"""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()
_SCREAMING_SNAKE_CASE  :		Any        =      logging.get_logger(__name__)



def 			A__						(  lowercase_    :  List[str]	)     ->       List[Any]:
      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 			A__						(  lowercase_    :  Any	)     ->       Any:
      # 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[:256, :]
            __lowerCamelCase								=				in_proj_bias[:256]
            __lowerCamelCase								=				in_proj_weight[256:512, :]
            __lowerCamelCase								=				in_proj_bias[256:512]
            __lowerCamelCase								=				in_proj_weight[-256:, :]
            __lowerCamelCase								=				in_proj_bias[-256:]



def 			A__						(  )     ->       Dict:
      __lowerCamelCase								=				'http://images.cocodataset.org/val2017/000000039769.jpg'
      __lowerCamelCase								=				Image.open(requests.get(lowercase_			,   stream=lowercase_	).raw	)

      return im



@torch.no_grad()
def 			A__						(  lowercase_    :  Optional[Any]			,   lowercase_    :  Dict			,   lowercase_    :  Union[str, Any]			,   lowercase_    :  Dict			,   lowercase_    :  Dict			,   lowercase_    :  Optional[int]			,   lowercase_    :  Dict			,   )     ->       Optional[int]:
      __lowerCamelCase								=				DeformableDetrConfig()
      # set config attributes
      if single_scale:
            __lowerCamelCase								=				1
      __lowerCamelCase								=				dilation
      __lowerCamelCase								=				with_box_refine
      __lowerCamelCase								=				two_stage
      # set labels
      __lowerCamelCase								=				91
      __lowerCamelCase								=				'huggingface/label-files'
      __lowerCamelCase								=				'coco-detection-id2label.json'
      __lowerCamelCase								=				json.load(open(cached_download(hf_hub_url(lowercase_			,   lowercase_			,   repo_type='dataset'	)	)			,   'r'	)	)
      __lowerCamelCase								=				{int(lowercase_	): 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=lowercase_			,   return_tensors='pt'	)
      __lowerCamelCase								=				encoding['pixel_values']

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

      # load original state dict
      __lowerCamelCase								=				torch.load(lowercase_			,   map_location='cpu'	)['model']
      # rename keys
      for key in state_dict.copy().keys():
            __lowerCamelCase								=				state_dict.pop(lowercase_	)
            __lowerCamelCase								=				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								=				'model.'
      for key in state_dict.copy().keys():
            if not key.startswith('class_embed'	) and not key.startswith('bbox_embed'	):
                  __lowerCamelCase								=				state_dict.pop(lowercase_	)
                  __lowerCamelCase								=				val
    # finally, create HuggingFace model and load state dict
      __lowerCamelCase								=				DeformableDetrForObjectDetection(lowercase_	)
      model.load_state_dict(lowercase_	)
      model.eval()

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

      __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], [-10.5634, -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], [-10.0665, -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], [-10.1178, -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(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								=				'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__":
     _SCREAMING_SNAKE_CASE  :		Any        =      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.'''
     )
     _SCREAMING_SNAKE_CASE  :		Tuple        =      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"""







from __future__ import annotations

from collections.abc import Iterable






class     _SCREAMING_SNAKE_CASE       :



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				float(_SCREAMING_SNAKE_CASE  ), float(_SCREAMING_SNAKE_CASE  )







   def __eq__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return self.x == other.x and self.y == other.y







   def __ne__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return not self == other







   def __gt__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x > other.x:
         return True
      elif self.x == other.x:
         return self.y > other.y
      return False







   def __lt__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return not self > other







   def __ge__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x > other.x:
         return True
      elif self.x == other.x:
         return self.y >= other.y
      return False







   def __le__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x < other.x:
         return True
      elif self.x == other.x:
         return self.y <= other.y
      return False







   def __repr__(  self  ):


      '''simple docstring'''

      return F'''({self.x}, {self.y})'''







   def __hash__(  self  ):


      '''simple docstring'''

      return hash(self.x  )



def 			A__						(  lowercase_    :  list[Point] | list[list[float]] | Iterable[list[float]]			,   )     ->       list[Point]:
   __lowerCamelCase								=				[]
   if list_of_tuples:
      for p in list_of_tuples:
         if isinstance(lowercase_			,   lowercase_	):
            points.append(lowercase_	)
         else:
            try:
               points.append(Point(p[0]			,   p[1]	)	)
            except (IndexError, TypeError):
               print(
                   F'''Ignoring deformed point {p}. All points'''
                   ' must have at least 2 coordinates.'	)
   return points



def 			A__						(  lowercase_    :  list[Point] | list[list[float]]	)     ->       list[Point]:

   if not hasattr(lowercase_			,   '__iter__'	):
      __lowerCamelCase								=				F'''Expecting an iterable object but got an non-iterable type {points}'''
      raise ValueError(lowercase_	)

   if not points:
      __lowerCamelCase								=				F'''Expecting a list of points but got {points}'''
      raise ValueError(lowercase_	)

   return _construct_points(lowercase_	)



def 			A__						(  lowercase_    :  Point			,   lowercase_    :  Point			,   lowercase_    :  Point	)     ->       float:
   __lowerCamelCase								=				(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
   return det



