






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






def  lowerCAmelCase__		(					a_  :     List[str]			)   -> str:




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

       return orig_key






def  lowerCAmelCase__		(					a_  :     Optional[Any]			)   -> Tuple:




       for i in range(6			):
              # read in weights + bias of input projection layer of self-attention
              UpperCAmelCase__	:   int												=	state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
              UpperCAmelCase__	:   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
              UpperCAmelCase__	:   List[str]												=	in_proj_weight[:2_5_6, :]
              UpperCAmelCase__	:   Optional[int]												=	in_proj_bias[:2_5_6]
              UpperCAmelCase__	:   Optional[int]												=	in_proj_weight[2_5_6:5_1_2, :]
              UpperCAmelCase__	:   Dict												=	in_proj_bias[2_5_6:5_1_2]
              UpperCAmelCase__	:   List[str]												=	in_proj_weight[-2_5_6:, :]
              UpperCAmelCase__	:   Optional[Any]												=	in_proj_bias[-2_5_6:]






def  lowerCAmelCase__		(					)   -> List[Any]:
       UpperCAmelCase__	:   Dict												=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCAmelCase__	:   Union[str, Any]												=	Image.open(requests.get(_a			,						stream=_a			).raw			)

       return im






@torch.no_grad()
def  lowerCAmelCase__		(					a_  :     Tuple			,						a_  :     List[str]			,						a_  :     Tuple			,						a_  :     Optional[Any]			,						a_  :     Tuple			,						a_  :     List[str]			,						a_  :     Union[str, Any]			,						)   -> Any:
       UpperCAmelCase__	:   str												=	DeformableDetrConfig()
       # set config attributes
       if single_scale:
              UpperCAmelCase__	:   Any												=	1
       UpperCAmelCase__	:   Any												=	dilation
       UpperCAmelCase__	:   Union[str, Any]												=	with_box_refine
       UpperCAmelCase__	:   str												=	two_stage
       # set labels
       UpperCAmelCase__	:   str												=	9_1
       UpperCAmelCase__	:   Optional[Any]												=	"""huggingface/label-files"""
       UpperCAmelCase__	:   Dict												=	"""coco-detection-id2label.json"""
       UpperCAmelCase__	:   Optional[Any]												=	json.load(open(cached_download(hf_hub_url(_a			,						_a			,						repo_type='''dataset'''			)			)			,						'''r'''			)			)
       UpperCAmelCase__	:   Dict												=	{int(_a			): v for k, v in idalabel.items()}
       UpperCAmelCase__	:   Any												=	idalabel
       UpperCAmelCase__	:   Tuple												=	{v: k for k, v in idalabel.items()}

       # load image processor
       UpperCAmelCase__	:   str												=	DeformableDetrImageProcessor(format='''coco_detection'''			)

       # prepare image
       UpperCAmelCase__	:   Tuple												=	prepare_img()
       UpperCAmelCase__	:   Tuple												=	image_processor(images=_a			,						return_tensors='''pt'''			)
       UpperCAmelCase__	:   Union[str, Any]												=	encoding["""pixel_values"""]

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

       # load original state dict
       UpperCAmelCase__	:   List[str]												=	torch.load(_a			,						map_location='''cpu'''			)["""model"""]
       # rename keys
       for key in state_dict.copy().keys():
              UpperCAmelCase__	:   int												=	state_dict.pop(_a			)
              UpperCAmelCase__	:   List[str]												=	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
       UpperCAmelCase__	:   Dict												=	"""model."""
       for key in state_dict.copy().keys():
              if not key.startswith('''class_embed'''			) and not key.startswith('''bbox_embed'''			):
                     UpperCAmelCase__	:   str												=	state_dict.pop(_a			)
                     UpperCAmelCase__	:   List[str]												=	val
    # finally, create HuggingFace model and load state dict
       UpperCAmelCase__	:   Union[str, Any]												=	DeformableDetrForObjectDetection(_a			)
       model.load_state_dict(_a			)
       model.eval()

       UpperCAmelCase__	:   int												=	"""cuda""" if torch.cuda.is_available() else """cpu"""
       model.to(_a			)
       # verify our conversion
       UpperCAmelCase__	:   Dict												=	model(pixel_values.to(_a			)			)

