




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    lowerCamelCase__			(		A__			:   Optional[int]      ):





       '''simple docstring'''



       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__			(		A__			:   int      ):





       '''simple docstring'''



       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    lowerCamelCase__			(		):





       '''simple docstring'''



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

       return im




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Optional[int]    ,     A__			:   str    ,     A__			:   Optional[int]    ,     A__			:   int    ,     A__			:   Any    ,     A__			:   List[str]    ,     ):





       '''simple docstring'''



       __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(A__    ,     A__    ,     repo_type="""dataset"""      )      )    ,     """r"""      )      )
       __lowerCamelCase  =		{int(A__      ): 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=A__    ,     return_tensors="""pt"""      )
       __lowerCamelCase  =		encoding["""pixel_values"""]

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

       # load original state dict
       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )["""model"""]
       # rename keys
       for key in state_dict.copy().keys():
              __lowerCamelCase  =		state_dict.pop(A__      )
              __lowerCamelCase  =		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
       __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(A__      )
                     __lowerCamelCase  =		val
    # finally, create HuggingFace model and load state dict
       __lowerCamelCase  =		DeformableDetrForObjectDetection(A__      )
       model.load_state_dict(A__      )
       model.eval()

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

       __lowerCamelCase  =		torch.tensor(
           [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]      )
       __lowerCamelCase  =		torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]      )

       if single_scale:
              __lowerCamelCase  =		torch.tensor(
                  [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]      )
              __lowerCamelCase  =		torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]      )

       if single_scale and dilation:
              __lowerCamelCase  =		torch.tensor(
                  [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]      )
              __lowerCamelCase  =		torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]      )

       if with_box_refine:
              __lowerCamelCase  =		torch.tensor(
                  [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]      )
              __lowerCamelCase  =		torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]      )

       if with_box_refine and two_stage:
              __lowerCamelCase  =		torch.tensor(
                  [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]      )
              __lowerCamelCase  =		torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]      )

       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:
              __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=A__    ,     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,
       )







from __future__ import annotations

from collections.abc import Iterable





class        lowerCamelCase__:
      def __init__(							self:					Dict       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					str  ):
             __lowerCamelCase,  __lowerCamelCase  =		float(UpperCamelCase_  ), float(UpperCamelCase_  )
      def __eq__(							self:					List[Any]       ,			UpperCamelCase_:					List[str]  ):
             return self.x == other.x and self.y == other.y
      def __ne__(							self:					Tuple       ,			UpperCamelCase_:					Dict  ):
             return not self == other
      def __gt__(							self:					List[Any]       ,			UpperCamelCase_:					List[Any]  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y > other.y
             return False
      def __lt__(							self:					str       ,			UpperCamelCase_:					int  ):
             return not self > other
      def __ge__(							self:					Dict       ,			UpperCamelCase_:					Any  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y >= other.y
             return False
      def __le__(							self:					Union[str, Any]       ,			UpperCamelCase_:					Optional[Any]  ):
             if self.x < other.x:
                    return True
             elif self.x == other.x:
                    return self.y <= other.y
             return False
      def __repr__(							self:					Union[str, Any]  ):
             return F'({self.x}, {self.y})'




      def __hash__(							self:					List[Any]  ):
             return hash(self.x  )




def    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]] | Iterable[list[float]]    ,     ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       if list_of_tuples:
              for p in list_of_tuples:
                     if isinstance(A__    ,     A__      ):
                            points.append(A__      )
                     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    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]]      ):





       '''simple docstring'''



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

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

       return _construct_points(A__      )




def    lowerCamelCase__			(		A__			:   Point    ,     A__			:   Point    ,     A__			:   Point      ):





       '''simple docstring'''



       __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    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )
       __lowerCamelCase  =		set()

       for i in range(n - 1      ):
              for j in range(i + 1    ,     A__      ):
                     __lowerCamelCase  =		__lowerCamelCase  =		False
                     __lowerCamelCase  =		True
                     for k in range(A__      ):
                            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(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       # 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(A__    ,     A__    ,     points[i]      )

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

       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )
       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )

       return sorted(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]    ,     A__			:   Point    ,     A__			:   Point    ,     A__			:   set[Point]      ):





       '''simple docstring'''



       if points:
              __lowerCamelCase  =		None
              __lowerCamelCase  =		float("""-inf"""      )
              __lowerCamelCase  =		[]

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

                     if det > 0:
                            candidate_points.append(A__      )

                            if det > extreme_point_distance:
                                   __lowerCamelCase  =		det
                                   __lowerCamelCase  =		p

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




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       __lowerCamelCase  =		points[:2]
       for i in range(2    ,     A__      ):
              __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(A__    ,     A__      ):
              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(A__      ) > 3 else convex_hull      )




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __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(A__      )

       __lowerCamelCase  =		convex_hull_recursive(A__      )
       assert results_bf == results_recursive

       __lowerCamelCase  =		convex_hull_melkman(A__      )
       assert results_bf == results_melkman

       print(A__      )


if __name__ == "__main__":
       main()