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)
   __lowerCamelCase								=				set()

   for i in range(n - 1	):
      for j in range(i + 1			,   lowercase_	):
         __lowerCamelCase								=				__lowerCamelCase								=				False
         __lowerCamelCase								=				True
         for k in range(lowercase_	):
            if k != i and k != j:
               __lowerCamelCase								=				_det(points[i]			,   points[j]			,   points[k]	)

               if det_k > 0:
                  __lowerCamelCase								=				True
               elif det_k < 0:
                  __lowerCamelCase								=				True
               else:
                  # point[i], point[j], point[k] all lie on a straight line
                  # if point[k] is to the left of point[i] or it's to the
                  # right of point[j], then point[i], point[j] cannot be
                  # part of the convex hull of A
                  if points[k] < points[i] or points[k] > points[j]:
                     __lowerCamelCase								=				False
                     break

            if points_left_of_ij and points_right_of_ij:
               __lowerCamelCase								=				False
               break

         if ij_part_of_convex_hull:
            convex_set.update([points[i], points[j]]	)

   return sorted(lowercase_	)



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)

   # divide all the points into an upper hull and a lower hull
   # the left most point and the right most point are definitely
   # members of the convex hull by definition.
   # use these two anchors to divide all the points into two hulls,
   # an upper hull and a lower hull.

   # all points to the left (above) the line joining the extreme points belong to the
   # upper hull
   # all points to the right (below) the line joining the extreme points below to the
   # lower hull
   # ignore all points on the line joining the extreme points since they cannot be
   # part of the convex hull

   __lowerCamelCase								=				points[0]
   __lowerCamelCase								=				points[n - 1]

   __lowerCamelCase								=				{left_most_point, right_most_point}
   __lowerCamelCase								=				[]
   __lowerCamelCase								=				[]

   for i in range(1			,   n - 1	):
      __lowerCamelCase								=				_det(lowercase_			,   lowercase_			,   points[i]	)

      if det > 0:
         upper_hull.append(points[i]	)
      elif det < 0:
         lower_hull.append(points[i]	)

   _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)
   _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)

   return sorted(lowercase_	)



def 			A__						(  lowercase_    :  list[Point]			,   lowercase_    :  Point			,   lowercase_    :  Point			,   lowercase_    :  set[Point]	)     ->       None:
   if points:
      __lowerCamelCase								=				None
      __lowerCamelCase								=				float('-inf'	)
      __lowerCamelCase								=				[]

      for p in points:
         __lowerCamelCase								=				_det(lowercase_			,   lowercase_			,   lowercase_	)

         if det > 0:
            candidate_points.append(lowercase_	)

            if det > extreme_point_distance:
               __lowerCamelCase								=				det
               __lowerCamelCase								=				p

      if extreme_point:
         _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)
         convex_set.add(lowercase_	)
         _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)

   __lowerCamelCase								=				points[:2]
   for i in range(2			,   lowercase_	):
      __lowerCamelCase								=				_det(convex_hull[1]			,   convex_hull[0]			,   points[i]	)
      if det > 0:
         convex_hull.insert(0			,   points[i]	)
         break
      elif det < 0:
         convex_hull.append(points[i]	)
         break
      else:
         __lowerCamelCase								=				points[i]
   i += 1

   for j in range(lowercase_			,   lowercase_	):
      if (
          _det(convex_hull[0]			,   convex_hull[-1]			,   points[j]	) > 0
          and _det(convex_hull[-1]			,   convex_hull[0]			,   points[1]	) < 0
      ):
         # The point lies within the convex hull
         continue

      convex_hull.insert(0			,   points[j]	)
      convex_hull.append(points[j]	)
      while _det(convex_hull[0]			,   convex_hull[1]			,   convex_hull[2]	) >= 0:
         del convex_hull[1]
      while _det(convex_hull[-1]			,   convex_hull[-2]			,   convex_hull[-3]	) <= 0:
         del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
   return sorted(convex_hull[1:] if len(lowercase_	) > 3 else convex_hull	)



def 			A__						(  )     ->       List[Any]:
   __lowerCamelCase								=				[
       (0, 3),
       (2, 2),
       (1, 1),
       (2, 1),
       (3, 0),
       (0, 0),
       (3, 3),
       (2, -1),
       (2, -4),
       (1, -3),
   ]
   # the convex set of points is
   # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
   __lowerCamelCase								=				convex_hull_bf(lowercase_	)

   __lowerCamelCase								=				convex_hull_recursive(lowercase_	)
   assert results_bf == results_recursive

   __lowerCamelCase								=				convex_hull_melkman(lowercase_	)
   assert results_bf == results_melkman

   print(lowercase_	)


if __name__ == "__main__":
     main()






