






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

       return orig_key






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






def  lowerCAmelCase__		(					)   -> Tuple:
       UpperCAmelCase__	:   Any												=	"http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCAmelCase__	:   Tuple												=	Image.open(requests.get(a_			,						stream=a_			).raw			)

       return im






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

       # load image processor
       UpperCAmelCase__	:   List[Any]												=	DeformableDetrImageProcessor(format='''coco_detection'''			)

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

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

       # load original state dict
       UpperCAmelCase__	:   Optional[Any]												=	torch.load(a_			,						map_location='''cpu'''			)["model"]
       # rename keys
       for key in state_dict.copy().keys():
              UpperCAmelCase__	:   List[str]												=	state_dict.pop(a_			)
              UpperCAmelCase__	:   int												=	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__	:   Optional[int]												=	state_dict.pop(a_			)
                     UpperCAmelCase__	:   str												=	val
    # finally, create HuggingFace model and load state dict
       UpperCAmelCase__	:   List[Any]												=	DeformableDetrForObjectDetection(a_			)
       model.load_state_dict(a_			)
       model.eval()

       UpperCAmelCase__	:   Optional[Any]												=	"cuda" if torch.cuda.is_available() else "cpu"
       model.to(a_			)
       # verify our conversion
       UpperCAmelCase__	:   Any												=	model(pixel_values.to(a_			)			)

       UpperCAmelCase__	:   Optional[int]												=	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__	:   List[str]												=	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__	:   Union[str, 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__	:   Optional[int]												=	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__	:   Union[str, 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__	:   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__	:   List[str]												=	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__	:   str												=	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[Any]												=	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__	:   Tuple												=	"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 importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def  A					(				):
					_snake_case							:       Optional[Any]												= ArgumentParser(
					    description=(
					        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
					    )					)

					# Optional arguments for the launch helper
					parser.add_argument("--num_cores"	,	type=UpperCAmelCase	,	default=1	,	help="Number of TPU cores to use (1 or 8)."					)

					# positional
					parser.add_argument(
					    "training_script"	,	type=UpperCAmelCase	,	help=(
					        "The full path to the single TPU training "
					        "program/script to be launched in parallel, "
					        "followed by all the arguments for the "
					        "training script"
					    )	,	)

					# rest from the training program
					parser.add_argument("training_script_args"	,	nargs=UpperCAmelCase					)

					return parser.parse_args()







def  A					(				):
					_snake_case							:       Any												= parse_args()

					# Import training_script as a module.
					_snake_case							:       Any												= Path(args.training_script					)
					sys.path.append(str(script_fpath.parent.resolve()					)					)
					_snake_case							:       Tuple												= script_fpath.stem
					_snake_case							:       str												= importlib.import_module(UpperCAmelCase					)

					# Patch sys.argv
					_snake_case							:       str												= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores					)]

					xmp.spawn(mod._mp_fn	,	args=()	,	nprocs=args.num_cores					)


if __name__ == "__main__":
							main()