	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=''''''
      if is_panoptic:
          a					='''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)
          a					=state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
          a					=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
          a					=in_proj_weight[:2_56, :]
          a					=in_proj_bias[:2_56]
          a					=in_proj_weight[2_56:5_12, :]
          a					=in_proj_bias[2_56:5_12]
          a					=in_proj_weight[-2_56:, :]
          a					=in_proj_bias[-2_56:]
      # 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
          a					=state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
          a					=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
          a					=in_proj_weight[:2_56, :]
          a					=in_proj_bias[:2_56]
          a					=in_proj_weight[2_56:5_12, :]
          a					=in_proj_bias[2_56:5_12]
          a					=in_proj_weight[-2_56:, :]
          a					=in_proj_bias[-2_56:]
          # read in weights + bias of input projection layer of cross-attention
          a					=state_dict.pop(
              f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
          a					=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
          a					=in_proj_weight_cross_attn[:2_56, :]
          a					=in_proj_bias_cross_attn[:2_56]
          a					=in_proj_weight_cross_attn[2_56:5_12, :]
          a					=in_proj_bias_cross_attn[2_56:5_12]
          a					=in_proj_weight_cross_attn[-2_56:, :]
          a					=in_proj_bias_cross_attn[-2_56:]
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      a					=Image.open(requests.get(lowercase ,					stream=lowercase	).raw	)
  
      return im
  @torch.no_grad()
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
          a					='''resnet101'''
      if "dc5" in model_name:
          a					=True
      a					='''panoptic''' in model_name
      if is_panoptic:
          a					=2_50
      else:
          a					=91
          a					='''huggingface/label-files'''
          a					='''coco-detection-id2label.json'''
          a					=json.load(open(hf_hub_download(lowercase ,					lowercase ,					repo_type='''dataset'''	) ,					'''r'''	)	)
          a					={int(lowercase	): v for k, v in idalabel.items()}
          a					=idalabel
          a					={v: k for k, v in idalabel.items()}
  
      # load image processor
      a					='''coco_panoptic''' if is_panoptic else '''coco_detection'''
      a					=DetrImageProcessor(format=lowercase	)
  
      # prepare image
      a					=prepare_img()
      a					=image_processor(images=lowercase ,					return_tensors='''pt'''	)
      a					=encoding['''pixel_values''']
  
      logger.info(f'''Converting model {model_name}...'''	)
  
      # load original model from torch hub
      a					=torch.hub.load('''facebookresearch/detr''' ,					lowercase ,					pretrained=lowercase	).eval()
      a					=detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
          if is_panoptic:
              a					='''detr.''' + src
          rename_key(lowercase ,					lowercase ,					lowercase	)
      a					=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
      a					='''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'''	)
              ):
                  a					=state_dict.pop(lowercase	)
                  a					=val
              elif "class_labels_classifier" in key or "bbox_predictor" in key:
                  a					=state_dict.pop(lowercase	)
                  a					=val
              elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
                  continue
              else:
                  a					=state_dict.pop(lowercase	)
                  a					=val
          else:
              if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
                  a					=state_dict.pop(lowercase	)
                  a					=val
      # finally, create HuggingFace model and load state dict
      a					=DetrForSegmentation(lowercase	) if is_panoptic else DetrForObjectDetection(lowercase	)
      model.load_state_dict(lowercase	)
      model.eval()
      # verify our conversion
      a					=detr(lowercase	)
      a					=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__":
         lowerCamelCase_     :     Tuple    		=						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."""
         )
         lowerCamelCase_     :     Any    		=						parser.parse_args()
         convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images