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 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
     UpperCAmelCase_      :      Tuple      =					state_dict.pop(__lowerCamelCase						)
     UpperCAmelCase_      :      str      =					val
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     UpperCAmelCase_      :      str      =					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_      :      List[Any]      =					value
        else:
           UpperCAmelCase_      :      List[str]      =					value
  
     return new_state_dict
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase=False						):
     UpperCAmelCase_      :      Union[str, Any]      =					""
     if is_panoptic:
        UpperCAmelCase_      :      Optional[Any]      =					"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_      :      Optional[int]      =					state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
        UpperCAmelCase_      :      Any      =					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_      :      Tuple      =					in_proj_weight[:256, :]
        UpperCAmelCase_      :      List[str]      =					in_proj_bias[:256]
        UpperCAmelCase_      :      Any      =					in_proj_weight[256:512, :]
        UpperCAmelCase_      :      Any      =					in_proj_bias[256:512]
        UpperCAmelCase_      :      List[str]      =					in_proj_weight[-256:, :]
        UpperCAmelCase_      :      List[Any]      =					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
        UpperCAmelCase_      :      Union[str, 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_      :      Union[str, Any]      =					in_proj_weight[:256, :]
        UpperCAmelCase_      :      List[str]      =					in_proj_bias[:256]
        UpperCAmelCase_      :      str      =					in_proj_weight[256:512, :]
        UpperCAmelCase_      :      Dict      =					in_proj_bias[256:512]
        UpperCAmelCase_      :      List[str]      =					in_proj_weight[-256:, :]
        UpperCAmelCase_      :      Optional[Any]      =					in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        UpperCAmelCase_      :      str      =					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_      :      int      =					in_proj_weight_cross_attn[:256, :]
        UpperCAmelCase_      :      Optional[int]      =					in_proj_bias_cross_attn[:256]
        UpperCAmelCase_      :      Dict      =					in_proj_weight_cross_attn[256:512, :]
        UpperCAmelCase_      :      Tuple      =					in_proj_bias_cross_attn[256:512]
        UpperCAmelCase_      :      List[str]      =					in_proj_weight_cross_attn[-256:, :]
        UpperCAmelCase_      :      int      =					in_proj_bias_cross_attn[-256:]
  
  
  
  
  
  
  
  def 			__a  (	):
     UpperCAmelCase_      :      Any      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_      :      List[Any]      =					Image.open(requests.get(__lowerCamelCase,	stream=__lowerCamelCase						).raw						)
  
     return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
     UpperCAmelCase_      :      Optional[int]      =					DetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
        UpperCAmelCase_      :      List[Any]      =					"resnet101"
     if "dc5" in model_name:
        UpperCAmelCase_      :      Union[str, Any]      =					True
     UpperCAmelCase_      :      Dict      =					"panoptic" in model_name
     if is_panoptic:
        UpperCAmelCase_      :      Tuple      =					250
     else:
        UpperCAmelCase_      :      Union[str, Any]      =					91
        UpperCAmelCase_      :      int      =					"huggingface/label-files"
        UpperCAmelCase_      :      List[Any]      =					"coco-detection-id2label.json"
        UpperCAmelCase_      :      List[str]      =					json.load(open(hf_hub_download(__lowerCamelCase,	__lowerCamelCase,	repo_type="dataset"						),	"r"						)						)
        UpperCAmelCase_      :      Union[str, Any]      =					{int(__lowerCamelCase						): v for k, v in idalabel.items()}
        UpperCAmelCase_      :      Union[str, Any]      =					idalabel
        UpperCAmelCase_      :      List[str]      =					{v: k for k, v in idalabel.items()}
  
     # load image processor
     UpperCAmelCase_      :      Optional[int]      =					"coco_panoptic" if is_panoptic else "coco_detection"
     UpperCAmelCase_      :      List[Any]      =					DetrImageProcessor(format=__lowerCamelCase						)
  
     # prepare image
     UpperCAmelCase_      :      int      =					prepare_img()
     UpperCAmelCase_      :      Tuple      =					image_processor(images=__lowerCamelCase,	return_tensors="pt"						)
     UpperCAmelCase_      :      int      =					encoding["pixel_values"]
  
     logger.info(f"""Converting model {model_name}..."""						)
  
     # load original model from torch hub
     UpperCAmelCase_      :      Optional[Any]      =					torch.hub.load("facebookresearch/detr",	__lowerCamelCase,	pretrained=__lowerCamelCase						).eval()
     UpperCAmelCase_      :      Tuple      =					detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
        if is_panoptic:
           UpperCAmelCase_      :      Any      =					"detr." + src
        rename_key(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						)
     UpperCAmelCase_      :      Optional[int]      =					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
     UpperCAmelCase_      :      Optional[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_      :      List[Any]      =					state_dict.pop(__lowerCamelCase						)
              UpperCAmelCase_      :      int      =					val
           elif "class_labels_classifier" in key or "bbox_predictor" in key:
              UpperCAmelCase_      :      Tuple      =					state_dict.pop(__lowerCamelCase						)
              UpperCAmelCase_      :      str      =					val
           elif key.startswith("bbox_attention"						) or key.startswith("mask_head"						):
              continue
           else:
              UpperCAmelCase_      :      Union[str, Any]      =					state_dict.pop(__lowerCamelCase						)
              UpperCAmelCase_      :      List[Any]      =					val
        else:
           if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
              UpperCAmelCase_      :      int      =					state_dict.pop(__lowerCamelCase						)
              UpperCAmelCase_      :      str      =					val
      # finally, create HuggingFace model and load state dict
     UpperCAmelCase_      :      Tuple      =					DetrForSegmentation(__lowerCamelCase						) if is_panoptic else DetrForObjectDetection(__lowerCamelCase						)
     model.load_state_dict(__lowerCamelCase						)
     model.eval()
     # verify our conversion
     UpperCAmelCase_      :      str      =					detr(__lowerCamelCase						)
     UpperCAmelCase_      :      int      =					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__":
      _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"""
  
  
  
  
  
  from .hash_table import HashTable
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     *lowercase_       ,     **lowercase_						):
  
       """simple docstring"""
       super().__init__(*lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=None						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					1
       UpperCAmelCase_      :      List[str]      =					self.hash_function(key + i * i						)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          i += 1
          UpperCAmelCase_      :      List[Any]      =					(
              self.hash_function(key + i * i						)
              if not self.balanced_factor() >= self.lim_charge
              else None
          )
  
          if new_key is None:
             break
  
       return new_key
  
  
  