


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

       return orig_key




def   snake_case_	(    snake_case	)    ->							Any:
       # transformer decoder self-attention layers
       for i in range(6	):
              # read in weights + bias of input projection layer of self-attention
              lowercase__:  str         =  state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'	)
              lowercase__:  Union[str, Any]         =  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
              lowercase__:  int         =  in_proj_weight[:2_56, :]
              lowercase__:  Optional[Any]         =  in_proj_bias[:2_56]
              lowercase__:  str         =  in_proj_weight[2_56:5_12, :]
              lowercase__:  List[Any]         =  in_proj_bias[2_56:5_12]
              lowercase__:  str         =  in_proj_weight[-2_56:, :]
              lowercase__:  Optional[int]         =  in_proj_bias[-2_56:]




def   snake_case_	(    )    ->							Optional[int]:
       lowercase__:  Any         =  'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:  Optional[Any]         =  Image.open(requests.get(snake_case     ,				stream=snake_case	).raw	)

       return im




@torch.no_grad()
def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				)    ->							str:
       lowercase__:  Dict         =  DeformableDetrConfig()
       # set config attributes
       if single_scale:
              lowercase__:  List[str]         =  1
       lowercase__:  Any         =  dilation
       lowercase__:  Tuple         =  with_box_refine
       lowercase__:  Dict         =  two_stage
       # set labels
       lowercase__:  List[str]         =  91
       lowercase__:  Optional[int]         =  'huggingface/label-files'
       lowercase__:  Tuple         =  'coco-detection-id2label.json'
       lowercase__:  str         =  json.load(open(cached_download(hf_hub_url(snake_case     ,				snake_case     ,				repo_type='dataset'	)	)     ,				'r'	)	)
       lowercase__:  List[Any]         =  {int(snake_case	): v for k, v in idalabel.items()}
       lowercase__:  str         =  idalabel
       lowercase__:  List[Any]         =  {v: k for k, v in idalabel.items()}

       # load image processor
       lowercase__:  Tuple         =  DeformableDetrImageProcessor(format='coco_detection'	)

       # prepare image
       lowercase__:  List[str]         =  prepare_img()
       lowercase__:  int         =  image_processor(images=snake_case     ,				return_tensors='pt'	)
       lowercase__:  Optional[Any]         =  encoding['pixel_values']

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

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

       lowercase__:  Dict         =  'cuda' if torch.cuda.is_available() else 'cpu'
       model.to(snake_case	)
       # verify our conversion
       lowercase__:  int         =  model(pixel_values.to(snake_case	)	)

       lowercase__:  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]]	)
       lowercase__:  List[str]         =  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:
              lowercase__:  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]]	)
              lowercase__:  Optional[int]         =  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:
              lowercase__:  List[str]         =  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]]	)
              lowercase__:  Any         =  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:
              lowercase__:  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]]	)
              lowercase__:  Tuple         =  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:
              lowercase__:  str         =  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]]	)
              lowercase__:  Any         =  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(snake_case	)     ,				atol=1e-4	)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,				expected_boxes.to(snake_case	)     ,				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(snake_case	).mkdir(exist_ok=snake_case	)
       model.save_pretrained(snake_case	)
       image_processor.save_pretrained(snake_case	)

       # Push to hub
       if push_to_hub:
              lowercase__:  Dict         =  '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=snake_case     ,				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,
 )






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Any:




        '''simple docstring'''
        super().__init__(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  str         =  (
            next_prime(value % self.size_table					)
            if not is_prime(value % self.size_table					)
            else value % self.size_table
        )  # gt = bigger than
        return next_prime_gt - (data % next_prime_gt)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        return (increment * self.__hash_function_a(lowerCAmelCase__							,					lowerCAmelCase__					)) % self.size_table
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__=None					)   ->							str:




        '''simple docstring'''
        lowercase__:  Dict         =  1
        lowercase__:  str         =  self.hash_function(lowerCAmelCase__					)

        while self.values[new_key] is not None and self.values[new_key] != key:
               lowercase__:  List[Any]         =  (
                   self.__hash_double_function(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
                   if self.balanced_factor() >= self.lim_charge
                   else None
               )
               if new_key is None:
                      break
               else:
                      i += 1

        return new_key


