






"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase   :						List[str]     						= logging.get_logger(__name__)




def 				a (     UpperCamelCase_							:				Tuple		)  ->		Union[str, Any]:
 if "backbone.0.body" in orig_key:
  __A	:       List[str]       =	orig_key.replace('backbone.0.body'     ,	'backbone.conv_encoder.model'		)
 if "transformer" in orig_key:
  __A	:       Union[str, Any]       =	orig_key.replace('transformer.'     ,	''		)
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   __A	:       Optional[int]       =	orig_key.replace('norm1'     ,	'self_attn_layer_norm'		)
  else:
   __A	:       Union[str, Any]       =	orig_key.replace('norm1'     ,	'encoder_attn_layer_norm'		)
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   __A	:       str       =	orig_key.replace('norm2'     ,	'final_layer_norm'		)
  else:
   __A	:       Tuple       =	orig_key.replace('norm2'     ,	'self_attn_layer_norm'		)
 if "norm3" in orig_key:
  __A	:       int       =	orig_key.replace('norm3'     ,	'final_layer_norm'		)
 if "linear1" in orig_key:
  __A	:       List[str]       =	orig_key.replace('linear1'     ,	'fc1'		)
 if "linear2" in orig_key:
  __A	:       Optional[Any]       =	orig_key.replace('linear2'     ,	'fc2'		)
 if "query_embed" in orig_key:
  __A	:       List[Any]       =	orig_key.replace('query_embed'     ,	'query_position_embeddings'		)
 if "cross_attn" in orig_key:
  __A	:       str       =	orig_key.replace('cross_attn'     ,	'encoder_attn'		)

 return orig_key




def 				a (     UpperCamelCase_							:				int		)  ->		Optional[int]:
 # transformer decoder self-attention layers
 for i in range(6		):
  # read in weights + bias of input projection layer of self-attention
  __A	:       List[str]       =	state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"		)
  __A	:       Dict       =	state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_bias"		)
  # next, add query, keys and values (in that order) to the state dict
  __A	:       Optional[Any]       =	in_proj_weight[:2_5_6, :]
  __A	:       Tuple       =	in_proj_bias[:2_5_6]
  __A	:       List[str]       =	in_proj_weight[2_5_6:5_1_2, :]
  __A	:       Optional[Any]       =	in_proj_bias[2_5_6:5_1_2]
  __A	:       Dict       =	in_proj_weight[-2_5_6:, :]
  __A	:       int       =	in_proj_bias[-2_5_6:]




def 				a (     )  ->		Union[str, Any]:
 __A	:       List[str]       =	'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A	:       Optional[int]       =	Image.open(requests.get(UpperCamelCase_     ,	stream=UpperCamelCase_		).raw		)

 return im




@torch.no_grad()
def 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				List[Any]     ,	)  ->		str:
 __A	:       List[Any]       =	DeformableDetrConfig()
 # set config attributes
 if single_scale:
  __A	:       Dict       =	1
 __A	:       Optional[int]       =	dilation
 __A	:       Tuple       =	with_box_refine
 __A	:       str       =	two_stage
 # set labels
 __A	:       Any       =	9_1
 __A	:       int       =	'huggingface/label-files'
 __A	:       int       =	'coco-detection-id2label.json'
 __A	:       List[str]       =	json.load(open(cached_download(hf_hub_url(UpperCamelCase_     ,	UpperCamelCase_     ,	repo_type='dataset'		)		)     ,	'r'		)		)
 __A	:       Optional[Any]       =	{int(UpperCamelCase_		): v for k, v in idalabel.items()}
 __A	:       Union[str, Any]       =	idalabel
 __A	:       Union[str, Any]       =	{v: k for k, v in idalabel.items()}

 # load image processor
 __A	:       Dict       =	DeformableDetrImageProcessor(format='coco_detection'		)

 # prepare image
 __A	:       Dict       =	prepare_img()
 __A	:       Tuple       =	image_processor(images=UpperCamelCase_     ,	return_tensors='pt'		)
 __A	:       Tuple       =	encoding['pixel_values']

 logger.info('Converting model...'		)

