

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[Any]					     =     logging.get_logger(__name__)






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

      return orig_key






def        __lowercase   (  _A      )  ->    Any:
      # transformer decoder self-attention layers
      for i in range(6      ):
            # read in weights + bias of input projection layer of self-attention
            SCREAMING_SNAKE_CASE			:   Tuple	  = state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"      )
            SCREAMING_SNAKE_CASE			:   Any	  = 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
            SCREAMING_SNAKE_CASE			:   str	  = in_proj_weight[:256, :]
            SCREAMING_SNAKE_CASE			:   List[Any]	  = in_proj_bias[:256]
            SCREAMING_SNAKE_CASE			:   Tuple	  = in_proj_weight[256:512, :]
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = in_proj_bias[256:512]
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = in_proj_weight[-256:, :]
            SCREAMING_SNAKE_CASE			:   Dict	  = in_proj_bias[-256:]






def        __lowercase   (  )  ->    int:
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE			:   Dict	  = Image.open(requests.get(_A    ,     stream=_A      ).raw      )

      return im






@torch.no_grad()
def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A    ,     _A    ,     _A    ,     _A    ,     )  ->    Optional[Any]:
      SCREAMING_SNAKE_CASE			:   List[str]	  = DeformableDetrConfig()
      # set config attributes
      if single_scale:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 1
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = dilation
      SCREAMING_SNAKE_CASE			:   int	  = with_box_refine
      SCREAMING_SNAKE_CASE			:   Any	  = two_stage
      # set labels
      SCREAMING_SNAKE_CASE			:   str	  = 91
      SCREAMING_SNAKE_CASE			:   int	  = """huggingface/label-files"""
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = """coco-detection-id2label.json"""
      SCREAMING_SNAKE_CASE			:   Dict	  = json.load(open(cached_download(hf_hub_url(_A    ,     _A    ,     repo_type="""dataset"""      )      )    ,     """r"""      )      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = {int(_A      ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE			:   Tuple	  = idalabel
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = {v: k for k, v in idalabel.items()}

      # load image processor
      SCREAMING_SNAKE_CASE			:   List[str]	  = DeformableDetrImageProcessor(format="""coco_detection"""      )

      # prepare image
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = prepare_img()
      SCREAMING_SNAKE_CASE			:   Tuple	  = image_processor(images=_A    ,     return_tensors="""pt"""      )
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = encoding["""pixel_values"""]

      logger.info("""Converting model..."""      )

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

      SCREAMING_SNAKE_CASE			:   Any	  = """cuda""" if torch.cuda.is_available() else """cpu"""
      model.to(_A      )
      # verify our conversion
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = model(pixel_values.to(_A      )      )

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

      if single_scale:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = torch.tensor(
                [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
            SCREAMING_SNAKE_CASE			:   str	  = 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:
            SCREAMING_SNAKE_CASE			:   Any	  = torch.tensor(
                [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
            SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

      if with_box_refine:
            SCREAMING_SNAKE_CASE			:   List[str]	  = torch.tensor(
                [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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:
            SCREAMING_SNAKE_CASE			:   Tuple	  = torch.tensor(
                [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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(_A      )    ,     atol=1e-4      )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,     expected_boxes.to(_A      )    ,     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(_A      ).mkdir(exist_ok=_A      )
      model.save_pretrained(_A      )
      image_processor.save_pretrained(_A      )

      # Push to hub
      if push_to_hub:
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """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=_A    ,     organization="""nielsr"""    ,     commit_message="""Add model"""      )


if __name__ == "__main__":
  UpperCAmelCase__			:  Tuple					     =     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__			:  int					     =     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,
  )







import itertools






def        __lowercase   (  _A = 1000000      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Tuple	  = [1]

      for i in itertools.count(len(_A      )      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
            for j in itertools.count(1      ):
                  SCREAMING_SNAKE_CASE			:   str	  = -1 if j % 2 == 0 else +1
                  SCREAMING_SNAKE_CASE			:   Any	  = (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(_A      )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")




