

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			      =		logging.get_logger(__name__)







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

      return orig_key







def 		_lowercase			(					lowercase__						):
      # transformer decoder self-attention layers
      for i in range(6						):
            # read in weights + bias of input projection layer of self-attention
            __lowerCAmelCase :						Optional[int] = state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""						)
            __lowerCAmelCase :						Optional[int] = 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
            __lowerCAmelCase :						Tuple = in_proj_weight[:2_5_6, :]
            __lowerCAmelCase :						Tuple = in_proj_bias[:2_5_6]
            __lowerCAmelCase :						int = in_proj_weight[2_5_6:5_1_2, :]
            __lowerCAmelCase :						Union[str, Any] = in_proj_bias[2_5_6:5_1_2]
            __lowerCAmelCase :						Union[str, Any] = in_proj_weight[-2_5_6:, :]
            __lowerCAmelCase :						Union[str, Any] = in_proj_bias[-2_5_6:]







def 		_lowercase			(					):
      __lowerCAmelCase :						Dict = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __lowerCAmelCase :						Optional[int] = Image.open(requests.get(lowercase__       ,     stream=lowercase__						).raw						)

      return im







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

      # load image processor
      __lowerCAmelCase :						Optional[int] = DeformableDetrImageProcessor(format='''coco_detection'''						)

      # prepare image
      __lowerCAmelCase :						str = prepare_img()
      __lowerCAmelCase :						Union[str, Any] = image_processor(images=lowercase__       ,     return_tensors='''pt'''						)
      __lowerCAmelCase :						str = encoding['''pixel_values''']

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

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

      __lowerCAmelCase :						List[str] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(lowercase__						)
      # verify our conversion
      __lowerCAmelCase :						str = model(pixel_values.to(lowercase__						)						)

      __lowerCAmelCase :						List[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]]						)
      __lowerCAmelCase :						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:
            __lowerCAmelCase :						Any = 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]]						)
            __lowerCAmelCase :						Union[str, 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:
            __lowerCAmelCase :						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]]						)
            __lowerCAmelCase :						Dict = 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:
            __lowerCAmelCase :						Optional[Any] = 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]]						)
            __lowerCAmelCase :						Dict = 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:
            __lowerCAmelCase :						Optional[Any] = 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]]						)
            __lowerCAmelCase :						str = 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(lowercase__						)       ,     atol=1E-4						)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,     expected_boxes.to(lowercase__						)       ,     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(lowercase__						).mkdir(exist_ok=lowercase__						)
      model.save_pretrained(lowercase__						)
      image_processor.save_pretrained(lowercase__						)

      # Push to hub
      if push_to_hub:
            __lowerCAmelCase :						List[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=lowercase__       ,     organization='''nielsr'''       ,     commit_message='''Add model'''						)


if __name__ == "__main__":
 _UpperCamelCase			      =		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			      =		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,
 )





def 		_lowercase			(					lowercase__       ,     lowercase__						):
      if len(str(lowercase__						)						) == 1 or len(str(lowercase__						)						) == 1:
            return a * b

      __lowerCAmelCase :						Any = max(len(str(lowercase__						)						)       ,     len(str(lowercase__						)						)						)
      __lowerCAmelCase :						Optional[int] = ma // 2

      __lowerCAmelCase,							__lowerCAmelCase :						Tuple = divmod(lowercase__       ,     1_0**ma						)
      __lowerCAmelCase,							__lowerCAmelCase :						Any = divmod(lowercase__       ,     1_0**ma						)

      __lowerCAmelCase :						str = karatsuba(lowercase__       ,     lowercase__						)
      __lowerCAmelCase :						Optional[int] = karatsuba((aa + aa)       ,     (ba + ba)						)
      __lowerCAmelCase :						List[Any] = karatsuba(lowercase__       ,     lowercase__						)

      return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)







def 		_lowercase			(					):
      print(karatsuba(1_5_4_6_3       ,     2_3_4_8_9						)						)


if __name__ == "__main__":
 main()