       UpperCAmelCase__	:   List[Any]												=	torch.tensor(
           [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-1_0.5_6_3_4, -5.3_379, -7.5_116]]			)
       UpperCAmelCase__	:   Tuple												=	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:
              UpperCAmelCase__	:   Tuple												=	torch.tensor(
                  [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-1_0.0_6_6_5, -5.8_470, -7.7_003]]			)
              UpperCAmelCase__	:   Optional[Any]												=	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:
              UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                  [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-1_0.1_1_7_8, -4.5_275, -6.2_671]]			)
              UpperCAmelCase__	:   Optional[Any]												=	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:
              UpperCAmelCase__	:   List[Any]												=	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]]			)
              UpperCAmelCase__	:   Optional[int]												=	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:
              UpperCAmelCase__	:   Any												=	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]]			)
              UpperCAmelCase__	:   List[str]												=	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:
              UpperCAmelCase__	:   int												=	"""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,
  )




import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
UpperCamelCase_									=   False
UpperCamelCase_									=   Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCamelCase_									=   datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Dict  ,lowerCamelCase_:     Optional[Any]=sys.argv[1:]							)	->      Optional[int]:
       UpperCAmelCase_     :       Union[str, Any]  		=					None
       UpperCAmelCase_     :       Any  		=					None
       UpperCAmelCase_     :       List[str]  		=					None
       UpperCAmelCase_     :       List[str]  		=					1
       UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					getopt.getopt(lowerCamelCase_  ,"""i:o:b:s"""  ,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]							)
       for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
               UpperCAmelCase_     :       List[Any]  		=					arg
           elif opt in ("-o", "--outfile"):
               UpperCAmelCase_     :       Optional[int]  		=					arg
           elif opt in ("-b", "--batch_size"):
               UpperCAmelCase_     :       Tuple  		=					int(lowerCamelCase_							)
           elif opt in ("-s", "--subset_list"):
               UpperCAmelCase_     :       Dict  		=					arg

       assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
       assert outputfile is not None and not os.path.isfile(lowerCamelCase_							), F'''{outputfile}'''
       if subset_list is not None:
           with open(os.path.realpath(lowerCamelCase_							)							) as f:
               UpperCAmelCase_     :       Union[str, Any]  		=					{self._vqa_file_split()[0] for x in tryload(lowerCamelCase_							)}
       else:
           UpperCAmelCase_     :       Optional[Any]  		=					None

       UpperCAmelCase_     :       Optional[int]  		=					CONFIG
       if torch.cuda.is_available():
           UpperCAmelCase_     :       Any  		=					"""cuda"""
       UpperCAmelCase_     :       str  		=					os.path.realpath(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					os.path.realpath(lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					Preprocess(self.config							)
       UpperCAmelCase_     :       List[str]  		=					GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""  ,config=self.config							)
       UpperCAmelCase_     :       Optional[Any]  		=					batch_size if batch_size != 0 else 1
       UpperCAmelCase_     :       Optional[int]  		=					DEFAULT_SCHEMA


   def       A__    (    self:     str  ,lowerCamelCase_:     Dict							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					int(file.split("""."""							)[0].split("""_"""							)[-1]							)
       UpperCAmelCase_     :       int  		=					os.path.join(self.inputdir  ,lowerCamelCase_							)
       return (img_id, filepath)


   @property
   def       A__    (    self:     Tuple							)	->      List[str]:
       UpperCAmelCase_     :       int  		=					[]
       for i, file in enumerate(os.listdir(self.inputdir							)							):
           if self.subset_list is not None and i not in self.subset_list:
               continue
           batch.append(self._vqa_file_split(lowerCamelCase_							)							)
           if len(lowerCamelCase_							) == self.batch:
               UpperCAmelCase_     :       Tuple  		=					batch
               UpperCAmelCase_     :       Union[str, Any]  		=					[]
               yield list(map(lowerCamelCase_  ,zip(*lowerCamelCase_							)							)							)

       for i in range(1							):
           yield list(map(lowerCamelCase_  ,zip(*lowerCamelCase_							)							)							)






   def __call__(    self:     Any							)	->      Optional[Any]:
       # make writer
       if not TEST:
           UpperCAmelCase_     :       Tuple  		=					datasets.ArrowWriter(features=self.schema  ,path=self.outputfile							)
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator							):
           UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					self.preprocess(lowerCamelCase_							)
           UpperCAmelCase_     :       Any  		=					self.model(
               lowerCamelCase_  ,lowerCamelCase_  ,scales_yx=lowerCamelCase_  ,padding="""max_detections"""  ,max_detections=self.config.MAX_DETECTIONS  ,pad_value=0  ,return_tensors="""np"""  ,location="""cpu"""  ,)
           UpperCAmelCase_     :       Dict  		=					output_dict.pop("""normalized_boxes"""							)
           if not TEST:
               UpperCAmelCase_     :       str  		=					np.array(lowerCamelCase_							)
               UpperCAmelCase_     :       int  		=					self.schema.encode_batch(lowerCamelCase_							)
               writer.write_batch(lowerCamelCase_							)
           if TEST:
               break
           # finalizer the writer
       if not TEST:
           UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					writer.finalize()
           print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''							)







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       Optional[int]  		=					json.load(_a    )
        try:
            UpperCAmelCase_     :       List[Any]  		=					list(data.keys()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					[d["""img_id"""] for d in data]
    except Exception:
        try:
            UpperCAmelCase_     :       str  		=					eval(stream.read()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					stream.read().split("""\n"""    )
    return data


if __name__ == "__main__":
      UpperCamelCase_									=   Extract(sys.argv[1:])
      extract()
      if not TEST:
            UpperCamelCase_									=   datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)
