"""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      : str       =logging.get_logger(__name__)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[str]						)    ->     Tuple:







     '''simple docstring'''


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

     return orig_key




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple						)    ->     Dict:







     '''simple docstring'''


     for i in range(6						):
          # read in weights + bias of input projection layer of self-attention
          __A					:					Dict								=		state_dict.pop(F'decoder.layers.{i}.self_attn.in_proj_weight'						)
          __A					:					Optional[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
          __A					:					Optional[Any]								=		in_proj_weight[:256, :]
          __A					:					str								=		in_proj_bias[:256]
          __A					:					Tuple								=		in_proj_weight[256:512, :]
          __A					:					List[str]								=		in_proj_bias[256:512]
          __A					:					List[Any]								=		in_proj_weight[-256:, :]
          __A					:					int								=		in_proj_bias[-256:]




def 						_lowercase				(  )    ->     int:







     '''simple docstring'''


     __A					:					Optional[int]								=		'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A					:					Union[str, Any]								=		Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,       stream=_SCREAMING_SNAKE_CASE						).raw						)

     return im




@torch.no_grad()
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				int  ,       _SCREAMING_SNAKE_CASE				:				Dict  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				Dict  ,       )    ->     List[str]:







     '''simple docstring'''


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

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

     # prepare image
     __A					:					Optional[int]								=		prepare_img()
     __A					:					Tuple								=		image_processor(images=_SCREAMING_SNAKE_CASE  ,       return_tensors='pt'						)
     __A					:					Optional[int]								=		encoding['pixel_values']

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

     # load original state dict
     __A					:					Optional[Any]								=		torch.load(_SCREAMING_SNAKE_CASE  ,       map_location='cpu'						)['model']
     # rename keys
     for key in state_dict.copy().keys():
          __A					:					List[str]								=		state_dict.pop(_SCREAMING_SNAKE_CASE						)
          __A					:					int								=		val
     # query, key and value matrices need special treatment
     read_in_q_k_v(_SCREAMING_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
     __A					:					Any								=		'model.'
     for key in state_dict.copy().keys():
          if not key.startswith('class_embed'						) and not key.startswith('bbox_embed'						):
               __A					:					List[Any]								=		state_dict.pop(_SCREAMING_SNAKE_CASE						)
               __A					:					Union[str, Any]								=		val
    # finally, create HuggingFace model and load state dict
     __A					:					Union[str, Any]								=		DeformableDetrForObjectDetection(_SCREAMING_SNAKE_CASE						)
     model.load_state_dict(_SCREAMING_SNAKE_CASE						)
     model.eval()

     __A					:					Any								=		'cuda' if torch.cuda.is_available() else 'cpu'
     model.to(_SCREAMING_SNAKE_CASE						)
     # verify our conversion
     __A					:					List[Any]								=		model(pixel_values.to(_SCREAMING_SNAKE_CASE						)						)

     __A					:					Union[str, Any]								=		torch.tensor(
         [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]						)
     __A					:					str								=		torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]						)

     if single_scale:
          __A					:					Optional[Any]								=		torch.tensor(
              [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]						)
          __A					:					List[str]								=		torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]						)

     if single_scale and dilation:
          __A					:					Any								=		torch.tensor(
              [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]						)
          __A					:					Optional[Any]								=		torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]						)

     if with_box_refine:
          __A					:					Tuple								=		torch.tensor(
              [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]						)
          __A					:					Any								=		torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]						)

     if with_box_refine and two_stage:
          __A					:					Union[str, Any]								=		torch.tensor(
              [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]						)
          __A					:					Union[str, Any]								=		torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]						)

     print('Logits:'  ,       outputs.logits[0, :3, :3]						)

     assert torch.allclose(outputs.logits[0, :3, :3]  ,       expected_logits.to(_SCREAMING_SNAKE_CASE						)  ,       atol=1E-4						)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,       expected_boxes.to(_SCREAMING_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(_SCREAMING_SNAKE_CASE						).mkdir(exist_ok=_SCREAMING_SNAKE_CASE						)
     model.save_pretrained(_SCREAMING_SNAKE_CASE						)
     image_processor.save_pretrained(_SCREAMING_SNAKE_CASE						)

     # Push to hub
     if push_to_hub:
          __A					:					List[str]								=		'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=_SCREAMING_SNAKE_CASE  ,       organization='nielsr'  ,       commit_message='Add model'						)


if __name__ == "__main__":
       lowerCamelCase      : List[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.'''
       )
       lowerCamelCase      : List[str]       =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

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



