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							:			List[str]				=logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  __lowerCAmelCase							:			str				=[]
  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 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      Any ):
      A__       =    state_dict.pop(lowerCamelCase__ )
      A__       =    val
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str ):
      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 		UpperCamelCase       (							_lowerCamelCase      :      Any		, _lowerCamelCase      :      Union[str, Any]=False ):
      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 		UpperCamelCase       (							):
      A__       =    "http://images.cocodataset.org/val2017/000000039769.jpg"
      A__       =    Image.open(requests.get(lowerCamelCase__		, stream=lowerCamelCase__ ).raw )
  
      return im
  
  
  
  
  
  @torch.no_grad()
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Any ):
      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(lowerCamelCase__		, lowerCamelCase__		, repo_type="dataset" )		, "r" ) )
          A__       =    {int(lowerCamelCase__ ): 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=lowerCamelCase__ )
  
      # prepare image
      A__       =    prepare_img()
      A__       =    image_processor(images=lowerCamelCase__		, 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"		, lowerCamelCase__		, pretrained=lowerCamelCase__ ).eval()
      A__       =    detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
          if is_panoptic:
              A__       =    "detr." + src
          rename_key(lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__ )
      A__       =    rename_backbone_keys(lowerCamelCase__ )
      # query, key and value matrices need special treatment
      read_in_q_k_v(lowerCamelCase__		, is_panoptic=lowerCamelCase__ )
      # 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(lowerCamelCase__ )
                  A__       =    val
              elif "class_labels_classifier" in key or "bbox_predictor" in key:
                  A__       =    state_dict.pop(lowerCamelCase__ )
                  A__       =    val
              elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
                  continue
              else:
                  A__       =    state_dict.pop(lowerCamelCase__ )
                  A__       =    val
          else:
              if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
                  A__       =    state_dict.pop(lowerCamelCase__ )
                  A__       =    val
          # finally, create HuggingFace model and load state dict
      A__       =    DetrForSegmentation(lowerCamelCase__ ) if is_panoptic else DetrForObjectDetection(lowerCamelCase__ )
      model.load_state_dict(lowerCamelCase__ )
      model.eval()
      # verify our conversion
      A__       =    detr(lowerCamelCase__ )
      A__       =    model(lowerCamelCase__ )
      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(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
      model.save_pretrained(lowerCamelCase__ )
      image_processor.save_pretrained(lowerCamelCase__ )
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			List[Any]				=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							:			int				=parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
      A__       =    copy.deepcopy(_lowerCamelCase )
      A__       =    sorted(_lowerCamelCase )
      A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
      return quantized
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
      A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
      A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
      # count number of elements per group
      A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
      A__       =    [0] + bins + [np.inf]
      logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
      logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
      return groups
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
       if not isinstance(lowercase_		, lowercase_							):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
       A__       =    sampler
       A__       =    group_ids
       A__       =    batch_size
   def __iter__(       self   :int							)->							Tuple:
       A__       =    defaultdict(lowercase_							)
       A__       =    defaultdict(lowercase_							)
  
       A__       =    0
       for idx in self.sampler:
           A__       =    self.group_ids[idx]
           buffer_per_group[group_id].append(lowercase_							)
           samples_per_group[group_id].append(lowercase_							)
           if len(buffer_per_group[group_id]							) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]							) < self.batch_size
  
       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       A__       =    len(self							)
       A__       =    expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           A__       =    []
           for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
               batch_idx.extend(lowercase_							)
               if len(lowercase_							) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   A__       =    batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(lowercase_							) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0
  
  
  
   def __len__(       self   :int							)->							Tuple:
       return (len(self.sampler							) + self.batch_size - 1) // self.batch_size
  
  
  
  