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()
lowerCAmelCase__					=				logging.get_logger(__name__)






def    __lowerCamelCase	(		lowerCAmelCase__			):
       if "backbone.0.body" in orig_key:
              lowerCAmelCase__  			=      orig_key.replace('backbone.0.body'			,						'backbone.conv_encoder.model'			)
       if "transformer" in orig_key:
              lowerCAmelCase__  			=      orig_key.replace('transformer.'			,						''			)
       if "norm1" in orig_key:
              if "encoder" in orig_key:
                     lowerCAmelCase__  			=      orig_key.replace('norm1'			,						'self_attn_layer_norm'			)
              else:
                     lowerCAmelCase__  			=      orig_key.replace('norm1'			,						'encoder_attn_layer_norm'			)
       if "norm2" in orig_key:
              if "encoder" in orig_key:
                     lowerCAmelCase__  			=      orig_key.replace('norm2'			,						'final_layer_norm'			)
              else:
                     lowerCAmelCase__  			=      orig_key.replace('norm2'			,						'self_attn_layer_norm'			)
       if "norm3" in orig_key:
              lowerCAmelCase__  			=      orig_key.replace('norm3'			,						'final_layer_norm'			)
       if "linear1" in orig_key:
              lowerCAmelCase__  			=      orig_key.replace('linear1'			,						'fc1'			)
       if "linear2" in orig_key:
              lowerCAmelCase__  			=      orig_key.replace('linear2'			,						'fc2'			)
       if "query_embed" in orig_key:
              lowerCAmelCase__  			=      orig_key.replace('query_embed'			,						'query_position_embeddings'			)
       if "cross_attn" in orig_key:
              lowerCAmelCase__  			=      orig_key.replace('cross_attn'			,						'encoder_attn'			)

       return orig_key






def    __lowerCamelCase	(		lowerCAmelCase__			):
       # transformer decoder self-attention layers
       for i in range(6			):
              # read in weights + bias of input projection layer of self-attention
              lowerCAmelCase__  			=      state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
              lowerCAmelCase__  			=      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__  			=      in_proj_weight[:2_5_6, :]
              lowerCAmelCase__  			=      in_proj_bias[:2_5_6]
              lowerCAmelCase__  			=      in_proj_weight[2_5_6:5_1_2, :]
              lowerCAmelCase__  			=      in_proj_bias[2_5_6:5_1_2]
              lowerCAmelCase__  			=      in_proj_weight[-2_5_6:, :]
              lowerCAmelCase__  			=      in_proj_bias[-2_5_6:]






def    __lowerCamelCase	(		):
       lowerCAmelCase__  			=      'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__  			=      Image.open(requests.get(lowerCAmelCase__			,						stream=lowerCAmelCase__			).raw			)

       return im






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

       # load image processor
       lowerCAmelCase__  			=      DeformableDetrImageProcessor(format='coco_detection'			)

       # prepare image
       lowerCAmelCase__  			=      prepare_img()
       lowerCAmelCase__  			=      image_processor(images=lowerCAmelCase__			,						return_tensors='pt'			)
       lowerCAmelCase__  			=      encoding['pixel_values']

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

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

       lowerCAmelCase__  			=      'cuda' if torch.cuda.is_available() else 'cpu'
       model.to(lowerCAmelCase__			)
       # verify our conversion
       lowerCAmelCase__  			=      model(pixel_values.to(lowerCAmelCase__			)			)

       lowerCAmelCase__  			=      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__  			=      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__  			=      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__  			=      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__  			=      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__  			=      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__  			=      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__  			=      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__  			=      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__  			=      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(lowerCAmelCase__			)			,						atol=1e-4			)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,						expected_boxes.to(lowerCAmelCase__			)			,						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(lowerCAmelCase__			).mkdir(exist_ok=lowerCAmelCase__			)
       model.save_pretrained(lowerCAmelCase__			)
       image_processor.save_pretrained(lowerCAmelCase__			)

       # Push to hub
       if push_to_hub:
              lowerCAmelCase__  			=      '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=lowerCAmelCase__			,						organization='nielsr'			,						commit_message='Add model'			)


if __name__ == "__main__":
      lowerCAmelCase__					=				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.'
      )
      lowerCAmelCase__					=				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    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       if target == 0:
              answer.append(path.copy()			)
       else:
              for index in range(lowerCAmelCase__			,						len(lowerCAmelCase__			)			):
                     if target >= candidates[index]:
                            path.append(candidates[index]			)
                            backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						target - candidates[index]			,						lowerCAmelCase__			)
                            path.pop(len(lowerCAmelCase__			) - 1			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      []  # type: list[int]
       lowerCAmelCase__  			=      []  # type: list[int]
       backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						0			)
       return answer






def    __lowerCamelCase	(		):
       print(combination_sum([-8, 2.3, 0]			,						1			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
