


"""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()
__lowercase          = logging.get_logger(__name__)


def 	lowercase     (							A_			)-> Dict:
  '''simple docstring'''







  if "backbone.0.body" in orig_key:
    a		:						List[Any] 				=     orig_key.replace("backbone.0.body"    ,  "backbone.conv_encoder.model"			)
  if "transformer" in orig_key:
    a		:						int 				=     orig_key.replace("transformer."    ,  ""			)
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      a		:						int 				=     orig_key.replace("norm1"    ,  "self_attn_layer_norm"			)
    else:
      a		:						Optional[int] 				=     orig_key.replace("norm1"    ,  "encoder_attn_layer_norm"			)
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      a		:						Optional[int] 				=     orig_key.replace("norm2"    ,  "final_layer_norm"			)
    else:
      a		:						Tuple 				=     orig_key.replace("norm2"    ,  "self_attn_layer_norm"			)
  if "norm3" in orig_key:
    a		:						Optional[int] 				=     orig_key.replace("norm3"    ,  "final_layer_norm"			)
  if "linear1" in orig_key:
    a		:						int 				=     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		:						Dict 				=     orig_key.replace("query_embed"    ,  "query_position_embeddings"			)
  if "cross_attn" in orig_key:
    a		:						List[Any] 				=     orig_key.replace("cross_attn"    ,  "encoder_attn"			)

  return orig_key


def 	lowercase     (							A_			)-> Union[str, Any]:
  '''simple docstring'''







  for i in range(6			):
    # read in weights + bias of input projection layer of self-attention
    a		:						Tuple 				=     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		:						int 				=     in_proj_weight[:256, :]
    a		:						int 				=     in_proj_bias[:256]
    a		:						str 				=     in_proj_weight[256:512, :]
    a		:						Dict 				=     in_proj_bias[256:512]
    a		:						Optional[Any] 				=     in_proj_weight[-256:, :]
    a		:						Optional[int] 				=     in_proj_bias[-256:]


def 	lowercase     (							)-> int:
  '''simple docstring'''







  a		:						Dict 				=     "http://images.cocodataset.org/val2017/000000039769.jpg"
  a		:						Tuple 				=     Image.open(requests.get(A_    ,  stream=A_			).raw			)

  return im


@torch.no_grad()
def 	lowercase     (							A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  A_    ,  )-> int:
  '''simple docstring'''







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

  # load image processor
  a		:						Union[str, Any] 				=     DeformableDetrImageProcessor(format="coco_detection"			)

  # prepare image
  a		:						Tuple 				=     prepare_img()
  a		:						Dict 				=     image_processor(images=A_    ,  return_tensors="pt"			)
  a		:						Any 				=     encoding["pixel_values"]

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

  # load original state dict
  a		:						Any 				=     torch.load(A_    ,  map_location="cpu"			)["model"]
  # rename keys
  for key in state_dict.copy().keys():
    a		:						Union[str, Any] 				=     state_dict.pop(A_			)
    a		:						int 				=     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
  a		:						List[Any] 				=     "model."
  for key in state_dict.copy().keys():
    if not key.startswith("class_embed"			) and not key.startswith("bbox_embed"			):
      a		:						str 				=     state_dict.pop(A_			)
      a		:						List[Any] 				=     val
    # finally, create HuggingFace model and load state dict
  a		:						Optional[int] 				=     DeformableDetrForObjectDetection(A_			)
  model.load_state_dict(A_			)
  model.eval()

  a		:						Optional[int] 				=     "cuda" if torch.cuda.is_available() else "cpu"
  model.to(A_			)
  # verify our conversion
  a		:						Union[str, Any] 				=     model(pixel_values.to(A_			)			)

  a		:						Optional[Any] 				=     torch.tensor(
      [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]			)
  a		:						Dict 				=     torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]			)

  if single_scale:
    a		:						Tuple 				=     torch.tensor(
        [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]			)
    a		:						List[Any] 				=     torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]			)

  if single_scale and dilation:
    a		:						Optional[int] 				=     torch.tensor(
        [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]			)
    a		:						Optional[int] 				=     torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]			)

  if with_box_refine:
    a		:						Optional[int] 				=     torch.tensor(
        [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]			)
    a		:						Optional[int] 				=     torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]			)

  if with_box_refine and two_stage:
    a		:						str 				=     torch.tensor(
        [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]			)
    a		:						Optional[int] 				=     torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]			)

  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:
    a		:						Tuple 				=     "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__":
 __lowercase          = 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."""
 )
 __lowercase          = 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"""







class 		_A    :







    """simple docstring"""




    def __init__(   self							:       Any):
      a		:						Tuple 				=     [0]
      a		:						str 				=     0






    def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       int):
      a		:						Any 				=     self.__heap[i]
      while i // 2 > 0:
        if self.__heap[i] > self.__heap[i // 2]:
          a		:						Tuple 				=     self.__heap[i // 2]
          a		:						Any 				=     temporary
        i //= 2






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       int):
      self.__heap.append(__UpperCAmelCase)
      self.__size += 1
      self.__swap_up(self.__size)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       int):
      while self.__size >= 2 * i:
        if 2 * i + 1 > self.__size:
          a		:						List[Any] 				=     2 * i
        else:
          if self.__heap[2 * i] > self.__heap[2 * i + 1]:
            a		:						Tuple 				=     2 * i
          else:
            a		:						str 				=     2 * i + 1
        a		:						Optional[int] 				=     self.__heap[i]
        if self.__heap[i] < self.__heap[bigger_child]:
          a		:						Any 				=     self.__heap[bigger_child]
          a		:						Union[str, Any] 				=     temporary
        a		:						Dict 				=     bigger_child






    def       __snake_case				(   self							:       List[str]):
      a		:						int 				=     self.__heap[1]
      a		:						Tuple 				=     self.__heap[self.__size]
      self.__size -= 1
      self.__heap.pop()
      self.__swap_down(1)
      return max_value






    @property
    def       __snake_case				(   self							:       List[str]):
      return self.__heap[1:]






    def __len__(   self							:       Optional[int]):
      return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 __lowercase          = BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(10)
 binary_heap.insert(15)
 binary_heap.insert(12)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))






