






'''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()
__lowerCAmelCase							=logging.get_logger(__name__)

def 							a	(   _UpperCAmelCase )				->				Optional[Any]:






     """simple docstring"""







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

     return orig_key

def 							a	(   _UpperCAmelCase )				->				Optional[Any]:






     """simple docstring"""







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

def 							a	(   )				->				Any:






     """simple docstring"""







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

     return im

@torch.no_grad()
def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  )				->				Tuple:






     """simple docstring"""







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

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

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

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

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

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

     a_									=   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_									=   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_									=   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_									=   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_									=   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_									=   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_									=   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_									=   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_									=   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_									=   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_									=   '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__":
      __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,
      )









'''simple docstring'''
def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				list[int]:






					"""simple docstring"""







					if hue < 0 or hue > 3_6_0:
										raise Exception('hue should be between 0 and 360' )

					if saturation < 0 or saturation > 1:
										raise Exception('saturation should be between 0 and 1' )

					if value < 0 or value > 1:
										raise Exception('value should be between 0 and 1' )

					a_									=   value * saturation
					a_									=   hue / 6_0
					a_									=   chroma * (1 - abs(hue_section % 2 - 1 ))
					a_									=   value - chroma

					if hue_section >= 0 and hue_section <= 1:
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (match_value) )
					elif hue_section > 1 and hue_section <= 2:
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (match_value) )
					elif hue_section > 2 and hue_section <= 3:
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
					elif hue_section > 3 and hue_section <= 4:
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
					elif hue_section > 4 and hue_section <= 5:
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
					else:
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )

					return [red, green, blue]

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				list[float]:






					"""simple docstring"""







					if red < 0 or red > 2_5_5:
										raise Exception('red should be between 0 and 255' )

					if green < 0 or green > 2_5_5:
										raise Exception('green should be between 0 and 255' )

					if blue < 0 or blue > 2_5_5:
										raise Exception('blue should be between 0 and 255' )

					a_									=   red / 2_5_5
					a_									=   green / 2_5_5
					a_									=   blue / 2_5_5
					a_									=   max(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )
					a_									=   value - min(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )
					a_									=   0 if value == 0 else chroma / value

					if chroma == 0:
										a_									=   0.0
					elif value == float_red:
										a_									=   6_0 * (0 + (float_green - float_blue) / chroma)
					elif value == float_green:
										a_									=   6_0 * (2 + (float_blue - float_red) / chroma)
					else:
										a_									=   6_0 * (4 + (float_red - float_green) / chroma)

					a_									=   (hue + 3_6_0) % 3_6_0

					return [hue, saturation, value]





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				bool:






					"""simple docstring"""







					a_									=   abs(hsv_a[0] - hsv_a[0] ) < 0.2
					a_									=   abs(hsv_a[1] - hsv_a[1] ) < 0.0_0_2
					a_									=   abs(hsv_a[2] - hsv_a[2] ) < 0.0_0_2

					return check_hue and check_saturation and check_value