 # load original state dict
 __A	:       List[str]       =	torch.load(UpperCamelCase_     ,	map_location='cpu'		)['model']
 # rename keys
 for key in state_dict.copy().keys():
  __A	:       List[str]       =	state_dict.pop(UpperCamelCase_		)
  __A	:       str       =	val
 # query, key and value matrices need special treatment
 read_in_q_k_v(UpperCamelCase_		)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 __A	:       Optional[int]       =	'model.'
 for key in state_dict.copy().keys():
  if not key.startswith('class_embed'		) and not key.startswith('bbox_embed'		):
   __A	:       Optional[Any]       =	state_dict.pop(UpperCamelCase_		)
   __A	:       List[Any]       =	val
    # finally, create HuggingFace model and load state dict
 __A	:       int       =	DeformableDetrForObjectDetection(UpperCamelCase_		)
 model.load_state_dict(UpperCamelCase_		)
 model.eval()

 __A	:       Optional[Any]       =	'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(UpperCamelCase_		)
 # verify our conversion
 __A	:       int       =	model(pixel_values.to(UpperCamelCase_		)		)

 __A	:       Any       =	torch.tensor(
     [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]		)
 __A	:       List[Any]       =	torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]		)

 if single_scale:
  __A	:       Dict       =	torch.tensor(
      [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]		)
  __A	:       List[Any]       =	torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]		)

 if single_scale and dilation:
  __A	:       List[str]       =	torch.tensor(
      [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]		)
  __A	:       Tuple       =	torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]		)

 if with_box_refine:
  __A	:       Optional[Any]       =	torch.tensor(
      [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]		)
  __A	:       Any       =	torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]		)

 if with_box_refine and two_stage:
  __A	:       str       =	torch.tensor(
      [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]		)
  __A	:       Optional[int]       =	torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]		)

 print('Logits:'     ,	outputs.logits[0, :3, :3]		)

 assert torch.allclose(outputs.logits[0, :3, :3]     ,	expected_logits.to(UpperCamelCase_		)     ,	atol=1E-4		)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,	expected_boxes.to(UpperCamelCase_		)     ,	atol=1E-4		)

 print('Everything ok!'		)

 # Save model and image processor
 logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."		)
 Path(UpperCamelCase_		).mkdir(exist_ok=UpperCamelCase_		)
 model.save_pretrained(UpperCamelCase_		)
 image_processor.save_pretrained(UpperCamelCase_		)

 # Push to hub
 if push_to_hub:
  __A	:       str       =	'deformable-detr'
  model_name += "-single-scale" if single_scale else ""
  model_name += "-dc5" if dilation else ""
  model_name += "-with-box-refine" if with_box_refine else ""
  model_name += "-two-stage" if two_stage else ""
  print('Pushing model to hub...'		)
  model.push_to_hub(repo_path_or_name=UpperCamelCase_     ,	organization='nielsr'     ,	commit_message='Add model'		)


if __name__ == "__main__":
   _UpperCamelCase   :						List[str]     						= argparse.ArgumentParser()

   parser.add_argument(
       """--checkpoint_path""",
       type=str,
       default="""/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth""",
       help="""Path to Pytorch checkpoint (.pth file) you'd like to convert.""",
   )
   parser.add_argument("""--single_scale""", action="""store_true""", help="""Whether to set config.num_features_levels = 1.""")
   parser.add_argument("""--dilation""", action="""store_true""", help="""Whether to set config.dilation=True.""")
   parser.add_argument("""--with_box_refine""", action="""store_true""", help="""Whether to set config.with_box_refine=True.""")
   parser.add_argument("""--two_stage""", action="""store_true""", help="""Whether to set config.two_stage=True.""")
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the folder to output PyTorch model.""",
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )
   _UpperCamelCase   :						List[str]     						= parser.parse_args()
   convert_deformable_detr_checkpoint(
       args.checkpoint_path,
       args.single_scale,
       args.dilation,
       args.with_box_refine,
       args.two_stage,
       args.pytorch_dump_folder_path,
       args.push_to_hub,
   )













"""simple docstring"""


import math




def 				a (     UpperCamelCase_							:				int		)  ->		list[int]:
 if not isinstance(UpperCamelCase_     ,	UpperCamelCase_		):
  __A	:       Any       =	F"Input value of [number={number}] must be an integer"
  raise TypeError(UpperCamelCase_		)
 if number < 1:
  __A	:       int       =	F"Input value of [number={number}] must be a positive integer"
  raise ValueError(UpperCamelCase_		)
 __A	:       Optional[Any]       =	[number]
 while number != 1:
  if number % 2 == 0:
   __A	:       Optional[int]       =	math.floor(math.sqrt(UpperCamelCase_		)		)
  else:
   __A	:       List[str]       =	math.floor(
       math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		)		)
  sequence.append(UpperCamelCase_		)
 return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()





