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()
  __lowercase			:		List[Any] =			logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  __lowercase			:		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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Dict     ):
  					__a    : Optional[Any]      					=						state_dict.pop(lowercase_     )
  					__a    : Tuple      					=						val
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str     ):
  					__a    : List[Any]      					=						OrderedDict()
  					for key, value in state_dict.items():
  										if "backbone.0.body" in key:
  															__a    : Any      					=						key.replace('backbone.0.body'		,   'backbone.conv_encoder.model'     )
  															__a    : Tuple      					=						value
  										else:
  															__a    : Any      					=						value
  
  					return new_state_dict
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]=False     ):
  					__a    : Optional[int]      					=						''
  					if is_panoptic:
  										__a    : Dict      					=						'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    : Any      					=						state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
  										__a    : Optional[int]      					=						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    : Union[str, Any]      					=						in_proj_weight[:256, :]
  										__a    : str      					=						in_proj_bias[:256]
  										__a    : List[str]      					=						in_proj_weight[256:512, :]
  										__a    : Dict      					=						in_proj_bias[256:512]
  										__a    : Tuple      					=						in_proj_weight[-256:, :]
  										__a    : Dict      					=						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
  										__a    : Tuple      					=						state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
  										__a    : Optional[int]      					=						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    : Any      					=						in_proj_weight[:256, :]
  										__a    : int      					=						in_proj_bias[:256]
  										__a    : int      					=						in_proj_weight[256:512, :]
  										__a    : Optional[Any]      					=						in_proj_bias[256:512]
  										__a    : Dict      					=						in_proj_weight[-256:, :]
  										__a    : Any      					=						in_proj_bias[-256:]
  										# read in weights + bias of input projection layer of cross-attention
  										__a    : List[str]      					=						state_dict.pop(
  										    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""     )
  										__a    : Dict      					=						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    : int      					=						in_proj_weight_cross_attn[:256, :]
  										__a    : List[str]      					=						in_proj_bias_cross_attn[:256]
  										__a    : List[str]      					=						in_proj_weight_cross_attn[256:512, :]
  										__a    : List[str]      					=						in_proj_bias_cross_attn[256:512]
  										__a    : Optional[Any]      					=						in_proj_weight_cross_attn[-256:, :]
  										__a    : Optional[int]      					=						in_proj_bias_cross_attn[-256:]
  
  
  
  
  
  
  def 						lowerCamelCase							():
  					__a    : Dict      					=						'http://images.cocodataset.org/val2017/000000039769.jpg'
  					__a    : int      					=						Image.open(requests.get(lowercase_		,   stream=lowercase_     ).raw     )
  
  					return im
  
  
  
  
  
  
  @torch.no_grad()
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     List[str]     ):
  					__a    : List[Any]      					=						DetrConfig()
  					# set backbone and dilation attributes
  					if "resnet101" in model_name:
  										__a    : str      					=						'resnet101'
  					if "dc5" in model_name:
  										__a    : Any      					=						True
  					__a    : Tuple      					=						'panoptic' in model_name
  					if is_panoptic:
  										__a    : str      					=						250
  					else:
  										__a    : Optional[Any]      					=						91
  										__a    : Optional[int]      					=						'huggingface/label-files'
  										__a    : List[str]      					=						'coco-detection-id2label.json'
  										__a    : Optional[int]      					=						json.load(open(hf_hub_download(lowercase_		,   lowercase_		,   repo_type='dataset'     )		,   'r'     )     )
  										__a    : Optional[Any]      					=						{int(lowercase_     ): v for k, v in idalabel.items()}
  										__a    : Union[str, Any]      					=						idalabel
  										__a    : Dict      					=						{v: k for k, v in idalabel.items()}
  
  					# load image processor
  					__a    : Tuple      					=						'coco_panoptic' if is_panoptic else 'coco_detection'
  					__a    : Union[str, Any]      					=						DetrImageProcessor(format=lowercase_     )
  
  					# prepare image
  					__a    : Union[str, Any]      					=						prepare_img()
  					__a    : Optional[int]      					=						image_processor(images=lowercase_		,   return_tensors='pt'     )
  					__a    : Optional[int]      					=						encoding['pixel_values']
  
  					logger.info(F"""Converting model {model_name}..."""     )
  
  					# load original model from torch hub
  					__a    : Union[str, Any]      					=						torch.hub.load('facebookresearch/detr'		,   lowercase_		,   pretrained=lowercase_     ).eval()
  					__a    : int      					=						detr.state_dict()
  					# rename keys
  					for src, dest in rename_keys:
  										if is_panoptic:
  															__a    : List[str]      					=						'detr.' + src
  										rename_key(lowercase_		,   lowercase_		,   lowercase_     )
  					__a    : int      					=						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    : List[str]      					=						'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    : int      					=						state_dict.pop(lowercase_     )
  																				__a    : Optional[Any]      					=						val
  															elif "class_labels_classifier" in key or "bbox_predictor" in key:
  																				__a    : Any      					=						state_dict.pop(lowercase_     )
  																				__a    : Union[str, Any]      					=						val
  															elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
  																				continue
  															else:
  																				__a    : Any      					=						state_dict.pop(lowercase_     )
  																				__a    : Optional[Any]      					=						val
  										else:
  															if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
  																				__a    : Tuple      					=						state_dict.pop(lowercase_     )
  																				__a    : int      					=						val
      # finally, create HuggingFace model and load state dict
  					__a    : List[Any]      					=						DetrForSegmentation(lowercase_     ) if is_panoptic else DetrForObjectDetection(lowercase_     )
  					model.load_state_dict(lowercase_     )
  					model.eval()
  					# verify our conversion
  					__a    : Tuple      					=						detr(lowercase_     )
  					__a    : Optional[int]      					=						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__":
  							__lowercase			:		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.'
  							)
  							__lowercase			:		Union[str, Any] =			parser.parse_args()
  							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import itertools
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 1_000_000     ):
  					__a    : str      					=						[1]
  
  					for i in itertools.count(len(_SCREAMING_SNAKE_CASE     )     ):
  										__a    : Dict      					=						0
  										for j in itertools.count(1     ):
  															__a    : int      					=						-1 if j % 2 == 0 else +1
  															__a    : Optional[int]      					=						(j * j * 3 - j) // 2
  															if index > i:
  																				break
  															item += partitions[i - index] * sign
  															item %= number
  															index += j
  															if index > i:
  																				break
  															item += partitions[i - index] * sign
  															item %= number
  
  										if item == 0:
  															return i
  										partitions.append(_SCREAMING_SNAKE_CASE     )
  
  					return 0
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  	print(f'''{solution() = }''')
  
  
  
  