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()
  A           =   logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  A           =   []
  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   snake_case_       (							_a     :							Optional[int] ,					_a     :							Optional[Any] ,					_a     :							Optional[int]			):
        snake_case_						       =							state_dict.pop(_a			)
        snake_case_						       =							val
  def   snake_case_       (							_a     :							Optional[Any]			):
        snake_case_						       =							OrderedDict()
        for key, value in state_dict.items():
              if "backbone.0.body" in key:
                    snake_case_						       =							key.replace("backbone.0.body" ,					"backbone.conv_encoder.model"			)
                    snake_case_						       =							value
              else:
                    snake_case_						       =							value
  
        return new_state_dict
  def   snake_case_       (							_a     :							Any ,					_a     :							Dict=False			):
        snake_case_						       =							""
        if is_panoptic:
              snake_case_						       =							"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)
              snake_case_						       =							state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''			)
              snake_case_						       =							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
              snake_case_						       =							in_proj_weight[:256, :]
              snake_case_						       =							in_proj_bias[:256]
              snake_case_						       =							in_proj_weight[256:512, :]
              snake_case_						       =							in_proj_bias[256:512]
              snake_case_						       =							in_proj_weight[-256:, :]
              snake_case_						       =							in_proj_bias[-256:]
        # 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
              snake_case_						       =							state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
              snake_case_						       =							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
              snake_case_						       =							in_proj_weight[:256, :]
              snake_case_						       =							in_proj_bias[:256]
              snake_case_						       =							in_proj_weight[256:512, :]
              snake_case_						       =							in_proj_bias[256:512]
              snake_case_						       =							in_proj_weight[-256:, :]
              snake_case_						       =							in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              snake_case_						       =							state_dict.pop(
                  F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''			)
              snake_case_						       =							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
              snake_case_						       =							in_proj_weight_cross_attn[:256, :]
              snake_case_						       =							in_proj_bias_cross_attn[:256]
              snake_case_						       =							in_proj_weight_cross_attn[256:512, :]
              snake_case_						       =							in_proj_bias_cross_attn[256:512]
              snake_case_						       =							in_proj_weight_cross_attn[-256:, :]
              snake_case_						       =							in_proj_bias_cross_attn[-256:]
  def   snake_case_       (							):
        snake_case_						       =							"http://images.cocodataset.org/val2017/000000039769.jpg"
        snake_case_						       =							Image.open(requests.get(_a ,					stream=_a			).raw			)
  
        return im
  @torch.no_grad()
  def   snake_case_       (							_a     :							Dict ,					_a     :							str			):
        snake_case_						       =							DetrConfig()
        # set backbone and dilation attributes
        if "resnet101" in model_name:
              snake_case_						       =							"resnet101"
        if "dc5" in model_name:
              snake_case_						       =							True
        snake_case_						       =							"panoptic" in model_name
        if is_panoptic:
              snake_case_						       =							250
        else:
              snake_case_						       =							91
              snake_case_						       =							"huggingface/label-files"
              snake_case_						       =							"coco-detection-id2label.json"
              snake_case_						       =							json.load(open(hf_hub_download(_a ,					_a ,					repo_type="dataset"			) ,					"r"			)			)
              snake_case_						       =							{int(_a			): v for k, v in idalabel.items()}
              snake_case_						       =							idalabel
              snake_case_						       =							{v: k for k, v in idalabel.items()}
  
        # load image processor
        snake_case_						       =							"coco_panoptic" if is_panoptic else "coco_detection"
        snake_case_						       =							DetrImageProcessor(format=_a			)
  
        # prepare image
        snake_case_						       =							prepare_img()
        snake_case_						       =							image_processor(images=_a ,					return_tensors="pt"			)
        snake_case_						       =							encoding["pixel_values"]
  
        logger.info(F'''Converting model {model_name}...'''			)
  
        # load original model from torch hub
        snake_case_						       =							torch.hub.load("facebookresearch/detr" ,					_a ,					pretrained=_a			).eval()
        snake_case_						       =							detr.state_dict()
        # rename keys
        for src, dest in rename_keys:
              if is_panoptic:
                    snake_case_						       =							"detr." + src
              rename_key(_a ,					_a ,					_a			)
        snake_case_						       =							rename_backbone_keys(_a			)
        # query, key and value matrices need special treatment
        read_in_q_k_v(_a ,					is_panoptic=_a			)
        # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
        snake_case_						       =							"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"			)
                    ):
                          snake_case_						       =							state_dict.pop(_a			)
                          snake_case_						       =							val
                    elif "class_labels_classifier" in key or "bbox_predictor" in key:
                          snake_case_						       =							state_dict.pop(_a			)
                          snake_case_						       =							val
                    elif key.startswith("bbox_attention"			) or key.startswith("mask_head"			):
                          continue
                    else:
                          snake_case_						       =							state_dict.pop(_a			)
                          snake_case_						       =							val
              else:
                    if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
                          snake_case_						       =							state_dict.pop(_a			)
                          snake_case_						       =							val
      # finally, create HuggingFace model and load state dict
        snake_case_						       =							DetrForSegmentation(_a			) if is_panoptic else DetrForObjectDetection(_a			)
        model.load_state_dict(_a			)
        model.eval()
        # verify our conversion
        snake_case_						       =							detr(_a			)
        snake_case_						       =							model(_a			)
        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(_a			).mkdir(exist_ok=_a			)
        model.save_pretrained(_a			)
        image_processor.save_pretrained(_a			)
  
  
  if __name__ == "__main__":
       A           =   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           =   parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  def   snake_case_       (							_a     :							List[str] ,					_a     :							List[str]			):
        snake_case_						       =							1
        while b > 0:
              if b & 1:
                    res *= a
  
              a *= a
              b >>= 1
  
        return res
  def   snake_case_       (							_a     :							Union[str, Any] ,					_a     :							Tuple ,					_a     :							Optional[Any]			):
        snake_case_						       =							1
        while b > 0:
              if b & 1:
                    snake_case_						       =							((res % c) * (a % c)) % c
  
              a *= a
              b >>= 1
  
        return res
  
  